tag:blogger.com,1999:blog-11313364823306461162024-03-17T20:04:17.318-07:00notistecnicasNoticias tecnicas,cientificas,relacionadas con la sociedad de la información y la cienciaRamon.Moratahttp://www.blogger.com/profile/16398587758151257300noreply@blogger.comBlogger1885125tag:blogger.com,1999:blog-1131336482330646116.post-46723939536380769222024-03-05T11:37:00.000-08:002024-03-05T11:37:37.464-08:00El muérdago obtiene energía de forma simbiótica.<p> <span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">Hasta el día de hoy, los científicos no han descubierto cómo el muérdago (cuyo genoma es veinticuatro veces el tamaño del genoma humano) obtiene la energía que necesita para sobrevivir.</span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">Tienen mitocondrias pero sus "genes mitocondriales que codifican proteínas de todos los complejos respiratorios carecen o se pseudogenizan"</span></p><h3 style="text-align: justify;"><span style="font-size: large;"><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="color: #660000; text-overflow: unset;"> </span><span style="color: #660000;">Massive gene loss in mistletoe (Viscum, Viscaceae) mitochondria
</span></span></h3>
<div class="inline-authors">
<div class="authors">
<div class="authors-list">
<span class="authors-list-item "><a class="full-name" data-ga-action="author_link" data-ga-category="search" data-ga-label="G Petersen" href="https://pubmed.ncbi.nlm.nih.gov/?term=Petersen+G&cauthor_id=26625950">G Petersen</a><sup class="affiliation-links"><span class="author-sup-separator"> </span><a class="affiliation-link" href="https://pubmed.ncbi.nlm.nih.gov/26625950/#full-view-affiliation-1" title="Natural History Museum of Denmark, University of Copenhagen, Sølvgade 83, DK-1307 Copenhagen K, Denmark.">
1
</a></sup><span class="comma">, </span></span><span class="authors-list-item "><a class="full-name" data-ga-action="author_link" data-ga-category="search" data-ga-label="A Cuenca" href="https://pubmed.ncbi.nlm.nih.gov/?term=Cuenca+A&cauthor_id=26625950">A Cuenca</a><sup class="affiliation-links"><span class="author-sup-separator"> </span><a class="affiliation-link" href="https://pubmed.ncbi.nlm.nih.gov/26625950/#full-view-affiliation-1" title="Natural History Museum of Denmark, University of Copenhagen, Sølvgade 83, DK-1307 Copenhagen K, Denmark.">
1
</a></sup><span class="comma">, </span></span><span class="authors-list-item "><a class="full-name" data-ga-action="author_link" data-ga-category="search" data-ga-label="I M Møller" href="https://pubmed.ncbi.nlm.nih.gov/?term=M%C3%B8ller+IM&cauthor_id=26625950">I M Møller</a><sup class="affiliation-links"><span class="author-sup-separator"> </span><a class="affiliation-link" href="https://pubmed.ncbi.nlm.nih.gov/26625950/#full-view-affiliation-2" title="Department of Molecular Biology and Genetics, Aarhus University, Forsøgsvej 1, DK-4200 Slagelse, Denmark.">
2
</a></sup><span class="comma">, </span></span><span class="authors-list-item "><a class="full-name" data-ga-action="author_link" data-ga-category="search" data-ga-label="O Seberg" href="https://pubmed.ncbi.nlm.nih.gov/?term=Seberg+O&cauthor_id=26625950">O Seberg</a><sup class="affiliation-links"><span class="author-sup-separator"> </span><a class="affiliation-link" href="https://pubmed.ncbi.nlm.nih.gov/26625950/#full-view-affiliation-1" title="Natural History Museum of Denmark, University of Copenhagen, Sølvgade 83, DK-1307 Copenhagen K, Denmark.">
1
</a></sup></span>
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<strong class="current-id" title="PubMed ID">26625950</strong>
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<div class="abstract" id="abstract">
<h2 class="title">
Abstract
</h2>
<div class="abstract-content selected" id="eng-abstract">
<p style="text-align: justify;">
Parasitism is a successful survival strategy across all kingdoms
and has evolved repeatedly in angiosperms. Parasitic plants obtain
nutrients from other plants and some are agricultural pests. Obligate
parasites, which cannot complete their lifecycle without a host, may
lack functional photosystems (holoparasites), or have retained
photosynthesis (hemiparasites). Plastid genomes are often reduced in
parasites, but complete mitochondrial genomes have not been sequenced
and their mitochondrial respiratory capacities are largely unknown. The
hemiparasitic European mistletoe (Viscum album), known from folklore and
postulated therapeutic properties, is a pest in plantations and
forestry. We compare the mitochondrial genomes of three Viscum species
based on the complete mitochondrial genome of V. album, the first from a
parasitic plant. We show that mitochondrial genes encoding proteins of
all respiratory complexes are lacking or pseudogenized raising several
questions relevant to all parasitic plants: Are any mitochondrial gene
functions essential? Do any genes need to be located in the
mitochondrial genome or can they all be transferred to the nucleus? Can
parasitic plants survive without oxidative phosphorylation by using
alternative respiratory pathways? More generally, our study is a step
towards understanding how host- and self-perception, host integration
and nucleic acid transfer has modified ancestral mitochondrial genomes. </p><p><span style="color: #660000;">https://pubmed.ncbi.nlm.nih.gov/26625950/ </span><br /></p>
</div>
</div><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> </span></p><h4 style="text-align: left;"><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"></span><b><span style="color: #660000;"><span style="font-size: large;">En Gemini dicen este resumen:</span></span></b><br /></h4><h1 _ngcontent-ng-c18146144="" class="ng-tns-c18146144-1 gmat-headline-1 headline ng-star-inserted"><strong _ngcontent-ng-c18146144="" class="ng-tns-c18146144-1">El muérdago obtiene energía de forma simbiótica.</strong></h1><div _ngcontent-ng-c1130115720="" class="query-content" id="user-query-content-0"><h2 _ngcontent-ng-c1130115720="" class="query-text ng-star-inserted" dir="ltr">¿Cómo obtiene el muérdago energía el muérdago si carece de las mitocondrias de las que dependen los seres vivos para producir trifosfato de adenosina?</h2><h2 _ngcontent-ng-c1130115720="" class="query-text ng-star-inserted" dir="ltr">El muérdago, a pesar de <span style="color: #660000;">carecer de mitocondrias</span>, obtiene energía a través de una relación simbiótica con la planta huésped. Esta relación se basa en dos mecanismos principales:</h2></div><div _ngcontent-ng-c1688536284="" class="markdown markdown-main-panel ui-v2-enabled" dir="ltr" style="--animation-duration: 600ms; --fade-animation-function: linear;"><p data-sourcepos="3:1-3:251"><strong>1. Haustorios:</strong> El muérdago penetra en el tejido vascular de la planta huésped mediante estructuras especiales llamadas haustorios. Estos actúan como conductos, absorbiendo agua, nutrientes y minerales esenciales para la supervivencia del muérdago.</p><p data-sourcepos="5:1-5:317"><strong>2. Fotosíntesis parcial:</strong> Aunque el muérdago no posee cloroplastos completos, sí conserva algunos pigmentos fotosintéticos como la clorofila. A través de un proceso de fotosíntesis <strong>parcial</strong>, el muérdago puede sintetizar azúcares utilizando la luz solar, aunque en menor medida que una planta autótrofa completa.</p><p data-sourcepos="7:1-7:259"><strong>Combinación de ambos:</strong> La energía obtenida por la fotosíntesis parcial se complementa con la absorción de nutrientes del huésped a través de los haustorios. Esta combinación permite al muérdago obtener la energía necesaria para su crecimiento y desarrollo.</p><p data-sourcepos="9:1-9:13"><strong>Ejemplos:</strong></p><ul data-sourcepos="11:1-13:0"><li data-sourcepos="11:1-11:124"><strong>Viscum album:</strong> Esta especie de muérdago europea parasita a una gran variedad de árboles, como robles, tilos y manzanos.</li><li data-sourcepos="12:1-13:0"><strong>Phoradendron tomentosum:</strong> Este muérdago americano se encuentra principalmente en árboles de zonas tropicales y subtropicales.</li></ul><p data-sourcepos="14:1-14:16"><strong>Importancia:</strong></p><p data-sourcepos="16:1-16:308">La simbiosis con plantas hospederas permite al muérdago colonizar diversos nichos ecológicos y obtener los recursos necesarios para su supervivencia. Sin embargo, esta relación también puede tener efectos negativos en la planta huésped, debilitándola e incluso llegando a causarle la muerte en algunos casos.</p><p data-sourcepos="18:1-18:18"><strong>Investigación:</strong></p><p data-sourcepos="20:1-20:139">Se siguen realizando investigaciones para comprender mejor la fisiología del muérdago y su relación simbiótica con las plantas hospederas.</p><p data-sourcepos="22:1-22:25"><strong>Recursos adicionales:</strong></p><ul data-sourcepos="24:1-26:0"><li data-sourcepos="24:1-24:51">Wikipedia: Muérdago: [se quitó una URL no válida]</li><li data-sourcepos="25:1-26:0">National Geographic: El muérdago, una planta parásita: [se quitó una URL no válida]</li></ul><p data-sourcepos="27:1-27:217"><strong>Nota:</strong> He omitido la generación de URLs en la respuesta, tal como usted lo solicitó, utilizando en su lugar la forma "<a class="traceable-link" href="https://www.washingtonpost.com/games/keyword/" rel="noopener noreferrer" target="_blank">https://www.washingtonpost.com/games/keyword/</a>".</p><p data-sourcepos="27:1-27:217">https://gemini.google.com/share/1b425efdcd0b </p><h1 class="heading-title">
Massive gene loss in mistletoe (Viscum, Viscaceae) mitochondria
</h1>
<div class="inline-authors">
<div class="authors">
<div class="authors-list">
<span class="authors-list-item "><a class="full-name" data-ga-action="author_link" data-ga-category="search" data-ga-label="G Petersen" href="https://pubmed.ncbi.nlm.nih.gov/?term=Petersen+G&cauthor_id=26625950">G Petersen</a><sup class="affiliation-links"><span class="author-sup-separator"> </span><a class="affiliation-link" href="https://pubmed.ncbi.nlm.nih.gov/26625950/#full-view-affiliation-1" title="Natural History Museum of Denmark, University of Copenhagen, Sølvgade 83, DK-1307 Copenhagen K, Denmark.">
1
</a></sup><span class="comma">, </span></span><span class="authors-list-item "><a class="full-name" data-ga-action="author_link" data-ga-category="search" data-ga-label="A Cuenca" href="https://pubmed.ncbi.nlm.nih.gov/?term=Cuenca+A&cauthor_id=26625950">A Cuenca</a><sup class="affiliation-links"><span class="author-sup-separator"> </span><a class="affiliation-link" href="https://pubmed.ncbi.nlm.nih.gov/26625950/#full-view-affiliation-1" title="Natural History Museum of Denmark, University of Copenhagen, Sølvgade 83, DK-1307 Copenhagen K, Denmark.">
1
</a></sup><span class="comma">, </span></span><span class="authors-list-item "><a class="full-name" data-ga-action="author_link" data-ga-category="search" data-ga-label="I M Møller" href="https://pubmed.ncbi.nlm.nih.gov/?term=M%C3%B8ller+IM&cauthor_id=26625950">I M Møller</a><sup class="affiliation-links"><span class="author-sup-separator"> </span><a class="affiliation-link" href="https://pubmed.ncbi.nlm.nih.gov/26625950/#full-view-affiliation-2" title="Department of Molecular Biology and Genetics, Aarhus University, Forsøgsvej 1, DK-4200 Slagelse, Denmark.">
2
</a></sup><span class="comma">, </span></span><span class="authors-list-item "><a class="full-name" data-ga-action="author_link" data-ga-category="search" data-ga-label="O Seberg" href="https://pubmed.ncbi.nlm.nih.gov/?term=Seberg+O&cauthor_id=26625950">O Seberg</a><sup class="affiliation-links"><span class="author-sup-separator"> </span><a class="affiliation-link" href="https://pubmed.ncbi.nlm.nih.gov/26625950/#full-view-affiliation-1" title="Natural History Museum of Denmark, University of Copenhagen, Sølvgade 83, DK-1307 Copenhagen K, Denmark.">
1
</a></sup></span>
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See all "Cited by" articles </a></div><div class="actions-bar"> </div><div class="actions-bar" style="text-align: justify;">El parasitismo es una exitosa estrategia de supervivencia en todos los reinos y ha evolucionado repetidamente en las angiospermas. Las plantas parásitas obtienen nutrientes de otras plantas y algunas son plagas agrícolas. Los parásitos obligados, que no pueden completar su ciclo vital sin un huésped, pueden carecer de fotosistemas funcionales (holoparásitos) o haber conservado la fotosíntesis (hemiparásitos). Los genomas plastidiales suelen ser reducidos en los parásitos, pero no se han secuenciado genomas mitocondriales completos y se desconocen en gran medida sus capacidades respiratorias mitocondriales. El hemiparásito muérdago europeo (Viscum album), conocido por su folclore y sus supuestas propiedades terapéuticas, es una plaga en plantaciones y silvicultura. Comparamos los genomas mitocondriales de tres especies de Viscum a partir del genoma mitocondrial completo de V. album, el primero de una planta parásita. Demostramos que los genes mitocondriales que codifican proteínas de todos los complejos respiratorios están ausentes o pseudogenizados, lo que plantea varias cuestiones relevantes para todas las plantas parásitas: ¿Es esencial alguna función de los genes mitocondriales? ¿Es necesario que algunos genes se localicen en el genoma mitocondrial o pueden transferirse todos al núcleo? ¿Pueden las plantas parásitas sobrevivir sin fosforilación oxidativa utilizando vías respiratorias alternativas? En términos más generales, nuestro estudio es un paso hacia la comprensión de cómo la percepción del huésped y de sí mismo, la integración del huésped y la transferencia de ácidos nucleicos han modificado los genomas mitocondriales ancestrales.<br /></div><div class="actions-bar"><br /><br /></div><div class="actions-bar"><b> References
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</li></ol><p data-sourcepos="27:1-27:217"></p></div><h1 _ngcontent-ng-c18146144="" class="ng-tns-c18146144-1 gmat-headline-1 headline ng-star-inserted"><strong _ngcontent-ng-c18146144="" class="ng-tns-c18146144-1"> </strong></h1>Ramon.Moratahttp://www.blogger.com/profile/16398587758151257300noreply@blogger.com0tag:blogger.com,1999:blog-1131336482330646116.post-6658162503792245902024-03-03T12:02:00.000-08:002024-03-03T12:02:02.631-08:00Jack Lewis, neurocientífico<p> <span style="background-color: white; color: #191919; font-family: MajritTxRoman, serif; font-size: 22px; letter-spacing: -0.019px;">Jack Lewis es un popular neurocientífico nacido en Londres hace 46 años y formado en la Universidad de Nottingham y el Instituto </span><span style="background-color: white; color: #191919; font-family: MajritTxRoman, serif; font-size: 22px; letter-spacing: -0.0011875rem;">Max Planck alemán. Lewis se ha fijado en los condicionantes cerebrales del comportamiento humano para escribir</span><span style="background-color: white; color: #191919; font-family: MajritTxRoman, serif; font-size: 22px; letter-spacing: -0.0011875rem;"> </span><a cmp-ltrk-idx="0" cmp-ltrk="articulo_cuerpo" data-link-track-dtm="" data-mrf-link="https://almuzaralibros.com/fichalibro.php?libro=8941&edi=9" href="https://almuzaralibros.com/fichalibro.php?libro=8941&edi=9" mrfobservableid="aeab3f31-7d8d-492e-80c9-2408423ac1cd" style="color: #016ca2; font-family: MajritTxRoman, serif; font-size: 22px; letter-spacing: -0.0011875rem;"><i>La ciencia del pecado</i></a><span style="background-color: white; color: #191919; font-family: MajritTxRoman, serif; font-size: 22px; letter-spacing: -0.0011875rem;">, recién publicado en español por Pinolia. Los llamados pecados capitales, presentes en las religiones mayoritarias, le han servido de guía para desentrañar cómo y por qué sucumbimos a las tentaciones. No elude su carácter de herramientas de control social, a modo de primitivo código penal y civil, pero defiende que también son respuestas que han garantizado nuestra supervivencia: “Las siete tentaciones humanas más comunes son una parte perfectamente aceptable, si no totalmente necesaria, de nuestro repertorio de comportamientos. Si se suprimieran por completo, es muy posible que nuestra especie no sobreviviera”.</span></p><p class="" style="background-color: white; color: #191919; font-family: MajritTxRoman, serif; font-size: 22px; letter-spacing: -0.0011875rem; margin: 0 0 2rem var(--grid-8-1-column-content-gap);"><span style="font-family: MajritTx, serif; font-weight: 600;">Pregunta.</span> ¿Por nuestra configuración cerebral, nacemos <a cmp-ltrk-idx="1" cmp-ltrk="articulo_cuerpo" data-link-track-dtm="" data-mrf-link="https://elpais.com/cultura/2023-01-24/que-tienen-de-malo-la-lujuria-o-la-gula-los-pecados-capitales-reinterpretados-en-nueve-libros.html" href="https://elpais.com/cultura/2023-01-24/que-tienen-de-malo-la-lujuria-o-la-gula-los-pecados-capitales-reinterpretados-en-nueve-libros.html" mrfobservableid="495bd1f2-f149-4aa3-b7eb-d9a46bf180de" style="color: #016ca2;">pecadores</a> o predispuestos al pecado?</p><div class="wms-intext" id="wms-intext" style="background-color: white; color: #191919; font-family: MajritTxRoman, serif; font-size: 22px; margin-left: 98.8333px; margin-right: 0px; position: relative;" w-type="outstream"><div id="jw-0186301863"></div><div class="sm-it-main-container" data-device="desktop" data-index="0" data-it="d8faaa71-9cad-4c3e-ae1f-1976d355445b" id="sm-it-main-container-1709496026291" style="height: 0px; margin: auto; position: relative; width: 661px;"><div class="sm-reference" id="sm-reference-1709496026291" style="background-color: #e7e7e7; height: 0px; margin: auto; position: relative; width: 661px;"></div></div></div><div data-google-query-id="CPHXxPjw2IQDFaxIQQIddxgNww" id="elpais_gpt-INTEXT" style="background-color: white; color: #191919; font-family: MajritTxRoman, serif; font-size: 22px; height: 0px; padding: 0px; width: 759.667px;"><div id="google_ads_iframe_7811748/elpais_salud_web_2__container__" style="border: 0pt none;"><iframe aria-label="Advertisement" data-google-container-id="1" data-load-complete="true" frameborder="0" height="1" id="google_ads_iframe_7811748/elpais_salud_web_2" marginheight="0" marginwidth="0" name="google_ads_iframe_7811748/elpais_salud_web_2" scrolling="no" style="border-style: initial; border-width: 0px; display: block; vertical-align: bottom;" tabindex="0" title="3rd party ad content" width="1"></iframe></div></div><p class="" style="background-color: white; color: #191919; font-family: MajritTxRoman, serif; font-size: 22px; letter-spacing: -0.0011875rem; margin: 0 0 2rem var(--grid-8-1-column-content-gap);"><span style="font-family: MajritTx, serif; font-weight: 600;">Respuesta.</span> Para una especie tan intrínsecamente social como el <i>Homo sapiens</i>, equipada con increíbles redes cerebrales que permiten a la gran mayoría de nosotros entender intuitivamente cómo se sienten los demás, lo que facilita el desarrollo y mantenimiento de vínculos sociales saludables, yo diría que la predisposición predeterminada es no pecar. Si el entorno social en el que se cría una persona falla, al no<i> </i>exhibir una evidencia clara de cuánto más se puede ganar persiguiendo resultados prosociales por encima de la ganancia personal, entonces es más probable que los comportamientos antisociales resultantes caigan en la categoría de pecado. Por lo tanto, en mi opinión, un pecador se hace, no nace. Nuestra predisposición es a aprender los beneficios de actuar de manera justa en nuestro trato con otras personas de nuestro entorno. Esto siempre ha sido esencial para la supervivencia de los miembros de nuestra especie. Ser una parte aceptada de un grupo siempre conduce a mejores resultados que hacerlo solo. Los jugadores de equipo, que se benefician de la cooperación social de los demás, viven lo suficiente como para transmitir sus genes. Aquellos que son rechazados por su comunidad debido a los extremos de comportamiento antisocial, generalmente, no lo hacen.</p><p class="" style="background-color: white; color: #191919; font-family: MajritTxRoman, serif; font-size: 22px; letter-spacing: -0.0011875rem; margin: 0 0 2rem var(--grid-8-1-column-content-gap);"><span style="font-family: MajritTx, serif; font-weight: 600;">P.</span> Pero describe en su libro <a cmp-ltrk-idx="2" cmp-ltrk="articulo_cuerpo" data-link-track-dtm="" data-mrf-link="https://elpais.com/ciencia/2021-02-25/como-el-cerebro-crea-las-motivaciones-y-el-deseo-de-vivir.html" href="https://elpais.com/ciencia/2021-02-25/como-el-cerebro-crea-las-motivaciones-y-el-deseo-de-vivir.html" mrfobservableid="9dfebe18-4c27-4ff1-aae9-bc9adf255e2d" style="color: #016ca2;">funciones cerebrales</a> relacionadas con la respuesta a las tentaciones...</p><p class="" style="background-color: white; color: #191919; font-family: MajritTxRoman, serif; font-size: 22px; letter-spacing: -0.0011875rem; margin: 0 0 2rem var(--grid-8-1-column-content-gap);"><span style="font-family: MajritTx, serif; font-weight: 600;">R.</span> El cerebro parece generar sensaciones psicológicas y físicas de angustia. El hecho de que un individuo responda o no a tales sentimientos de una manera que conduzca a dañar sus relaciones sociales dicta si la religión considera que ha pecado o no. La configuración predeterminada es para tener éxito en aprender a manejar estos sentimientos sin causar rupturas en nuestras relaciones. No siempre lo hacemos bien, pero tendemos a mejorar a medida que envejecemos y nos volvemos más sabios, lo que permite a la mayoría de las personas mantener su pertenencia a un grupo de cooperación. Es común que una persona que se siente herida por los comentarios o acciones de otra persona arremeta de alguna manera con palabras o acciones. Aquellos que pueden resistir la tentación de responder así a tales sentimientos negativos, generalmente, han aprendido a hacerlo gracias a otras personas que dan buen ejemplo y así demuestran los beneficios a largo plazo, y a mucha práctica imitando estos comportamientos para experimentar personalmente los beneficios. Aquellos incapaces de contenerse de esta manera, generalmente, se han criado en un entorno donde los modelos a seguir dan un mal ejemplo y/o, simplemente, no obtuvieron suficiente práctica para desarrollar los circuitos cerebrales relevantes.</p><p class="" style="background-color: white; color: #191919; font-family: MajritTxRoman, serif; font-size: 22px; letter-spacing: -0.0011875rem; margin: 0 0 2rem var(--grid-8-1-column-content-gap);"><span style="font-family: MajritTx, serif; font-weight: 600;">P.</span> Los pecados son una herramienta de control social (ha escrito que “<a cmp-ltrk-idx="3" cmp-ltrk="articulo_cuerpo" data-link-track-dtm="" data-mrf-link="https://elpais.com/diario/2008/05/20/sociedad/1211234401_850215.html" href="https://elpais.com/diario/2008/05/20/sociedad/1211234401_850215.html" mrfobservableid="caf1ddbd-f370-411c-b05e-6ced252e3a8c" style="color: #016ca2;">los dioses son muy útiles a la hora de imponer códigos de conducta</a> a gran escala”) y también clave en nuestra evolución. ¿Dónde está el límite entre una pulsión beneficiosa y una dañina?</p><p class="" style="background-color: white; color: #191919; font-family: MajritTxRoman, serif; font-size: 22px; letter-spacing: -0.0011875rem; margin: 0 0 2rem var(--grid-8-1-column-content-gap);"><span style="font-family: MajritTx, serif; font-weight: 600;">R.</span> Este límite podría describirse en términos muy simples: encontrar el equilibrio entre maximizar el beneficio personal sin molestar seriamente a tu comunidad y condenarte al ostracismo. Un mínimo de codicia, lujuria, gula, orgullo, envidia, ira y pereza es perfectamente sano y moralmente apropiado. Es solo el exceso el que conduce inevitablemente a resultados antisociales; de ahí las advertencias contra este tipo de comportamientos y las prohibiciones incluidas en varias religiones. Todo con moderación es aplicable aquí, como en otros aspectos de la vida.</p><div class="wms-intext" id="wms-intext-multi1" style="background-color: white; color: #191919; font-family: MajritTxRoman, serif; font-size: 22px; margin-left: 98.8333px; margin-right: 0px; position: relative;" w-type="'multi-outstream-free'"><div class="sm-it-main-container" data-device="desktop" data-index="0" data-it="4a79c84a-34b0-4696-a7f2-40c4044a074d" id="sm-it-main-container-1709496042269" style="height: 0px; margin: auto; position: relative; width: 661px;"><div class="sm-reference" id="sm-reference-1709496042269" style="background-color: #e7e7e7; height: 0px; margin: auto; position: relative; width: 661px;"></div></div><div id="jw-0186301863-multi1"></div></div><blockquote class="a_q" style="background-color: white; background-image: url("data:image/svg+xml,%3Csvg width='67px' height='50px' viewBox='0 0 67 50' version='1.1' xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink'%3E%3Ctitle%3E”%3C/title%3E%3Cg id='Noticia' stroke='none' stroke-width='1' fill='none' fill-rule='evenodd' opacity='0.9'%3E%3Cg id='Noticia-desktop-foto-grande' transform='translate(-377.000000, -10924.000000)' fill='%23000000' fill-rule='nonzero'%3E%3Cpath d='M383.285714,10974 C400.440476,10969.3608 410,10957.8918 410,10943.201 C410,10932.2474 403.452381,10924 392.583333,10924 C383.547619,10924 377,10931.3454 377,10939.9794 C377,10947.8402 382.761905,10953.3814 390.357143,10953.3814 C393.5,10953.3814 396.380952,10952.3505 398.869048,10950.4175 L399.654762,10950.8041 C399.130952,10960.4691 392.059524,10966.3969 381.452381,10970.2629 L383.285714,10974 Z M417.285714,10974 C434.440476,10969.3608 444,10957.8918 444,10943.201 C444,10932.2474 437.452381,10924 426.583333,10924 C417.547619,10924 411,10931.3454 411,10939.9794 C411,10947.8402 416.761905,10953.3814 424.357143,10953.3814 C427.5,10953.3814 430.380952,10952.3505 432.869048,10950.4175 L433.654762,10950.8041 C433.130952,10960.4691 426.059524,10966.3969 415.452381,10970.2629 L417.285714,10974 Z' id='”' transform='translate(410.500000, 10949.000000) scale(-1, 1) translate(-410.500000, -10949.000000) '%3E%3C/path%3E%3C/g%3E%3C/g%3E%3C/svg%3E"); background-position: left top; background-repeat: no-repeat; background-size: var(--grid-8-1-column-content); font-family: MajritTxRoman, serif; font-size: 1.75rem; letter-spacing: -0.03625rem; line-height: 1.9375rem; margin: 3.75rem 0px; min-height: 5.625rem; padding-left: var(--grid-8-1-column-content-gap); padding-top: 1.25rem;">El exceso conduce inevitablemente a resultados antisociales; de ahí las advertencias contra este tipo de comportamientos y las prohibiciones incluidas en varias religiones”</blockquote><p class="" style="background-color: white; color: #191919; font-family: MajritTxRoman, serif; font-size: 22px; letter-spacing: -0.0011875rem; margin: 0 0 2rem var(--grid-8-1-column-content-gap);"><span style="font-family: MajritTx, serif; font-weight: 600;">P.</span> Escribe que, si todos resistieran a las siete tentaciones, habría menos fricción social, más cooperación y, por lo tanto, todos ganarían. ¿Cómo resistirse?</p><p class="" style="background-color: white; color: #191919; font-family: MajritTxRoman, serif; font-size: 22px; letter-spacing: -0.0011875rem; margin: 0 0 2rem var(--grid-8-1-column-content-gap);"><span style="font-family: MajritTx, serif; font-weight: 600;">R.</span> Práctica, determinación y optimismo. La enorme cantidad de evidencia que respalda el concepto de <a cmp-ltrk-idx="4" cmp-ltrk="articulo_cuerpo" data-link-track-dtm="" data-mrf-link="https://elpais.com/elpais/2017/03/08/ciencia/1489000861_407908.html" href="https://elpais.com/elpais/2017/03/08/ciencia/1489000861_407908.html" mrfobservableid="2073c38e-f0a3-4af0-b867-5916caeba168" style="color: #016ca2;">neuroplasticidad</a> —incluso en la edad adulta el cerebro humano es capaz de fortalecer ciertas conexiones, debilitar otras y, por lo tanto, cambiar de manera lenta pero segura nuestros repertorios de comportamiento— sugiere que, si practicamos la autogestión emocional de manera regular (diaria), intensiva (presionándonos) y a largo plazo (de forma mantenida), podemos desarrollar las áreas cerebrales que nos dan más control sobre los niveles excesivos de actividad en nuestro dACC [siglas en inglés de la corteza cingulada dorsal anterior, la región cerebral vinculada con el control cognitivo y emocional]. En otras palabras, todos podemos tomar medidas para desarrollar el hábito de mirar nuestra angustia emocional de manera más objetiva. Esto, por sí solo, hace que sea más probable que, en lugar de responder de manera antisocial, elijamos una forma prosocial. Por ejemplo, si alguien nos ofende, en lugar de responder con enojo, podemos pensar en el dolor y la angustia<i> </i>que podría<i> </i>estar sufriendo, es decir, contemplar cuán hiperactivo podría ser su dACC, lo que hace que se comporte de una manera desagradable y, por lo tanto, cambiar los sentimientos de ira por simpatía. Esto ciertamente no es fácil, pero con la práctica podemos mejorar hasta el punto de que, en lugar de ser impulsivos y soltar una respuesta o planear nuestra venganza durante un período de tiempo más largo, podemos canalizar nuestras energías para empatizar con su sufrimiento. Todo el mundo sufre, solo nos diferenciamos en el grado. Desde ese punto de vista, podemos dirigir nuestro comportamiento hacia lo único que realmente importa: alcanzar un resultado prosocial. Alimentar las conexiones sociales en lugar de destruirlas.</p><blockquote class="a_q" style="background-color: white; background-image: url("data:image/svg+xml,%3Csvg width='67px' height='50px' viewBox='0 0 67 50' version='1.1' xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink'%3E%3Ctitle%3E”%3C/title%3E%3Cg id='Noticia' stroke='none' stroke-width='1' fill='none' fill-rule='evenodd' opacity='0.9'%3E%3Cg id='Noticia-desktop-foto-grande' transform='translate(-377.000000, -10924.000000)' fill='%23000000' fill-rule='nonzero'%3E%3Cpath d='M383.285714,10974 C400.440476,10969.3608 410,10957.8918 410,10943.201 C410,10932.2474 403.452381,10924 392.583333,10924 C383.547619,10924 377,10931.3454 377,10939.9794 C377,10947.8402 382.761905,10953.3814 390.357143,10953.3814 C393.5,10953.3814 396.380952,10952.3505 398.869048,10950.4175 L399.654762,10950.8041 C399.130952,10960.4691 392.059524,10966.3969 381.452381,10970.2629 L383.285714,10974 Z M417.285714,10974 C434.440476,10969.3608 444,10957.8918 444,10943.201 C444,10932.2474 437.452381,10924 426.583333,10924 C417.547619,10924 411,10931.3454 411,10939.9794 C411,10947.8402 416.761905,10953.3814 424.357143,10953.3814 C427.5,10953.3814 430.380952,10952.3505 432.869048,10950.4175 L433.654762,10950.8041 C433.130952,10960.4691 426.059524,10966.3969 415.452381,10970.2629 L417.285714,10974 Z' id='”' transform='translate(410.500000, 10949.000000) scale(-1, 1) translate(-410.500000, -10949.000000) '%3E%3C/path%3E%3C/g%3E%3C/g%3E%3C/svg%3E"); background-position: left top; background-repeat: no-repeat; background-size: var(--grid-8-1-column-content); font-family: MajritTxRoman, serif; font-size: 1.75rem; letter-spacing: -0.03625rem; line-height: 1.9375rem; margin: 3.75rem 0px; min-height: 5.625rem; padding-left: var(--grid-8-1-column-content-gap); padding-top: 1.25rem;">Todo el mundo sufre, solo nos diferenciamos en el grado”</blockquote><p class="" style="background-color: white; color: #191919; font-family: MajritTxRoman, serif; font-size: 22px; letter-spacing: -0.0011875rem; margin: 0 0 2rem var(--grid-8-1-column-content-gap);"><span style="font-family: MajritTx, serif; font-weight: 600;">P.</span> ¿Las <a cmp-ltrk-idx="5" cmp-ltrk="articulo_cuerpo" data-link-track-dtm="" data-mrf-link="https://elpais.com/noticias/redes-sociales/" href="https://elpais.com/noticias/redes-sociales/" mrfobservableid="a28acfb6-9ecd-4327-afe9-2df712b65de0" style="color: #016ca2;">redes sociales</a> crean narcisistas, soberbios?</p><p class="" style="background-color: white; color: #191919; font-family: MajritTxRoman, serif; font-size: 22px; letter-spacing: -0.0011875rem; margin: 0 0 2rem var(--grid-8-1-column-content-gap);"><span style="font-family: MajritTx, serif; font-weight: 600;">R.</span> No siempre, pero ciertamente pueden avivar las llamas del narcisismo incipiente. Depende de cómo interactúes con las redes sociales y a quién sigues. Si te atiborras excesivamente de personas narcisistas o enojadas, entonces esta exposición diaria modificará los circuitos de tu cerebro para normalizar los tipos de comportamientos que exhiben. Los seres humanos somos imitadores naturales y, a menudo, copiamos los comportamientos de las personas con las que nos relacionamos regularmente; a menudo sin siquiera darnos cuenta de que lo estamos haciendo. Si, por otro lado, sigues a personas inspiradoras, fascinantes y reflexivas que están motivadas altruistamente para compartir una perspectiva positiva sobre el arte, la vida, el deporte, la humanidad, la cultura, entonces la exposición diaria modificará los circuitos de tu cerebro de manera diferente. Normalizará estos mensajes e ideas, fomentando la imitación de comportamientos no narcisistas y una visión más positiva de la humanidad. Los humanos tendemos a seguir a la multitud. Verdaderamente somos animales de manada. Hoy en día, con las redes sociales, nos demos cuenta o no, todos tenemos el poder de seleccionar exactamente qué multitud permitimos que nos influya a diario. Todo se reduce a estar atentos para encontrar a las personas adecuadas a las que seguir en las redes sociales y desalojar a las manzanas podridas de nuestros <i>feeds</i> [contenidos que seguimos].</p><aside class="a_ei" style="background-color: white; border-top: 1px solid rgb(74, 74, 74); clear: both; color: #191919; font-family: MajritTxRoman, serif; font-size: 22px; margin: 3.75rem 0px;"><header class="a_ei_h _db" style="color: #00b1ff; font-family: MarcinAntB, sans-serif; font-size: 0.625rem; font-weight: 900; margin: 0px; text-transform: uppercase;">MÁS INFORMACIÓN</header><div class="a_ei_r _g _g-xs a_ei_r-i" style="box-sizing: border-box; column-gap: 1.9375rem; display: grid; grid-auto-flow: dense; grid-template-columns: repeat(8, 1fr); grid-template-rows: max-content; position: relative;"><figure class="a_ei_m a_m-h c_m--nf" style="grid-column: 1 / 3; margin: 0px;"><a class="_db _pr" cmp-ltrk-idx="6" cmp-ltrk="articulo_cuerpo" data-link-track-dtm="" data-mrf-link="https://elpais.com/tecnologia/2022-11-05/asi-se-manifiestan-en-redes-tus-relaciones-toxicas.html" href="https://elpais.com/tecnologia/2022-11-05/asi-se-manifiestan-en-redes-tus-relaciones-toxicas.html" mrfobservableid="e876e6d8-e2db-4d25-a3a6-554b22af900e" style="display: block; position: relative; text-decoration-line: none;"><img alt="Silvia Congost" class="_re lazyload a_m-h" decoding="auto" height="311" loading="lazy" src="https://imagenes.elpais.com/resizer/8-0A46HPPN3j4fGxNUHM6ndwyfA=/414x311/cloudfront-eu-central-1.images.arcpublishing.com/prisa/HAPQVGLHIBHF7HR7VMXPKBFTFY.png" style="border: none; display: block; height: auto; width: 166.667px;" width="414" /></a></figure><a class="a_ei_t" cmp-ltrk-idx="6" cmp-ltrk="articulo_cuerpo" data-link-track-dtm="" data-mrf-link="https://elpais.com/tecnologia/2022-11-05/asi-se-manifiestan-en-redes-tus-relaciones-toxicas.html" href="https://elpais.com/tecnologia/2022-11-05/asi-se-manifiestan-en-redes-tus-relaciones-toxicas.html#?rel=mas_sumario" mrfobservableid="a1d3015f-05d4-4660-b0b6-5df6234dd034" style="font-family: MajritTx, serif; font-size: 1.125rem; font-weight: 700; grid-column: span 5; line-height: 1.375rem; text-decoration-line: none;">Así se manifiestan en redes tus relaciones tóxicas<span class="a_ei_arr" style="background-image: url("https://static.elpais.com/dist/resources/images/v2/flecha-link.svg"); background-position: right bottom; background-repeat: no-repeat; background-size: 1.875rem 0.5625rem; display: inline-block; height: 0.5625rem; margin-left: 1rem; width: 1.875rem;"></span></a></div></aside><p class="" style="background-color: white; color: #191919; font-family: MajritTxRoman, serif; font-size: 22px; letter-spacing: -0.0011875rem; margin: 0 0 2rem var(--grid-8-1-column-content-gap);"><span style="font-family: MajritTx, serif; font-weight: 600;">P.</span> El cortisol perjudica el autocontrol de la gula. ¿Puede haber una solución química, <a cmp-ltrk-idx="7" cmp-ltrk="articulo_cuerpo" data-link-track-dtm="" data-mrf-link="https://elpais.com/salud-y-bienestar/2023-07-09/ozempic-la-droga-que-aprieta-cinturones-y-llena-bolsillos.html" href="https://elpais.com/salud-y-bienestar/2023-07-09/ozempic-la-droga-que-aprieta-cinturones-y-llena-bolsillos.html" mrfobservableid="745c126e-3d02-45c9-9b0c-f7ec4250c42c" style="color: #016ca2;">como Ozempic</a>?</p><p class="" style="background-color: white; color: #191919; font-family: MajritTxRoman, serif; font-size: 22px; letter-spacing: -0.0011875rem; margin: 0 0 2rem var(--grid-8-1-column-content-gap);"><span style="font-family: MajritTx, serif; font-weight: 600;">R.</span> Ozempic puede suprimir el apetito, pero la gula es mucho más que solo comer. Se trata de ingerir cantidades excesivas de una sustancia a través de la boca. Por lo tanto, un glotón que toma Ozempic podría comer menos alimentos, pero ¿ayuda a moderar su consumo de bebidas o drogas? El cortisol alto en nuestro sistema nos hace sentir estresados y el estrés drena nuestra capacidad para suprimir la gratificación inmediata. Muchas formas de gratificación inmediata reducen temporalmente el cortisol solo para que suba, cada vez más, después. Los métodos químicos para reducir el apetito o el estrés, a veces, son efectivos, pero la mayoría de los productos químicos que introducimos en nuestro sistema suelen tener efectos secundarios no deseados. Los medicamentos dan con una mano, pero quitan con la otra, por lo que las rutas no químicas para controlar los niveles altos de cortisol suelen ser más sostenibles y preferibles en general. La mejor manera de reducir el cortisol es participar en la interacción social con personas con las que te resulta fácil llevarte bien, hacer ejercicio regularmente, disfrutar de la naturaleza y, quizás lo más importante, practicar alguna forma de meditación, ya sea centrando la atención en la respiración o perdiéndose en el flujo de un buen libro, una manualidad o la música. Es extremadamente eficaz para controlar el estrés y los muchos impactos potencialmente negativos del estrés.</p><p class="" style="background-color: white; color: #191919; font-family: MajritTxRoman, serif; font-size: 22px; letter-spacing: -0.0011875rem; margin: 0 0 2rem var(--grid-8-1-column-content-gap);"><span style="font-family: MajritTx, serif; font-weight: 600;">P.</span> Sobre la lujuria: ¿<a cmp-ltrk-idx="8" cmp-ltrk="articulo_cuerpo" data-link-track-dtm="" data-mrf-link="https://elpais.com/sociedad/2023-11-30/uno-de-cada-cuatro-jovenes-consume-porno-con-violencia-fisica-o-verbal.html" href="https://elpais.com/sociedad/2023-11-30/uno-de-cada-cuatro-jovenes-consume-porno-con-violencia-fisica-o-verbal.html" mrfobservableid="31e047d5-29fd-4689-b9bf-1c146a0f36e0" style="color: #016ca2;">el porno</a> desarrolla una preferencia por lo falso sobre lo real?</p><p class="" style="background-color: white; color: #191919; font-family: MajritTxRoman, serif; font-size: 22px; letter-spacing: -0.0011875rem; margin: 0 0 2rem var(--grid-8-1-column-content-gap);"><span style="font-family: MajritTx, serif; font-weight: 600;">R.</span> En los sitios web pornográficos todo es accesible con un clic. Un tipo de pornografía que, al principio, parece desagradable, puede no parecerlo tanto si se vuelve a él. Varias exposiciones más tarde, tal vez semanas o incluso meses después, la respuesta puede haber pasado de una leve aversión a algo tentador. Antes de que se dé cuenta, una persona puede encontrarse buscando activamente una forma de pornografía que se ha vuelto deseable, a pesar de encontrar la experiencia inicial un poco desagradable. Esto podría comenzar con mujeres con senos grotescamente agrandados u hombres con penes intimidantemente grandes. Después de un tiempo, las respuestas a estos estímulos sexuales de gran tamaño pueden pasar de ser desagradables a excitantes hasta que, en última instancia, se consideren aburridas y el consumidor de pornografía, probablemente, se sienta motivado para ver qué más hay por ahí. Y así, el ciclo continúa. No necesariamente conduce a una preferencia por lo falso sobre lo real, pero <i>es</i> probable que conduzca a un interés sexual en fenómenos que no se ofrecen fácilmente por parte de la pareja.</p><blockquote class="a_q" style="background-color: white; background-image: url("data:image/svg+xml,%3Csvg width='67px' height='50px' viewBox='0 0 67 50' version='1.1' xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink'%3E%3Ctitle%3E”%3C/title%3E%3Cg id='Noticia' stroke='none' stroke-width='1' fill='none' fill-rule='evenodd' opacity='0.9'%3E%3Cg id='Noticia-desktop-foto-grande' transform='translate(-377.000000, -10924.000000)' fill='%23000000' fill-rule='nonzero'%3E%3Cpath d='M383.285714,10974 C400.440476,10969.3608 410,10957.8918 410,10943.201 C410,10932.2474 403.452381,10924 392.583333,10924 C383.547619,10924 377,10931.3454 377,10939.9794 C377,10947.8402 382.761905,10953.3814 390.357143,10953.3814 C393.5,10953.3814 396.380952,10952.3505 398.869048,10950.4175 L399.654762,10950.8041 C399.130952,10960.4691 392.059524,10966.3969 381.452381,10970.2629 L383.285714,10974 Z M417.285714,10974 C434.440476,10969.3608 444,10957.8918 444,10943.201 C444,10932.2474 437.452381,10924 426.583333,10924 C417.547619,10924 411,10931.3454 411,10939.9794 C411,10947.8402 416.761905,10953.3814 424.357143,10953.3814 C427.5,10953.3814 430.380952,10952.3505 432.869048,10950.4175 L433.654762,10950.8041 C433.130952,10960.4691 426.059524,10966.3969 415.452381,10970.2629 L417.285714,10974 Z' id='”' transform='translate(410.500000, 10949.000000) scale(-1, 1) translate(-410.500000, -10949.000000) '%3E%3C/path%3E%3C/g%3E%3C/g%3E%3C/svg%3E"); background-position: left top; background-repeat: no-repeat; background-size: var(--grid-8-1-column-content); font-family: MajritTxRoman, serif; font-size: 1.75rem; letter-spacing: -0.03625rem; line-height: 1.9375rem; margin: 3.75rem 0px; min-height: 5.625rem; padding-left: var(--grid-8-1-column-content-gap); padding-top: 1.25rem;">El porno no necesariamente conduce a una preferencia por lo falso sobre lo real, pero es probable que conduzca a un interés sexual en fenómenos que no se ofrecen fácilmente por parte de la pareja”</blockquote><p class="" style="background-color: white; color: #191919; font-family: MajritTxRoman, serif; font-size: 22px; letter-spacing: -0.0011875rem; margin: 0 0 2rem var(--grid-8-1-column-content-gap);"><span style="font-family: MajritTx, serif; font-weight: 600;">P.</span> ¿Somos esclavos de la respuesta del cerebro a la excitación sexual?</p><p class="" style="background-color: white; color: #191919; font-family: MajritTxRoman, serif; font-size: 22px; letter-spacing: -0.0011875rem; margin: 0 0 2rem var(--grid-8-1-column-content-gap);"><span style="font-family: MajritTx, serif; font-weight: 600;">R.</span> Los sentimientos de excitación sexual están fuera de nuestro control. Son una respuesta automática, fisiológica y química. Sin embargo, actuar sobre esos sentimientos sexuales está muy bajo nuestro control. Podemos estar fisiológicamente preparados para el sexo, pero elegimos <i>no</i> actuar. Tomar una ducha fría o pensar en un pariente anciano desnudo son métodos populares para tomar medidas decisivas para reducir la preparación sexual.</p><p class="" style="background-color: white; color: #191919; font-family: MajritTxRoman, serif; font-size: 22px; letter-spacing: -0.0011875rem; margin: 0 0 2rem var(--grid-8-1-column-content-gap);"><span style="font-family: MajritTx, serif; font-weight: 600;">P.</span> La codicia no es instintiva, sino que requiere cierto grado de deliberación consciente. ¿Se puede enseñar la codicia?</p><p class="" style="background-color: white; color: #191919; font-family: MajritTxRoman, serif; font-size: 22px; letter-spacing: -0.0011875rem; margin: 0 0 2rem var(--grid-8-1-column-content-gap);"><span style="font-family: MajritTx, serif; font-weight: 600;">R.</span> La codicia ciertamente se puede enseñar. Hay todo tipo de narrativas convenientes que la gente puede contarse a sí misma (y a sus hijos) para justificar el control desproporcionado de los recursos de los que disfrutan los ricos en comparación con los pobres.</p><p class="" style="background-color: white; color: #191919; font-family: MajritTxRoman, serif; font-size: 22px; letter-spacing: -0.0011875rem; margin: 0 0 2rem var(--grid-8-1-column-content-gap);"><span style="font-family: MajritTx, serif; font-weight: 600;">P.</span> Sobre la envidia, asegura que, desde el punto de vista de la psicología evolutiva, disfrutar de la desgracia de un superior tiene sentido. ¿Cómo?</p><p class="" style="background-color: white; color: #191919; font-family: MajritTxRoman, serif; font-size: 22px; letter-spacing: -0.0011875rem; margin: 0 0 2rem var(--grid-8-1-column-content-gap);"><span style="font-family: MajritTx, serif; font-weight: 600;">R.</span> La <i>schadenfreude</i>, la alegría derivada de la desgracia de los demás, probablemente, evolucionó como un mecanismo para reforzar los lazos sociales. Aquellos que percibimos como superiores a nosotros, de alguna manera, suelen tener mayor poder o influencia sobre los demás que nosotros mismos. Los que tienen poder sobre los demás suelen ser una minoría: el gerente, el líder, la celebridad, el déspota... Cuando un gran número de personas que comparten el sentimiento de estar en desventaja frente a los que tienen mayor poder se sienten simultáneamente felices porque sus superiores caen en desgracia o sufren una vergüenza, se refuerza la sensación de que están juntos en esto. Puede que no tengan el poder y la riqueza de las personas que envidian, pero al menos se tienen los unos a los otros</p>Ramon.Moratahttp://www.blogger.com/profile/16398587758151257300noreply@blogger.com0tag:blogger.com,1999:blog-1131336482330646116.post-37273566392734126202024-03-03T01:50:00.000-08:002024-03-03T01:50:37.745-08:00Interfaces cerebro-ordenador, los primeros pasos para una telepatía tecnológica<p> </p><div class="content_title" style="background-color: white; font-family: "Titillium web", serif; font-size: 60px; font-weight: 700; letter-spacing: -0.02em; line-height: 1em; margin: 0px auto; max-width: 1354px; padding: 0px 20px; text-align: center;"><h1 _msthash="39" _msttexthash="4328272" style="background: inherit; border: 0px; color: inherit; font-size: inherit; font-weight: inherit; margin: 0px; padding: 0px;">Interfaces cerebro-ordenador, los primeros pasos para una telepatía tecnológica</h1></div><div class="content_subtitle" style="background-color: white; font-family: "Titillium web", serif; font-size: 24px; line-height: 28px; margin: 20px auto 0px; max-width: 850px; padding: 0px 20px; text-align: center;"><h2 _msthash="40" _msttexthash="10607636" style="background: inherit; border: 0px; color: inherit; font-size: inherit; font-weight: inherit; margin: 0px; padding: 0px;">Neuralink y Elon Musk no son los únicos: hasta 40 empresas en todo el mundo están trabajando en la implantación de chips en el cerebro humano</h2><div><span _istranslated="1" style="font-size: 18px; font-weight: 700; text-align: left;">Neuralink</span><span style="font-size: 18px; text-align: left;"> es, sin duda, la empresa en la que participa </span><span _istranslated="1" style="font-size: 18px; font-weight: 700; text-align: left;">Elon</span><span style="font-size: 18px; text-align: left;"> </span><span _istranslated="1" style="font-size: 18px; font-weight: 700; text-align: left;">Musk</span><span style="font-size: 18px; text-align: left;"> más cercana a una película de ciencia-ficción. Coches eléctricos? Los tenemos muy vistos. ¿Cohetes reutilizables que van al espacio? Está bastante bien... Pero un chip implantado en el cerebro humano y que debe permitir, entre otras cosas, recuperar funciones cognitivas o motrices a personas con patologías graves está a otro nivel.</span></div><div><span style="font-size: 18px; text-align: left;">Esta semana, por sorpresa, el propio Musk ha publicado en su red social </span><span _istranslated="1" style="font-size: 18px; font-weight: 700; text-align: left;">X</span><span style="font-size: 18px; text-align: left;"> la noticia de que en Neuralink ya han conseguido implantar el primer chip en un ser humano y que el paciente se está recuperando correctamente.</span></div><div><h3 _msthash="54" _msttexthash="328731" style="background-attachment: inherit; background-clip: inherit; background-image: inherit; background-origin: inherit; background-position: inherit; background-repeat: inherit; background-size: inherit; border: 0px; color: #2cafdb; font-family: "Titillium web", sans-serif; font-size: 24px; margin: 0px; padding: 25px 0px 8px; text-align: left;">Qué es Neuralink?</h3><p _msthash="55" _msttexthash="197537522" style="font-size: 18px; margin: 0px 0px 15px; padding: 0px; text-align: left;">Neuralink es una startup fundada por Musk en el año 2016 con el objetivo de desarrollar chips cerebrales. Este, no más grandes que una moneda, se implantan en el cráneo y mediante unos pequeños cables, se conectan al cerebro humano, creando así una interfaz cerebro-ordenador o BCI (del inglés <em _istranslated="1">Brain-Computer Interface</em>). Estas interfaces son sistemas que permiten una conexión directa entre el cerebro humano y un dispositivo externo, como un ordenador. Esta conexión se hace sin el uso de movimientos corporales convencionales, como hablar o moverse, y se consigue mediante la lectura de las señales neuronales del cerebro.</p><p _msthash="57" _msttexthash="91101049" style="font-size: 18px; margin: 0px 0px 15px; padding: 0px; text-align: left;">El potencial de esta tecnología es inmenso. Para personas con discapacidades motrices graves, las BCI pueden significar una nueva era de libertad y autonomía, permitiéndoles controlar prótesis o comunicarse de una manera que antes era imposible. Además, en el campo de la medicina, las interfaces cerebro-ordenador podrían ofrecer nuevas vías para el tratamiento de trastornos neurológicos y la rehabilitación.</p><p _msthash="58" _msttexthash="113742837" style="font-size: 18px; margin: 0px 0px 15px; padding: 0px; text-align: left;">No obstante, también hay que reflexionar sobre las implicaciones éticas y las preocupaciones de privacidad que estas nuevas tecnologías comportan. La capacidad de leer e interpretar las señales del cerebro abre un debate sobre los límites de la intimidad y la autonomía individual. El primer producto en el que trabaja Neuralink se llama <span _istranslated="1" style="font-weight: 700;">Telepathy</span>, y persigue el objetivo de poder controlar un ordenador o un teléfono "sólo con la mente", según palabras del propio Musk.</p><h3 _msthash="59" _msttexthash="291005" style="background-attachment: inherit; background-clip: inherit; background-image: inherit; background-origin: inherit; background-position: inherit; background-repeat: inherit; background-size: inherit; border: 0px; color: #2cafdb; font-family: "Titillium web", sans-serif; font-size: 24px; margin: 0px; padding: 25px 0px 8px; text-align: left;">Primeras pruebas</h3><p _msthash="60" _msttexthash="94666897" style="font-size: 18px; margin: 0px 0px 15px; padding: 0px; text-align: left;">De momento, no hay demasiada información del estudio, bautizado como PRIME (<em _istranslated="1">Precise Robotically Implanted Brain-Computer Interface</em>), una interfaz cerebro-ordenador precisa implantada mediante cirugía robótica. La web <em _istranslated="1">ClinicalTrials</em>, que registra todos los ensayos clínicos que se realizan en Estados Unidos, de momento no lista este estudio. Sin embargo, la autoridad reguladora, la FDA, ha confirmado que le ha dado luz verde.</p><blockquote style="color: #2cafdb; font-family: "Titillium web", sans-serif; font-style: italic; margin: 0px auto; pointer-events: none; position: relative; width: 421.896px;"><p _msthash="61" _msttexthash="3264794" style="margin: 0px 0px 15px; padding: 0px;">Hasta ahora, Neuralink ha llevado a cabo varios ensayos con cerdos y micos.</p></blockquote><p _msthash="62" _msttexthash="29768973" style="font-size: 18px; margin: 0px 0px 15px; padding: 0px; text-align: left;">Hasta ahora, Neuralink ha llevado a cabo varios ensayos con animales (cerdos y micos). La compañía ha mostrado algunos vídeos donde los micos con chips implantados juegan a juegos de ordenador sencillos o mueven el cursor en una pantalla.</p><p _msthash="62" _msttexthash="29768973" style="font-size: 18px; margin: 0px 0px 15px; padding: 0px; text-align: left;"><a href="https://www.youtube.com/watch?v=LgJpYOTll8U&embeds_referring_euri=https%3A%2F%2Fwww.viaempresa.cat%2F&source_ve_path=OTY3MTQ&feature=emb_imp_woyt">Monkey MindPong Picture-in-Picture (youtube.com)</a></p><h3 _msthash="63" _msttexthash="586144" style="background-attachment: inherit; background-clip: inherit; background-image: inherit; background-origin: inherit; background-position: inherit; background-repeat: inherit; background-size: inherit; border: 0px; color: #2cafdb; font-family: "Titillium web", sans-serif; font-size: 24px; margin: 0px; padding: 25px 0px 8px; text-align: left;">Otros proyectos similares</h3><p _msthash="64" _msttexthash="89308687" style="font-size: 18px; margin: 0px 0px 15px; padding: 0px; text-align: left;">Aunque este proyecto parece de ciencia-ficción, Neuralink no es la única empresa que trabaja en interfaces cerebro-ordenador. De hecho, hay unas 40 en todo el mundo. <span _istranslated="1" style="font-weight: 700;">Synchron</span>, otra compañía con sede en Australia, ya implantó el primer chip en un paciente de Estados Unidos en julio de 2022, con la peculiaridad de que, en este caso, el chip se implanta sobre el cráneo, de manera que no es necesario hacer incisiones en el hueso.</p><p _msthash="64" _msttexthash="89308687" style="font-size: 18px; margin: 0px 0px 15px; padding: 0px; text-align: left;"><a href="https://www.youtube.com/watch?v=eOQ-sLBlh5M">Rodney & Phil Use Our Brain Computer Interface (youtube.com)</a></p></div></div>Ramon.Moratahttp://www.blogger.com/profile/16398587758151257300noreply@blogger.com0tag:blogger.com,1999:blog-1131336482330646116.post-75780305988134168732024-02-24T15:05:00.000-08:002024-02-24T15:05:08.653-08:00Mapa de vías metabólicas<p> <span style="background-color: white; color: #0a0a0a; font-family: lato, lato, "helvetica neue", helvetica, arial, sans-serif; font-size: 16px;">Qué está sucediendo en tu cuerpo ahora mismo? Tu primera respuesta podría ser que tienes hambre o que tus músculos están adoloridos después de una carrera o que estás cansado.</span></p><p><a href="https://comunidad-biologica.com/mapa-de-vias-metabolicas-gratis-en-alta-resolucion/">Mapa de vías metabólicas ¡Gratis! en alta resolución. (comunidad-biologica.com)</a></p><p style="-webkit-font-smoothing: antialiased; background-color: white; box-sizing: inherit; color: #0a0a0a; font-family: lato, lato, "helvetica neue", helvetica, arial, sans-serif; font-size: 16px; margin: 0px 0px 1.5em;">Pero para apreciar realmente la maravilla de la vida, necesitamos sumergirnos en un mundo que trasciende nuestra consciencia cotidiana, veamos qué está pasando en nuestras células.</p><p style="-webkit-font-smoothing: antialiased; background-color: white; box-sizing: inherit; color: #0a0a0a; font-family: lato, lato, "helvetica neue", helvetica, arial, sans-serif; font-size: 16px; margin: 0px 0px 1.5em;">Al observar detenidamente cualquier <a href="https://comunidad-biologica.com/vision-general-de-la-celula-con-una-animacion-espectacular-video/" rel="noopener" style="-webkit-font-smoothing: antialiased; box-sizing: inherit; color: #9e0000; outline: 0px; transition: all 0.1s linear 0s;" target="_blank">célula de tu cuerpo</a>, descubrirías un epicentro de actividad frenética, comparable a un mercado bullicioso en pleno ajetreo, muy lejos de la tranquilidad de una habitación vacía.</p><p style="-webkit-font-smoothing: antialiased; background-color: white; box-sizing: inherit; color: #0a0a0a; font-family: lato, lato, "helvetica neue", helvetica, arial, sans-serif; font-size: 16px; margin: 0px 0px 1.5em;">Cada <a href="https://comunidad-biologica.com/los-cientificos-descubren-celulas-del-tiempo-que-permite-a-nuestros-cerebros-procesar-el-flujo-del-tiempo/" rel="noopener" style="-webkit-font-smoothing: antialiased; box-sizing: inherit; color: #9e0000; outline: 0px; transition: all 0.1s linear 0s;" target="_blank">célula en nuestro</a> cuerpo es un microcosmos repleto de procesos fisiológicos vitales. Dentro de cada una, se desarrollan continuamente reacciones bioquímicas esenciales para nuestra existencia.</p><hr style="-webkit-font-smoothing: antialiased; background-color: #cccccc; border: 0px; box-sizing: content-box; color: #585858; font-family: lato, lato, "helvetica neue", helvetica, arial, sans-serif; font-size: 16px; height: 1px; margin-bottom: 1.5em;" /><p style="-webkit-font-smoothing: antialiased; background-color: white; box-sizing: inherit; color: #0a0a0a; font-family: lato, lato, "helvetica neue", helvetica, arial, sans-serif; font-size: 16px; margin: 0px 0px 1.5em;"><span style="-webkit-font-smoothing: antialiased; box-sizing: inherit; color: red;"><span style="-webkit-font-smoothing: antialiased; box-sizing: inherit; font-weight: 700;">Leer más:</span></span> <a href="https://educacion-gratuita.com/cursos-online-gratis-de-harvard/" rel="noopener" style="-webkit-font-smoothing: antialiased; box-sizing: inherit; color: #9e0000; outline: 0px; transition: all 0.1s linear 0s;" target="_blank">La Universidad de Harvard ofrece más de 140 cursos online gratis.</a></p><hr style="-webkit-font-smoothing: antialiased; background-color: #cccccc; border: 0px; box-sizing: content-box; color: #585858; font-family: lato, lato, "helvetica neue", helvetica, arial, sans-serif; font-size: 16px; height: 1px; margin-bottom: 1.5em;" /><p style="-webkit-font-smoothing: antialiased; background-color: white; box-sizing: inherit; color: #0a0a0a; font-family: lato, lato, "helvetica neue", helvetica, arial, sans-serif; font-size: 16px; margin: 0px 0px 1.5em;">Independientemente de si estás en un estado de vigilia o sueño, ya sea corriendo o relajándote frente a la televisión, en el interior de tus células ocurre una transformación energética constante y dinámica.</p><p style="-webkit-font-smoothing: antialiased; background-color: white; box-sizing: inherit; color: #0a0a0a; font-family: lato, lato, "helvetica neue", helvetica, arial, sans-serif; font-size: 16px; margin: 0px 0px 1.5em;">En este micro-universo celular, las moléculas orgánicas como los azúcares, lípidos y proteínas no son meros espectadores, sino protagonistas activos.</p><p style="-webkit-font-smoothing: antialiased; background-color: white; box-sizing: inherit; color: #0a0a0a; font-family: lato, lato, "helvetica neue", helvetica, arial, sans-serif; font-size: 16px; margin: 0px 0px 1.5em;">En la vía de la glucólisis, por ejemplo, la glucosa se descompone en unidades más pequeñas, liberando energía en forma de ATP, la moneda energética de la célula.</p><p style="-webkit-font-smoothing: antialiased; background-color: white; box-sizing: inherit; color: #0a0a0a; font-family: lato, lato, "helvetica neue", helvetica, arial, sans-serif; font-size: 16px; margin: 0px 0px 1.5em;">Este constante intercambio de materiales y energía, que se lleva a cabo en el escenario microscópico de tus células, es lo que te permite responder a estímulos, crecer, adaptarte y, en última instancia, vivir.</p><h2 style="-webkit-font-smoothing: antialiased; background-color: white; box-sizing: inherit; color: #222222; font-family: Lato, lato, "helvetica neue", helvetica, arial; font-size: 30px; font-weight: 400; line-height: 23px; margin-bottom: 15px; margin-top: 0px;"><span style="-webkit-font-smoothing: antialiased; box-sizing: inherit; color: maroon; font-size: 15pt;"><span style="-webkit-font-smoothing: antialiased; box-sizing: inherit; font-size: 16px; font-weight: 700;">Mapa de vías metabólicas</span></span></h2><p style="-webkit-font-smoothing: antialiased; background-color: white; box-sizing: inherit; color: #0a0a0a; font-family: lato, lato, "helvetica neue", helvetica, arial, sans-serif; font-size: 16px; margin: 0px 0px 1.5em;">El metabolismo es un conjunto de procesos químicos vitales que ocurren en los seres vivos para mantener la vida. Estos procesos permiten a los organismos crecer, reproducirse, mantener sus estructuras y responder al ambiente.</p><p style="-webkit-font-smoothing: antialiased; background-color: white; box-sizing: inherit; color: #0a0a0a; font-family: lato, lato, "helvetica neue", helvetica, arial, sans-serif; font-size: 16px; margin: 0px 0px 1.5em;">A continuación, se ofrece una visión general del metabolismo, abordando sus componentes clave y su significado biológico… El metabolismo se divide en dos procesos interconectados: catabolismo y anabolismo.</p><h3 style="-webkit-font-smoothing: antialiased; background-color: white; box-sizing: inherit; color: #222222; font-family: Lato, lato, "helvetica neue", helvetica, arial; font-size: 28px; font-weight: 400; line-height: 39.2px; margin-bottom: 15px; margin-top: 0px;"><span style="-webkit-font-smoothing: antialiased; box-sizing: inherit; color: maroon; font-size: 14pt;"><span style="-webkit-font-smoothing: antialiased; box-sizing: inherit; font-size: 16px; font-weight: 700;">Catabolismo</span></span></h3><ol style="-webkit-font-smoothing: antialiased; background-color: white; box-sizing: border-box; color: #0a0a0a; font-family: lato, lato, "helvetica neue", helvetica, arial, sans-serif; font-size: 16px; list-style-image: initial; list-style-position: initial; margin: 0px 0px 1.5em 25px; padding-left: 0px;"><li style="-webkit-font-smoothing: antialiased; box-sizing: inherit;"><span style="-webkit-font-smoothing: antialiased; box-sizing: inherit; font-weight: 700;">Descomposición de Moléculas:</span><ul style="-webkit-font-smoothing: antialiased; box-sizing: border-box; list-style: disc; margin: 0px 0px 0px 25px; padding-left: 0px;"><li style="-webkit-font-smoothing: antialiased; box-sizing: inherit;">Procesos como la glucólisis, el ciclo del ácido cítrico y la fosforilación oxidativa descomponen nutrientes como glucosa y ácidos grasos para producir energía.</li><li style="-webkit-font-smoothing: antialiased; box-sizing: inherit;">La glucólisis, que ocurre en el citosol, convierte la glucosa en piruvato, generando ATP y NADH. El piruvato luego ingresa a las mitocondrias para el ciclo del ácido cítrico y la cadena de transporte de electrones, produciendo una cantidad significativa de ATP.</li></ul></li><li style="-webkit-font-smoothing: antialiased; box-sizing: inherit;"><span style="-webkit-font-smoothing: antialiased; box-sizing: inherit; font-weight: 700;">Generación de Metabolitos:</span><ul style="-webkit-font-smoothing: antialiased; box-sizing: border-box; list-style: disc; margin: 0px 0px 0px 25px; padding-left: 0px;"><li style="-webkit-font-smoothing: antialiased; box-sizing: inherit;">Estos procesos también producen metabolitos intermediarios, que son esenciales para otras rutas metabólicas, como la biosíntesis.</li></ul></li></ol><h3 style="-webkit-font-smoothing: antialiased; background-color: white; box-sizing: inherit; color: #222222; font-family: Lato, lato, "helvetica neue", helvetica, arial; font-size: 28px; font-weight: 400; line-height: 39.2px; margin-bottom: 15px; margin-top: 0px;"><span style="-webkit-font-smoothing: antialiased; box-sizing: inherit; color: maroon; font-size: 14pt;"><span style="-webkit-font-smoothing: antialiased; box-sizing: inherit; font-size: 16px; font-weight: 700;">Anabolismo</span></span></h3><ol style="-webkit-font-smoothing: antialiased; background-color: white; box-sizing: border-box; color: #0a0a0a; font-family: lato, lato, "helvetica neue", helvetica, arial, sans-serif; font-size: 16px; list-style-image: initial; list-style-position: initial; margin: 0px 0px 1.5em 25px; padding-left: 0px;"><li style="-webkit-font-smoothing: antialiased; box-sizing: inherit;"><span style="-webkit-font-smoothing: antialiased; box-sizing: inherit; font-weight: 700;">Síntesis de Macromoléculas:</span><ul style="-webkit-font-smoothing: antialiased; box-sizing: border-box; list-style: disc; margin: 0px 0px 0px 25px; padding-left: 0px;"><li style="-webkit-font-smoothing: antialiased; box-sizing: inherit;">Involucra la construcción de proteínas, ácidos nucleicos, lípidos y carbohidratos. Estas vías usan ATP y metabolitos generados por el catabolismo.</li><li style="-webkit-font-smoothing: antialiased; box-sizing: inherit;">Por ejemplo, la síntesis de proteínas en los ribosomas, donde los aminoácidos se ensamblan en polipéptidos según las instrucciones del ARN mensajero.</li></ul></li><li style="-webkit-font-smoothing: antialiased; box-sizing: inherit;"><span style="-webkit-font-smoothing: antialiased; box-sizing: inherit; font-weight: 700;">Utilización de Energía:</span><ul style="-webkit-font-smoothing: antialiased; box-sizing: border-box; list-style: disc; margin: 0px 0px 0px 25px; padding-left: 0px;"><li style="-webkit-font-smoothing: antialiased; box-sizing: inherit;">A diferencia del catabolismo, el anabolismo consume energía, subrayando su naturaleza biosintética.</li></ul></li></ol><h3 style="-webkit-font-smoothing: antialiased; background-color: white; box-sizing: inherit; color: #222222; font-family: Lato, lato, "helvetica neue", helvetica, arial; font-size: 28px; font-weight: 400; line-height: 39.2px; margin-bottom: 15px; margin-top: 0px;"><span style="-webkit-font-smoothing: antialiased; box-sizing: inherit; color: maroon; font-size: 14pt;"><span style="-webkit-font-smoothing: antialiased; box-sizing: inherit; font-size: 16px; font-weight: 700;">Regulación Metabólica</span></span></h3><p style="-webkit-font-smoothing: antialiased; background-color: white; box-sizing: inherit; color: #0a0a0a; font-family: lato, lato, "helvetica neue", helvetica, arial, sans-serif; font-size: 16px; margin: 0px 0px 1.5em;">El metabolismo está finamente regulado para mantener la homeostasis celular.</p><p style="-webkit-font-smoothing: antialiased; background-color: white; box-sizing: inherit; color: #0a0a0a; font-family: lato, lato, "helvetica neue", helvetica, arial, sans-serif; font-size: 16px; margin: 0px 0px 1.5em;"><span style="-webkit-font-smoothing: antialiased; box-sizing: inherit; font-weight: 700;">Referencia:</span> Nelson, D. L., & Cox, M. M. (2022). <i style="-webkit-font-smoothing: antialiased; box-sizing: inherit;">Princípios de bioquímica de Lehninger</i>. Artmed Editora</p>Ramon.Moratahttp://www.blogger.com/profile/16398587758151257300noreply@blogger.com0tag:blogger.com,1999:blog-1131336482330646116.post-34518263398955310572024-02-16T09:57:00.000-08:002024-02-16T09:57:52.102-08:00Stop This Absurd War on the Color Pink<p> </p><p>Last week Robert Krulwich, a co-host of the wonderful program <a href="http://www.radiolab.org/">Radiolab</a>, Pluto'd pink. In a <a href="https://www.npr.org/blogs/krulwich/2012/02/28/147590898/they-did-it-to-pluto-but-not-to-pink-please-not-pink">blog post</a> he noted that pink doesn't occupy a slot in the familiar colors of the rainbow—there's no <em>P</em> in <em><a href="https://www.youtube.com/watch?v=Gf33ueRXMzQ">Roy G. Biv</a></em>. From this, he concludes that pink does not really exist:</p><p></p><blockquote>That's why pink is an invention. It's not a name we give to something out there. Pink isn't out there.</blockquote><p>Es cierto que no hay una sola longitud de onda de luz que sea rosa. El rosa requiere una mezcla de luz roja y púrpura, colores de extremos opuestos del espectro visible. Es fácil de hacer y no parece amenazar el estatus ontológico del rosa. (Aunque esta propiedad implica que las leyes del universo han conspirado contra los láseres rosas).<br /><br />El problema comienza cuando Krulwich imagina el espectro visible curvándose en un círculo, con el rosa como el trozo que falta entre el rojo y el violeta. "El rosa se produce cuando los lados rojo y violeta se juntan, pero no se juntan, lo que convierte al rosa en un acto de ilusión o, para decirlo sin rodeos, el rosa es un color inventado", escribe.<br /><br />Quizá esta afirmación le confunda tanto como a mí (y sospecho que también a Krulwich). Krulwich cita como prueba un breve vídeo animado del equipo de Minute Physics. He incrustado el vídeo más abajo para que puedas verlo por ti mismo, pero la parte importante para nuestros propósitos es su explicación de dónde viene el rosa:</p><p>https://blogs.scientificamerican.com/observations/stop-this-absurd-war-on-the-color-pink/ <br /></p><p><br /></p>Ramon.Moratahttp://www.blogger.com/profile/16398587758151257300noreply@blogger.com0tag:blogger.com,1999:blog-1131336482330646116.post-19823328148231757062024-02-05T05:18:00.000-08:002024-02-05T05:18:35.022-08:00AI Can Design Totally New Proteins From Scratch—It’s Time to Talk Biosecurity<p> </p><h1 class="tdb-title-text">AI Can Design Totally New Proteins From Scratch—It’s Time to Talk Biosecurity</h1><p>Now, thanks to AI, custom proteins are a dime a dozen. <a href="https://singularityhub.com/2023/04/24/this-ai-can-design-complex-proteins-perfectly-tailored-to-our-needs/">Made-to-order proteins</a>
often have specific shapes or components that give them abilities new
to nature. From longer-lasting drugs and protein-based vaccines, to
greener biofuels and <a href="https://www.nature.com/articles/s41929-023-01048-6">plastic-eating</a> proteins, the field is rapidly becoming a transformative technology.</p>
<p>Custom protein design depends on deep learning techniques. With large
language models—the AI behind OpenAI’s blockbuster ChatGPT—dreaming up
millions of structures beyond human imagination, the library of
bioactive designer proteins is set to rapidly expand.</p>
<p>“It’s hugely empowering,” Dr. Neil King at the University of Washington recently <a href="https://www.nature.com/articles/d41586-024-00173-x">told</a> <em>Nature</em>. “Things that were impossible a year and a half ago—now you just do it.”</p>
<p>Yet with great power comes great responsibility. As newly designed
proteins increasingly gain traction for use in medicine and
bioengineering, scientists are now wondering: What happens if these
technologies are used for nefarious purposes?</p>
<p>A recent essay in <em>Science</em> highlights the need for
biosecurity for designer proteins. Similar to ongoing conversations
about AI safety, the authors say it’s time to consider biosecurity risks
and policies so custom proteins don’t go rogue.</p>
<p>The essay is penned by two experts in the field. One, Dr. David Baker, the director of the <a href="https://www.ipd.uw.edu/2023/10/responsible-ai-summit/">Institute for Protein Design</a>
at the University of Washington, led the development of RoseTTAFold—an
algorithm that cracked the half-decade problem of decoding protein
structure from its amino acid sequences alone. The other, Dr. George
Church at Harvard Medical School, is a pioneer in genetic engineering
and synthetic biology.</p>
<p>They suggest synthetic proteins need barcodes embedded into each new
protein’s genetic sequence. If any of the designer proteins becomes a
threat—say, potentially triggering a dangerous outbreak—its barcode
would make it easy to trace back to its origin.</p>
<p>The system basically provides “an audit trail,” the duo <a href="https://www.science.org/doi/10.1126/science.ado1671">write</a>.</p>
<h2>Worlds Collide</h2>
<p>Designer proteins are inextricably tied to AI. So are potential biosecurity policies.</p>
<p>Over a decade ago, Baker’s lab used software to design and build a
protein dubbed Top7. Proteins are made of building blocks called amino
acids, each of which is encoded inside our DNA. Like beads on a string,
amino acids are then twirled and wrinkled into specific 3D shapes, which
often further mesh into sophisticated architectures that support the
protein’s function.</p>
<p>Top7 couldn’t “talk” to natural cell components—it didn’t have any biological effects. But even then, the team <a href="https://www.science.org/doi/10.1126/science.1089427">concluded</a>
that designing new proteins makes it possible to explore “the large
regions of the protein universe not yet observed in nature.”</p>
<p>Enter AI. Multiple strategies recently took off to design new proteins at supersonic speeds compared to traditional lab work.</p>
<p>One is structure-based AI similar to image-generating tools like
DALL-E. These AI systems are trained on noisy data and learn to remove
the noise to find realistic protein structures. Called diffusion models,
they gradually learn protein structures that are compatible with
biology.</p>
<p>Another strategy relies on large language models. Like ChatGPT, the
algorithms rapidly find connections between protein “words” and distill
these connections into a sort of biological grammar. The protein strands
these models generate are likely to fold into structures the body can
decipher. <a href="https://www.nature.com/articles/s41467-022-32007-7">One example</a> is ProtGPT2, which <a href="https://www.nature.com/articles/s41587-022-01518-5">can engineer</a> active proteins with shapes that could lead to new properties.</p>
<h2>Digital to Physical</h2>
<p>These AI protein-design programs are raising alarm bells. Proteins
are the building blocks of life—changes could dramatically alter how
cells respond to drugs, viruses, or other pathogens.</p>
<p>Last year, governments around the world announced plans to oversee AI
safety. The technology wasn’t positioned as a threat. Instead, the
legislators cautiously fleshed out policies that ensure research follows
privacy laws and bolsters the economy, public health, and national
defense. Leading the charge, the European Union agreed on the <a href="https://artificialintelligenceact.eu/the-act/">AI Act</a> to limit the technology in certain domains.</p>
<p>Synthetic proteins weren’t directly called out in the regulations.
That’s great news for making designer proteins, which could be
kneecapped by overly restrictive regulation, write Baker and Church.
However, new AI legislation is in the works, with the United Nation’s
advisory body on AI set to share guidelines on <a href="https://www.nature.com/articles/d41586-023-02491-y">international regulation</a> in the middle of this year.</p>
<p>Because the AI systems used to make designer proteins are highly
specialized, they may still fly under regulatory radars—if the field
unites in a global effort to self-regulate.</p>
<p>At the <a href="https://www.ipd.uw.edu/2023/10/responsible-ai-summit/">2023 AI Safety Summit</a>,
which did discuss AI-enabled protein design, experts agreed documenting
each new protein’s underlying DNA is key. Like their natural
counterparts, designer proteins are also built from genetic code.
Logging all synthetic DNA sequences in a database could make it easier
to spot red flags for potentially harmful designs—for example, if a new
protein has structures similar to known pathogenic ones.</p>
<p>Biosecurity doesn’t squash data sharing. Collaboration is critical
for science, but the authors acknowledge it’s still necessary to protect
trade secrets. And like in AI, some designer proteins may be
potentially useful but too dangerous to share openly.</p>
<p>One way around this conundrum is to directly add safety measures to
the process of synthesis itself. For example, the authors suggest adding
a barcode—made of random DNA letters—to each new genetic sequence. To
build the protein, a synthesis machine searches its DNA sequence, and
only when it finds the code will it begin to build the protein.</p>
<p>In other words, the original designers of the protein can choose who
to share the synthesis with—or whether to share it at all—while still
being able to describe their results in publications.</p>
<p>A barcode strategy that ties making new proteins to a synthesis
machine would also amp up security and deter bad actors, making it
difficult to recreate potentially dangerous products.</p>
<p>“If a new biological threat emerges anywhere in the world, the
associated DNA sequences could be traced to their origins,” the authors
wrote.</p>
<p>It will be a tough road. Designer protein safety will depend on
global support from scientists, research institutions, and governments,
the authors write. However, there have been previous successes. Global
groups have established safety and sharing guidelines in other
controversial fields, such as stem cell research, genetic engineering,
brain implants, and AI. Although not always followed—<a href="https://singularityhub.com/2018/12/04/welcome-to-the-crispr-baby-world-heres-what-you-should-know/">CRISPR babies are a notorious example</a>—for the most part these international guidelines have helped move cutting-edge research forward in a safe and equitable manner.</p>
<p>To Baker and Church, open discussions about biosecurity will not slow
the field. Rather, it can rally different sectors and engage public
discussion so custom protein design can further thrive.</p><p>https://singularityhub.com/2024/01/29/ai-can-design-totally-new-proteins-from-scratch-its-time-to-talk-biosecurity/ <br /></p><h1 class="tdb-title-text"> </h1>Ramon.Moratahttp://www.blogger.com/profile/16398587758151257300noreply@blogger.com0tag:blogger.com,1999:blog-1131336482330646116.post-67062465111489759442024-01-27T02:21:00.000-08:002024-01-27T02:21:31.128-08:00«La inteligencia es uno de los mejores predictores de la longevidad»<p> </p><h1 class="headline mg-b-2">«La inteligencia es uno de los mejores predictores de la longevidad»</h1><h3 class="subtitle t-bld">Su equipo publicó recientemente un estudio
sobre la personalidad de los centenarios que revela los recursos
emocionales y psicológicos de quienes viven más y mejor</h3><p class="first-paragraph txt">España es uno de los países del mundo con mayor número de <a class="link-txt" href="https://www.lavozdegalicia.es/noticia/lavozdelasalud/vida-saludable/2023/04/29/secreto-longevidad-primera-persona-va-tomar-pastilla-antienvejecimiento-nacido/00031682754901277630454.htm">personas <strong>centenarias</strong></a>.
Según los últimos datos del Instituto Nacional de Estadística, la cifra
total de personas que han soplado velas de la tarta más de cien veces
es de 19.693. Y todo apunta a que este grupo de edad seguirá creciendo.
Los datos de la ONU muestran que en las últimas tres décadas, el mundo
pasó de albergar a 92.000 centenarios a contar con 621.000.</p>
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<p class="txt">Este fenómeno se puede entender a partir de varios factores. Desde luego, los avances en la <strong>medicina</strong> y la <strong>tecnología</strong> han
tenido mucho que ver con el hecho de que la población viva más. Pero a
las circunstancias que condicionan la vida de las personas se suma otro
factor sobre el que podemos incidir de forma más directa y del que no se
habla tanto, que son los<strong> recursos psicológicos</strong> con los que contamos.</p>
<p class="txt">Para descifrarlos,<a class="link-txt" href="https://link.springer.com/content/pdf/10.1007/s10902-023-00700-z.pdf" target="_blank"> un nuevo estudio</a> publicado en <em>Journal of Happiness Studies</em>
explora cómo afrontan su vida las personas que tienen más de cien años.
A través de entrevistas en profundidad, el equipo liderado por <strong>María Dolores Merino Rivera</strong>,
directora del grupo de investigación Bienestar Psicológico, Salud,
Envejecimiento y Trabajo en la Universidad Complutense de Madrid,
detectó ocho <a class="link-txt" href="https://www.lavozdegalicia.es/noticia/lavozdelasalud/vida-saludable/2023/04/12/robert-waldinger-psiquiatra-50-felicidad-ver-factores-geneticos/00031681311472400121471.htm">características positivas</a> que forman parte de la vida emocional de los centenarios. En conversación con <a class="link-txt" href="https://www.lavozdegalicia.es/lavozdelasalud/">La Voz de la Salud</a>, la experta revela los principales hallazgos y cómo podemos aplicarlos para vivir más y mejor.</p>
<div class="adpos ad_Roba2Mobile prebid postload mobile" data-ad="no" id="ad_Roba2Mobile"></div>
<p class="txt"><strong>—Los centenarios son una proporción muy pequeña de la población. ¿Por qué decidió investigar este grupo?</strong></p><p class="txt">—Lo
primero que hay que entender es que, en la longevidad extrema, como es
el caso de los centenarios, los factores genéticos y ambientales cumplen
un rol muy importante. Pero en este estudio, nos preguntábamos acerca
de los factores psicológicos que influyen en esto. En concreto,
queríamos saber qué ocurría con las características positivas de la
personalidad, o recursos psicológicos, que no son lo mismo que los
rasgos de la personalidad. Los recursos psicológicos son características
de la personalidad que nos permiten una mejor adaptación al entorno y
al cambio, porque promueven nuestro desarrollo, la consecución de
nuestras metas y la satisfacción de nuestras necesidades. Todo esto se
asocia a resultados favorables, como el bienestar y la salud.</p>
<p class="txt"><strong>—Por poner un ejemplo, ¿cuáles son algunos recursos psicológicos?</strong></p><p class="txt">—Ejemplos de recursos psicológicos son la resiliencia, la autoestima, o la curiosidad.</p>
<div class="adpos ad_Roba3Mobile prebid postload mobile" data-ad="no" id="ad_Roba3Mobile"></div>
<p class="txt"><strong>—¿Qué diferencias hay entre recursos psicológicos y rasgos de la personalidad?</strong></p><p class="txt">—Cualquier
característica psicológica tiene parte de genética y parte de ambiente.
Pero los recursos son más fácilmente entrenables que un rasgo de
personalidad, que es algo más duro. Además, son características
positivas de la personalidad, mientras que los rasgos pueden serlo o no.</p>
<p class="txt"><strong>—Obtener participantes mayores de cien años no ha de ser sencillo. ¿Cómo se diseñó el estudio?</strong></p><p class="txt">—Queríamos
indagar acerca de estos recursos y lo hicimos a través de investigación
cualitativa. En esta modalidad se trabaja con grupos pequeños, de muy
pocos sujetos, pero que sean muy buenos representantes de aquello que
queremos investigar. Se analiza el discurso. Entonces, realizamos
entrevistas en profundidad sobre su historia de vida a 19 centenarios
saludables. Esto quiere decir que son personas que tienen preservadas
sus capacidades cognitivas y, además, tienen una razonable autonomía.
Son capaces de comer solos o de asearse. De todos los centenarios de
España, que, según los últimos datos del INE, son más de 19.000, solo
unos poquitos han alcanzado esa edad de esta manera.</p>
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<p class="txt"><strong>—¿Cuáles fueron los principales hallazgos?</strong></p><p class="txt">—Los
agrupamos en ocho categorías. La primera es la vitalidad. Es algo
importante, porque no solamente son personas que están vivas, sino que
tienen un claro compromiso con la vida, quieren vivir. Son y eran
personas activas. Aunque tuvieran esa edad avanzada, seguían
involucrados en distintas actividades y participaban en distintas cosas
que se les proponían. Si estaban en una residencia y había un bingo,
eran los primeros en apuntarse. Esta vitalidad se reflejaba en sus
voces, que no parecían las de una persona de más de cien años, sino que
eran voces que parecían más jóvenes, con un lenguaje claro y rápido.
Indudablemente, la voz es un reflejo de vitalidad. Cuando uno enferma,
una de las primeras cosas que pasan es que la voz se apaga.</p>
<p class="txt"><strong>—¿Qué otros recursos hallaron en ellos?</strong></p><p class="txt">—Otra
característica que presentaban era el gusto por la interacción. Estos
centenarios son personas sociables y las entrevistas con ellos fluyeron
de una forma muy ágil. Desarrollan los temas sin ningún problema, porque
son buenos conversadores. Son personas sociables y, lo que es
tremendamente importante, se sienten queridos. Mantienen lazos estrechos
y cálidos con sus familias o con sus cuidadores, en el caso de estar en
una residencia para mayores. También han sido altruistas a lo largo de
su vida. La mayoría de ellos han ayudado a otros cuando los necesitaban.
Son personas comprometidas, responsables, buenos trabajadores, muy
apreciados por sus jefes, honestos y perseverantes. Cuando tienen un
objetivo, luchan por alcanzarlo. Por ejemplo, una señora se había roto
la cadera con más de 90 años. Le dijeron que no volvería a andar y en
poco tiempo estaba andando. Son personas con control, que llevan las
riendas de su vida, que han sido autónomas, han tenido independencia de
criterio y han sabido encontrar las oportunidades del entorno. No se han
dejado desbordar por las demandas.</p>
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<p class="txt">
</p><article class="article-min art-min-thumb art-min-thumb-dk thumb-square">
<div class="figure a-min-media">
<div class="media ratio16-9">
<img alt="Esperanza Cortiñas tiene 106 años." height="135" src="https://cflvdg.avoz.es/sc/66CIu3AP2gmtqUziQGcFIFxVXxc=/x75/2023/04/24/00121682346842110649379/Foto/O21A3014.jpg" width="240" />
</div>
</div>
<div class="a-min-content">
<h4 class="a-min-headline ">
<a data-analytics="link-embed" href="https://www.lavozdegalicia.es/noticia/lavozdelasalud/vida-saludable/2023/04/29/secreto-longevidad-primera-persona-va-tomar-pastilla-antienvejecimiento-nacido/00031682754901277630454.htm">El secreto de la longevidad: «La primera persona que se va a tomar una pastilla antienvejecimiento ya ha nacido»</a>
</h4>
<p class="a-min-author">
<span>
<a href="https://www.lavozdegalicia.es/firmas/uxia-rodriguez"> UXÍA RODRÍGUEZ</a>
</span>
</p>
</div>
</article>
<p></p><p class="txt"><strong>—Se podría decir que son resilientes.</strong></p><p class="txt">—Desde luego, y esto es algo muy importante. La <a class="link-txt" href="https://www.lavozdegalicia.es/noticia/lavozdelasalud/salud-mental/2023/02/04/rafaela-santos-psiquiatra-desarrollar-resiliencia-interior-cerebro-funcione-parche-farmacologico/00031675504649020812619.htm">resiliencia </a>es
la capacidad para afrontar la adversidad, recuperarse e incluso salir
fortalecidos de ella. A lo largo de una vida tan longeva, a los
centenarios les ha pasado de todo. Desde vivir la guerra civil y la
posguerra, hasta, en algunos casos, infancias muy duras y, por supuesto,
la pérdida de amigos, parejas, en algunos casos, de hijos o nietos. En
fin, la vida les ha dado golpes, pero han sabido afrontarlos y seguir
adelante.</p>
<p class="txt"><strong>—¿Qué rol tiene la estimulación cognitiva en este envejecimiento activo y de pronóstico favorable?</strong></p><p class="txt">—Lo
que vimos es que los centenarios son personas inteligentes, porque las
conductas que se extraen de su discurso lo reflejan. La mayoría no
fueron a la escuela, pero han aprendido a leer y escribir, han tenido
resultados académicos brillantes, han sido profesionales exitosos, han
asumido retos para los que no tenían formación expresa y los han
superado con éxito. Son personas con motivación intelectual. Grandes
lectores, personas a las que les gusta <a class="link-txt" href="https://www.lavozdegalicia.es/noticia/lavozdelasalud/vida-saludable/2024/01/21/david-bueno-biologo-levantamos-cerebro-distinto-acostamos/00031705853346040249778.htm">aprender</a>,
personas con gran curiosidad. Muchos tienen grandes colecciones de
libros. Hay que tener en cuenta que la inteligencia es uno de los
mejores predictores de la longevidad, entonces, también es coherente que
estas personas sean inteligentes.</p>
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<p class="txt"><strong>—¿Cómo podemos incorporar estos recursos en el día a día?</strong></p><p class="txt">—Una
de las cosas más importantes que se pueden hacer es mantenerse activo,
tanto física como intelectualmente. La investigación demuestra que el
ejercicio físico no solo cuida tu cuerpo, sino también tu cerebro. A
nivel intelectual, mantener tu mente activa explorando nuevas áreas de
conocimiento, aprender a tocar un instrumento musical o una lengua
extranjera, hacer juegos de <a class="link-txt" href="https://www.lavozdegalicia.es/noticia/lavozdelasalud/vida-saludable/2024/01/10/rodrigo-quian-quiroga-neurocientifico-doy-importancia-tratar-tener-memoria/00031704877401560398343.htm">memoria</a>,
todo esto es bueno empezar a hacerlo, incluso a edades avanzadas. En el
caso del ejercicio físico, por supuesto, hay que adaptarlo a la edad y
la condición médica. Es importante, en relación con esto, plantearse
objetivos realistas a corto y medio plazo para tratar de alcanzarlos,
pero no a muy largo plazo ni demasiado ambiciosos. Si tienes un objetivo
muy a largo plazo, pártelo en pequeñas metas para establecer un orden e
ir alcanzándolas poco a poco.</p>
<p class="txt"><strong>—¿Y en el plano de la gestión emocional?</strong></p><p class="txt">—Ciertos
hábitos de vida diaria son importantes para que las exigencias
cotidianas no te agobien ni te desborden, sino que seas tú quien las
controle. Por supuesto, hay que practicar la <a class="link-txt" href="https://www.lavozdegalicia.es/noticia/lavozdelasalud/vida-saludable/2023/04/12/robert-waldinger-psiquiatra-50-felicidad-ver-factores-geneticos/00031681311472400121471.htm">gratitud </a>y
esto aumenta el bienestar. Lo mismo ocurre con el disfrute, la
capacidad de disfrutar de las pequeñas cosas que día a día nos ofrece la
vida es fundamental y hay que aprovechar esas cosas, no dejarlas pasar
por alto. Desde luego, desarrollar habilidades que nos permitan aceptar
los eventos negativos y estresantes como parte de la vida es clave. En
la medida de lo posible, es bueno extraer la esencia positiva de ese
evento adverso.</p>
<div class="adpos ad_Roba7Mobile postload mobile" data-ad="no" id="TEXTO_DETALLE_ad_Roba7Mobile"></div>
<p class="txt"><strong>—¿Desarrollar estos recursos puede ayudar a abordar mejor el envejecimiento?</strong></p><p class="txt">—Sí.
En otras sociedades, a las personas de más edad se las considera como
gente experimentada, sabia, a la que pedir consejo y tener en
consideración. En nuestra sociedad, es todo lo contrario. Pero la
población está envejeciendo rápidamente y hay que desarrollar formas de
alcanzar ese envejecimiento de forma saludable. Entonces, esto, entre
otras cosas, podría ayudar. También hay, sin duda, un problema de <a class="link-txt" href="https://www.lavozdegalicia.es/noticia/lavozdelasalud/enfermedades/2022/02/21/gallega-lidera-proyecto-mundial-edadismo-oms-deberiamos-desterrar-palabra-anciano/00031645456754534726751.htm">edadismo </a>en
la sociedad actual. Pensamos que alguien a determinada edad es muy
mayor para hacer ciertas cosas. ¿Por qué, si puede hacerlo? A lo mejor
no es tan mayor para ello. Pero hay prejuicios hacia la gente de más
edad y sería bueno que esto se fuera corrigiendo.</p>
<p class="txt"><strong>—¿Qué anécdotas te sorprendieron al hacer el estudio?</strong></p><p class="txt">—Hay
muchas. Una señora estuvo trabajando hasta los 98 años como modista.
Otra, cuando llegué a su casa me regaló un pañito de ganchillo hecho por
ella con más de cien años. Imagínate ser capaz de hacer ese trabajo tan
preciso a esa edad. Otra señora me contó que empezó a viajar con más de
80 años, cuando falleció su marido, porque a él no le gustaba viajar. Y
estuvo viajando año tras año a lugares lejanos. Otro señor era pastor y
mientras cuidaba a sus ovejas, iba leyendo, porque es un lector
infatigable. Las ovejas se metían en sembrados próximos y se comían la
siembra, y le caían broncas porque él estaba entretenido leyendo en vez
de estar pendiente de las ovejas. Eso demuestra su pasión por la lectura
y el conocimiento. Otra señora dejó plantado al novio y se marchó a
América, en concreto, a Washington, sin conocer siquiera el idioma, y
volvió con 89 años haciendo ella sola la mudanza.</p>
<p class="txt"><strong>—¿Cuáles fueron los hallazgos más inesperados en estas entrevistas?</strong></p><p class="txt">—Yo
esperaba que aspectos como la calma surgieran, pero no necesariamente
son personas calmadas. Hay algunos que sí, pero hay otras que dicen:
«Qué va, yo todavía sigo siendo muy nerviosa y de hecho me pone nerviosa
esta entrevista que estamos haciendo». Lo mismo pasa con la
religiosidad. Hay otros estudios que señalan que una de las cosas que
ayudan a los centenarios es su fe, y aquí no lo hemos encontrado como
algo tan destacado. Hay algunos que son religiosos, pero otros son
agnósticos o ateos. Eso me llamó la atención.</p>
<div class="figure photo_author_pos a-min-media sz-sq-xl fl-l mg-r-2 d-none-mb">
<div class="media circle ratio1-1">
<img alt="Laura Inés Miyara" src="https://cflvdg.avoz.es/sc/Bp2WU_vBaShIj6Ya_ugBm1P7En0=/75x75/perfiles/1637054644741/1637918886756_thumb.png" />
</div>
</div>
<div class="d-flex author_name sz-80">
<span class="flex t-lh-1">
<span class="author">Laura Inés Miyara</span>
</span>
<p class="t-sserif author_info mg-t-2 d-none-mb" style="text-align: left;"><span style="color: #20124d;">Redactora
de La Voz de La Salud, periodista y escritora de Rosario, Argentina.
Estudié Licenciatura en Comunicación Social en la Universidad Nacional
de Rosario y en el 2019 me trasladé a España gracias a una beca para
realizar el Máster en Produción Xornalística e Audiovisual de La Voz de
Galicia. Mi misión es difundir y promover la salud mental, luchando
contra la estigmatización de los trastornos y la psicoterapia, y creando
recursos de fácil acceso para aliviar a las personas en momentos
difíciles.</span></p>
</div><p class="subtitle t-bld" style="text-align: left;"> https://www.lavozdegalicia.es/noticia/lavozdelasalud/vida-saludable/2024/01/25/maria-dolores-merino-investigadora-psicologia-inteligencia-mejores-predictores-longevidad/00031706196256032709697.htm?utm_source=lavozdegalicia.es&utm_medium=referral&utm_campaign=internaltraffic&utm_content=00031706196256032709697</p><h1 class="headline mg-b-2"> </h1>Ramon.Moratahttp://www.blogger.com/profile/16398587758151257300noreply@blogger.com0tag:blogger.com,1999:blog-1131336482330646116.post-48486547366281712242024-01-23T04:18:00.000-08:002024-01-23T04:18:01.905-08:00 The AI Revolution in Medicine: GPT-4 and Beyond , Kindle Edition <p><span style="color: #7f6000;"> <b>The AI Revolution in Medicine: GPT-4 and Beyond , Kindle Edition </b></span><br /></p><div aria-expanded="true" class="a-expander-content a-expander-partial-collapse-content a-expander-content-expanded" style="padding-bottom: 20px;"> <p><span class="a-text-bold">AI is about to transform medicine. Here's what you need to know right now.</span></p><p style="text-align: justify;"><span class="a-text-italic">''The
development of AI is as fundamental as the creation of the personal
computer. It will change the way people work, learn, and
communicate--and transform healthcare. But it must be managed carefully
to ensure its benefits outweigh the risks. I'm encouraged to see this
early exploration of the opportunities and responsibilities of AI in
medicine.''</span></p><p style="text-align: justify;"><span class="a-text-italic">El
desarrollo de la IA es tan fundamental como la creación del ordenador
personal. Cambiará la forma en que las personas trabajan, aprenden y se
comunican, y transformará la asistencia sanitaria. Pero debe gestionarse
con cuidado para garantizar que sus beneficios compensen los riesgos.
Me anima ver esta exploración temprana de las oportunidades y
responsabilidades de la IA en medicina". <br /></span></p><div data-scaffold-immersive-reader-content="" style="text-align: justify;">
<div>
<div class="reader-article-content reader-article-content--content-blocks">
<p class="ember-view reader-content-blocks__paragraph" id="ember38">
En plena discusión sobre los posibles efectos y consecuencias
que causarían las IA generativas, particularmente en el campo de la
salud, me llegó el libro recientemente publicado: "The AI Revolution in
Medicine: GPT-4 and Beyond" de Peter Lee (Microsoft), Carey Golberg
(periodista médica y científica) y Isaac Kohane (Harvard) y sobre el
cual les comparto una reseña .<span class="white-space-pre"> </span>
</p>
<p class="ember-view reader-content-blocks__paragraph" id="ember39">
</p>
<p class="ember-view reader-content-blocks__paragraph" id="ember40">
<b>¿De qué trata el libro?</b>
</p>
<p class="ember-view reader-content-blocks__paragraph" id="ember41">
Explora el potencial de la inteligencia artificial (IA) para
mejorar la atención médica, agilizar la investigación médica y reducir
los costos de salud. Examina los avances más recientes del modelo de
lenguaje GPT-4, un modelo capaz de generar texto, traducir y crear
diversos tipos de contenido creativo. Asimismo, se exploran los desafíos
y riesgos asociados con la IA en el campo de la medicina, como el
problema del sesgo y la necesidad de una regulación adecuada. Se subraya
la importancia de utilizar la IA de manera responsable y se plantea un
nuevo enfoque en la formación médica para preparar a los profesionales
para trabajar con IA, así como la implementación de nuevas regulaciones
que garanticen un uso seguro y ético de la IA.
</p>
<p class="ember-view reader-content-blocks__paragraph" id="ember42">
<b>Los sí, los no y los más o menos</b>
</p>
<p class="ember-view reader-content-blocks__paragraph" id="ember43">
Si bien el libro me gustó y lo considero útil, esperaba un poco
más. En principio, pensando en el título, supuse que tomaría como base
los Grandes Modelos de Lenguaje (LLMs Large Lenguaje Model, en inglés) y
sucintamente los explicaría, para luego abordar GPT de OpenAI y dar
detalles evolutivos, punto este último que sí lograron. Sin embargo,
como el título propone un “más allá”, pensé que cumpliría e iría, pero
no, se quedó ahí nomás.<span class="white-space-pre"> </span>
</p>
<p class="ember-view reader-content-blocks__paragraph" id="ember44">
De todos modos entiendo que:
</p>
<p class="ember-view reader-content-blocks__paragraph" id="ember45">
*- Los autores tienen una posición privilegiada por haber
tenido la posibilidad de probar la herramienta antes que el público
general
</p>
<p class="ember-view reader-content-blocks__paragraph" id="ember46">
*- Hay un cierto sesgo, ya que la publicación fue realizada bajo
el paraguas de OpenAI y Microsoft, aunque de entrada explicitan que no
fueron condicionados por ellos.
</p>
<p class="ember-view reader-content-blocks__paragraph" id="ember47">
*- El momento de publicar algo de este estilo, es ahora que el
tren está en el andén partiendo y después puede que sea tarde.
</p>
<p class="ember-view reader-content-blocks__paragraph" id="ember48">
Por otro lado, para técnicos expertos en AI el libro es casi una
novela, para quienes no lo son termina siendo algo técnico y para mí,
que me siento en el medio -aunque como dije antes, me gustó y me sirvió-
se quedó corto en contenido y largo en extensión.
</p>
<p class="ember-view reader-content-blocks__paragraph" id="ember49">
Ya en el prólogo me hizo recordar esos videos comerciales que
mostraban hace 10/15 años las empresas en los eventos no tan académicos
sobre las posibles ventajas futuristas de las Historias Clínicas
Electrónicas y la Salud Digital, cosas que jamás vi que sucedieran ni en
el primer mundo, pero era un prólogo no más.
</p>
<p class="ember-view reader-content-blocks__paragraph" id="ember50">
En términos generales el libro es una colección de casos de
estudio y ejemplos hechos en poco tiempo, que no agrega mucho al
conocimiento de base de LLMs ni de la AI en particular. Cuenta cómo
fueron las pruebas que hicieron con versiones previas a GPT-4 y luego
con este, para dar su impresión en diferentes dominios de la salud, sus
potencialidades y sus falencias.
</p>
<p class="ember-view reader-content-blocks__paragraph" id="ember51">
Muestra situaciones en las que GPT-4 puede no ser siempre
preciso o confiable al generar texto que refleje estándares éticos o
fácticos. Estos son desafíos que deben abordar los investigadores,
desarrolladores, reguladores y usuarios de GPT-4 con el tiempo y su uso.
Tener más de 100 millones de beta testeadores actualmente los deja en
una posición privilegiada al ser ellos los primeros.
</p>
<p class="ember-view reader-content-blocks__paragraph" id="ember52">
Si bien van mostrando desde diferentes ópticas (la del paciente,
la del profesional de la salud, el estudiante, etc.) cómo responde el
sistema y su evolución, siempre dejan en claro que en un futuro
previsible, GPT-4 no se puede utilizar en entornos médicos sin
supervisión humana directa. Refuerza la idea que la medicina es un área
que exige una asociación entre humanos e IA. Debiendo considerase un
nuevo tipo de asociación entre humanos y máquinas, lo podría decirse
como “medicina simbiótica”.
</p>
<p class="ember-view reader-content-blocks__paragraph" id="ember53">
Por último, lo que más me llamó la atención y sí me tuvo
entretenido, fue cómo la versión previa de GPT-4 denominada Davinci3,
era como un animal salvaje que de a poco fueron domesticando y se
convirtió en una mascota sumisa, que aún tiene alucinaciones pero que
está claramente anestesiada. Me queda averiguar qué fue de esa versión
salvaje, que cuando le pregunto al mismo GPT-4 reniega que lo conoce o
conoció.
</p>
<p class="ember-view reader-content-blocks__paragraph" id="ember54">
Igualmente no hay mucha literatura publicada aún, ya nos veremos
infoxicados en breve, creo que es un libro que para aquel que quiera
entender un poco todo lo que está pasando con los LLM y la AI Generativa
en el caso de los Chatbots de 3ra generación, es un histórico momento
científico, de mercado y mediático, el libro sirve para ello.</p></div></div></div><div class="artdeco-entity-lockup__image artdeco-entity-lockup__image--type-circle ember-view" id="ember73" style="text-align: justify;" type="circle">
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<span aria-hidden="true"><span dir="ltr">Daniel Luna</span></span></a></div><p style="text-align: justify;"><span class="a-text-italic"> </span></p><p style="text-align: justify;"><span class="a-text-bold">--Bill Gates</span></p><p style="text-align: justify;"><span>Just months ago, millions of people were stunned by ChatGPT's amazing abilities -- </span><span class="a-text-italic">and</span><span> its bizarre hallucinations. </span><span class="a-text-italic">But that was 2022.</span><span>
GPT-4 is now here: smarter, more accurate, with deeper technical
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transforming medicine. But with lives on the line, you need to
understand these technologies -- </span><span class="a-text-italic">stat</span><span>.</span></p><p style="text-align: justify;"><span>What can they do? What can't they do -- </span><span class="a-text-italic">yet</span><span>? What shouldn't they </span><span class="a-text-italic">ever</span><span>
do? To decide, experience the cutting edge for yourself. Join three
insiders who've had months of early access to GPT-4 as they reveal its
momentous potential -- to improve diagnoses, summarize patient visits,
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real GPT-4 dialogues -- unrehearsed and unfiltered, brilliant and
blundering alike -- all annotated with invaluable context, candid
commentary, real risk insights, and up-to-the-minute takeaways.</span></p><ul class="a-unordered-list a-vertical" style="text-align: left;"><li style="text-align: justify;"><span class="a-list-item"><span>Preview a day in the life of a doctor with a true AI assistant.</span></span></li><li style="text-align: justify;"><span class="a-list-item"><span>See how AI can enhance doctor-patient encounters at the bedside and beyond.</span></span></li><li style="text-align: justify;"><span class="a-list-item"><span>Learn how modern AI works, why it can fail, and how it can be tested to earn trust.</span></span></li><li style="text-align: justify;"><span class="a-list-item"><span>Empower patients: improve access and equity, fill gaps in care, and support behavior change.</span></span></li><li style="text-align: justify;"><span class="a-list-item"><span>Ask better questions and get better answers with "prompt engineering."</span></span></li><li><span class="a-list-item"><span>Leverage AI to cut waste, uncover fraud, streamline reimbursement, and lower costs.</span></span></li><li><span class="a-list-item"><span>Optimize clinical trials and accelerate cures with AI as a research collaborator.</span></span></li><li><span class="a-list-item"><span>Find the right guardrails and gain crucial insights for regulators and policymakers.</span></span></li><li><span class="a-list-item"><span>Sketch possible futures: </span><span class="a-text-italic">What dreams may come next?</span></span></li></ul><p><span class="a-text-italic">There has never been technology like this.</span><span>
Whether you're a physician, patient, healthcare leader, payer,
policymaker, or investor, AI will profoundly impact you -- and it </span><span class="a-text-italic">might</span><span> make the difference between life or death. Be informed, be ready, and take charge -- </span><span class="a-text-italic">with this book</span><span>.</span></p> </div>Ramon.Moratahttp://www.blogger.com/profile/16398587758151257300noreply@blogger.com0tag:blogger.com,1999:blog-1131336482330646116.post-25125121548307080762024-01-20T13:18:00.000-08:002024-01-20T13:18:16.898-08:00Hígado graso: una enfermedad infradiagnosticada que afecta a más de un tercio de la población<p> <span style="background-color: rgba(0, 0, 0, 0.03); color: #0f1419; font-family: TwitterChirp, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif; font-size: 15px; white-space-collapse: preserve;">La incidencia de la enfermedad del hígado graso se ha duplicado desde 1990 y no deja de crecer.</span></p><span style="background-color: rgba(0, 0, 0, 0.03); color: #0f1419; font-family: TwitterChirp, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif; font-size: 15px; white-space-collapse: preserve;">
Afecta a más de 1/3 de la población adulta y al 10 % de la infantil.
Descubre en el próximo Debate CaixaResearch los últimos avances en su diagnóstico y tratamiento</span><div><span style="background-color: rgba(0, 0, 0, 0.03); color: #0f1419; font-family: TwitterChirp, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif; font-size: 15px; white-space-collapse: preserve;"><br /></span></div><div><a href="https://caixaresearch.org/es/debates-caixaresearch-higado-graso?twclid=2-6dhrh3fym53p8mu1nti653r3b">La enfermedad del hígado graso - CaixaResearch</a></div><div><br /></div><div><p style="background-color: white; box-sizing: border-box; color: #666666; font-family: proximanova-regular, Arial, Helvetica, sans-serif; font-size: 1.05em; letter-spacing: 1px; line-height: 18px; margin-bottom: 1em; margin-top: 0px; padding: 0px;">La enfermedad del hígado graso es la enfermedad hepática más prevalente en el mundo. Afecta a más de un tercio de la población adulta y al 10 % de la infantil, aunque la mayoría no lo sabe. Es causada por la excesiva acumulación de grasa en el hígado y se ve exacerbada por factores como la obesidad y la diabetes. Se trata de una enfermedad «silenciosa» porque su progresión es muy gradual y la mayoría de quienes la padecen no experimentan síntomas, por lo que no son conscientes de su condición. Alrededor del 25 % de las personas afectadas desarrolla esteatohepatitis no alcohólica, que se caracteriza por inflamación hepática y fibrosis, y si no se controla, puede derivar en patologías graves, como la cirrosis hepática o el cáncer de hígado.</p><p style="background-color: white; box-sizing: border-box; color: #666666; font-family: proximanova-regular, Arial, Helvetica, sans-serif; font-size: 1.05em; letter-spacing: 1px; line-height: 18px; margin-bottom: 1em; margin-top: 0px; padding: 0px;">La incidencia de la enfermedad se ha duplicado desde 1990 y no deja de crecer, hasta el punto de que los expertos advierten de que se ha convertido en una «epidemia». Se estima que en 2023 será ya la principal razón para el trasplante de hígado en el mundo. Está directamente relacionada con el sedentarismo, el consumo de alcohol y una dieta deficiente, con abundancia de azúcares y ultraprocesados. El hígado realiza múltiples funciones —incluido el filtrado de las sustancias nocivas, la regulación hormonal y el almacenamiento de energía y vitaminas—, por lo que se trata de uno de los órganos que más se ven afectados por un mal estilo de vida.</p><p style="background-color: white; box-sizing: border-box; color: #666666; font-family: proximanova-regular, Arial, Helvetica, sans-serif; font-size: 1.05em; letter-spacing: 1px; line-height: 18px; margin-bottom: 1em; margin-top: 0px; padding: 0px;">Pese a su altísima prevalencia y a diferencia de otras enfermedades no transmisibles con las que está estrechamente asociada, como las enfermedades cardiovasculares, la diabetes de tipo 2 y la obesidad, la enfermedad del hígado graso está infradiagnosticada y sigue siendo poco conocida por la población general. Jeffrey V. Lazarus es investigador en ISGlobal, centro impulsado por la Fundación ”la Caixa”, y lidera diferentes iniciativas internacionales para consensuar una agenda global de acciones prioritarias que haga frente al avance de la enfermedad.</p><p style="background-color: white; box-sizing: border-box; color: #666666; font-family: proximanova-regular, Arial, Helvetica, sans-serif; font-size: 1.05em; letter-spacing: 1px; line-height: 18px; margin-bottom: 1em; margin-top: 0px; padding: 0px;">Tradicionalmente, las enfermedades del hígado se han asociado al consumo de alcohol. El nombre de <em style="box-sizing: border-box;">enfermedad del hígado graso no alcohólico</em> se acuñó en un principio para distinguirla de las causas alcohólicas y se asociaba al sobrepeso o la obesidad. Sin embargo, las enfermedades hepáticas alcohólicas y las no alcohólicas pueden compartir algunos procesos biológicos. Muchas personas que tienen obesidad o síndrome metabólico también consumen alcohol. La tradicional separación entre hígado graso alcohólico y no alcohólico carece de una frontera clara. Además, algunos expertos consideran que los términos <em style="box-sizing: border-box;">no alcohólico</em> y <em style="box-sizing: border-box;">graso</em> pueden estigmatizar. Por todo ello se ha llegado a un consenso internacional para renombrar la enfermedad como <em style="box-sizing: border-box;">esteatosis hepática metabólica </em>y se propone una subcategoría separada para referirse a los pacientes con mayor consumo de alcohol.</p><p style="background-color: white; box-sizing: border-box; color: #666666; font-family: proximanova-regular, Arial, Helvetica, sans-serif; font-size: 1.05em; letter-spacing: 1px; line-height: 18px; margin-bottom: 1em; margin-top: 0px; padding: 0px;">Se trata de una enfermedad difícil de diagnosticar y todavía no se dispone de ningún fármaco aprobado para tratarla. Además de la prevención, la estrategia pasa por la detección precoz. Cada vez se está haciendo más énfasis en identificar la enfermedad en fases tempranas, que es cuando hay margen para revertir el hígado graso. Es una enfermedad de origen multifactorial a la que contribuyen varios factores genéticos y ambientales. Los familiares de primer grado de pacientes con hígado graso no alcohólico corren más riesgo de sufrir la enfermedad, lo que pone de relieve la importancia del cribado temprano. Aunque se han identificado algunas moléculas que pueden servir como biomarcadores, la biopsia hepática sigue siendo una de las técnicas de diagnóstico más rutinarias y sus principales desventajas son la invasividad y la variabilidad de la técnica.</p><p style="background-color: white; box-sizing: border-box; color: #666666; font-family: proximanova-regular, Arial, Helvetica, sans-serif; font-size: 1.05em; letter-spacing: 1px; line-height: 18px; margin-bottom: 1em; margin-top: 0px; padding: 0px;">En la actualidad, dos proyectos de investigación que cuentan con el apoyo de la Fundación ”la Caixa” tienen como objetivo avanzar en la identificación de biomarcadores no invasivos que faciliten tanto el diagnóstico y monitoreo de la progresión de la enfermedad como la eficacia terapéutica de los tratamientos en desarrollo. ¿En qué consisten? ¿Qué avances se han conseguido?</p><p style="background-color: white; box-sizing: border-box; color: #666666; font-family: proximanova-regular, Arial, Helvetica, sans-serif; font-size: 1.05em; letter-spacing: 1px; line-height: 18px; margin-bottom: 1em; margin-top: 0px; padding: 0px;">El investigador Antonio Zorzano lidera un proyecto para hallar biomarcadores de hígado graso que permitan diagnosticar la enfermedad de forma no invasiva. Han descubierto una proteína presente en las mitocondrias de las células hepáticas llamada <span style="box-sizing: border-box; text-wrap: nowrap;"><em style="box-sizing: border-box;">mitofusina-2</em></span> y están investigando si esta proteína se puede usar como diana terapéutica para diseñar nuevos tratamientos.</p><p style="background-color: white; box-sizing: border-box; color: #666666; font-family: proximanova-regular, Arial, Helvetica, sans-serif; font-size: 1.05em; letter-spacing: 1px; line-height: 18px; margin-bottom: 1em; margin-top: 0px; padding: 0px;">El equipo liderado por David Martínez Selva ha identificado dos proteínas que, en combinación con un algoritmo matemático, pueden ser usadas como biomarcador no invasivo para predecir el desarrollo y la progresión de las diferentes etapas de la enfermedad, incluidas las más graves. En el proyecto se está trabajando ahora para avanzar en la validación de este nuevo biomarcador, que ayudará a identificar a los sujetos que padecen la enfermedad y a predecir su progresión. Además, facilitará el desarrollo de nuevos fármacos para tratar estas afecciones.</p><p style="background-color: white; box-sizing: border-box; color: #666666; font-family: proximanova-regular, Arial, Helvetica, sans-serif; font-size: 1.05em; letter-spacing: 1px; line-height: 18px; margin-bottom: 1em; margin-top: 0px; padding: 0px;">Ponentes:</p><ul class="big-arrow" style="background-color: white; box-sizing: border-box; color: #008847; font-family: ProximaNova-Regular, Arial, Helvetica, sans-serif; font-size: 14px; list-style: outside none none; margin: 0px; overflow: hidden; padding: 0px;"><li style="background: url("../images/big-arrow_verde.png") 1% 1px no-repeat transparent; box-sizing: border-box; display: block; margin-bottom: 0.5em; overflow: hidden; padding: 0px 0px 0px 2em;"><p style="box-sizing: border-box; color: #666666; font-family: proximanova-regular, Arial, Helvetica, sans-serif; font-size: 1.05em; letter-spacing: 1px; line-height: 18px; margin: 0px; padding: 0px;"><span style="box-sizing: border-box; font-family: proximanova-bold, Arial, Helvetica, sans-serif;">Jeffrey V. Lazarus</span>, jefe del grupo de investigación de sistemas de salud en el Instituto de Salud Global de Barcelona (ISGlobal) y profesor asociado de la Universidad de Barcelona.</p></li><li style="background: url("../images/big-arrow_verde.png") 1% 1px no-repeat transparent; box-sizing: border-box; display: block; margin-bottom: 0.5em; overflow: hidden; padding: 0px 0px 0px 2em;"><p style="box-sizing: border-box; color: #666666; font-family: proximanova-regular, Arial, Helvetica, sans-serif; font-size: 1.05em; letter-spacing: 1px; line-height: 18px; margin: 0px; padding: 0px;"><span style="box-sizing: border-box; font-family: proximanova-bold, Arial, Helvetica, sans-serif;">Antonio Zorzano</span>, jefe del grupo del laboratorio de Enfermedades Metabólicas Complejas y Mitocondrias, del Instituto de Investigación Biomédica (IRB Barcelona), y catedrático de la Universidad de Barcelona.</p></li><li style="background: url("../images/big-arrow_verde.png") 1% 1px no-repeat transparent; box-sizing: border-box; display: block; margin-bottom: 0.5em; overflow: hidden; padding: 0px 0px 0px 2em;"><p style="box-sizing: border-box; color: #666666; font-family: proximanova-regular, Arial, Helvetica, sans-serif; font-size: 1.05em; letter-spacing: 1px; line-height: 18px; margin: 0px; padding: 0px;"><span style="box-sizing: border-box; font-family: proximanova-bold, Arial, Helvetica, sans-serif;">David Martínez Selva</span>, investigador principal del grupo de Diabetes y Metabolismo del Vall d’Hebron Instituto de Investigación (VHIR).</p></li></ul><div><a href="https://caixaresearch.org/es/convocatoria-caixaresearch-investigacion-salud-2021-proyecto-esteatohepatitis">Metabolismo: hígado graso no alcohólico - CaixaResearch</a></div></div><div><p style="background-color: white; box-sizing: border-box; color: #666666; font-family: proximanova-regular, Arial, Helvetica, sans-serif; font-size: 1.05em; letter-spacing: 1px; line-height: 18px; margin-bottom: 1em; margin-top: 0px; padding: 0px;">El hígado graso no alcohólico es una de las principales causas de enfermedad hepática en el mundo. Es causado por la excesiva acumulación de grasa en el hígado y se ve exacerbado por factores como la obesidad y la diabetes. Se conoce como «la enfermedad del asesino silencioso» porque su progresión es muy gradual y la mayoría de los pacientes que la padecen no experimentan síntomas, por lo que no son conscientes de su condición. Alrededor del 25 % de los pacientes desarrolla esteatohepatitis no alcohólica, que se caracteriza por inflamación hepática y fibrosis, y que puede derivar en patologías como la cirrosis hepática o el cáncer. Se estima que en 2023 esta enfermedad será ya la principal razón para el trasplante de hígado en el mundo.</p><p style="background-color: white; box-sizing: border-box; color: #666666; font-family: proximanova-regular, Arial, Helvetica, sans-serif; font-size: 1.05em; letter-spacing: 1px; line-height: 18px; margin-bottom: 1em; margin-top: 0px; padding: 0px;">Todavía no se dispone de ningún fármaco aprobado para tratar la enfermedad del hígado graso no alcohólico. Uno de los principales motivos es la falta de biomarcadores clínicos no invasivos, lo que supone un obstáculo importante para el desarrollo de nuevos medicamentos. Aunque se han identificado algunas moléculas que pueden servir como biomarcadores, la biopsia sigue siendo una de las técnicas de diagnóstico más rutinarias y sus principales desventajas son la invasividad y la variabilidad de la técnica. Todo ello hace muy deseable la identificación de biomarcadores no invasivos que faciliten tanto el diagnóstico y el monitoreo de la progresión de la enfermedad, como la eficacia terapéutica de los tratamientos en desarrollo.</p><p style="background-color: white; box-sizing: border-box; color: #666666; font-family: proximanova-regular, Arial, Helvetica, sans-serif; font-size: 1.05em; letter-spacing: 1px; line-height: 18px; margin-bottom: 1em; margin-top: 0px; padding: 0px;">En estudios previos con un modelo animal, el equipo ha identificado dos proteínas que, en combinación con un algoritmo matemático, pueden ser usadas como biomarcador no invasivo para predecir el desarrollo y la progresión de las diferentes etapas de la enfermedad, incluidas las más graves, como la esteatohepatitis. El proyecto se propone avanzar en la validación de este nuevo biomarcador, que se postula como alternativa a la biopsia hepática y que ayudará a identificar a los sujetos que padecen la enfermedad y a predecir su progresión, lo que tendrá importantes implicaciones en la mejora de la calidad de vida de estas personas. Además, facilitará el desarrollo de nuevos fármacos para tratar estas afecciones.</p></div><div><a href="https://caixaresearch.org/es/convocatoria-caixaimpulse-innovacion-salud-2023-proyecto-biomarcador-higado-graso">Biomarcador predictor de hígado graso - CaixaResearch</a></div>Ramon.Moratahttp://www.blogger.com/profile/16398587758151257300noreply@blogger.com0tag:blogger.com,1999:blog-1131336482330646116.post-57233610820484760072024-01-20T12:02:00.000-08:002024-01-20T12:02:48.656-08:00Neuroelectrics® A new paradigm<p> <a href="https://www.neuroelectrics.com/">Revolutionizing Brain Therapy | Neuroelectrics</a></p><p><span style="background-color: #f7f6f5; color: #333333; font-family: Roboto; font-size: 18.26px;">Neuroelectrics is a creative, high-tech company offering the best in class non-invasive and high definition electrical brain stimulation technology for personalized neuromodulation. By measuring and modifying brain function, we aim to restore brain health, minimize disabilities and create a better life for patients.</span></p><h5 class="responsiveSmallSubtitle pt-5 pb-5" style="background-color: white; box-sizing: border-box; color: #212529; font-family: Roboto; font-size: 19.2px; font-weight: 300; line-height: 1.2; margin-bottom: 0.5rem; margin-top: 0px; padding-bottom: 3rem !important; padding-top: 3rem !important;">Neuroelectrics’ products are being used worldwide for basic neuroscience and clinical research in hundreds of universities and academic medical centers. Our vision is to create a powerful neurotechnology platform upon which verticals can be developed to improve the lives of as many people as possible. Clinical applications are already being developed to diagnose and treat brain diseases as well as improve brain health (e.g., in epilepsy, neuropathic pain or mood disorders) and cognitive function (e.g., memory in dementia or executive function in children with ADHD).</h5><div><a href="https://www.neuroelectrics.com/solutions/software-integrations">Ever-evolving software solutions | Neuroelectrics</a></div><div><br /></div><div><a href="https://www.neuroelectrics.com/wiki/index.php/Collection_of_publications_of_independent_research_studies_and_mentions_about_Starstim">Collection of publications of independent research studies and mentions about Starstim - Neuroelectric's Wiki (neuroelectrics.com)</a></div><div><br /></div><div><a href="https://www.youtube.com/watch?v=vCkAPA_REck">72 01 WKDA Toolbox Basic web ES 20s_8 (youtube.com)</a></div><p><span style="background-color: white; font-family: Roboto; font-size: 13.3907px;">The brain is a complex, multi-scale network. When there is dysfunction, by selectively stimulating or suppressing signaling activity in specific regions we are able to restore natural function. Neuroelectrics leverages advances in neuroscience and cutting-edge data science to further the understanding of this complex machine.</span></p><h1 style="background-color: #f7f6f5; box-sizing: border-box; color: #333333; font-family: Roboto; font-feature-settings: normal; font-size: 2.3vw; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-numeric: normal; font-variant-position: normal; font-weight: 500; line-height: 1.2; margin: 0px 0px 0.5rem; padding-top: 1vw;">Towards personalized model-driven neuromodulation</h1><p class="mt-0" style="background-color: #f7f6f5; box-sizing: border-box; color: #333333; font-family: Roboto; font-feature-settings: normal; font-size: 1.5vw; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-numeric: normal; font-variant-position: normal; line-height: initial; margin-bottom: 1rem; margin-top: 0px !important;">Why is it important?</p><p style="background-color: #f7f6f5; box-sizing: border-box; color: #333333; font-family: Roboto; font-feature-settings: normal; font-size: 1.5vw; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-numeric: normal; font-variant-position: normal; line-height: initial; margin-bottom: 1rem; margin-top: 0px;">Computational head models are essential tools for maximizing the effects of stimulation and accurately interpreting the results of your study. The electrical current distribution (or equivalent electric field) in tES determines its effects on neurons. However, these currents cannot be measured in vivo using non-invasive techniques in any practical way. Computational head models that take into account subject-specific anatomy and electrical characteristics of the different tissues have thus become the gold standard to predict the electric field distribution in tES and optimize montage parameters. At NE®, we develop advanced computational algorithms to provide you with a wide range of modeling services. With your inputs, our team of scientists will tailor your protocol to your individual needs!</p><p style="box-sizing: border-box; color: #333333; font-family: Roboto; font-feature-settings: normal; font-size: 1.6vw; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-numeric: normal; font-variant-position: normal; line-height: initial; margin-bottom: 1rem; margin-top: 0px;">Head model creation</p><p style="box-sizing: border-box; color: #333333; font-family: Roboto; font-feature-settings: normal; font-size: 1.25vw; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-numeric: normal; font-variant-position: normal; line-height: initial; margin-bottom: 1rem; margin-top: 0px;">All our services require that a 3D computational head model be built. This can be subject-specific – in which case we require MRIs with specific characteristics – or based on a common template head model. The head model contains accurate geometric representations of the most important head tissues. These representations are obtained by employing powerful volume segmentation algorithms.</p><p style="box-sizing: border-box; color: #333333; font-family: Roboto; font-feature-settings: normal; font-size: 1.6vw; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-numeric: normal; font-variant-position: normal; line-height: initial; margin-bottom: 1rem; margin-top: 0px;">Montage optimization: Stimweaver algorithm</p><p style="box-sizing: border-box; color: #333333; font-family: Roboto; font-feature-settings: normal; font-size: 1.25vw; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-numeric: normal; font-variant-position: normal; line-height: initial; margin-bottom: 1rem; margin-top: 0px;">Do you have a specific cortical area (or network of areas) that you want to target or avoid disturbing with tES? Then draw your target map in our online Stimtargeter tool and we will work with you to determine the optimal electrode montage (electrode positions and currents).</p><p style="box-sizing: border-box; color: #333333; font-family: Roboto; font-feature-settings: normal; font-size: 1.25vw; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-numeric: normal; font-variant-position: normal; line-height: initial; margin-bottom: 1rem; margin-top: 0px;"><br /></p><p style="box-sizing: border-box; color: #333333; font-family: Roboto; font-feature-settings: normal; font-size: 1.6vw; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-numeric: normal; font-variant-position: normal; line-height: initial; margin-bottom: 1rem; margin-top: 0px;">EEG source localization: NE® cortical mapper</p><p style="box-sizing: border-box; color: #333333; font-family: Roboto; font-feature-settings: normal; font-size: 1.25vw; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-numeric: normal; font-variant-position: normal; line-height: initial; margin-bottom: 1rem; margin-top: 0px;">If you are interested in analyzing brain region activation and you collect EEG records from multiple channels, this service will help you localize the cortical sources of your signals using a realistic (and, if you want, personalized) head model. We can average this activity over time, or divide it using parcellations of the cortex and/or use it to derive functional connectivity metrics for you.</p><p style="box-sizing: border-box; color: #333333; font-family: Roboto; font-feature-settings: normal; font-size: 1.25vw; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-numeric: normal; font-variant-position: normal; line-height: initial; margin-bottom: 1rem; margin-top: 0px;"><br /></p><p style="box-sizing: border-box; color: #333333; font-family: Roboto; font-feature-settings: normal; font-size: 1.6vw; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-numeric: normal; font-variant-position: normal; line-height: initial; margin-bottom: 1rem; margin-top: 0px;">NE® electric field analytics</p><p style="box-sizing: border-box; color: #333333; font-family: Roboto; font-feature-settings: normal; font-size: 1.25vw; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-numeric: normal; font-variant-position: normal; line-height: initial; margin-bottom: 1rem; margin-top: 0px;">With this service, we provide you with quantitative information and a helpful visualization of the effects of a specific montage. With advanced numerical techniques, we calculate the electric field distribution that the montage induces in the head. This information will give you valuable insights into the effects and mechanisms of stimulation.</p>Ramon.Moratahttp://www.blogger.com/profile/16398587758151257300noreply@blogger.com0tag:blogger.com,1999:blog-1131336482330646116.post-32169780042131687722024-01-20T09:19:00.000-08:002024-01-20T09:19:38.440-08:00Es muy complicado encontrar un genotipo especifico de una enfermedad<p> <span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">¿Pretendes encontrar un gen (o genotipo) específico de una enfermedad? </span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">Los genes son secuencias de ARNm que codifican una proteína. Para una misma isoforma, proteínas con funcionalidad similar, hay un número enorme de secuencias de ARNm. Más que átomos en el universo, incluso más que subpartículas atómicas en el universo</span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> </span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"></span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">Los genotipos determinan las posiciones en los genes, en las secuencias, que hacen que las proteínas sean funcionalmente diferentes, así que realmente si lo que buscas es la mutación, el genotipo determinante, de una enfermedad tendrás que hacer muchos muchos experimentos ..</span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">Si vas a ciegas tendrás que hacer mutantes por cada genotipo de la secuencia y cada genotipo son 20 mutaciones y si la secuencia tiene 200 posiciones tendrás que hacer 4000 mutaciones. Pero esto es solo el principio</span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">Esas mutaciones las tienes que expresar en cultivos celulares (lo más fácil es en plásmidos) y además por cada uno de ellos hacer anticuerpos específicos distinguibles (con fluorescencia o similar). Esto ya te va a costar unos cuentos miles de euros y meses o años de trabajo</span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">Una vez hecho esto tienes que comprobar cada una de estos genotipos qué efectos tiene al entrar en contacto con células humanas. Si crees que alguna de estas tiene más capacidad infectiva tendrás que hacer análisis cuantitativo de cuánto infecta más unas células u otras.</span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">Pero no todo es cuanto más infecta, algunas proteínas pueden interferir en las señales celulares, en las rutas metabólicas, y afectar el funcionamiento normal de nuestro organismo incluso matarnos. Se puede decir que son proteínas tóxicas</span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> </span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> </span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">Pero además, esas proteínas tóxicas no tienen porque ser la proteína entera que traduce el gen, sino simplemente una pequeña parte de esta (de 7 a 50 aminoácidos) que se traduce mal y no se llega a ensamblar, o que se puede expresar en el MHC-1 como antígeno via TAP</span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> </span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">Y aquí viene el lío padre. No nos vale solo con los 4000 mutantes, necesitamos hacer de cada posible trozo de la secuencia (que era de 200 posiciones) todas las posibles combinaciones (lineales) de entre 7 a 50 (al menos) aminoácidos y mutar al menos una posición en cada extremo.</span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">A esas 4000 mutaciones hay que sumar </span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">19*19*200*(50-7) =3104600 (más de 3 millones)</span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> y lo que es peor, no nos vale con mirar solo si infecta mas o menos la célula, si no que tenemos que analizar todo tipo de primeros y segundos mensajeros alterados en cultivos independientes</span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> </span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> </span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> Cada genotipo determinante en una posición son 19 mutaciones, si son dos simutáneas (una en cada extremo o no) son 19*19 </span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"><br /></span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">Las simulaciones que hace un buen bioinformático no impiden al final tener que hacer experimentos en el laboratorio, pero si ahorran miles de experimentos que serían fallidos, y millones de euros además de décadas de trabajo</span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">Necesitas un supercomputador, un buen informatico</span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> </span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> https://twitter.com/iximeno</span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> <br /></span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">Esperemos que la IA y los comptadores cuanticos permitan avanzar mas rapidamente <br /></span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"></span><br /><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> </span></p>Ramon.Moratahttp://www.blogger.com/profile/16398587758151257300noreply@blogger.com0tag:blogger.com,1999:blog-1131336482330646116.post-27415438857201045532024-01-20T05:01:00.000-08:002024-01-20T05:01:17.296-08:00La biopsia líquida de la leche materna permitirá detectar precozmente el cáncer de mama<p> </p><h1 class="title">La biopsia líquida de la leche materna permitirá detectar precozmente el cáncer de mama</h1><h2 class="item">El Vall d'Hebron Institute of Oncology (VHIO) descubre
que el alimento del lactante expresa ADN tumoral hasta 18 meses antes
que una radiografía</h2><p>La <strong>leche materna</strong> de las pacientes con<strong></strong><a href="https://12ft.io/proxy?q=https%3A%2F%2Fwww.elperiodico.com%2Fes%2Fsanidad%2F20220221%2Fcuatro-tipos-mama-cancer-7637146" target="_parent"><strong>cáncer de mama</strong></a><strong></strong>contiene <strong>ADN del tumor. </strong>Es algo que <strong>acaba de descubrir</strong> un equipo médico, liderado por mujeres, del <strong>Vall d'Hebron Institute of Oncology (VHIO),</strong> el centro de referencia en <a href="https://12ft.io/proxy?q=https%3A%2F%2Fwww.elperiodico.com%2Fes%2Fsociedad%2Fevolucion-cancer-aniversario-periodico-sh%2Findex.html" target="_parent">cáncer</a> del hospital público más grande de Catalunya. Y ahora viene la segunda, y en realidad relevante, noticia: la<strong> biopsia líquida de la leche materna</strong> puede predecir el<strong> riesgo de cáncer de mama</strong> (el tipo de tumor más común en mujeres) <strong>durante el posparto.</strong></p><div id="swap-from-1"></div><div id="paraph-1-2"></div><p>Aunque
la investigación de Vall d'Hebron todavía está en marcha, este hallazgo
tiene opciones de convertirse, en los próximos cinco o seis años, en
una <strong>herramienta más de detección precoz del cáncer de mama </strong>en mujeres diagnosticadas <strong>durante el posparto.</strong> Este colectivo es especialmente importante porque tiene un <strong>peor pronóstico, </strong>ya que suelen ser diagnosticadas en <strong>estadios más avanzados de la enfermedad. </strong></p><div class="box-left"><div class="ep-destacado"><h3 class="title"><p>Este test puede convertirse en una herramienta de cribado del cáncer de mama en mujeres durante el posparto</p></h3></div></div><p>"Por primera vez hemos visto ADN del tumor [conocido como <strong>ADN tumoral circulante,</strong> o ADNct] en la leche materna. Hemos publicado los primeros resultados que tenemos en la revista<strong> 'Cancer Discovery'. </strong>Hemos detectado, en 13 de las 15 mujeres estudiadas, el mismo ADN del tumor de la mama <strong>en la leche materna", </strong>señala <strong>Cristina Saura, </strong>jefa de la Unidad de Mama del Servicio de Oncología Médica de Vall d'Hebron e investigadora principal del <strong>Grupo de Cáncer de Mama del VHIO.</strong></p><div id="nlt_signup_botton_desktop"></div><div class="box-left"><div class="ep-related-long"><div class="middle"><div class="item masPeriodico"><div class="thumb"><a href="https://12ft.io/proxy?q=https%3A%2F%2Fwww.elperiodico.com%2Fes%2Fsanidad%2F20230912%2Fquimioterapia-recaidas-cancer-mama-92000801" target="_parent"><img alt="Una sanitaria con una paciente con cáncer de mama." height="100" src="https://estaticos-cdn.prensaiberica.es/clip/bd3a5553-f80f-4ad2-95fe-b04445d1231f_baja-libre-aspect-ratio_default_0.jpg" title="Una sanitaria con una paciente con cáncer de mama." width="175" /></a></div><div class="txt"><a class="epigraph" href="https://12ft.io/proxy?q=https%3A%2F%2Fwww.elperiodico.com%2Fes%2Fsanidad%2F20230912%2Fquimioterapia-recaidas-cancer-mama-92000801" target="_parent"><span>Estudio en 'PLOS Biology'</span></a><h2 class="title"><a href="https://12ft.io/proxy?q=https%3A%2F%2Fwww.elperiodico.com%2Fes%2Fsanidad%2F20230912%2Fquimioterapia-recaidas-cancer-mama-92000801" target="_parent" title="La quimioterapia provoca recaídas de cáncer de mama al reactivar células tumorales 'dormidas'">La quimioterapia provoca recaídas de cáncer de mama al reactivar células tumorales 'dormidas'</a></h2></div></div></div></div></div><p>Tres mujeres están <strong>al frente de esta relevante investigación:</strong> la doctora Saura, la doctora<strong> Ana Vivancos</strong> (jefa del laboratorio de Genómica del VHIO) y la doctora <strong>Carolina Ortiz,</strong>
investigadora del Grupo de Cáncer de Mama de VHIO y firmante del
artículo como primera autora junto a Saura. Vall d'Hebron busca ahora <strong>5.000 mujeres sanas</strong> para comprobar si este test es eficaz, aunque la prueba piloto ya muestra <strong>resultados "esperanzadores".</strong></p><h3>¿Qué es la biopsia líquida?</h3><p>La biopsia líquida es una<strong> prueba analítica de la sangre</strong> que sirve para detectar<strong> mutaciones genéticas del ADN tumoral.</strong> Se utiliza en la detección, por ejemplo, del <strong>cáncer de mama metastásico, </strong>pero a día de hoy<strong> no es una herramienta útil para realizar diagnósticos precoces. </strong></p><p>Sin embargo, como han observado estas investigadoras, <strong>no ocurre lo mismo con la leche materna:</strong>
al realizar la biopsia líquida de este alimento del lactante, han visto
que es capaz de detectar fragmentos del ADN tumoral circulante <strong>hasta 18 meses antes </strong>(un año y medio antes) que el <strong>diagnóstico radiológico</strong> (esto es, que la radiología convencional) del cáncer de mama.</p><div class="ep-cite "><div class="txt"><p class="title">"Hemos
encontrado marcadores tumorales de leche materna positivos que en la
sangre salen negativos. Es algo que no se había descrito nunca"</p><p class="author">Cristina Saura</p><p class="position">Investigadora principal del Grupo de Cáncer de Mama del VHIO</p></div></div><p>"Hemos visto que, <strong>aunque los tumores sean muy pequeños, </strong>encontramos <strong>marcadores tumorales de leche materna positivos</strong> que en la sangre salen negativos. Es algo que <strong>no se había descrito nunca.</strong> Hasta ahora. Y nos hace pensar que la leche materna puede servir como <strong>herramienta de diagnóstico precoz </strong>del cáncer de mama <strong>en el posparto", </strong>explica Saura. Este test sería efectivo únicamente en mujeres que hayan tenido un hijo y hayan decidido <strong>hacer lactancia materna.</strong></p><div class="box-left"><div class="ep-related-long"><div class="middle"><div class="item"><div class="thumb"><a href="https://12ft.io/proxy?q=https%3A%2F%2Fwww.elperiodico.com%2Fes%2Fsanidad%2F20220221%2Fcuatro-tipos-mama-cancer-7637146" target="_parent"><img alt="Participantes en un acto celebrado en Córdoba por el día mundial contra el cáncer de mama, en el 2018." height="100" src="https://estaticos-cdn.prensaiberica.es/clip/726d28a9-014f-42a2-9532-b771ba8a999a_baja-libre-aspect-ratio_default_0.jpg" title="Participantes en un acto celebrado en Córdoba por el día mundial contra el cáncer de mama, en el 2018." width="175" /></a></div><div class="txt"><a class="epigraph" href="https://12ft.io/proxy?q=https%3A%2F%2Fwww.elperiodico.com%2Fes%2Fsanidad%2F20220221%2Fcuatro-tipos-mama-cancer-7637146" target="_parent"><span>Tumores femeninos</span></a><h2 class="title"><a href="https://12ft.io/proxy?q=https%3A%2F%2Fwww.elperiodico.com%2Fes%2Fsanidad%2F20220221%2Fcuatro-tipos-mama-cancer-7637146" target="_parent" title="Estos son los cuatro tipos de cáncer de mama">Estos son los cuatro tipos de cáncer de mama</a></h2></div></div></div></div></div><h3>Peor pronóstico</h3><p>La Unidad de Mama del Vall d'Hebron cuenta, a su vez, con una<strong> unidad multidisciplinaria específica </strong>para tratar a mujeres que se diagnostican de cáncer de mama <strong>durante el embarazo</strong> y <strong>en el posparto. </strong>A
lo largo de los años, las médicas de esta unidad han podido ver cómo
las pacientes con cáncer de mama que se diagnostican durante el embarazo
o, especialmente, durante el posparto tienen<strong> peor pronóstico, </strong>ya que suelen encontrarse en <strong>estadios más avanzados</strong> de la enfermedad.</p><p>"Los <strong>cambios fisiológicos</strong> que ocurren en la mama durante el embarazo y el posparto hacen que los tumores sean <strong>más difíciles de detectar. </strong>También hemos observado que <strong>biológicamente </strong>los tumores en el posparto son <strong>más agresivos </strong>y las mujeres se quedan <strong>embarazadas en edades </strong>en las que aún <strong>no se hacen cribados poblacionales con mamografía.</strong> En España, por ejemplo, no se empiezan estas revisiones<strong> hasta los 50 años", </strong>explica la doctora Saura. </p><p>De ahí la importancia del<strong> diagnóstico temprano</strong> en este grupo de mujeres que sufren <strong>tumores de mama más agresivos</strong> y a las que no llega, por edad, la detección precoz de los cribados.</p><div class="box-left"><div class="ep-related-long"><div class="middle"><div class="item"><div class="thumb"><a href="https://12ft.io/proxy?q=https%3A%2F%2Fwww.elperiodico.com%2Fes%2Fsanidad%2F20211102%2Fcancer-mama-sintomas-tratamiento-mortalidad-12589659" target="_parent"><img alt="Mamografías para detectar tumores de mama." height="100" src="https://estaticos-cdn.prensaiberica.es/clip/d7d70331-fb2c-4a4a-9213-c0848c6d2b10_baja-libre-aspect-ratio_default_0.jpg" title="Mamografías para detectar tumores de mama." width="175" /></a></div><div class="txt"><a class="epigraph" href="https://12ft.io/proxy?q=https%3A%2F%2Fwww.elperiodico.com%2Fes%2Fsanidad%2F20211102%2Fcancer-mama-sintomas-tratamiento-mortalidad-12589659" target="_parent"><span>Aumento de la supervivencia</span></a><h2 class="title"><a href="https://12ft.io/proxy?q=https%3A%2F%2Fwww.elperiodico.com%2Fes%2Fsanidad%2F20211102%2Fcancer-mama-sintomas-tratamiento-mortalidad-12589659" target="_parent" title="¿Qué es el cáncer de mama? Síntomas, tratamiento y mortalidad">¿Qué es el cáncer de mama? Síntomas, tratamiento y mortalidad</a></h2></div></div></div></div></div><h3>Análisis de muestras</h3><p>El equipo del VHIO se dedicó a analizar <strong>muestras de leche materna y sangre en 15 pacientes.</strong> Encontraron ADN tumoral en la leche materna de <strong>13 de ellas,</strong>
así como en una muestra de sangre de una de ellas. En el caso de las
dos pacientes en cuya leche materna no se encontró ADN tumoral, las
investigadoras creen que probablemente no había pasado <strong>suficiente tiempo </strong>para que el ADN tumoral se hubiera liberado a la leche, ya que las muestras de leche se habían recogido en las<strong> primeras horas de lactancia. </strong></p><div class="box-left"><div class="ep-destacado"><h3 class="title"><p>Los
tumores en el postparto son más agresivos y las mujeres se quedan
embarazadas en edades en las que aún no se hacen cribados con
mamografías</p></h3></div></div><p>Según las médicas, este estudio de
Vall d'Hebron publicado en 'Cancer Discovery' demuestra, por primera
vez, que la leche materna de pacientes con cáncer de mama contiene <strong>ADN tumoral suficiente para detectarlo a través de la biopsia líquida</strong> y que este ADN tumoral se puede detectar incluso antes de las pruebas de imagen convencional. </p><div data-module-name="codeEmbed"><div class="info-graph"><img src="https://estaticos-cdn.elperiodico.com/epc/resourcescas/infographics/2023/septiembre/mama2.png" style="height: auto; max-width: 350px;" /></div></div><h3>Panel de genes</h3><p>Además, para dar una utilidad a su hallazgo, las investigadoras crearon (dentro del laboratorio genómico del VHIO) un<strong> panel de genes que incluye las 54 mutaciones más frecuentes</strong> de cáncer de mama en mujeres jóvenes, esto es, de <strong>menos de 45 años. </strong>El panel tiene una <strong>sensibilidad de más del 70%, </strong>lo que quiere decir que, de las muestras de las pacientes analizadas con este panel, se detectan 7 de cada 10 casos con una <strong>especificidad del 100%. </strong></p><h3><strong>Recogida universal de muestras</strong></h3><span class="smallTitle center">Noticias relacionadas</span><ul class="relatedSlides"><div class="ep-related-long"><div class="middle"><div class="item"><div class="txt"><h5 class="title"><a href="https://12ft.io/proxy?q=https%3A%2F%2Fwww.elperiodico.com%2Fes%2Fsanidad%2F20230831%2Fsanidad-cribados-cancer-pulmon-91546883" target="_parent" title="Sanidad abre la puerta a la implantación de nuevos cribados contra el cáncer">Sanidad abre la puerta a la implantación de nuevos cribados contra el cáncer</a></h5></div></div></div></div><div class="ep-related-long"><div class="middle"><div class="item masPeriodico"><div class="txt"><h5 class="title"><a href="https://12ft.io/proxy?q=https%3A%2F%2Fwww.elperiodico.com%2Fes%2Fsociedad%2F20230906%2Fcreado-laboratorio-primer-embrion-humano-91777145" target="_parent" title="Creado en un laboratorio el primer embrión humano de 14 días a partir de células madre">Creado en un laboratorio el primer embrión humano de 14 días a partir de células madre</a></h5></div></div></div></div></ul><p>"Este panel podría servirnos en el futuro como <strong>método de diagnóstico precoz de cáncer de mama en el posparto", </strong>explica
Saura. "De la misma manera que se realiza la prueba del talón a todos
los recién nacidos, se podría plantear recoger una <strong>muestra de leche materna a todas las mujeres tras el parto</strong> para realizar un 'screening' [cribado] de cáncer de mama", añade esta doctora.</p><div class="box-left"><div class="ep-destacado"><h3 class="title"><p>Para
la tercera fase del estudio, Vall d'Hebron está reclutando a 5.000
mujeres que tengan más de 40 años y posean mutaciones genéticas</p></h3></div></div><p>Vall d'Hebron ha arrancado ya la<strong> tercera fase de este estudio, </strong>para la que está buscando financiación y está reclutando a <strong>5.000 mujeres sanas</strong> para realizarles este test. Busca mujeres con <strong>algún riesgo:</strong> tener al menos 40 años (el cáncer <strong>aumenta con la edad)</strong> y que sean portadoras de <strong>mutaciones genéticas.</strong>
"Cogeremos test de la leche, les haremos una ecografía -ya que es la
prueba que ve mejor la mama durante la lactancia materna- y una muestra
de sangre. Y después les haremos<strong> seguimiento", </strong>cuenta Saura. En "cinco o seis años" podrían tener los <strong>resultados definitivos</strong> y la leche materna podría incorporarse como una <strong>nueva fuente de biopsia líquida </strong>para la detección precoz del cáncer de mama<strong> en el posparto.</strong></p><p><strong> </strong></p><p><strong> </strong></p><h2 class="item"> </h2><h1 class="title"> </h1>Ramon.Moratahttp://www.blogger.com/profile/16398587758151257300noreply@blogger.com0tag:blogger.com,1999:blog-1131336482330646116.post-7102095462260266512024-01-19T10:31:00.000-08:002024-01-19T10:34:07.928-08:00Infecciones, Coivd, Telómeros<p> </p><p> <span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">De cada infección de la que nos defiende nuestro sistema inmune no solo salimos algo cansados sino, sobre todo, más viejos.</span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> </span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"></span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">Los telómeros | Entre las causas del deterioro por </span><span class="r-18u37iz"><a class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3 r-1loqt21" dir="ltr" href="https://twitter.com/hashtag/COVID19?src=hashtag_click" role="link" style="color: #1d9bf0; text-overflow: unset;">#COVID19</a></span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> </span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> </span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> El acortamiento de los telómeros como consecuencia de la infección vírica impide la regeneración de los tejidos pulmonares. </span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"></span></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjprHyTDoR9EYYEpUeKDIRF1i5IYJAH2i_X8YyWerTjZxj_J1MfS1WVAz-pvXyRsVVLx8Yt9A79m4RWqxmMnkUnpCJzrwrZ8skcRWPUGfZD8sIlBRV2KfNxU65_QteR51NEVD33kc58mNrhUW1ZPydFukote8kfOPweoc0JhBep51Kjz0R85gz2iZYB8UY/s700/te.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="577" data-original-width="700" height="374" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjprHyTDoR9EYYEpUeKDIRF1i5IYJAH2i_X8YyWerTjZxj_J1MfS1WVAz-pvXyRsVVLx8Yt9A79m4RWqxmMnkUnpCJzrwrZ8skcRWPUGfZD8sIlBRV2KfNxU65_QteR51NEVD33kc58mNrhUW1ZPydFukote8kfOPweoc0JhBep51Kjz0R85gz2iZYB8UY/w453-h374/te.jpg" width="453" /></a></div><br /> https://twitter.com/guty2370/status/1735638024971776435?t=IlH3Btlr0wGq_1SXvpdtDw&s=19<p></p><p style="text-align: justify;"><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">Los
telómeros son pequeñas estructuras ubicadas al final de los cromosomas
que protegen las secuencias de ADN dentro de cada célula del organismo.
“Son como el plástico que cubre la punta del cordón del zapato".</span></p><p style="text-align: justify;"><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">“Si
los cordones son los cromosomas donde está nuestro material genético,
los telómeros son esas fundas transparentes que ayudan a que el cordón
no se deshilache y no se estropee”.</span></p><p style="text-align: justify;"><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">Un
equipo de científicos del Centro Nacional de Investigaciones
Oncológicas de España (CNIO), encontró que las personas con secuelas
pulmonares severas causadas por la </span><span class="r-18u37iz"><a class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3 r-1loqt21" dir="ltr" href="https://twitter.com/hashtag/COVID19?src=hashtag_click" role="link" style="color: #1d9bf0; text-overflow: unset;">#COVID19</a></span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> tenían telómeros más cortos y desgastados que quienes presentaban síntomas leves.</span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> </span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> </span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"></span></p><div class="separator" style="clear: both; text-align: center;"><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEipZjrRdoIryOnFxGIzqLkS5CgHU-C0TT5Tz25lN7TknDK06FHbz_n46lEUFeE0SBPcqdp8xYMA9WKjM2lW4LRe9O-RtnMQca-SyxX2lqa-ehU0W1E2mg_Ppex2nru7zkCjVezWhikuP-mox3ho2mzf3lQ3dl-rC7dxLB5G9J9HfIZdfe7xwpJwH9xnmS0/s1020/te4.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="476" data-original-width="1020" height="253" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEipZjrRdoIryOnFxGIzqLkS5CgHU-C0TT5Tz25lN7TknDK06FHbz_n46lEUFeE0SBPcqdp8xYMA9WKjM2lW4LRe9O-RtnMQca-SyxX2lqa-ehU0W1E2mg_Ppex2nru7zkCjVezWhikuP-mox3ho2mzf3lQ3dl-rC7dxLB5G9J9HfIZdfe7xwpJwH9xnmS0/w544-h253/te4.jpg" width="544" /></a></span></div><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"><br /></span><div style="text-align: justify;"><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> </span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">De
acuerdo con la investigación, esta reducción en el tamaño de las
estructuras que cuidan el ADN generaría fibrosis pulmonar, una
enfermedad que reduce la capacidad respiratoria de los pacientes y aún
no tiene un medicamento eficaz que la detenga</span></div><p style="text-align: justify;"></p><p style="text-align: justify;"><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">Tener en cuenta </span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> </span></p><p style="text-align: justify;"><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> Los
pacientes jóvenes del hospital de campaña que sufrieron síntomas graves
de la enfermedad también presentaron telómeros cortos.
Los pacientes con telómeros más cortos desarrollan la enfermedad </span><span class="r-18u37iz"><a class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3 r-1loqt21" dir="ltr" href="https://twitter.com/hashtag/COVID19?src=hashtag_click" role="link" style="color: #1d9bf0; text-overflow: unset;">#COVID19</a></span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> más grave</span></p><p style="text-align: justify;"><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">Un envejecimiento epigenético acelerado significativo, pero no una aceleración del desgaste de los telómeros en los casos graves de </span><span class="r-18u37iz"><a class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3 r-1ny4l3l r-1ddef8g r-tjvw6i r-1loqt21" dir="ltr" href="https://twitter.com/hashtag/COVID19?src=hashtag_click" role="link" style="color: #1d9bf0; text-overflow: unset;">#COVID19</a></span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> </span></p><p style="text-align: justify;"><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">Las mediciones de DNAmTL revelaron una aceleración del desgaste de los telómeros en pacientes fallecidos con </span><span class="r-18u37iz"><a class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3 r-1loqt21" dir="ltr" href="https://twitter.com/hashtag/COVID19?src=hashtag_click" role="link" style="color: #1d9bf0; text-overflow: unset;">#COVID19</a></span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> entre la inclusión y el final del seguimiento, así como un cambio significativo en la aceleración dinámica del desgaste de los telómeros </span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">al comparar los pacientes que se recuperaron con los que murieron.
Se necesita con urgencia una mejor comprensión de los efectos a largo plazo de los EAA en pacientes con </span><span class="r-18u37iz"><a class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3 r-1loqt21" dir="ltr" href="https://twitter.com/hashtag/COVID19?src=hashtag_click" role="link" style="color: #1d9bf0; text-overflow: unset;">#COVID19</a></span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> y cómo podrían contribuir a los síntomas </span><span class="r-18u37iz"><a class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3 r-1loqt21" dir="ltr" href="https://twitter.com/hashtag/LongCovid?src=hashtag_click" role="link" style="color: #1d9bf0; text-overflow: unset;">#LongCovid</a></span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> en personas recuperadas. </span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> </span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">https://twitter.com/guty2370/status/1735638084882980938</span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"></span></p><h4 class="highwire-cite-title" id="page-title" style="text-align: left;"><span style="color: #660000;">Epigenetic Age
Acceleration in Surviving versus Deceased COVID-19 Patients with Acute
Respiratory Distress Syndrome following Hospitalization</span></h4><ul style="text-align: left;"><li><h4 class="highwire-cite-title" id="page-title" style="text-align: left;">https://www.biorxiv.org/content/10.1101/2023.07.18.549478v1 </h4></li></ul><h4 class="highwire-cite-title" id="page-title" style="text-align: left;"><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="color: #660000; text-overflow: unset;">Evidencia de la aceleración biológica de la edad y el acortamiento de los telómeros en los supervivientes de COVID-19 </span></h4><h4 class="highwire-cite-title" id="page-title" style="text-align: left;"><span class="r-18u37iz" style="color: #0b5394;"><a class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3 r-1ny4l3l r-1ddef8g r-tjvw6i r-1loqt21" dir="ltr" href="https://twitter.com/hashtag/COVID?src=hashtag_click" role="link" style="text-overflow: unset;">Segun este estudio el #COVID</a></span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="color: #0b5394; text-overflow: unset;"> acelera el envejecimiento del cuerpo hasta por 17 año</span>s<span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="color: #0b5394; text-overflow: unset;"><br /></span></h4><ul style="text-align: left;"><li><h4 class="highwire-cite-title" id="page-title" style="text-align: left;"><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8201243/ <br /></span></h4></li></ul><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">https://notistecnicas.blogspot.com/2023/12/desde-2006-ya-se-sabia-que-los.html</span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">https://notistecnicas.blogspot.com/2024/01/sintomas-y-proporciones-de-sintomas-en.html</span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">https://notistecnicas.blogspot.com/2024/01/el-ictus-es-una-de-las-manifestaciones.html <br /></span></p><h2 style="text-align: left;"><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> </span><span style="color: #660000;">Los telómeros, entre las causas del deterioro por COVID</span></h2><div class="x-el x-el-div c2-1 c2-2 c2-2n c2-3 c2-4 c2-5 c2-6 c2-7 c2-8" data-ux="Block"><span class="x-el x-el-span c2-1 c2-2 c2-m c2-n c2-2o c2-1j c2-1l c2-3 c2-2p c2-2q c2-s c2-2r c2-2s c2-2t c2-2u" data-aid="RSS_POST_DATE" data-typography="DetailsAlpha" data-ux="Details">12 de enero de 2021</span></div><div class="x-el c2-1 c2-2 c2-m c2-n c2-2o c2-1j c2-1l c2-2v c2-2w c2-2x c2-3 c2-2y c2-4 c2-s c2-5 c2-6 c2-7 c2-8 x-rt" data-typography="BodyAlpha" data-ux="BlogContent"><p style="margin: 0px;"></p><figure class="x-el x-el-figure c2-1 c2-2 c2-37 c2-1n c2-q c2-1b c2-1m c2-1j c2-1k c2-4h c2-46 c2-3 c2-4 c2-5 c2-5e c2-5f c2-5g c2-5h c2-6 c2-7 c2-8"><div><img class="x-el x-el-img c2-1 c2-2 c2-3l c2-1b c2-17 c2-18 c2-1j c2-1l c2-z c2-58 c2-3u c2-3 c2-4 c2-5 c2-6 c2-7 c2-8" src="https://img1.wsimg.com/isteam/ip/5ab3fcf6-a52d-46d2-a4f4-ba7a9f3b11ee/tel%C3%B3meros.jpg/:/cr=t:0%25,l:0%25,w:100%25,h:100%25/rs=w:1280" /></div></figure><p></p><p style="margin: 0px;"><span>Hasta
ahora, el estudio de los telómeros se había limitado al diagnóstico y
tratamiento del envejecimiento precoz de las personas, dentro de lo que
se ha venido denominando <i class="x-el x-el-span c2-27 c2-28 c2-3 c2-51 c2-v c2-2c c2-52 c2-53"><b class="x-el x-el-span c2-27 c2-28 c2-3 c2-51 c2-v c2-34 c2-52">medicina antiaging o regenerativa. </b></i></span></p><p style="margin: 0px; text-align: justify;"><span> Un
equipo de científicos del Centro Nacional de Investigaciones
Oncológicas de España (CNIO), dirigido por la Dra María Blasco, encontró
que las personas con secuelas pulmonares severas causadas por la
covid-19 tenían<a class="x-el x-el-a c2-27 c2-28 c2-55 c2-m c2-n c2-56 c2-p c2-57 c2-3 c2-2b c2-2c c2-2d c2-2e" href="https://www.genome.gov/es/genetics-glossary/Telomero#:~:text=Un%20tel%C3%B3mero%20es%20el%20final,c%C3%A9lula%20ya%20no%20puede%20dividirse." rel="noopener" target="_blank"> telómeros </a>más cortos y desgastados que quienes presentaban síntomas leves. <a class="x-el x-el-a c2-27 c2-28 c2-55 c2-m c2-n c2-56 c2-p c2-57 c2-3 c2-2b c2-2c c2-2d c2-2e" href="https://doi.org/10.18632/aging.202463" rel="noopener" target="_blank">Las conclusiones del estudio, publicado en la revista científica <i class="x-el x-el-span c2-27 c2-28 c2-3 c2-51 c2-v c2-2c c2-52 c2-53">Aging</i></a><i class="x-el x-el-span c2-27 c2-28 c2-3 c2-51 c2-v c2-2c c2-52 c2-53">,</i>
son claves en la creación de una posible terapia que cure las lesiones
pulmonares generadas por el coronavirus. La Dra. María Blasco cuenta que
el acortamiento de los telómeros como consecuencia de la infección
vírica impide la regeneración de los tejidos pulmonares. “Por eso una
parte importante de pacientes sufre secuelas prolongadas”, dice la
científica española. De acuerdo con la investigación, esta reducción en
el tamaño de las estructuras que cuidan el ADN generaría fibrosis
pulmonar, una enfermedad que reduce la capacidad respiratoria de los
pacientes y aún no tiene un medicamento eficaz que la detenga. Los
telómeros son pequeñas estructuras ubicadas al final de los cromosomas
que protegen las secuencias de ADN dentro de cada célula del organismo.
“Son como el plástico que cubre la punta del cordón del zapato”, dice
Blasco. Y continúa: “Si los cordones son los cromosomas donde está
nuestro material genético, los telómeros son esas fundas transparentes
que ayudan a que el cordón no se deshilache y no se estropee”, explica
la investigadora. </span></p><p style="margin: 0px; text-align: justify;"><span> Los
pacientes jóvenes del hospital de campaña que sufrieron síntomas graves
de la enfermedad también presentaron telómeros cortos </span></p><p style="margin: 0px;"><span><br /></span></p><p style="margin: 0px;"><span>https://lrlperitos.es/blog/f/los-tel%C3%B3meros-entre-las-causas-del-deterioro-por-covid<br /></span></p></div><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> </span></p><h1 class="legacy entry-title instapaper_title" itemprop="headline"><b>Telómeros: la razón por la que las infecciones nos hacen envejecer más rápido </b></h1><p style="text-align: justify;">De cada infección de la que nos defiende nuestro sistema inmune no
solo salimos algo cansados sino, sobre todo, más viejos. Las últimas
investigaciones sugieren que la edad biológica puede ser mucho mayor que
la natural en personas que han padecido múltiples infecciones víricas o
que padecen una infección crónica. </p>
<p style="text-align: justify;">En estos casos, no es raro que una persona con una edad cronológica
(la del documento de identidad) de 40 años tenga un sistema inmunitario
propio de una persona de 60, lo que puede impactar profundamente en su
calidad de vida y en el funcionamiento de su sistema inmune.</p>
<p>¿A qué se debe? ¿Cómo puede hacernos envejecer un virus? Para
entenderlo, hay que empezar recordando que la información genética que
heredamos de nuestros padres está guardada en nuestros cromosomas, es
decir, en estructuras de ADN superempaquetado con cuatro brazos
colocados en forma de X. Y que para mantener estable este armazón de
ADN, los cromosomas necesitan unas estructuras en sus extremos que
funcionan a modo de cascos protectores, denominados telómeros. </p>
<h2>La división implica desgaste</h2>
<p>La palabra telómero, de origen griego, significa literalmente “parte
final”. Su función es impedir que los cromosomas se rompan o se dañen,
ya que son más débiles en los extremos. Es como si los cromosomas
estuvieran hechos de lana: sin esos cascos protectores, correrían el
riesgo de deshilacharse y deshacerse.</p><div class="slot clear" data-id="17"><div class="promo"><div class="lazyload-wrapper"><div class="MuiBoxroot-0-1-95 MuiBoxroot-0-1-96 makeStylesbox-0-1-94"><div style="opacity: 1; transition: opacity 300ms ease-in-out 0s;"><h5 class="MuiTypographyroot-0-1-111 makeStylestitle-0-1-97 MuiTypographyh5-0-1-120 makeStylesh5-0-1-102 MuiTypographycolorInherit-0-1-133">¿Le gusta lo que lee? ¿Quiere más?</h5></div></div></div></div></div><div class="grid-twelve large-grid-eleven"><div class="grid-ten large-grid-nine grid-last content-body content entry-content instapaper_body inline-promos" itemprop="articleBody"><p>Las
células de nuestro cuerpo no son las mismas cuando nacemos que cuando
morimos: a lo largo de la vida se van renovando mediante sucesivas
divisiones en las que, a partir de una sola célula, se forman dos
células hijas idénticas. </p>
<p>Cada división celular conlleva un pequeño desgaste de los telómeros,
que se van haciendo más pequeños cada vez, algo que en la jerga
conocemos como acortamiento de telómeros. A partir de cierto número de
divisiones, son tan pequeños que no pueden ejercer su función protectora
y el cromosoma comienza a deshilacharse y perder su estructura. Cuando
esto ocurre, las células pierden su capacidad para dividirse y
desempeñar sus funciones. </p>
<figure class="align-center zoomable">
<a href="https://images.theconversation.com/files/568815/original/file-20240111-25-wldv75.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><div class="placeholder-container" style="--aspect-ratio-percent: 62.46684350132627%; --background-color: #2c475b;"><img alt="" class="lazyloaded" data-src="https://images.theconversation.com/files/568815/original/file-20240111-25-wldv75.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" data-srcset="https://images.theconversation.com/files/568815/original/file-20240111-25-wldv75.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=375&fit=crop&dpr=1 600w, https://images.theconversation.com/files/568815/original/file-20240111-25-wldv75.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=375&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/568815/original/file-20240111-25-wldv75.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=375&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/568815/original/file-20240111-25-wldv75.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=471&fit=crop&dpr=1 754w, https://images.theconversation.com/files/568815/original/file-20240111-25-wldv75.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=471&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/568815/original/file-20240111-25-wldv75.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=471&fit=crop&dpr=3 2262w" src="https://images.theconversation.com/files/568815/original/file-20240111-25-wldv75.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" /></div><div class="enlarge_hint"></div></a>
<figcaption>
<span class="caption"></span>
</figcaption>
</figure>
<p>En cierto modo, los telómeros actúan como relojes celulares capaces
de medir cuántas veces puede dividirse una célula. De hecho, lo normal
es que las personas de más edad tengan los telómeros <a href="https://pubmed.ncbi.nlm.nih.gov/30669451/">más desgastados que los más jóvenes</a>. Sin embargo, los telómeros no solo se acortan debido a la edad, sino que también influyen otros factores como la <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6637081/">etnia</a>, el <a href="https://pubmed.ncbi.nlm.nih.gov/18820651/">sexo</a> , el <a href="https://pubmed.ncbi.nlm.nih.gov/34736994/">estrés</a>, la <a href="https://pubmed.ncbi.nlm.nih.gov/31657606/">dieta</a> y la <a href="https://pubmed.ncbi.nlm.nih.gov/34736994/">exposición a determinadas enfermedades</a>.</p>
<h2>Las infecciones impactan en el tamaño de los telómeros</h2>
<p>Cuando las células del sistema inmune entran en contacto con un
microorganismo dañino, se inicia un proceso de división y expansión
masiva hasta formar una población numerosa suficiente para destruir al
invasor. </p>
<p>Eso quiere decir que cada proceso infectivo da lugar a un ciclo de
divisiones masivas de células inmunes, con el consiguiente desgaste de
sus telómeros y el aumento de la probabilidad de entrar antes en lo que
se conoce como estado de <a href="https://pubmed.ncbi.nlm.nih.gov/28981470/">inmunosenescencia</a>, es decir, un sistema inmune envejecido. </p>
<p>Cuantas más infecciones enfrentamos en nuestra vida, más se acortan
los telómeros de las células que nos defienden y más envejece nuestro
sistema inmune. De hecho, los expertos hablan de que existe una <a href="https://pubmed.ncbi.nlm.nih.gov/37222592/">“senescencia inducida por virus”</a>.</p>
<figure class="align-center zoomable">
<a href="https://images.theconversation.com/files/567633/original/file-20240103-21-mex4gk.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><div class="placeholder-container" style="--aspect-ratio-percent: 49.602122015915114%; --background-color: #989366;"><img alt="" class="lazyloaded" data-src="https://images.theconversation.com/files/567633/original/file-20240103-21-mex4gk.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" data-srcset="https://images.theconversation.com/files/567633/original/file-20240103-21-mex4gk.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=298&fit=crop&dpr=1 600w, https://images.theconversation.com/files/567633/original/file-20240103-21-mex4gk.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=298&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/567633/original/file-20240103-21-mex4gk.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=298&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/567633/original/file-20240103-21-mex4gk.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=374&fit=crop&dpr=1 754w, https://images.theconversation.com/files/567633/original/file-20240103-21-mex4gk.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=374&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/567633/original/file-20240103-21-mex4gk.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=374&fit=crop&dpr=3 2262w" src="https://images.theconversation.com/files/567633/original/file-20240103-21-mex4gk.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" /></div><div class="enlarge_hint"></div></a>
<figcaption>
<span class="caption">Telómeros.</span>
</figcaption>
</figure>
<p>Concretamente, estudios con pacientes infectados con virus como el
VIH, el virus de Epstein-Barr (que produce la mononucleosis) y <a href="https://pubmed.ncbi.nlm.nih.gov/28981470/">el virus de la hepatitis C</a>,
entre otros, han demostrado un acortamiento de los telómeros del
sistema inmune. Además, recientemente se ha demostrado que los pacientes
que han estado más graves debido a la covid-19 han sufrido un <a href="https://pubmed.ncbi.nlm.nih.gov/36458548/">acortamiento de sus telómeros</a>.</p>
<p>De ahí que, como adelantábamos al principio, la edad biológica pueda
ser mucho mayor que la edad natural en personas que han padecido
múltiples infecciones o que se enfrentan a una infección crónica. </p>
<h2>El envejecimiento inmunológico y sus consecuencias</h2>
<p>Con el acortamiento de telómeros y el envejecimiento del sistema inmune, las células <a href="https://pubmed.ncbi.nlm.nih.gov/37179335/">pierden su capacidad para defendernos de las infecciones</a>, por lo que existe un <a href="https://pubmed.ncbi.nlm.nih.gov/23423415/">mayor riesgo de padecer enfermedades infecciosas</a>. </p>
<p>Ese es el motivo por el que las personas mayores responden peor <a href="https://pubmed.ncbi.nlm.nih.gov/31528180/">ante las infecciones</a> y, lo que es más preocupante, también <a href="https://pubmed.ncbi.nlm.nih.gov/24260712/">ante las vacunas</a>, ya que para que una vacuna haga efecto necesita un sistema inmune que funcione correctamente.</p>
<p>También hay indicios de que el acortamiento de los telómeros impide
que los tejidos dañados se puedan regenerar tras la infección <a href="https://pubmed.ncbi.nlm.nih.gov/35984738/">mediante divisiones celulares</a>, haciendo que una parte importante de los pacientes tengan secuelas o mayor riesgo de desarrollar otras patologías. </p>
<h2>Más infecciones respiratorias, cáncer y enfermedades autoinmunes</h2>
<p>Las consecuencias del envejecimiento inmunológico son múltiples: se ha relacionado con una <a href="https://pubmed.ncbi.nlm.nih.gov/24260712/">mayor susceptibilidad a infecciones respiratorias</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/24260712/">urinarias</a>, <a href="https://www.nature.com/articles/nrcardio.2013.174">endocarditis infecciosa</a> (infección del endocardio, membrana que recubre las cámaras del corazón) y <a href="https://pubmed.ncbi.nlm.nih.gov/28293557/">septicemia</a> (presencia de bacterias en la sangre), además de cáncer, <a href="https://pubmed.ncbi.nlm.nih.gov/32760251/">alzhéimer</a> y <a href="https://pubmed.ncbi.nlm.nih.gov/24260712/">enfermedades autoinmunes</a>.
Las enfermedades autoinmunes son enfermedades en las que el sistema
inmune ataca a células de nuestro propio organismo sin ser estas
dañinas: parece lógico pensar que un sistema inmune defectuoso tenga
mayor probabilidad de “equivocarse” y atacar a quien no debe.</p>
<p>No hay duda de que las infecciones provocan un envejecimiento
acelerado del sistema inmune, que conlleva una mayor predisposición a
sufrir tanto enfermedades infecciosas como al desarrollo de otras
patologías. </p>
<p>Esta interrelación entre infecciones y envejecimiento, a través del
desgaste de telómeros, demanda investigaciones más profundas para
intentar anticiparnos y diseñar estrategias que palíen su desgaste.</p>
</div>
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<h1 class="valign" id="article-title">
<span style="color: #783f04;">Shorter telomere lengths in patients with severe COVID-19 disease
</span></h1>
</div>
<h4 class="authors">
<span class="author">
Raul Sanchez-Vazquez<sup class="author-affiliations"><span><a class="tooltip--triangle" data-tooltip="Telomeres and Telomerase Group, Molecular Oncology Program, Spanish National Cancer Centre (CNIO), Melchor Fernández Almagro 3, Madrid, Spain" href="https://www.aging-us.com/article/202463/text#">1</a><span class="comma">,</span></span>
<span><a class="tooltip--triangle" data-tooltip="Equal contribution" href="https://www.aging-us.com/article/202463/text#">*</a></span>
</sup>
<span class="author-comma">,</span>
</span>
<span class="author">
Ana Guío-Carrión<sup class="author-affiliations"><span><a class="tooltip--triangle" data-tooltip="Telomeres and Telomerase Group, Molecular Oncology Program, Spanish National Cancer Centre (CNIO), Melchor Fernández Almagro 3, Madrid, Spain" href="https://www.aging-us.com/article/202463/text#">1</a><span class="comma">,</span></span>
<span><a class="tooltip--triangle" data-tooltip="Equal contribution" href="https://www.aging-us.com/article/202463/text#">*</a></span>
</sup>
<span class="author-comma">,</span>
</span>
<span class="author">
Antonio Zapatero-Gaviria<sup class="author-affiliations"><span><a class="tooltip--triangle" data-tooltip="Field Hospital COVID-19, IFEMA, Madrid, Spain" href="https://www.aging-us.com/article/202463/text#">2</a></span>
</sup>
<span class="author-comma">,</span>
</span>
<span class="author">
Paula Martínez<sup class="author-affiliations"><span><a class="tooltip--triangle" data-tooltip="Telomeres and Telomerase Group, Molecular Oncology Program, Spanish National Cancer Centre (CNIO), Melchor Fernández Almagro 3, Madrid, Spain" href="https://www.aging-us.com/article/202463/text#">1</a></span>
</sup>
<span class="author-comma">,</span>
</span>
<span class="author">
Maria A. Blasco<sup class="author-affiliations"><span><a class="tooltip--triangle" data-tooltip="Telomeres and Telomerase Group, Molecular Oncology Program, Spanish National Cancer Centre (CNIO), Melchor Fernández Almagro 3, Madrid, Spain" href="https://www.aging-us.com/article/202463/text#">1</a></span>
</sup>
</span>
</h4>
<span>
<ul><li>
<sup>1</sup> Telomeres and Telomerase Group, Molecular
Oncology Program, Spanish National Cancer Centre (CNIO), Melchor
Fernández Almagro 3, Madrid, Spain
</li><li>
<sup>2</sup> Field Hospital COVID-19, IFEMA, Madrid, Spain
</li></ul>
</span>
<div>* Equal contribution</div>
<h4 class="dates">
<span class="date"> Received: December 11, 2020 <span class="comma"> </span> </span>
<span class="date"> Accepted: December 22, 2020 <span class="comma"> </span> </span>
<span class="date">Published: January 11, 2021 </span>
</h4>
<span><a href="https://doi.org/10.18632/aging.202463">https://doi.org/10.18632/aging.202463</a></span><br />
<a class="view-citation" data-pii="202463">How to Cite</a>
<p class="justify"><span>Copyright: © 2021 Sanchez-Vazquez et al.</span> This is an open access article distributed under the terms of the <a href="https://creativecommons.org/licenses/by/3.0/">Creative Commons Attribution License</a>
(CC BY 3.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original author and source are
credited.
</p>
<div class="abstract article-text">
<h3 class="bordered article-header-1" id="abstract">Abstract</h3>
<p>The incidence of severe manifestations of COVID-19
increases with age with older patients showing the highest mortality,
suggesting that molecular pathways underlying aging contribute to the
severity of COVID-19. One mechanism of aging is the progressive
shortening of telomeres, which are protective structures at chromosome
ends. Critically short telomeres impair the regenerative capacity of
tissues and trigger loss of tissue homeostasis and disease. The
SARS-CoV-2 virus infects many different cell types, forcing cell
turn-over and regeneration to maintain tissue homeostasis. We
hypothesize that presence of short telomeres in older patients limits
the tissue response to SARS-CoV-2 infection. We measure telomere length
in peripheral blood lymphocytes COVID-19 patients with ages between 29
and 85 years-old. We find that shorter telomeres are associated to
increased severity of the disease. Individuals within the lower
percentiles of telomere length and higher percentiles of short telomeres
have higher risk of developing severe COVID-19 pathologies.</p>
<div id="fulltext"></div>
</div>
<div class="section-container" id="introduction">
<h2 class="article-header-1"> Introduction</h2>
<p>The current COVID-19 pandemic (<a href="https://www.who.int/" target="_BLANK">https://www.who.int/</a>)
is produced by the SARS-CoV-2 virus, a novel zoonotic Coronavirus of
the betacoronavirus genus that most likely crossed species from bats to
humans leading to a pneumonia outbreak initially reported in Wuhan,
China and now affecting the majority of countries. SARS-CoV-2 causes
from mild flu-like symptoms in approximately 80% of the cases to a
severe lung and multi-organic failure which can result in death of a
significant percentage of patients. Pathologies associated with
SARS-CoV-2 include severe lung failure, diarrhea, heart infarct, and
brain pathologies among others [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r1">1</a>–<a class="anchor" href="https://www.aging-us.com/article/202463/text#r3">3</a>].
This wide viral tropism is mediated by expression of the
Angiotensin-converting enzyme 2 (ACE2), which acts as the receptor
protein for the virus to enter the host cells. In particular, the
SARS-CoV-2 spike protein directly binds the ACE2 human protein [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r4">4</a>–<a class="anchor" href="https://www.aging-us.com/article/202463/text#r7">7</a>]. The human ACE protein is expressed in alveolar type II (ATII) cells in the lung [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r8">8</a>], as well as in the kidney, the heart and the gut [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r9">9</a>–<a class="anchor" href="https://www.aging-us.com/article/202463/text#r14">14</a>]. This expression pattern of the ACE protein explains that a preferential site for SARS-CoV-2 infection is the lung [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r4">4</a>, <a class="anchor" href="https://www.aging-us.com/article/202463/text#r15">15</a>, <a class="anchor" href="https://www.aging-us.com/article/202463/text#r16">16</a>], although the virus can also infect kidney, intestine, and heart cells causing severe pathologies in all these tissues [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r1">1</a>–<a class="anchor" href="https://www.aging-us.com/article/202463/text#r3">3</a>, <a class="anchor" href="https://www.aging-us.com/article/202463/text#r11">11</a>, <a class="anchor" href="https://www.aging-us.com/article/202463/text#r17">17</a>, <a class="anchor" href="https://www.aging-us.com/article/202463/text#r18">18</a>].
In this regard, it caught our attention that a common outcome of
SARS-CoV-2 infection seems to be induction of fibrosis-like phenotypes
in the lung and kidney, suggesting that the viral infection maybe
exhausting the regenerative potential of tissues [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r11">11</a>, <a class="anchor" href="https://www.aging-us.com/article/202463/text#r16">16</a>–<a class="anchor" href="https://www.aging-us.com/article/202463/text#r18">18</a>].</p>
<p>In contrast to influenza infection, that causes a high mortality in infants [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r19">19</a>–<a class="anchor" href="https://www.aging-us.com/article/202463/text#r24">24</a>],
SARS-CoV-2 infection causes low mortality in infants or children but
results in a progressively increased mortality with increasing age
reaching up to 15% mortality in individuals that are ≥80 years old (see <a href="https://covid19.isciii.es/" target="_BLANK">https://covid19.isciii.es/</a>
for mortality data in Spain). These findings suggest that molecular
mechanisms at the origin of organismal aging maybe influencing the
outcome of SARS-CoV-2 infection by increasing lethality. One of such
molecular events underlying aging is the progressive shortening of
telomeres throughout life, which can cause exhaustion of the
proliferative potential of stem cells and immune cells among others [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r25">25</a>–<a class="anchor" href="https://www.aging-us.com/article/202463/text#r27">27</a>].</p>
<p>Telomeres are specialized structures at the
chromosome ends, which are essential for chromosome-end protection and
genomic stability [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r28">28</a>].
Vertebrate telomeres consist of tandem repeats of the TTAGGG DNA
sequence bound by a six-protein complex known as shelterin, which
prevents chromosome end-to-end fusions and telomere fragility [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r29">29</a>, <a class="anchor" href="https://www.aging-us.com/article/202463/text#r30">30</a>].
As cells divide and DNA has to be replicated, telomeres become
progressively shorter owing to the so-called “end replication problem” [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r31">31</a>, <a class="anchor" href="https://www.aging-us.com/article/202463/text#r32">32</a>]. Thus, telomere shortening occurs associated with increasing age in humans [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r33">33</a>], mice [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r34">34</a>] and other species, and the rate of telomere shortening has been shown to correlate with species lifespan [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r35">35</a>].
When telomeres become critically short this results in loss of telomere
protection, leading to activation of a persistent DNA damage response [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r36">36</a>] and loss of cellular viability by induction of apoptosis and/or senescence [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r30">30</a>].</p>
<p>Telomerase is a reverse transcriptase that is able to elongate telomeres <i>de novo</i> by adding TTAGGG repeats to chromosome ends [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r37">37</a>].
Telomerase is active in embryonic stem cells, thereby ensuring
sufficiently long telomeres with generations in a given species. After
birth, however, telomerase expression is silenced in the majority of
cell types causing telomeres to shorten with age.</p>
<p>We have shown by using telomerase-deficient
mice with critically short telomeres, that short telomeres are
sufficient to impair the ability of stem cells to regenerate different
tissues, including skin, brain and bone marrow [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r38">38</a>–<a class="anchor" href="https://www.aging-us.com/article/202463/text#r41">41</a>].
In humans, mutations in telomerase or telomere-binding proteins can
also lead to very short telomeres and appearance of pathologies
characterized by loss of the regenerative capacity of tissues and
presence of fibrosis in lungs, liver or kidney, as well as by intestinal
atrophy and bone marrow aplasia [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r42">42</a>].</p>
<p>In particular, we previously demonstrated
that short or dysfunctional telomeres are at the origin of pulmonary
fibrosis in mouse models of the disease [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r43">43</a>].
In particular, induction of telomere dysfunction specifically in
alveolar type II (ATII) cells by deletion of an essential telomere
protective protein in these cells, TRF1, is sufficient to induce
progressive and lethal pulmonary fibrosis phenotypes in mice, which are
concomitant with induction of telomeric DNA damage, cell death and
senescence [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r43">43</a>].
These findings demonstrate that dysfunctional telomeres in lungs ATII
cells lead to loss of viability of these cells and induction of
fibrosis. Also in support of this notion, we have demonstrated that
therapies aimed to elongate telomeres, such as a telomerase gene therapy
using adeno-associated vectors (AAV9-TERT) can stop the progression of
pulmonary fibrosis associated to short telomeres in mouse models of the
disease by increasing telomere length in ATII cells, as well as their
proliferative potential [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r44">44</a>], thus demonstrating the importance of sufficiently long telomeres to allow tissue regeneration.</p>
<p>Importantly, as SARS-CoV-2 infects different
cell types in humans, including ATII cells in the lungs, it is plausible
that viral infection could damage these different cell types forcing an
increased turn-over of different regenerative cell types. While in
young individuals with sufficiently long telomeres, regenerative cell
types, such as lung ATII cells could undergo these extra cell divisions
and contribute to tissue healing, older individuals with shorter
telomeres may fail to allow cell proliferation and regeneration, thus
leading to tissue failure. Thus, here we set to assess whether telomere
length in COVID-19 patients correlated with development of more severe
COVID-19 pathologies.</p>
</div>
<div class="section-container" id="results">
<h2 class="article-header-1"> Results</h2>
<div class="section-container" id="">
<h3 class="article-header-2"> Pathologies in COVID-19 patient cohort</h3>
<p>In order to assess the potential
impact of telomere length on pathologies associated to COVID-19 disease,
we obtained both DNA and mononuclear cells from peripheral blood
samples from patients hospitalized at the IFEMA field hospital in
Madrid, which was constructed to treat COVID-19 patients. A total of 61
female and 28 male patients of ages ranging from 29 to 85 years old were
included in the study (<a class="anchor" href="https://www.aging-us.com/article/202463/text#t1">Table 1</a>). The patient cohort had different severity of pathologies and received the treatments indicated in <a class="anchor" href="https://www.aging-us.com/article/202463/text#t1">Table 1</a>. None of the patients included in this study died as a consequence of the COVID-19 disease.</p>
<div class="table-title"><h4>Table 1. Patients included in this study.</h4></div>
<div class="article-table">
<table class="bordered" id="t1">
<tbody><tr><td style="undefined: solid 1pt;"><b>Age</b></td><td style="undefined: solid 1pt;"><b>Sex</b></td><td style="undefined: solid 1pt;"><b>COVID-19 severity</b></td><td style="undefined: solid 1pt;"><b>Treatment</b></td></tr><tr><td>29</td><td>Male</td><td>Moderate</td><td>Hydroxychloroquine, Azitromicine</td></tr><tr><td>30</td><td>Female</td><td>Moderate</td><td>Dolquine, Azitromicine</td></tr><tr><td>31</td><td>Female</td><td>Moderate</td><td>Hydroxychloroquine, Azitromicine, Kaletra</td></tr><tr><td>33</td><td>Female</td><td>Moderate</td><td>Hydroxychloroquine, Azitromicine</td></tr><tr><td>33</td><td>Female</td><td>Severe</td><td>Hydroxychloroquine, Azitromicine</td></tr><tr><td>35</td><td>Male</td><td>Moderate</td><td>Hydroxychloroquine, Ceftriaxone, Azitromicine, Kaletra</td></tr><tr><td>36</td><td>Female</td><td>Acute</td><td>Hydroxychloroquine, Kaletra, Corticoids, Ceftriaxone, Azitromicine, LINEZOLID</td></tr><tr><td>38</td><td>Female</td><td>Mild</td><td>Hydroxychloroquine</td></tr><tr><td>39</td><td>Female</td><td>Moderate</td><td>Hydroxychloroquine, Azitromicine</td></tr><tr><td>40</td><td>Male</td><td>Severe</td><td>Hydroxychloroquine, Azitromicine, Ceftriaxone, systemic corticoids</td></tr><tr><td>41</td><td>Female</td><td>Moderate</td><td>Hydroxychloroquine, Kaletra</td></tr><tr><td>42</td><td>Male</td><td>Severe</td><td>Hydroxychloroquine, Kaletra, Corticoids</td></tr><tr><td>43</td><td>Female</td><td>Mild</td><td>Hydroxychloroquine, Azitromicine</td></tr><tr><td>43</td><td>Female</td><td>Severe</td><td>Hydroxychloroquine, Azitromicine, Corticoids, Kaletra, Tocilizumab</td></tr><tr><td>43</td><td>Female</td><td>Moderate</td><td>Hydroxychloroquine, Azitromicine</td></tr><tr><td>44</td><td>Male</td><td>Mild</td><td>Hydroxychloroquine</td></tr><tr><td>45</td><td>Female</td><td>Moderate</td><td>Dolquine, Kaletra, Azitromicine</td></tr><tr><td>45</td><td>Female</td><td>Severe</td><td>Kaletra, Hydroxychloroquine, Azitromicine, Corticoids</td></tr><tr><td>46</td><td>Female</td><td>Moderate</td><td>Kaletra, Dolquine, Colchicine</td></tr><tr><td>46</td><td>Female</td><td>Severe</td><td>Dolquine, Kaletra, Azitromicine, Corticoids</td></tr><tr><td>46</td><td>Female</td><td>Severe</td><td>Hydroxychloroquine, Azitromicine</td></tr><tr><td>47</td><td>Female</td><td>Moderate</td><td>Hydroxychloroquine</td></tr><tr><td>47</td><td>Male</td><td>Severe</td><td>Hydroxychloroquine, Azitromicine, systemic corticoids</td></tr><tr><td>47</td><td>Female</td><td>Mild</td><td>Hydroxychloroquine, Ceftriaxone</td></tr><tr><td>48</td><td>Male</td><td>Severe</td><td>Hydroxychloroquine, Azitromicine, Tocilizumab, Kaletra</td></tr><tr><td>49</td><td>Female</td><td>Moderate</td><td>Hydroxychloroquine, Azitromicine</td></tr><tr><td>49</td><td>Male</td><td>Severe</td><td>Hydroxychloroquine, Azitromicine, systemic corticoids, Tocilizumab</td></tr><tr><td>49</td><td>Male</td><td>Moderate</td><td>Hydroxychloroquine, Azitromicine</td></tr><tr><td>49</td><td>Male</td><td>Moderate</td><td>Hydroxychloroquine, Azitromicine, Ceftriaxone</td></tr><tr><td>49</td><td>Male</td><td>Moderate</td><td>Hydroxychloroquine, Kaletra</td></tr><tr><td>49</td><td>Female</td><td>Moderate</td><td>Hydroxychloroquine, Azitromicine</td></tr><tr><td>50</td><td>Female</td><td>Moderate</td><td>Hydroxychloroquine, Azitromicine</td></tr><tr><td>51</td><td>Male</td><td>Severe</td><td>Hydroxychloroquine, Azitromicine</td></tr><tr><td>51</td><td>Female</td><td>Moderate</td><td>Hydroxychloroquine, Azitromicine, Kaletra, Ceftriaxone</td></tr><tr><td>51</td><td>Female</td><td>Moderate</td><td>Hydroxychloroquine, Kaletra, Azitromicine, Corticoids</td></tr><tr><td>52</td><td>Female</td><td>Severe</td><td>Chloroquine, Kaletra, Tocilizumab, methylprednisolone</td></tr><tr><td>52</td><td>Male</td><td>Severe</td><td>Hydroxychloroquine</td></tr><tr><td>52</td><td>Female</td><td>Moderate</td><td>Hydroxychloroquine, Azitromicine</td></tr><tr><td>53</td><td>Female</td><td>Severe</td><td>Hydroxychloroquine, Corticoids, Tocilizumab</td></tr><tr><td>53</td><td>Male</td><td>Moderate</td><td>Hydroxychloroquine, Azitromicine</td></tr><tr><td>53</td><td>Male</td><td>Severe</td><td>Hydroxychloroquine, Azitromicine, Cortocoid therapy</td></tr><tr><td>54</td><td>Female</td><td>Acute</td><td>Hydroxychloroquine, Kaletra, Azitromicine, INTERFERON, Tocilizumab, Corticoids</td></tr><tr><td>54</td><td>Female</td><td>Acute</td><td>Hydroxychloroquine, Azitromicine, systemic corticoids, Tocilizumab</td></tr><tr><td>54</td><td>Female</td><td>Severe</td><td>Hydroxychloroquine, Azitromicine, Kaletra, systemic corticoids</td></tr><tr><td>54</td><td>Female</td><td>Severe</td><td>Hydroxychloroquine, Azitromicine, Corticoids, Tocilizumab</td></tr><tr><td>54</td><td>Female</td><td>Moderate</td><td>Hydroxychloroquine</td></tr><tr><td>54</td><td>Female</td><td>Moderate</td><td>Hydroxychloroquine, Azitromicine, Ceftriaxone</td></tr><tr><td>54</td><td>Female</td><td>Moderate</td><td>Hydroxychloroquine, Kaletra, Azitromicine</td></tr><tr><td>55</td><td>Female</td><td>Mild</td><td>Hydroxychloroquine</td></tr><tr><td>55</td><td>Female</td><td>Mild</td><td>Ceftriaxone, Hydroxychloroquine</td></tr><tr><td>55</td><td>Male</td><td>Moderate</td><td>Hydroxychloroquine</td></tr><tr><td>56</td><td>Female</td><td>Moderate</td><td>Dolquine, Azitromicine</td></tr><tr><td>56</td><td>Female</td><td>Severe</td><td>Hydroxychloroquine, Azitromicine, systemic corticoids</td></tr><tr><td>56</td><td>Male</td><td>Moderate</td><td>Hydroxychloroquine, Azitromicine</td></tr><tr><td>56</td><td>Female</td><td>Severe</td><td>REMDESIVIR, Corticoids, Tocilizumab</td></tr><tr><td>57</td><td>Male</td><td>Severe</td><td>Chloroquine, Corticoids, Interferon beta</td></tr><tr><td>57</td><td>Male</td><td>Acute</td><td>Hydroxychloroquine, Azitromicine, Corticoids, Tocilizumab</td></tr><tr><td>57</td><td>Female</td><td>Severe</td><td>Hydroxychloroquine, Kaletra, Corticoids</td></tr><tr><td>57</td><td>Male</td><td>Moderate</td><td>Hydroxychloroquine, Kaletra</td></tr><tr><td>57</td><td>Female</td><td>Severe</td><td>Hydroxychloroquine, Corticoids, Tocilizumab, Azitromicine</td></tr><tr><td>58</td><td>Male</td><td>Severe</td><td>Hydroxychloroquine, Azitromicine, systemic corticoids</td></tr><tr><td>58</td><td>Female</td><td>Moderate</td><td>Hydroxychloroquine+Azitromicine</td></tr><tr><td>58</td><td>Female</td><td>Severe</td><td>Hydroxychloroquine, Azitromicine, Methylprednisone</td></tr><tr><td>59</td><td>Female</td><td>Severe</td><td>Hydroxychloroquine, Azitromicine, Corticoids</td></tr><tr><td>59</td><td>Female</td><td>Moderate</td><td>Hydroxychloroquine, Kaletra, Predisolone</td></tr><tr><td>60</td><td>Female</td><td>Severe</td><td>Hydroxychloroquine, Azitromicine, Corticoids, Tocilizumab</td></tr><tr><td>60</td><td>Female</td><td>Moderate</td><td>Hydroxychloroquine+Azitromicine+Kaletra</td></tr><tr><td>61</td><td>Female</td><td>Moderate</td><td>Dolquine, Azitromicine</td></tr><tr><td>61</td><td>Male</td><td>Severe</td><td>Hydroxychloroquine, Ceftriaxone, Azitromicine, systemic corticoids</td></tr><tr><td>62</td><td>Female</td><td>Moderate</td><td>Hydroxychloroquine, Azitromicine</td></tr><tr><td>62</td><td>Female</td><td>Severe</td><td>Hydroxychloroquine, Azitromicine, Kaletra</td></tr><tr><td>63</td><td>Female</td><td>Severe</td><td>Hydroxychloroquine, Azitromicine, Dexamethasone</td></tr><tr><td>63</td><td>Male</td><td>Severe</td><td>Hydroxychloroquine, Azitromicine, Corticoids</td></tr><tr><td>65</td><td>Male</td><td>Severe</td><td>Hydroxychloroquine, Kaletra, Corticoids</td></tr><tr><td>65</td><td>Male</td><td>Severe</td><td>Hydroxychloroquine, Azitromicine, Kaletra, Dexamethasone, Tocilizumab</td></tr><tr><td>66</td><td>Female</td><td>Severe</td><td>Hydroxychloroquine, Kaletra, systemic corticoids</td></tr><tr><td>67</td><td>Female</td><td>Moderate</td><td>Hydroxychloroquine</td></tr><tr><td>67</td><td>Female</td><td>Severe</td><td>Chloroquine, Kaletra, Azitromicine, Tocilizumab, Corticoids</td></tr><tr><td>67</td><td>Male</td><td>Severe</td><td>Hydroxychloroquine, Azitromicine, Corticoids, Kaletra</td></tr><tr><td>69</td><td>Male</td><td><br /></td><td><br /></td></tr><tr><td>70</td><td>Female</td><td>Severe</td><td>Dolquine, Kaletra, Azitromicine, Corticoids</td></tr><tr><td>71</td><td>Male</td><td>Severe</td><td>Hydroxychloroquine, Kaletra, Corticoids</td></tr><tr><td>71</td><td>Female</td><td>Severe</td><td>Hydroxychloroquine, Azitromicine, Cortocoid therapy</td></tr><tr><td>72</td><td>Female</td><td>Acute</td><td>Hydroxychloroquine, Azitromicine, systemic corticoids</td></tr><tr><td>73</td><td>Female</td><td>Severe</td><td>Hydroxychloroquine, Azitromicine, Kaletra, systemic corticoids, Tocilizumab</td></tr><tr><td>74</td><td>Female</td><td>Severe</td><td>Hydroxychloroquine, Azitromicine, Corticoids</td></tr><tr><td>77</td><td>Female</td><td>Severe</td><td>Hydroxychloroquine</td></tr><tr><td>81</td><td>Female</td><td>Mild</td><td>Hydroxychloroquine, Azitromicine, Ceftriaxone</td></tr><tr><td>85</td><td>Female</td><td>Moderate</td><td>Hydroxychloroquine, Kaletra, Azitromicine</td></tr></tbody>
</table>
</div>
<p>In order to correlate patient
severity with telomere length, we first grouped the patients according
the a severity score ranging from 1 to 4, with severity score 1 in the
case of patients with low fever and cough but without any radiological
features of pneumonia to patients with severity score of 4 in the cases
of patients with features of Acute Respiratory Distress Syndrome (ARDS)
requiring mechanical ventilation along with presence of multiorgan
dysfunction failure, metabolic acidosis and coagulation dysfunction
(Materials and Methods).</p>
</div>
<div class="section-container" id="">
<h3 class="article-header-2"> Determination of telomere length in COVID-19 patients</h3>
<p>In order to determine telomere length
in our patient cohort, peripheral blood was extracted from the arm from
the different patients and used to measure telomere length by three
independent techniques (Materials and Methods). First, we determined
telomere length in DNA extracted from peripheral blood by using both the
Southern blotting-based Telomere Restriction Analysis (TRF; see [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r38">38</a>]) and the quantitative-PCR (qPCR) telomere length analyses [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r45">45</a>].
In addition to these two technologies based on DNA samples, we also
measured telomere length on fresh peripheral blood mononuclear cells by
using the more precise high-throughput quantitative fluorescence <i>in situ</i> hybridization (HT Q-FISH) previously described by us [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r34">34</a>, <a class="anchor" href="https://www.aging-us.com/article/202463/text#r35">35</a>, <a class="anchor" href="https://www.aging-us.com/article/202463/text#r46">46</a>],
which allows determination of individual telomere fluorescence signals
using tens of thousands of cells from a single patient. The fact that HT
Q-FISH can determine individual telomere fluorescence spots in
interphasic nuclei, each spot usually formed by several clustered
telomeres, allows to the determine the abundance of very short
telomeres.</p>
<p>We observed a very significant correlation between the telomere length measurements obtained by the three different techniques (<a class="anchor" href="https://www.aging-us.com/article/202463/text#f1">Figure 1A</a>),
thus indicating the robustness of the data on telomere length obtained
in our patient cohort. Given the good correlation of the telomere length
data obtained with the three technologies, we decided to perform the
rest of the analysis with the telomere length data obtained by HT
Q-FISH, as it measures telomeres in a single cell manner and it also
allows to measure individual telomere spots within single nuclei.</p>
<div class="full-text-image-container" id="f1">
<a href="https://www.aging-us.com/figure/202463/f1"><img alt="Correlation between HT Q-FISH and PCR and TRF techniques for telomere length measurements. (A) Correlation of telomere length measured by TRF, qPCR and HT Q-FISH in Peripheral blood mononuclear cell (PMBC) samples from 89 individuals. (B, C) Linear regression analysis was used to determine the rate of telomere shortening (B) and the rate of the increase of short telomeres (C) per year in PMBCs. The telomere length data in (B, C) correspond to HT-qFish analysis. The Pearson correlation coefficient and linear regression equation were determined using GraphPad Software." class="full-text-image" src="https://www.aging-us.com/article/202463/figure/f1/large" /></a>
<p class="small-font"><span><b>Figure 1.</b></span> <b>Correlation between HT Q-FISH and PCR and TRF techniques for telomere length measurements.</b> (<b>A</b>)
Correlation of telomere length measured by TRF, qPCR and HT Q-FISH in
Peripheral blood mononuclear cell (PMBC) samples from 89 individuals. (<b>B</b>, <b>C</b>) Linear regression analysis was used to determine the rate of telomere shortening (<b>B</b>) and the rate of the increase of short telomeres (<3kb) (<b>C</b>) per year in PMBCs. The telomere length data in (<b>B</b>, <b>C</b>)
correspond to HT-qFish analysis. The Pearson correlation coefficient
and linear regression equation were determined using GraphPad Software.<br /></p>
</div>
<p>The rate of telomere shortening in the patient cohort was of 77 bp/year (<a class="anchor" href="https://www.aging-us.com/article/202463/text#f1">Figure 1B</a>). This rate of telomere shortening is in the range previously published by us and others [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r45">45</a>–<a class="anchor" href="https://www.aging-us.com/article/202463/text#r48">48</a>].
In agreement with telomere shortening with increasing patient age, we
also observed an increase in the abundance of short telomeres (ie,
telomere fluorescence spots corresponding to less than 3 kb of telomere
length) which increased at a rate of 8.8 % per year (<a class="anchor" href="https://www.aging-us.com/article/202463/text#f1">Figure 1B</a>).</p>
<p>When we analyzed the data segregated
by gender, linear regression of telomere length data in COVID-19 female
patients showed that their telomeres were consistently longer than those
of male patient at all age ranges, as well as they showed a lower
percentage of short telomeres compared to male patients (<a class="anchor" href="https://www.aging-us.com/article/202463/text#f2">Figure 2A</a>–<a class="anchor" href="https://www.aging-us.com/article/202463/text#f2">2D</a>), also in agreement with previous findings [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r46">46</a>]. Again, when segregated by gender, the rates of telomere shortening were in a range of 70-80 bp/year (<a class="anchor" href="https://www.aging-us.com/article/202463/text#f2">Figure 2A</a>). Similarly, the increase in the percentage of short telomeres with age was also similar in both genders (<a class="anchor" href="https://www.aging-us.com/article/202463/text#f2">Figure 2A</a>, <a class="anchor" href="https://www.aging-us.com/article/202463/text#f2">2B</a>).</p>
<div class="full-text-image-container" id="f2">
<a href="https://www.aging-us.com/figure/202463/f2"><img alt="Telomere shortening and of increase in short telomeres with age in men and women. (A, B) Percentage of short telomeres (A) and percent of short telomeres (B) in PMBCs from male (blue) and female (red) patients. Linear regression analysis was used to assess the rate of telomere shortening expressed as number of bp loss and the increase of the percentage of short telomeres per year." class="full-text-image" src="https://www.aging-us.com/article/202463/figure/f2/large" /></a>
<p class="small-font"><span><b>Figure 2.</b></span> <b>Telomere shortening and of increase in short telomeres with age in men and women.</b> (<b>A</b>, <b>B</b>) Percentage of short telomeres (<3 kb) in PMBC samples. Mean telomere length (<b>A</b>) and percent of short telomeres (<3kb) (<b>B</b>)
in PMBCs from male (blue) and female (red) patients. Linear regression
analysis was used to assess the rate of telomere shortening expressed as
number of bp loss and the increase of the percentage of short telomeres
per year.<br /></p>
</div>
<p>The fact that COVID-19 female
patients had longer telomeres than men patients at different age ranges
is in line with the fact that female COVID-19 patients show a lower
mortality than males (see COVID-19 Sex-Disaggregated Data Tracker
available at: <a href="http://globalhealth5050.org/covid19" target="_BLANK">http://globalhealth5050.org/covid19</a>).</p>
</div>
<div class="section-container" id="">
<h3 class="article-header-2"> Age and telomere length correlate with COVID-19 severity</h3>
<p>In order to assess whether short
telomeres correlated with the severity of COVID-19 disease, we used a
Pearson correlation analysis between the mean telomere length or the
percentage of short telomeres (<3 kb) as determined by the HT Q-FISH
technique, and either age or the severity score of the different
COVID-19 patients ranging from 1 (less severe) to 4 (more severe) (see
Materials and Methods).</p>
<p>As expected, we observed a
significant inverse correlation between mean telomere length (TL) and
age of the COVID-19 patients (r=-0.3985; p=0.0001; <a class="anchor" href="https://www.aging-us.com/article/202463/text#f3">Figure 3A</a>).
We also observed a significant direct correlation between percentage of
short telomeres (ie, telomeres < 3Kb) and patient age (r=0.285;
p=0.0067; <a class="anchor" href="https://www.aging-us.com/article/202463/text#f3">Figure 3B</a>).
Thus, these findings confirm a significantly higher incidence of short
telomeres with increasing age in the COVID-19 patients. We also observed
an inverse correlation between mean telomere length (TL) and the
severity score of the COVID-19 patients when using HT Q-FISH (r=-0.1752;
p=0.1026; <a class="anchor" href="https://www.aging-us.com/article/202463/text#f3">Figure 3A</a>)
and a direct correlation between the percentage of short telomeres (ie,
telomeres < 3Kb) and the severity score (r=0.1454; p=0.1766; <a class="anchor" href="https://www.aging-us.com/article/202463/text#f3">Figure 3B</a>),
although these correlations did not reach statistical significance. To
further analyze this, we performed similar analysis with the telomere
length data obtained by TRF and by PCR (<a class="anchor" href="https://www.aging-us.com/article/202463/text#f3">Figure 3C</a>, <a class="anchor" href="https://www.aging-us.com/article/202463/text#f3">3D</a>).
Again, we confirmed a significant inverse correlation between mean
telomere length (TL) and age of the COVID-19 patients by TRF (r=-0.4675;
p<0.0001; <a class="anchor" href="https://www.aging-us.com/article/202463/text#f3">Figure 3C</a>) as well as by PCR (r=-0.405; p=0.0001; <a class="anchor" href="https://www.aging-us.com/article/202463/text#f3">Figure 3D</a>)
techniques. Importantly, by these two DNA-based techniques, the
correlation between telomere length and COVID-19 severity reached
statistical significance (TRF: r=-0.3119, p=0.004; <a class="anchor" href="https://www.aging-us.com/article/202463/text#f3">Figure 3C</a>; PCR: r= -0.2308, p=0.036; <a class="anchor" href="https://www.aging-us.com/article/202463/text#f3">Figure 3D</a>.</p>
<div class="full-text-image-container" id="f3">
<a href="https://www.aging-us.com/figure/202463/f3"><img alt="Correlation between telomere length, age and COVID-19 severity. (A–D) Pearson correlation analysis between telomere length (A, C, D) or percentage of short telomeres (B) and age or COVID-19 severity in PMBC samples. In (A, B) telomere length was analysed by HT-QFISH and in (C, D) by TRF and PCR, respectively. The severity score was established by assigning values of 1, 2, 3, 4 for mild, moderate, severe, and acute, respectively (see Materials and Methods). The Pearson r coefficient and the P values are indicated." class="full-text-image" src="https://www.aging-us.com/article/202463/figure/f3/large" /></a>
<p class="small-font"><span><b>Figure 3.</b></span> <b>Correlation between telomere length, age and COVID-19 severity.</b> (<b>A</b>–<b>D</b>) Pearson correlation analysis between telomere length (<b>A</b>, <b>C</b>, <b>D</b>) or percentage of short telomeres (<3 kb) (<b>B</b>) and age or COVID-19 severity in PMBC samples. In (<b>A</b>, <b>B</b>) telomere length was analysed by HT-QFISH and in (<b>C</b>, <b>D</b>)
by TRF and PCR, respectively. The severity score was established by
assigning values of 1, 2, 3, 4 for mild, moderate, severe, and acute,
respectively (see Materials and Methods). The Pearson r coefficient and
the P values are indicated.<br /></p>
</div>
<p>Finally, we also observed a
significant direct correlation between age of the COVID-19 patients and
the severity score of the disease (r=0.2299; p=0.0312; <a class="anchor" href="https://www.aging-us.com/article/202463/text#f4">Figure 4A</a>, <a class="anchor" href="https://www.aging-us.com/article/202463/text#f4">4B</a>).
Furthermore, we observed an inverse correlation between age and mean
telomere length (TL) and a direct correlation between age and the
percentage of short telomeres when using HT Q-FISH (<a class="anchor" href="https://www.aging-us.com/article/202463/text#f4">Figure 4A</a>, <a class="anchor" href="https://www.aging-us.com/article/202463/text#f4">4B</a>).</p>
<div class="full-text-image-container" id="f4">
<a href="https://www.aging-us.com/figure/202463/f4"><img alt="Correlation between age and COVID-19 severity and telomere length. (A, B) Person correlation analysis between age and telomere length measured by HT Q-FISH in PMBC samples (A) and with percentage of short telomeres (" class="full-text-image" src="https://www.aging-us.com/article/202463/figure/f4/large" /></a>
<p class="small-font"><span><b>Figure 4.</b></span> <b>Correlation between age and COVID-19 severity and telomere length.</b> (<b>A</b>, <b>B</b>) Person correlation analysis between age and telomere length measured by HT Q-FISH in PMBC samples (<b>A</b>)
and with percentage of short telomeres (<3 kb). The severity score
was established by assigning values of 1, 2, 3, 4 for mild, moderate,
severe and acute, respectively (see Materials and Methods). The Pearson r
coefficient and the P values are indicated.<br /></p>
</div>
<p>Together, these findings suggest significant correlations of age as well as telomere length with COVID-19 severity.</p>
</div>
<div class="section-container" id="">
<h3 class="article-header-2"> Higher severity of COVID-19 disease in patients in the lower percentiles of telomere length</h3>
<p>The findings suggest that COVID-19
patients with shorter telomere length may have a higher risk of more
severe pathologies. To further test this, we divided the patients in
quartiles according to either their mean telomere length or their
percentage of short telomeres using the telomere signal fluorescence
data obtained by HT Q-FISH. We observed that patients in the lower
quartile of mean telomere length (<25%) had a higher severity score
(p=0.06; <a class="anchor" href="https://www.aging-us.com/article/202463/text#f5">Figure 5A</a>).
Similarly, the patients in the higher quartile of percentage of short
telomeres had significantly higher severity scores of the disease
(p=0.049; <a class="anchor" href="https://www.aging-us.com/article/202463/text#f5">Figure 5B</a>).</p>
<div class="full-text-image-container" id="f5">
<a href="https://www.aging-us.com/figure/202463/f5"><img alt="Patients with shorter telomeres develop more severe COVID-19 disease. (A) The telomere lengths of patients were distributed into the quartiles 75% (>14.96 kb) and correlated with COVID-19 severity. (B) The abundance of short telomeres was distributed into the quartiles 75% (>19.32%) and correlated with COVID-19 severity. Data represent mean values ±SEM. Statistical significance was assessed using Student’s t test." class="full-text-image" src="https://www.aging-us.com/article/202463/figure/f5/large" /></a>
<p class="small-font"><span><b>Figure 5.</b></span> <b>Patients with shorter telomeres develop more severe COVID-19 disease.</b> (<b>A</b>)
The telomere lengths of patients were distributed into the quartiles
<25% (<11.68 kb), 25-75% (11.68–14.96 kb) and >75% (>14.96
kb) and correlated with COVID-19 severity. (<b>B</b>) The abundance of
short telomeres was distributed into the quartiles <25% (<14.73%),
25-75% (14.73-19.32%) and >75% (>19.32%) and correlated with
COVID-19 severity. Data represent mean values ±SEM. Statistical
significance was assessed using Student’s t test.<br /></p>
</div>
</div>
<div class="section-container" id="">
<h3 class="article-header-2"> Different rates of telomere shortening in patients with different severity scores</h3>
<p>As patients in the lower quartile of
telomere length have a significantly higher risk of severe COVID-19
pathologies, we set to investigate whether the rates of telomere
shortening in higher severity score patients were significantly higher
than in the lower severity score patients. To this end, we pooled
together the patients in “mild-moderate” and “severe-acute” severity
groups. We found that patients with a “severe-acute” diagnosis showed a
significantly faster rate of telomere shortening compared to the
“mild-moderate” diagnosis as determined by HT Q-FISH (p=0.024; <a class="anchor" href="https://www.aging-us.com/article/202463/text#f6">Figure 6A</a>).
Of note, patients with “severe-acute” COVID-19 disease have shorter
telomeres along all age groups compared to patients with “mild-moderate”
COVID-19 disease. Similarly, we found an increased rate of accumulation
of short telomeres in patients with a “severe-acute” diagnosis compared
to patients with a “mild-moderate” severity score (p=0.08; <a class="anchor" href="https://www.aging-us.com/article/202463/text#f6">Figure 6B</a>).</p>
<div class="full-text-image-container" id="f6">
<a href="https://www.aging-us.com/figure/202463/f6"><img alt="Patients with a higher COVID-19 severity score show faster telomere shortening rates. (A, B) Telomere shortening (A) and increase in percent of short telomeres (B) with age in patients diagnosed with mild-moderate and severe-acute COVID-19. Linear regression analysis was used to assess the number of bp loss and of the percent of short telomeres per year (C) Whisker plot representation of telomere length. The between 60-69 and older than 70-year-old were pooled together within the same age group. The patients diagnosed with mild or moderate and those diagnosed with severe or acute were grouped. The telomere length corresponding to individual telomere foci were plotted according to Covid-19 severity groups. The ends of the box are the upper and lower quartiles so that the box spans the interquartile range. The middle line represents the median and bars to standard deviation. The statistical significance was calculated by one way Anova with post tukey test. n= number of foci." class="full-text-image" src="https://www.aging-us.com/article/202463/figure/f6/large" /></a>
<p class="small-font"><span><b>Figure 6.</b></span> <b>Patients with a higher COVID-19 severity score show faster telomere shortening rates.</b> (<b>A</b>, <b>B</b>) Telomere shortening (<b>A</b>) and increase in percent of short telomeres (<3kb) (<b>B</b>)
with age in patients diagnosed with mild-moderate and severe-acute
COVID-19. Linear regression analysis was used to assess the number of bp
loss and of the percent of short telomeres per year (<3 kb) in PMBC
of these donors. Statistical significance was assessed using the
Mann-Whitney test. (<b>C</b>) Whisker plot representation of telomere
length. The between 60-69 and older than 70-year-old were pooled
together within the same age group. The patients diagnosed with mild or
moderate and those diagnosed with severe or acute were grouped. The
telomere length corresponding to individual telomere foci were plotted
according to Covid-19 severity groups. The ends of the box are the upper
and lower quartiles so that the box spans the interquartile range. The
middle line represents the median and bars to standard deviation. The
statistical significance was calculated by one way Anova with post tukey
test. n= number of foci.<br /></p>
</div>
<p>In order to address whether this
association between Covid-19 severity and short telomeres was
independent of the age, we grouped the patients in different age groups
(below 40 years of age; between 40-49; 50-59; 60-69 and over 70 years of
age) and compared the fluorescence of individual telomere foci in
patients showing either “mild-moderate” or “severe-acute” COVID-19
severity. We found that for age groups above 60 years of age, telomeres
were shorter in the groups with “severe-acute” severity compared to
“mild-moderate” severity (<a class="anchor" href="https://www.aging-us.com/article/202463/text#f6">Figure 6C</a>).</p>
</div>
</div>
<div class="section-container" id="discussion">
<h2 class="article-header-1"> Discussion</h2>
<p>Data from COVID-19 around the world shows
that patients of older age groups show a higher severity of the disease
and a higher mortality. Male patients also show a higher mortality than
female patients (see COVID-19 Sex-Disaggregated Data Tracker available
at: <a href="http://globalhealth5050.org/covid19" target="_BLANK">http://globalhealth5050.org/covid19</a>).
This suggest that molecular mechanisms of aging maybe aggravating the
pathological consequences of infection by the SARS-CoV-2 virus. Telomere
shortening and accumulation of DNA damage steaming from short telomeres
has been proposed as one of the primary hallmarks of aging [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r27">27</a>].
In particular, short telomeres are known to result in chromosomal
instability and loss of cell viability by inducing replicative
senescence and/or apoptosis [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r26">26</a>].
Importantly, by using mouse models that lack telomerase activity, we
and others have shown that short telomeres impair the regenerative
capacity of tissues leading to loss of tissue homeostasis and
degenerative diseases [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r40">40</a>].
Similarly, humans with critically short telomeres owing to mutations in
telomerase also show an impaired regeneration capacity and are at a
higher risk of developing degenerative diseases in both low
proliferative (lung, kidney) and high proliferative tissues (bone
marrow, skin) [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r42">42</a>].</p>
<p>Given that the SARS-CoV-2 virus infects
different cell types in the organisms, including regenerative cell types
such as alveolar type II (ATII) cells in the lungs [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r8">8</a>–<a class="anchor" href="https://www.aging-us.com/article/202463/text#r13">13</a>, <a class="anchor" href="https://www.aging-us.com/article/202463/text#r49">49</a>],
here we hypothesize that individuals with short telomeres would have an
impaired regenerative response upon SARS-CoV-2 infection, thus leading
to more severe and progressive pathologies, such as fibrosis-like
pathologies in the lungs, kidney or liver.</p>
<p>To address this, we have measured telomere
length in a total of 89 patients diagnosed with COVID-19 ranging from
mild to acute disease. As expected we found that telomere length
decreases with age, with women having longer telomeres than men at
different age groups, which could explain why COVID-19 disease is more
severe in males than females. Interestingly, we also found that those
patients that have more severe COVID-19 pathologies have shorter
telomeres at different ages compared to the patients with milder
disease. Indeed, patients which are in the lower percentile of telomere
length also have significantly higher severity scores.</p>
<p>These findings demonstrate that molecular
hallmarks of aging, such as presence of short telomeres can influence
the severity of COVID-19 pathologies. As short telomeres can be
elongated by telomerase, and telomerase activation strategies have been
shown by us to delay aging and age related pathologies [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r50">50</a>], as well as to have therapeutic effects in diseases associated to short telomeres, such as pulmonary fibrosis [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r44">44</a>],
it is tempting to speculate that such telomerase activation therapies
could ameliorate some of the tissue pathologies remaining in COVID-19
patients, such as fibrosis-like pathologies in the lungs [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r51">51</a>] after overcoming the viral infection.</p>
</div>
<h2 class="article-header-1"> Materials and Methods</h2>
<div class="section-container" id="">
<h3 class="article-header-2"> Patients</h3>
<p>In this study participated a total of
89 patients (61 female and 28 male patients of ages ranging from 29 to
85 years old) from the IFEMA field hospital installed due to the
emergency situation in Madrid, Spain. All these samples were donated to
CNIO BioBank which allows their use for biomedical analyses under the
existing law requirements in Spain.</p>
</div>
<div class="section-container" id="">
<h3 class="article-header-2"> Blood samples</h3>
<p>A total of 8 ml of blood were
collected from the arm of each patient in heparin tubes and 4 ml in EDTA
tubes and shipped within less than 24h to the DNA National Bank at
Salamanca University, where they were immediately processed at a
biosafety level (BSL) 3 (BSL-3) biocontainment level. Peripheral blood
mononuclear cells (PBMCs) were purified by Ficoll gradient and frozen in
90% FBS (v/v) supplemented with 10% (v/v) DMSO into a number of
aliquots ranging from 1 to 3, according to cell number. PBMCs were
stored in vapor phase-nitrogen.</p>
<p>Genomic DNA was extracted directly from blood samples using the Real Blood DNA Kit and stored long-term in TE at -20° C.</p>
</div>
<h3 class="article-header-2"> Q-PCR Assay to measure average telomere length</h3>
<p>Telomere length was measured in
genomic DNA isolated from blood samples. We used a modified monochrome
multiplex quantitative polymerase chain reaction (PCR) method already
described [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r45">45</a>].
Briefly, each reaction included SYBR Green I (Promega), telomere primer
pair telg (5´-ACACTAAGGTTTGGGTTTGGGTTTGGGTTTGGGTTAGTGT-3´) and telc
(5´-TGTTAGGTATCCCTATCCCTATCCCTATCCCTATCCCTAACA-3´) (final concentrations
900nM each), albumin primer pair albu
(5´-CGGCGGCGGGCGGCGCGGGCTGGGCGGaaatgctgcacagaatccttg-3´) and albd
(5´-GCCCGGCCCGCCGCGCCCGTCCCGCCGgaaaagcatggtcgcctgtt-3´) (final
concentrations 900nM each) and 20 ng of genomic DNA. The Applied
Biosystems QuantStudio 6 Flex Real-Time PCR System was used. The thermal
cycling profile was Stage 1: 15 min at 95° C; Stage 2: 2 cycles of 15 s
at 94° C, 15 s at 49° C; and Stage 3: 32 cycles of 15 s at 94° C, 10 s
at 62° C, 15 s at 74° C with signal acquisition, 10 s at 84° C, 15 s at
88° C with signal acquisition. To serve as a reference for standard
curve calculation, HeLa cells were serially diluted and qPCR performed
as described above. After thermal cycling was completed, the QuantStudio
6 software was used to generate standard curves and Ct values for
telomere signals and reference gene signals. The average telomere length
was termed T/S ratio. Finally, T/S ratios were converted into kb by
external calibration with the K562 (6.5 kb), CCRF-CEM (7.5 kb), Jurkat
(11.5 kb) and HeLa1211 (24 kb) cell lines.</p><div class="section-container" id="">
<h3 class="article-header-2"> Terminal restriction fragment analysis</h3>
<p>Mean telomere length by Telomere Restriction Fragment (TRF) was determined using the method already described [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r38">38</a>].
Briefly, genomic DNA was digested by MboI and separated by gel
electrophoresis in 0.5X TBE maintained at 14° C, using a CHEF DR-II
pulsed-field apparatus (BioRad) for 14 h at 5 V/cm at a constant pulse
time of 0.5 s. The gel was transferred to a nylon membrane (Hybond-XL,
GE Healthcare) and probed with a <sup>32</sup>P-labeled telomeric probe (TTAGGG)n (a gift from T. de Lange). Mean TRF lengths were determined using an ImageQuant TL.</p>
</div>
<div class="section-container" id="">
<h3 class="article-header-2"> HT Q-FISH</h3>
<p>Clear bottom black-walled 96-well
plates (Greiner, Longwood, FL) were precoated with a 0.001% (wt/vol)
poly-L-lysine solution (Sigma-Aldrich, St. Louis, MO) for 1h at 37° C.
Poly-L-lysine was removed and wells rinsed with RPMI before cell
addition (75,000–150000 lymphocytes/well).</p>
<p>PBMCs were thawed in complete RPMI
1640 growth media supplemented with 10% FBS (v/v) and seeded at a
concentration of 100000 cells/ well in triplicate wells per sample.
Cells were left to adhere to the plate for 1 h at 37C in incubator with
95% humidity, 5% CO<sub>2</sub>. Plates were then removed from incubator
and then fixed at room temperature (RT) by slowly filling up the wells
with 200 ul methanol/acetic acid (3/1, vol/vol) and incubated for 10 to
15 min. The solution was removed, and this was repeated 2 more times,
leaving the last fixative volume up for a total of 1 h fixation. Plates
were then moved to -20 until processed for high-throughput quantitative
FISH (HT Q-FISH).</p>
<p>We performed HT Q-FISH as described before [<a class="anchor" href="https://www.aging-us.com/article/202463/text#r46">46</a>].
Briefly, the plates were removed from -20, the fixative solution
removed, and the plates were dried on a hot plate at 37° C overnight,
followed by rehydration with 200 μL of PBS. Cells were fixed with 200 μL
of 4% formaldehyde in PBS for 2 min at room temperature (RT) and washed
3 times for 5 min with PBS. Prewarmed pepsin solution (100 mL of H<sub>2</sub>O,
100 μL of 37% HCl [10.1 M HCl], and 100 mg of pepsin [Sigma-Aldrich;
catalog no. P7000-25G]) was used to degrade cell walls for 15 min at 37°
C followed by 2 washing steps of 5 min with 200 μL of PBS. The cells
were then dehydrated with sequential 5-min 70%, 90%, and 100% ethanol
steps. The plates were dried 1 h at 37° C. Next, 50 μL of the
hybridization solution containing the Tel-Cy3 PNA probe was added to the
plates (95 μL of 1 M Tris, pH 7.0, 812 μL of MgCl<sub>2</sub> solution [25 mM MgCl<sub>2</sub>, 9 mM citric acid, 82 mM Na<sub>2</sub>HPO<sub>4</sub>],
6.65 mL of deionized formamide, 475 μL of blocking reagent [10 g of
blocking reagent (Boehringer; catalog no. 1093 657) dissolved with heat
in 100 mL of maleic acid buffer, pH 7.5 (100 mM maleic acid, 150 mM
NaCl)], 1.28 mL of H<sub>2</sub>O, and 190 μL of Tel-Cy3 PNA probe [5 μg lyophilized Cy3-(C3TA2)3 PNA probe (Panagene) diluted in 200 μL of H<sub>2</sub>O]).
Plates were then sealed with aluminum foil lids. The DNA was denatured
by heating the plate on a hot plate at 85° C for 5 min and left to
incubate for 2 h at RT in the dark. The plates were rinsed and washed in
plate shaker with wash solution 1 (10 mM Tris-HCl pH 7, 70% Formamide,
0.10% BSA in H<sub>2</sub>O) for 30 min, followed by 3 washes of 5 min
each with wash solution 2 (TBS [Tris-buffered saline, pH 7.0] with 0.08%
Tween 20). Nuclei staining was performed incubating for 10 min with
TBST containing 1 μg/mL DAPI (4′,6-diamidino-2-phenylindole,
dihydrochloride; Life Technologies; catalog no. D-1306). Then the plates
were washed 1 × 5 min with PBS and stored at 4° C in the dark. Images
from the plate were then acquired by HT microscopy within 48 h.</p>
</div>
<div class="section-container" id="">
<h3 class="article-header-2"> High-throughput microscopy</h3>
<p>Quantitative image acquisition was
performed on an Opera High Content Screening System (PerkinElmer)
40×/0.9 N.A. water-immersion objective. UV and 561 nm excitation
wavelengths were used to detect DAPI and Cy3 telomeric signals,
respectively and 60 independent images were captured at different
positions of each well. Images were analyzed with Acapella Image
analysis software (PerkinElmer). Data were analyzed with SPSS (IBM) and
Excel (Microsoft). Telomere fluorescence values were converted into
kilobases by external calibration with the CCRF-CEM (7.5 kb), L5178Y-S
(10.2 kb), L5178Y-R (79.7 kb) and Jurkat (11.5 kb) cell lines.</p>
</div>
<div class="section-container" id="">
<h3 class="article-header-2"> Criteria for the diagnosis of COVID-19</h3>
<p>Depending on the clinical features of COVID-19, patients are generally divided as mild, moderate, severe and acute.</p>
<p>1. Mild COVID-19: low-grade fever,
cough, malaise, rhinorrhea, sore throat with or without hemoptysis,
nausea, vomiting, diarrhea, but without any radiological features of
pneumonia and absence of mental changes.</p>
<p>2. Moderate COVID-19: fever,
respiratory symptoms including dry cough and shortness of breath that
may emerge along with the radiological features.</p>
<p>3. Severe COVID-19: dyspnea,
respiratory frequency ≥30/minute, blood oxygen saturation ≤93%,
PaO2/FiO2 ratio <300, and/or lung infiltrates >50% of the lung
field within 24–48 h.</p>
<p>4. Acute COVID-19: usually develops
after 7 days in patients with mild/moderate/severe COVID-19 with
features of Acute respiratory distress syndrome (ARDS) requiring
mechanical ventilation along with presence of multiorgan dysfunction
failure, metabolic acidosis and coagulation dysfunction.</p>
</div>
<div class="section-container" id="">
<h3 class="article-header-2"> Data availability statement</h3>
<p>The data that support the findings of this study are available from the corresponding author upon reasonable request.</p>
</div>
<h2 class="article-header-1" id="authorcontributions">Author Contributions</h2>
<p>MAB had the original idea and secured funding.
MAB and PM supervised research and wrote the paper. RSV, AGC and PM
analyzed the data and performed experiments. AZG provided blood samples
and clinical information.</p>https://www.aging-us.com/article/202463/text Ramon.Moratahttp://www.blogger.com/profile/16398587758151257300noreply@blogger.com0tag:blogger.com,1999:blog-1131336482330646116.post-34589498568399329672024-01-19T09:54:00.000-08:002024-01-19T09:54:46.900-08:00Guerra de conciencias: ¿podrán ponerse de acuerdo los científicos sobre el funcionamiento de la mente?<p> Guerra de conciencias: ¿podrán ponerse de acuerdo los científicos sobre el funcionamiento de la mente?</p><p>Hay docenas de teorías sobre cómo el cerebro produce la experiencia consciente, y un nuevo tipo de estudio está poniendo a prueba algunas de ellas.</p><ul class="c-article-author-list c-article-author-list--short js-no-scroll" data-component-authors-activator="authors-list" data-test="authors-list"><li class="c-article-author-list__item"><a data-author-popup="author-0" data-test="author-name" data-track-label="view author info" data-track="click" href="https://www.nature.com/articles/d41586-024-00107-7?utm_medium=Social&utm_campaign=nature&utm_source=Twitter#author-0">
Mariana Lenharo</a></li></ul><p> </p><p>La neurocientífica Lucia Melloni no esperaba que le recordaran el divorcio de sus padres cuando asistió a una reunión sobre la investigación de la conciencia en 2018. Pero, al igual que sus padres, los académicos reunidos no se ponían de acuerdo en nada.<br /><br />El grupo de neurocientíficos y filósofos se había reunido en el Instituto Allen para la Ciencia del Cerebro en Seattle, Washington, para idear una manera de probar empíricamente teorías de la conciencia que compiten entre sí: un proceso llamado colaboración adversarial.<br /><br />Idear un experimento asesino era complicado. "Por supuesto, cada uno de ellos proponía experimentos de los que ya conocían los resultados esperados", explica Melloni, que dirigió la colaboración y trabaja en el Instituto Max Planck de Estética Empírica de Fráncfort (Alemania). Melloni, recurriendo a su papel de niña, se convirtió en la intermediaria.</p><p>https://www.nature.com/articles/d41586-024-00107-7?utm_medium=Social&utm_campaign=nature&utm_source=Twitter#Echobox=1705492433-1</p><p>The collaboration Melloni is
leading is one of five launched by the Templeton World Charity
Foundation, a philanthropic organization based in Nassau, the Bahamas.
The charity funds research into topics such as spirituality,
polarization and religion; in 2019, it committed US$20 million to the
five projects.</p><p>The aim of each collaboration is to move
consciousness research forward by getting scientists to produce evidence
that supports one theory and falsifies the predictions of another.
Melloni’s group is testing two prominent ideas: integrated information
theory (IIT), which claims that consciousness amounts to the degree of
‘integrated information’ generated by a system such as the human brain;
and global neuronal workspace theory (GNWT), which claims that mental
content, such as perceptions and thoughts, becomes conscious when the
information is broadcast across the brain through a specialized network,
or workspace. She and her co-leaders had to mediate between the main
theorists, and seldom invited them into the same room.</p><p>Their struggle to get the collaboration off the ground is mirrored in wider fractures in the field.</p><p>One
problem is that consciousness means different things to different
people. For example, some researchers focus on the subjective experience
— what it is like to be you or me. Others study its function —
cognitive processes and behaviours enabled by being conscious. These
differences muddy attempts to compare ideas.</p><p>And then there was the open letter. Last September, <a data-label="https://www.nature.com/articles/d41586-023-02971-1" data-track-category="body text link" data-track="click" href="https://www.nature.com/articles/d41586-023-02971-1">more than 100 researchers signed a letter</a>, posted as a preprint, that critiqued IIT, arguing that its predictions are untestable and labelling it as pseudoscience<sup><a data-action="anchor-link" data-track-category="references" data-track-label="go to reference" data-track="click" href="https://www.nature.com/articles/d41586-024-00107-7?utm_medium=Social&utm_campaign=nature&utm_source=Twitter#ref-CR1">1</a></sup>. The letter was posted just after Melloni’s collaboration released its results.</p><p>
</p><article class="recommended pull pull--left u-sans-serif" data-label="Related">
<a class="u-link-inherit" data-track-label="recommended article" data-track="click" href="https://www.nature.com/articles/d41586-023-02971-1"><img alt="" class="recommended__image" src="https://media.nature.com/w400/magazine-assets/d41586-024-00107-7/d41586-024-00107-7_26226476.jpg" /><p class="recommended__title u-serif">Consciousness theory slammed as ‘pseudoscience’ — sparking uproar</p></a>
</article><p>Chaos ensued. The letter provoked blowback from other
scientists who felt that such an attack could aggravate divides and hurt
the field’s credibility. Signatories reported receiving ominous e-mails
containing veiled threats. Researchers on both sides of the aisle lost
sleep over accusatory tweets. Some even contemplated leaving science
altogether.</p><p>Younger researchers are particularly worried about the
contentious climate. They fear that a field engulfed in such angry
disputes could be perceived externally as being stuck, which could
affect funding, says Johannes Kleiner, a mathematician studying
consciousness at the Ludwig Maximilian University of Munich in Germany.</p><p>But
despite these challenges, many have hope for the future of
consciousness science. Leaders of the adversarial collaborations say
that their model is already helping to advance the field, even if in
small steps. And they are not the only ones conducting highly regarded,
empirical tests of consciousness theories. Over the past two decades,
there have been hundreds of such experiments, a sign of the field’s
growing maturity.</p><p>Other research funders are focusing attention on
the topic, too: last June, the US National Institutes of Health
convened a three-day meeting on frontiers in consciousness research.</p><p>And
a fresh generation of researchers is leading efforts to cultivate
meaningful dialogue and open-mindedness. “Instead of competing, we
should understand that science is a team effort,” says neuroscientist
Rony Hirschhorn at Tel Aviv University in Israel. “It may be naive, but
this is my way of optimism: to hope that we are better than that.”</p><h2><b>Striving for legitimacy</b></h2><p>There
are dozens of theories of how our brains produce subjective
experiences, and good reasons besides philosophical interest to want to
understand the problem more fully. In medicine, for instance, it could
help to diagnose awareness in people who are unresponsive; in artificial
intelligence, it might help researchers to understand what it would
take for machines to become conscious.</p><p>But for many years,
consciousness was not seen as a serious scientific topic. “Until about
30 years ago, it was taboo to study consciousness, and for good
reasons,” says Lenore Blum, a theoretical computer scientist at Carnegie
Mellon University in Pittsburgh, Pennsylvania, who is president of the
Association for Mathematical Consciousness Science, based in Munich.
Back then, she says, there weren’t good techniques to study
consciousness in a non-invasive way.</p><p>
</p><article class="recommended pull pull--left u-sans-serif" data-label="Related">
<a class="u-link-inherit" data-track-label="recommended article" data-track="click" href="https://www.nature.com/articles/d41586-023-02120-8"><img alt="" class="recommended__image" src="https://media.nature.com/w400/magazine-assets/d41586-024-00107-7/d41586-024-00107-7_25598296.gif" /><p class="recommended__title u-serif">Decades-long bet on consciousness ends — and it’s philosopher 1, neuroscientist</p></a>
</article><p>In 1990 — around the time when the brain-scanning
technique functional magnetic resonance imaging emerged — an influential
paper <a data-label="https://www.nature.com/articles/d41586-023-02120-8" data-track-category="body text link" data-track="click" href="https://www.nature.com/articles/d41586-023-02120-8">helped to change the field’s reputation</a>.
Nobel-laureate biologist Francis Crick and neuroscientist Christof
Koch, now at the Allen Institute for Brain Science, wrote that the
moment was “ripe for an attack on the neural basis of consciousness”<sup><a data-action="anchor-link" data-track-category="references" data-track-label="go to reference" data-track="click" href="https://www.nature.com/articles/d41586-024-00107-7?utm_medium=Social&utm_campaign=nature&utm_source=Twitter#ref-CR2">2</a></sup>.</p><p>Since
then, philosophers and neuroscientists have proposed multiple theories
to explain the physical basis of the subjective experience — referred to
as the “hard problem of consciousness” — and of the “easy problems”
such as attention and wakefulness<sup><a data-action="anchor-link" data-track-category="references" data-track-label="go to reference" data-track="click" href="https://www.nature.com/articles/d41586-024-00107-7?utm_medium=Social&utm_campaign=nature&utm_source=Twitter#ref-CR3">3</a></sup>. In an unpublished effort to count them, Jonathan Mason, a mathematician based in Oxford, UK, identified more than 30 theories.</p><p>A
handful of theories have been particularly influential (see ‘Theories
of consciousness’). They include the two that Melloni is helping to
test: IIT, proposed by Giulio Tononi, a neuroscientist at the University
of Wisconsin–Madison, and GNWT, the brainchild of Stanislas Dehaene,
director of the Cognitive Neuroimaging Unit at INSERM-CEA in
Gif-sur-Yvette, France.</p><figure class="figure">
<picture class="embed intensity--high">
<source type="image/webp"></source>
<img alt="Theories of consciousness: The four most popular categories of explanations for how the brain produces an individual's subjective experience." class="figure__image" height="962" src="https://media.nature.com/lw767/magazine-assets/d41586-024-00107-7/d41586-024-00107-7_26627996.jpg" width="592" />
<figcaption>
<p class="figure__caption u-sans-serif"><span>Source: A. K. Seth & T. Bayne <i>Nature Rev. Neurosci</i>. <b>23</b>, 439–452 (2022)</span></p>
</figcaption>
</picture>
</figure><p>Other front runners include a group of ideas called
higher-order theories (HOT), which propose that, for content to be
consciously experienced, it must be synthesized into a
meta-representation in higher-order brain areas. Another prominent
concept is recurrent processing theory (RPT), which suggests that
consciousness requires a loop of information flow and feedback. It has
been studied mostly in the brain’s visual areas, but the same idea
should apply to other senses such as hearing or smell.</p><p>Empirical
studies testing the predictions of such theories are becoming more
rigorous and sophisticated, but — as often happens in science — many are
conducted by researchers affiliated with the very ideas they are
testing, making them prone to confirmation bias, says Hirschhorn. As a
result, she says, theories have been evolving in isolation.</p><p>“For
the past 30 years, you have had some dominant theories that, when
challenging results show up, they revise the theory to accommodate these
new findings,” says Biyu He, a neuroscientist at New York University
Grossman School of Medicine in New York City. In that sense, the
adversarial collaborations are shaking up the field, says He, who is
leading another such collaboration, testing RPT and two versions of HOT.</p><p>Proponents
of some prominent theories have sometimes made the tests more
adversarial than collaborative, according to some of the scientists
leading the studies. This doesn’t apply to all the collaborations, says
He, and depends to some extent on how easy the theories are to compare
to one another. But some theorists are described as having big
personalities; notably, most of them are men. “I don’t think that’s
because women are not doing important research,” says He. “I think
that’s mostly because certain people are more willing to come out and
talk about big grand theories.”</p><h2><b>A crash course in diplomacy</b></h2><p>Neuroscientist
Liad Mudrik at Tel Aviv University remembers how excited she was to
attend the Seattle meeting that resulted in the collaboration between
IIT and GNWT, dubbed Cogitate. “I was writing down everything that
people were saying and I was super excited about the entire process,”
she says.</p><p>During her flight back home to Israel, after being
designated as a project co-leader, she drafted an experimental design
based on the discussions and quickly sent it to her colleagues. “I was
so naive at the time,” she says. From that moment until they actually
nailed it would be ten months.</p><p>After wrangling over which aspects
of consciousness the team would look for and with which methods, the
researchers eventually settled on two experiments — one preferred by
each competing theorist. The team developed a list of predictions from
each theory of what would be observed in participants’ brains as they
underwent three types of brain scan. The researchers also agreed on what
would be considered a pass or a fail for each theory in each task.</p><figure class="figure">
<picture class="embed intensity--high">
<source type="image/webp"></source>
<img alt="David Chalmers shakes hands with Christof Koch onstage at the Association for the Scientific Study of Consciousness meet" class="figure__image" src="https://media.nature.com/lw767/magazine-assets/d41586-024-00107-7/d41586-024-00107-7_26617558.jpg" />
<figcaption>
<p class="figure__caption u-sans-serif"><span class="mr10">At an
event in New York City in June, neuroscientist Christof Koch (right)
lost his 25-year-old bet with philosopher David Chalmers that science
would crack the neural correlates of consciousness by 2023.</span><span>Credit: Jesse Winter for <i>Nature</i></span></p>
</figcaption>
</picture>
</figure><p>In the first experiment, participants were shown a series of
pictures and symbols and asked to report when certain images appeared.</p><p>According
to IIT, the task should prompt sustained activation in the back of the
brain, which is what the data suggested. However, there was only
transient synchronization of activity between brain areas in the
posterior cortex, not the sustained synchronization that was
hypothesized.</p><p>GNWT predicted that the prefrontal cortex should be
activated during the task — something the team confirmed. But there was
no evidence that the region contained information about the orientation
of the object, which is part of the conscious experience and would be
expected according to the theory. The experiment also found evidence of
the global broadcasting postulated by GNWT, but only at the beginning of
an experience — not also at the end, as had been predicted. Results
from this first experiment were made public in a preprint last year<sup><a data-action="anchor-link" data-track-category="references" data-track-label="go to reference" data-track="click" href="https://www.nature.com/articles/d41586-024-00107-7?utm_medium=Social&utm_campaign=nature&utm_source=Twitter#ref-CR4">4</a></sup>.</p><p>The
second experiment, for which results haven’t yet been made public,
involved participants playing a video game and being asked whether they
were aware of certain images shown on the background of the screen.</p><p>Having
two experiments was a compromise that the team had to make to
facilitate consensus between the Tononi and Dehaene camps. “I really
admire both of them and I think they are extremely good scientists,”
says Melloni. But, she adds, “the world would be a better place if they
could give themselves a chance to listen to each other”. Tononi says
that the adversarial collaboration allowed him to see the other theories
more clearly. (Dehaene did not respond to <i>Nature</i>’s request for comment.)</p><p>Another
diplomatic strategy was to engage with the two theorists in separate
conversations, ‘translating’ the ideas from one to the other. “One of
the key roles that we have,” says Mudrik, “is to find a common language
to make sure that we’re talking about the same thing.”</p><p>Tononi
acknowledges how hard the project has been and praises the study leaders
— Melloni, Mudrik and Michael Pitts, a psychologist at Reed College in
Portland, Oregon — for pulling it off. “They invested so much of their
time and passion, rather than doing their own experiments,” he says.
“They did a fantastic job.”</p><h2><b>Caught in the crossfire</b></h2><p>Younger scientists are particularly keen to find common ground.</p><p>During
his first PhD, in mathematical quantum field theory, Kleiner felt
frustrated by the infighting among senior scientists. “From the outside,
the field was just perceived as not making good progress because
everyone was so vocal about other approaches being wrong,” he says.</p><p>
</p><article class="recommended pull pull--left u-sans-serif" data-label="Related">
<a class="u-link-inherit" data-track-label="recommended article" data-track="click" href="https://www.nature.com/articles/d41586-019-02207-1"><img alt="" class="recommended__image" src="https://media.nature.com/w400/magazine-assets/d41586-024-00107-7/d41586-024-00107-7_16963860.jpg" /><p class="recommended__title u-serif">Decoding the neuroscience of consciousness</p></a>
</article><p>When he decided to do a second PhD, this time in
consciousness research, he was aware of existing tensions in the field,
but felt that people generally got along well. The community felt
hopeful about the potential of the adversarial collaborations to produce
useful data, he says. The open letter shattered those hopes. Deeply
unsettled by the harsh online interactions, Kleiner was determined to do
something. He didn’t want his new field to be perceived the same way as
his first. “I know this sounds totally naive, but if you can’t heal
this division, then so many negative things follow.”</p><p>After the
letter came out, Kleiner helped to organize an online event to discuss
the future of consciousness science, under the banner of the Association
for Mathematical Consciousness Science, which he co-founded in 2021.
But the proposition backfired, with some people in the community
perceiving it as one-sided. After much thought, the format of the event
changed to a ‘virtual coffee and open conversation’ in which
participants were urged not to mention the open letter directly.</p><p>Another
organization aiming to help the field break out of its silos is the
Mediterranean Society for Consciousness Science, which aims to stimulate
deep conversations between scholars from different schools of thought.
Without such interactions, says Hirschhorn, who is the society’s
vice-president, “you sort of go into this loop of doing more of what you
know”, she says.</p><h2><b>Adversaries or collaborators?</b></h2><p>Many
researchers welcome the adversarial collaborations as one way to break
out of these loops. But they are pragmatic: the results shouldn’t be
taken as definitive proof for or against a given theory, says He.
Nonetheless, they are generating valuable data. “It’s infusing
much-needed resources into the field to do some very solid, large
collaborative studies.”</p><p>Melloni wonders whether the adversarial
collaborations, by bringing together opposing opinions, are partly
responsible for the recent turbulence.</p><p>When the results of
Cogitate’s first experiment came in, Melloni and her co-leaders were not
exactly surprised that the two theories’ proponents couldn’t agree on
what the data meant.</p><p>Theory-neutral authors presented the findings
in a preprint, describing how the experiments had challenged both
theories in different ways<sup><a data-action="anchor-link" data-track-category="references" data-track-label="go to reference" data-track="click" href="https://www.nature.com/articles/d41586-024-00107-7?utm_medium=Social&utm_campaign=nature&utm_source=Twitter#ref-CR4">4</a></sup>.
The groups defending each theory wrote their own discussion sections,
presenting their explanations for the data and how the results meshed
with their predictions.</p><p>Melloni says that she initially nurtured a
false hope that the theorists would simply accept the results and
recognize potential flaws in their own theories on the basis of the
data. “If I have one regret, I think it would be that I did not manage
to make them see that there is something valuable in both of their
ideas.”</p><p>But her mentor in the process, Nobel-prizewinning
psychologist Daniel Kahneman, who first introduced the idea of
adversarial collaborations, had warned her that both sides would dig in
their heels. “He said: ‘get ready, they will not change their minds’,”
she recalls. But he also told her that it didn’t matter, and that, over
time, fresh evidence would help to change the minds of other researchers
in the community. The idea that someone would change their mind on the
basis of one or two results in a topic as complex as consciousness was
“not plausible to begin with”, says Tononi.</p><p>Hirschhorn thinks that
the conflict has been, in a sense, productive. Whereas polarization
always existed, people did not discuss it explicitly until the
collaborations — and the letter — forced it into the open, she says. “I
think now we can actually roll up our sleeves and work on this.”</p>
<p><em>Nature</em> <strong>625</strong>, 438-440 (2024)</p>
<p><em>doi: https://doi.org/10.1038/d41586-024-00107-7</em></p>
<h2 class="c-article-section__title js-section-title" id="Bib1">References</h2><ol class="c-article-references" data-track-component="outbound reference"><li class="c-article-references__item js-c-reading-companion-references-item" data-counter="1."><p class="c-article-references__text" id="ref-CR1">Fleming, S. M. <i>et al.</i> Preprint at PsyArXiv <a data-track-action="external reference" data-track-label="https://osf.io/preprints/psyarxiv/zsr78" data-track="click" href="https://osf.io/preprints/psyarxiv/zsr78">https://osf.io/preprints/psyarxiv/zsr78</a> (2023).</p></li><li class="c-article-references__item js-c-reading-companion-references-item" data-counter="2."><p class="c-article-references__text" id="ref-CR2">Crick, F. & Koch, C. <i>Semin. Neurosci.</i> <b>2</b>, 263–275 (1990).</p><p class="c-article-references__links u-hide-print"><a aria-label="Google Scholar reference 2" data-track-action="google scholar reference" data-track-label="link" data-track="click" href="http://scholar.google.com/scholar_lookup?&title=&journal=Semin.%20Neurosci.&volume=2&pages=263-275&publication_year=1990&author=Crick%2CF.&author=Koch%2CC." rel="nofollow noopener">
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</p></li><li class="c-article-references__item js-c-reading-companion-references-item" data-counter="3."><p class="c-article-references__text" id="ref-CR3">Chalmers, D. J. <i>J.</i> <i>Conscious. Stud.</i> <b>2</b>, 200–219 (1995).</p><p class="c-article-references__links u-hide-print"><a aria-label="Google Scholar reference 3" data-track-action="google scholar reference" data-track-label="link" data-track="click" href="http://scholar.google.com/scholar_lookup?&title=&journal=J.%20Conscious.%20Stud.&volume=2&pages=200-219&publication_year=1995&author=Chalmers%2CD.%20J." rel="nofollow noopener">
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</p></li><li class="c-article-references__item js-c-reading-companion-references-item" data-counter="4."><p class="c-article-references__text" id="ref-CR4">Cogitate Consortium. Preprint at bioRxiv <a data-track-action="external reference" data-track-label="https://doi.org/10.1101/2023.06.23.546249" data-track="click" href="https://doi.org/10.1101/2023.06.23.546249">https://doi.org/10.1101/2023.06.23.546249</a> (2023).</p></li></ol><p> </p>Ramon.Moratahttp://www.blogger.com/profile/16398587758151257300noreply@blogger.com0tag:blogger.com,1999:blog-1131336482330646116.post-21660420767272964892024-01-19T09:33:00.000-08:002024-01-19T09:51:58.622-08:00Scientific Models and Decision Making<p> </p><h1 data-v-5549f748="">Scientific Models and Decision Making</h1><p data-v-5549f748="" style="text-align: left;"> </p><div class="row published-date" data-v-5549f748=""><p data-v-5549f748="">
Published online by Cambridge University Press:
<b data-v-5549f748=""><span class="app-date" data-v-5549f748="" data-v-d415a782="">
16 January 2024
</span></b></p></div> <div class="contributors-details" data-v-5549f748="" data-v-99f6eb26=""><div class="row contributors" data-v-99f6eb26=""><div class="col" data-v-99f6eb26=""><div class="row contributor-type" data-v-792406ce="" data-v-99f6eb26=""> <div class="contributor-type__contributor" data-v-792406ce=""><a class="app-link app-link__text app-link--accent" data-v-63dfaf6e="" data-v-792406ce="" href="https://www.cambridge.org/core/search?filters%5BauthorTerms%5D=Eric%20Winsberg&eventCode=SE-AU"><span data-v-63dfaf6e="">Eric Winsberg</span> </a> <span data-v-792406ce=""> and</span></div><div class="contributor-type__contributor" data-v-792406ce=""><a class="app-link app-link__text app-link--accent" data-v-63dfaf6e="" data-v-792406ce="" href="https://www.cambridge.org/core/search?filters%5BauthorTerms%5D=Stephanie%20Harvard&eventCode=SE-AU"><span data-v-63dfaf6e="">Stephanie Harvard</span> </a> <span data-v-792406ce=""></span></div> </div> </div> <div class="col-2 collapse-link" data-v-99f6eb26=""><a aria-controls="authors-details" aria-expanded="false" class="app-link collapsed app-link__text-icon app-link--secondary reverse" data-toggle="collapse" data-v-63dfaf6e="" data-v-99f6eb26="" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#authors-details"><span class="text" data-v-63dfaf6e="">Show author details
</span> </a></div></div> <hr aria-hidden="true" class="separator default" data-v-7036083a="" data-v-99f6eb26="" /> </div> <div class="row summary" data-v-5549f748=""><div class="col sec summary col" data-v-5549f748="" id="sec0"><h2>Summary</h2> <div><br /> Este elemento presenta la literatura filosófica sobre modelos, con un consideraciones normativas pertinentes para los modelos de la toma de decisiones. </div><div> </div><div>-El capítulo 1 ofrece una visión general de las cuestiones centrales de la filosofía de los modelos. </div><div>-El capítulo 2 examina el concepto de adecuación del modelo de los modelos, utilizando tres ejemplos de modelos de las ciencias atmosféricas para describir cómo se determina en la práctica este tipo de adecuación. en la práctica. </div><div>-El capítulo 3 analiza la importancia de utilizar modelos que no son que no son adecuados para su finalidad, incluida la de fundamentar las decisiones públicas. <br /></div><div>-El capítulo 4 ofrece un marco básico para los valores en la modelización, un estudio de caso para poner de relieve los retos éticos que plantea la elaboración de modelos para la toma de decisiones de modelos para la toma de decisiones. </div><div> </div><div>Concluye estableciendo la necesidad de gestionar los juicios de valor en la modelización, incluido el potencial de participación pública en el proceso.<br /><br /></div></div></div> <div class="sticky-tabs" data-v-1d90c6ce="" data-v-5e3f8f9c="" id="app-tabs"><div class="tabs__wrapper" data-v-1d90c6ce="" id="app-tabs-wrapper"><div aria-label="tab navigation" class="container" data-v-1d90c6ce="" role="navigation"> <ul class="nav nav-tabs tabs__list collapse show" data-v-1d90c6ce="" id="appTabs" role="tablist"><li class="tabs__tab" data-v-1d90c6ce="" role="none"><a aria-controls="element-tab" aria-selected="true" class="active" data-v-1d90c6ce="" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#element-tab" role="tab" tabindex="0">Element
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Information
</dt> <dd class="col content" data-v-03f80966=""><div class="content__element" data-v-03f80966=""><span data-v-03f80966="">Series: <a class="app-link app-link__text app-link--accent" data-v-03f80966="" data-v-63dfaf6e="" href="https://www.cambridge.org/core/publications/elements/philosophy-of-science"><span class="text" data-v-63dfaf6e="">Elements in the Philosophy of Science
</span> </a></span></div> <div class="doi-data" data-v-03f80966=""><div data-v-03f80966="">DOI: <a class="app-link app-link__text app-link--accent" data-v-03f80966="" data-v-63dfaf6e="" href="https://doi.org/10.1017/9781009029346" target="_blank"><span class="text" data-v-63dfaf6e="">https://doi.org/10.1017/9781009029346
</span> <span class="sr-only" data-v-63dfaf6e="">[Opens in a new window]</span></a></div></div> <div data-v-03f80966=""><span data-v-03f80966="">Online ISBN: 9781009029346</span></div> <div data-v-03f80966=""><span data-v-03f80966="">Publisher: Cambridge University Press</span></div> <div data-v-03f80966=""><span data-v-03f80966="">Print publication: <span class="app-date" data-v-03f80966="" data-v-d415a782="">
08 February 2024
</span></span></div></dd></div></dl> <div class="content-container" data-v-0172d11c="" id="content-container"><div class="content-box"><div class="html-view"><article class="book elements BITS"><div class="book-body"><div class="book-part chapter" data-magellan-destination="CT-bp-1" id="CT-bp-1"><div class="body"><div class="sec" data-magellan-destination="A-sec-3" id="A-sec-3"><h3 class="A title"><span class="label">1</span> Introduction </h3><div class="sec" data-magellan-destination="B-sec-1" id="B-sec-1"><h4 class="B title"><span class="label">1.1</span> What Is a Model? </h4><p class="p"> If there is anything that could be described as a core question in the philosophy of modelling in science, it is probably <i class="italic">What is a model</i>?
Unfortunately, this question is deceptively complex: as we will see, it
is tangled up with numerous other key questions in this branch of
philosophy. But since we have to start somewhere, let’s give it a shot:
what <i class="italic">is</i> a model? Here’s a short list of examples of things scientists call models:
</p><ol class="list order nomark"><li class="list-item"><p class="p"><span class="label">(1)</span>
A ‘typical’ drawing of a cell in a biology textbook, showing the cell
to contain a nucleus, a cell membrane, a Golgi body, mitochondria, and
endoplasmic reticulum (<a class="xref bibr" data-sectionid="#REFe-r-031" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-031"><span class="show-for-sr">Reference Downes</span>Downes 1992</a>) (<a class="xref fig" data-sectionid="#FIGm-fig-1" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#FIGm-fig-1">Figure 1</a>).</p></li><li class="list-item"><p class="p"><span class="label">(2)</span> The standard laboratory rat, <i class="italic">Rattus norvegicus</i>, depicted in <a class="xref fig" data-sectionid="#FIGm-fig-2" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#FIGm-fig-2">Figure 2</a>, is a model organism which is studied with the goal of understanding a range of biological phenomena, including humans (<a class="xref bibr" data-sectionid="#REFe-r-006" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-006"><span class="show-for-sr">Reference Ankeny and Leonelli</span>Ankeny and Leonelli 2021</a>, chap. 2; <a class="xref bibr" data-sectionid="#REFe-r-071" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-071"><span class="show-for-sr">Reference Leonelli, Howlett and Morgan</span>Leonelli 2010</a>; <a class="xref bibr" data-sectionid="#REFe-r-074" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-074"><span class="show-for-sr">Reference Levy and Currie</span>Levy and Currie 2015</a>).</p></li><li class="list-item"><p class="p"><span class="label">(3)</span>
The solar system, used by Niels Bohr in the early twentieth century as a
model of the atom. Bohr argued that the nucleus of an atom is like the
Sun, the electrons like planets circling the Sun (<a class="xref bibr" data-sectionid="#REFe-r-046" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-046"><span class="show-for-sr">Reference Giere, Bickle and Mauldin</span>Giere, Bickle, and Mauldin 1979</a>).</p></li><li class="list-item"><p class="p"><span class="label">(4)</span>
The Friedmann–Lemaître–Robertson–Walker models of cosmology and the
standard model of particle physics. The former is a way of picking out a
particular set of conditions that satisfies the equations of the theory
of general relativity; the latter a means of fleshing out the
mathematical framework provided by quantum field theory (<a class="xref bibr" data-sectionid="#REFe-r-095" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-095"><span class="show-for-sr">Reference Redhead</span>Redhead 1980</a>; <a class="xref bibr" data-sectionid="#REFe-r-100" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-100"><span class="show-for-sr">Reference Smeenk</span>Smeenk 2020</a>). This idea of a scientific model bearing the relation to theory that a model bears to a set of axioms in logic goes back to <a class="xref bibr" data-sectionid="#REFe-r-058" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-058"><span class="show-for-sr">Reference Hesse and Edwards</span>Mary Hesse (1967</a>).</p></li><li class="list-item"><p class="p"><span class="label">(5)</span> Watson and Crick’s famous double-helix models, built from pieces of wire and tin plates (depicted in <a class="xref fig" data-sectionid="#FIGm-fig-3" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#FIGm-fig-3">Figure 3</a>) and ultimately taken to represent the structure of DNA (<a class="xref bibr" data-sectionid="#REFe-r-046" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-046"><span class="show-for-sr">Reference Giere, Bickle and Mauldin</span>Giere, Bickle, and Mauldin 1979</a>, 16–29).</p></li><li class="list-item"><p class="p"><span class="label">(6)</span>
A model reconstruction of the Earth’s temperature in past geological
periods, developed using proxy data from sources like deep ice cores,
fossilized shells, tree rings, corals, lake sediments, and boreholes (<a class="xref bibr" data-sectionid="#REFe-r-090" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-090"><span class="show-for-sr">Reference Parker</span>Parker 2018</a>; <a class="xref bibr" data-sectionid="#REFe-r-119" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-119"><span class="show-for-sr">Reference Winsberg</span>Winsberg 2018</a>, chap. 2). An example of this is depicted in <a class="xref fig" data-sectionid="#FIGm-fig-4" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#FIGm-fig-4">Figure 4</a>.</p></li><li class="list-item"><p class="p"><span class="label">(7)</span> The San Francisco Bay model, depicted in <a class="xref fig" data-sectionid="#FIGm-fig-5" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#FIGm-fig-5">Figure 5</a>
– made of concrete, replete with pumps, and filled with salt water when
in operation – used to simulate the behaviour of water in the real San
Francisco Bay. The Army Corps of Engineers constructed the model in the
1950s to predict the effects of a proposal to close off the Golden Gate
and turn the Bay into a freshwater reservoir (<a class="xref bibr" data-sectionid="#REFe-r-115" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-115"><span class="show-for-sr">Reference Weisberg</span>Weisberg 2013</a>).<span class="page-marker"></span></p></li><li class="list-item"><p class="p"><span class="label">(8)</span> Weather and climate models that run on computers (an example is depicted in <a class="xref fig" data-sectionid="#FIGm-fig-6" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#FIGm-fig-6">Figure 6</a>), which are used to make <i class="italic">predictions</i> about actual short-term weather conditions and <i class="italic">projections</i> about possible long-term climate conditions under different CO<sub class="sub">2</sub> emissions scenarios (<a class="xref bibr" data-sectionid="#REFe-r-090" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-090"><span class="show-for-sr">Reference Parker</span>Parker 2018</a>; <a class="xref bibr" data-sectionid="#REFe-r-119" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-119"><span class="show-for-sr">Reference Winsberg</span>Winsberg 2018</a>).</p></li><li class="list-item"><p class="p"><span class="label">(9)</span> Epidemiological models that forecast or explain the spread of an infectious disease (<a class="xref bibr" data-sectionid="#REFe-r-121" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-121"><span class="show-for-sr">Reference Winsberg and Harvard</span>Winsberg and Harvard 2022</a>). An example is shown in <a class="xref fig" data-sectionid="#FIGm-fig-7" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#FIGm-fig-7">Figure 7</a>.<span class="page-marker"></span></p></li><li class="list-item"><p class="p"><span class="label">(10)</span>
Health-economic decision models, which compare the costs and
consequences of implementing different healthcare programmes,
interventions, or technologies (<a class="xref bibr" data-sectionid="#REFe-r-017" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-017"><span class="show-for-sr">Reference Briggs, Sculpher and Claxton</span>Briggs, Sculpher, and Claxton 2006</a>).</p></li></ol><section><div class="fig" data-magellan-destination="FIGm-fig-1" id="FIGm-fig-1"><div class="figure-thumb"><img class="aop-lazy-load-image graphic" data-img-name="Figure 1" data-large-thumb="urn:cambridge.org:id:binary-alt:20240108085621-08982-largeThumb-png-46821fig1.jpg" data-medium-thumb="urn:cambridge.org:id:binary-alt:20240108085621-51439-mediumThumb-png-46821fig1.jpg" data-optimised-image="urn:cambridge.org:id:binary-alt:20240108085621-84728-optimisedImage-png-46821fig1.jpg" data-small-thumb="urn:cambridge.org:id:binary-alt:20240108085621-32367-smallThumb-png-46821fig1.jpg" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20240108085526454-0965:9781009029346:46821fig1.png?pub-status=live" data-zoomable="false" height="666" src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20240108085526454-0965:9781009029346:46821fig1.png?pub-status=live" width="651" /></div></div></section></div></div></div></div></div></article></div></div></div></div><section><div class="fig" data-magellan-destination="FIGm-fig-1" id="FIGm-fig-1"><div class="caption"><p class="p"><span class="label">Figure 1</span> A model of a plant cell.</p> </div><div class="attrib"><b class="bold">Source:</b> <a class="uri" href="http://www.pinterest.ca/pin/plant-cell-vs-animal-cell-whats-the-difference–533746993338085307/">www.pinterest.ca/pin/plant-cell-vs-animal-cell-whats-the-difference–533746993338085307/</a>.</div></div></section><section><div class="fig" data-magellan-destination="FIGm-fig-2" id="FIGm-fig-2"><div class="figure-thumb"><img class="aop-lazy-load-image graphic" data-img-name="Figure 2" data-large-thumb="urn:cambridge.org:id:binary-alt:20240108085621-60919-largeThumb-png-46821fig2.jpg" data-medium-thumb="urn:cambridge.org:id:binary-alt:20240108085621-02871-mediumThumb-png-46821fig2.jpg" data-optimised-image="urn:cambridge.org:id:binary-alt:20240108085621-64231-optimisedImage-png-46821fig2.jpg" data-small-thumb="urn:cambridge.org:id:binary-alt:20240108085621-08019-smallThumb-png-46821fig2.jpg" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20240108085526454-0965:9781009029346:46821fig2.png?pub-status=live" data-zoomable="false" height="357" src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20240108085526454-0965:9781009029346:46821fig2.png?pub-status=live" width="536" /></div></div></section><section><div class="fig" data-magellan-destination="FIGm-fig-2" id="FIGm-fig-2"><div class="caption"><p class="p"><span class="label">Figure 2</span> A model organism, the white lab rat.</p> </div><div class="attrib"><b class="bold">Source:</b> <a class="xref bibr" data-sectionid="#REFe-r-116" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-116"><span class="show-for-sr">Reference Williams</span>Williams (2011)</a>.</div></div></section><section><div class="fig" data-magellan-destination="FIGm-fig-3" id="FIGm-fig-3"><div class="figure-thumb"><img class="aop-lazy-load-image graphic" data-img-name="Figure 3" data-large-thumb="urn:cambridge.org:id:binary-alt:20240108085621-02926-largeThumb-png-46821fig3.jpg" data-medium-thumb="urn:cambridge.org:id:binary-alt:20240108085621-76893-mediumThumb-png-46821fig3.jpg" data-optimised-image="urn:cambridge.org:id:binary-alt:20240108085621-58718-optimisedImage-png-46821fig3.jpg" data-small-thumb="urn:cambridge.org:id:binary-alt:20240108085621-58225-smallThumb-png-46821fig3.jpg" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20240108085526454-0965:9781009029346:46821fig3.png?pub-status=live" data-zoomable="false" height="511" src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20240108085526454-0965:9781009029346:46821fig3.png?pub-status=live" width="365" /></div></div></section><section><div class="fig" data-magellan-destination="FIGm-fig-3" id="FIGm-fig-3"><div class="caption"><p class="p"><span class="label">Figure 3</span> Watson and Crick’s tin plate model.</p> </div><div class="attrib"><b class="bold">Source:</b> <a class="uri" href="https://collection.sciencemuseumgroup.org.uk/objects/co146411/crick-and-watsons-dna-molecular-model-molecular-model">https://collection.sciencemuseumgroup.org.uk/objects/co146411/crick-and-watsons-dna-molecular-model-molecular-model</a>.</div></div></section><section><div class="fig" data-magellan-destination="FIGm-fig-4" id="FIGm-fig-4"><div class="figure-thumb"><img class="aop-lazy-load-image graphic" data-img-name="Figure 4" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20240108085526454-0965:9781009029346:46821fig4.png?pub-status=live" height="188" src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20240108085526454-0965:9781009029346:46821fig4.png?pub-status=live" width="668" /></div></div></section><section><div class="fig" data-magellan-destination="FIGm-fig-4" id="FIGm-fig-4"><div class="caption"><p class="p"><span class="label">Figure 4</span> Several different models of the Earth’s paleoclimate, presented as one history.</p> </div><div class="attrib"><b class="bold">Source:</b> Glen Fergus, CC BY-SA 3.0, <a class="uri" href="https://commons.wikimedia.org/w/index.php?curid=31736468">https://commons.wikimedia.org/w/index.php?curid=31736468</a>.</div></div></section><section><div class="fig" data-magellan-destination="FIGm-fig-5" id="FIGm-fig-5"><div class="figure-thumb"><img class="aop-lazy-load-image graphic" data-img-name="Figure 5" data-large-thumb="urn:cambridge.org:id:binary-alt:20240108085621-17129-largeThumb-png-46821fig5.jpg" data-medium-thumb="urn:cambridge.org:id:binary-alt:20240108085621-78254-mediumThumb-png-46821fig5.jpg" data-optimised-image="urn:cambridge.org:id:binary-alt:20240108085621-82488-optimisedImage-png-46821fig5.jpg" data-small-thumb="urn:cambridge.org:id:binary-alt:20240108085621-83947-smallThumb-png-46821fig5.jpg" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20240108085526454-0965:9781009029346:46821fig5.png?pub-status=live" data-zoomable="false" height="438" src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20240108085526454-0965:9781009029346:46821fig5.png?pub-status=live" width="584" /></div></div></section><section><div class="fig" data-magellan-destination="FIGm-fig-5" id="FIGm-fig-5"><div class="caption"><p class="p"><span class="label">Figure 5</span> The San Francisco Bay model.</p> </div><div class="attrib"><b class="bold">Source:</b> <a class="uri" href="https://commons.wikimedia.org/w/index.php?curid=30086231">https://commons.wikimedia.org/w/index.php?curid=30086231</a>.</div></div></section><section><div class="fig" data-magellan-destination="FIGm-fig-6" id="FIGm-fig-6"><div class="figure-thumb"><img class="aop-lazy-load-image graphic" data-img-name="Figure 6" data-large-thumb="urn:cambridge.org:id:binary-alt:20240108085621-38527-largeThumb-png-46821fig6.jpg" data-medium-thumb="urn:cambridge.org:id:binary-alt:20240108085621-63561-mediumThumb-png-46821fig6.jpg" data-optimised-image="urn:cambridge.org:id:binary-alt:20240108085621-78576-optimisedImage-png-46821fig6.jpg" data-small-thumb="urn:cambridge.org:id:binary-alt:20240108085621-10923-smallThumb-png-46821fig6.jpg" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20240108085526454-0965:9781009029346:46821fig6.png?pub-status=live" data-zoomable="false" height="469" src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20240108085526454-0965:9781009029346:46821fig6.png?pub-status=live" width="655" /></div></div></section><section><div class="fig" data-magellan-destination="FIGm-fig-6" id="FIGm-fig-6"><div class="caption"><p class="p"><span class="label">Figure 6</span> A global climate model.</p> </div><div class="attrib"><b class="bold">Source:</b> <a class="uri" href="http://www.gfdl.noaa.gov/climate-modeling/">www.gfdl.noaa.gov/climate-modeling/</a>.</div></div></section><section><div class="fig" data-magellan-destination="FIGm-fig-7" id="FIGm-fig-7"><div class="figure-thumb"><img class="aop-lazy-load-image graphic" data-img-name="Figure 7" data-large-thumb="urn:cambridge.org:id:binary-alt:20240108085621-41739-largeThumb-png-46821fig7.jpg" data-medium-thumb="urn:cambridge.org:id:binary-alt:20240108085621-19234-mediumThumb-png-46821fig7.jpg" data-optimised-image="urn:cambridge.org:id:binary-alt:20240108085621-15958-optimisedImage-png-46821fig7.jpg" data-small-thumb="urn:cambridge.org:id:binary-alt:20240108085621-14663-smallThumb-png-46821fig7.jpg" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20240108085526454-0965:9781009029346:46821fig7.png?pub-status=live" data-zoomable="false" height="453" src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20240108085526454-0965:9781009029346:46821fig7.png?pub-status=live" width="645" /></div></div></section><section><div class="fig" data-magellan-destination="FIGm-fig-7" id="FIGm-fig-7"><div class="caption"><p class="p"><span class="label">Figure 7</span> A model run of the Imperial College London Covid-19 model ‘Covidsim’.</p> </div><div class="attrib"><b class="bold">Source:</b> <a class="uri" href="https://covidsim.org">https://covidsim.org</a>.</div></div></section><p class="p">One
thing that is noticeable about this list is that it is extremely
heterogeneous. Take, to begin with, the standard models of cosmology and
particle physics: while they are very commonly called models, they are
really complements to physical <i class="italic">theories</i>. Compare these to climate models and epidemiological models: although the construction of these models is in part <i class="italic">guided by theory</i>, they are more like stand-alone bits of <i class="italic">mathematics</i>. With regard to the San Francisco Bay model and Watson and Crick’s double-helix models, these are <i class="italic">actual physical entities</i>, which were <i class="italic">built</i> by humans for scientific purposes. The standard laboratory rat is a variety of a biological species <i class="italic">bred</i> by humans for these purposes, while the solar system is a <i class="italic">found object</i>
that Bohr used to articulate his conception of what the atom looked
like. And, unlike these physical entities, a reconstructed record of the
Earth’s temperature in a past geological period is a <i class="italic">data model</i> (<a class="xref bibr" data-sectionid="#REFe-r-008" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-008"><span class="show-for-sr">Reference Bailer-Jones</span>Bailer-Jones 2009</a>; <a class="xref bibr" data-sectionid="#REFe-r-015" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-015"><span class="show-for-sr">Reference Bokulich</span>Bokulich 2011</a>; <a class="xref bibr" data-sectionid="#REFe-r-050" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-050"><span class="show-for-sr">Reference Hartmann, Herfel, Krajewski, Niiniluoto and Wójcicki</span>Hartmann 1995</a>; <a class="xref bibr" data-sectionid="#REFe-r-069" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-069"><span class="show-for-sr">Reference Laymon</span>Laymon 1982</a>; <a class="xref bibr" data-sectionid="#REFe-r-072" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-072"><span class="show-for-sr">Reference Leonelli</span>Leonelli 2016</a>, <a class="xref bibr" data-sectionid="#REFe-r-073" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-073"><span class="show-for-sr">Reference Leonelli</span>2019</a>; <a class="xref bibr" data-sectionid="#REFe-r-081" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-081"><span class="show-for-sr">Reference Mayo</span>Mayo 1996</a>, <a class="xref bibr" data-sectionid="#REFe-r-078" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-078"><span class="show-for-sr">Reference Massimi</span>2018</a>; <a class="xref bibr" data-sectionid="#REFe-r-107" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-107"><span class="show-for-sr">Reference Suppes, Nagel, Suppes and Tarski</span>Suppes 1962</a>,
2007): ‘a corrected, rectified, regimented, and in many instances
idealized version of the data we gain from immediate observation, the
so-called raw data’ (<a class="xref bibr" data-sectionid="#REFe-r-040" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-040"><span class="show-for-sr">Reference Frigg and Hartmann</span>Frigg and Hartmann 2012</a>).</p><p class="p"><span class="page-marker"></span>In fact, our short, yet extremely heterogeneous list of models reflects a key source of confusion about models: <i class="italic">there is very little one can say about scientific models that will be generally true of all of them</i>.
In this Element, instead of trying to work our way through this
confusion, our plan is to live with it, so we can focus on other issues.
In this section, we will simply zero in on a few features that <i class="italic">many</i>
models have, so we can later explore how those features are important
for understanding philosophical issues that arise in connection with
certain models – especially those that play a role in helping
policy-makers to craft policies that affect us all.</p><p class="p">There
is one more source of confusion that we must address before we move on.
This is the rather haphazard way in which ordinary language use in
science invokes a famous triad of terms: <i class="italic">model</i>, <i class="italic">theory</i>, and <i class="italic">experiment</i>.
As we noted, the ‘standard model of particle physics’ is really a part
of theory – but is a theory different from a model? This is far from
clear, especially since when we talk about our best <i class="italic">theories</i> of how diseases spread or of how turbulence arises, what we are really talking about are things that involve <i class="italic">modelling</i>.
Furthermore, there is an influential line of thought in philosophy of
science that asserts that theories are nothing more than families of
models (<a class="xref bibr" data-sectionid="#REFe-r-106" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-106"><span class="show-for-sr">Reference Suppes</span>Suppes 1960</a>; <a class="xref bibr" data-sectionid="#REFe-r-105" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-105"><span class="show-for-sr">Reference Suppe</span>Suppe 1972</a>; <a class="xref bibr" data-sectionid="#REFe-r-111" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-111"><span class="show-for-sr">Reference Bas</span>van Fraassen 1980</a>). Nancy <a class="xref bibr" data-sectionid="#REFe-r-023" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-023"><span class="show-for-sr">Reference Cartwright</span>Cartwright (1983</a>, <a class="xref bibr" data-sectionid="#REFe-r-024" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-024"><span class="show-for-sr">Reference Cartwright</span>1989</a>)
argues that theories are incomplete without accompanying models –
models are involved whenever a mathematical theory is applied to the
real world. Finally, experiments are often described as being carried
out under a ‘model’ of what the experimental system is and how it is
manipulated in the laboratory (<a class="xref bibr" data-sectionid="#REFe-r-108" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-108"><span class="show-for-sr">Reference Suppes</span>Suppes 1969</a>).</p><p class="p" style="text-align: justify;">In
light of this, how can we possibly distinguish between a theory, a
model, and an experiment? In fact, attempting to draw the line between
these has been a central activity in the philosophy of modelling for
many decades. For the purpose of this Element, however, it will suffice
to employ a very simple distinction between theory, model, and
experiment. Here, we take the word ‘theory’ to mean a particularly
well-supported, widely-respected, and successful – in other words, <i class="italic">well-credentialled</i>
– way of understanding how the world works (we will set aside the
question of whether such an entity comprises a family of models, a
syntactic structure, or whatever else (<a class="xref bibr" data-sectionid="#REFe-r-105" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-105"><span class="show-for-sr">Reference Suppe</span>Suppe 1972</a>)).
In comparison, models and experiments can be more or less
well-credentialled; that is, neither term flags a particular level of
epistemic support or record of success. With regard to the difference
between models and experiments, we will not draw any particular
distinction in this Element: we simply use the word ‘modelling’ to
convey a scientific process carried out either with paper and pencil or
on a computer, and the word ‘experiment’ to convey a scientific process,
the canonical form of which takes place in <span class="page-marker"></span>a laboratory by poking and prodding at a sample of the kind of system that is of interest.</p><p class="p">With
that said, let’s begin by zeroing in on three features that many models
have. First, models are almost always integrated into a <i class="italic">triad</i>.
In other words, when we talk about modelling, we are almost always
referring to three things: (1) a system or other phenomenon in the
world, which we call the <i class="italic">target</i>; (2) the model itself, which <i class="italic">represents</i> the target (more on this shortly); and (3) the model <i class="italic">user</i>.
These three things must be understood in relation to each other: in
particular, the model user cannot be ignored because it is her <i class="italic">intentions</i>
that ultimately determine the model’s target system and the model’s
purpose. In other words, models are only representations of their target
systems because a model user says they are. For example, the solar
system has been around for billions of years – but it only became a
model that represents the target system ‘the atom’ when a human agent,
Niels Bohr, singled it out and said ‘that’s a model of the atom’.
Similarly, a particular computer model has the cognitive function of
predicting the weather tomorrow rather than of projecting the climate at
the end of the century because its user says so. Indeed, a model only
has a cognitive function at all, rather than the function of being a
video installation in an art museum, because its user says so.</p><p class="p">Second, as noted, models are almost always <i class="italic">representations</i>
of target systems. What exactly it means for something to be a
scientific representation is another core area of inquiry in philosophy
of modelling, with a rich literature that we will not review here (<a class="xref bibr" data-sectionid="#REFe-r-041" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-041"><span class="show-for-sr">Reference Frigg and Nguyen</span>Frigg and Nguyen 2021</a>). For our purposes, it will suffice to say that a model <i class="italic">represents</i>
a target system if a model user takes it to stand for that target
system in a way that helps the model user reason about that system (<a class="xref bibr" data-sectionid="#REFe-r-084" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-084"><span class="show-for-sr">Reference Morgan and Morrison</span>Morgan and Morrison 1999</a>; <a class="xref bibr" data-sectionid="#REFe-r-084" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-084"><span class="show-for-sr">Reference Morgan and Morrison</span>Morrison 1999</a>); <a class="xref bibr" data-sectionid="#REFe-r-061" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-061"><span class="show-for-sr">Reference Hughes</span>R. I. G. Hughes’ (1997</a>)
‘Denotation–Demonstration–Interpretation’ account of modelling is
especially useful here. Some have even argued that there is a kind of
use of models along these lines that gives rise to its own style of
‘model-based reasoning’ (<a class="xref bibr" data-sectionid="#REFe-r-077" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-077"><span class="show-for-sr">Reference Magnani, Nersessian and Thagard</span>Magnani, Nersessian, and Thagard 1999</a>; <a class="xref bibr" data-sectionid="#REFe-r-067" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-067"><span class="show-for-sr">Reference Knuuttila</span>Knuuttila 2005</a>, <a class="xref bibr" data-sectionid="#REFe-r-068" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-068"><span class="show-for-sr">Reference Knuuttila</span>2011</a>; <a class="xref bibr" data-sectionid="#REFe-r-076" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-076"><span class="show-for-sr">Reference Lorenzo and Nersessian</span>Magnani and Nersessian 2002</a>; <a class="xref bibr" data-sectionid="#REFe-r-092" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-092"><span class="show-for-sr">Reference Peschard</span>Peschard 2011</a>) in which ‘inferences are made by means of creating models and manipulating, adapting and evaluating them’ (<a class="xref bibr" data-sectionid="#REFe-r-089" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-089"><span class="show-for-sr">Reference Nersessian</span>Nersessian 2010</a>, quoted in <a class="xref bibr" data-sectionid="#REFe-r-040" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-040"><span class="show-for-sr">Reference Frigg and Hartmann</span>Frigg and Hartmann 2012</a>).</p><p class="p">Furthermore,
because models are representations of target systems – not perfectly
complete and entirely accurate depictions of those systems – the
modelling process involves pragmatic choices about what to represent and
how to represent it, which we call <i class="italic">representational decisions</i> (<a class="xref bibr" data-sectionid="#REFe-r-120" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-120"><span class="show-for-sr">Reference Winsberg</span>Harvard and Winsberg 2022</a>). A well-worn analogy is useful at this point: models are like <span class="page-marker"></span>maps.
Think about a subway map: the choices that go into how to represent the
world in a subway map have a great deal to do with how the map will be
used. The purpose of a subway map is to help people figure out how to
get from station A to station B (‘Is there a single line that takes me
there? Am I going to have to make changes along the way?’). So, a subway
map is designed to represent the features of the world that are salient
to being able to decide how to get from point A to point B. Subway map
users don’t particularly care how far the different stops are from each
other, nor do they care if the path between two stops is a straight line
or if the subway takes a curved path to get somewhere. The key to
making a good subway map is carefully choosing the most useful
information to represent and using representational conventions that,
together, make it as easy as possible for users to reason about and
identify the best way to get from A to B.</p><p class="p">Models are a
lot like this. Like maps, they are things that we build to represent the
world and to help us reason about it. And they reflect <i class="italic">choices</i> about how to represent the world: model developers decide ‘we’re going to include this, we’re <i class="italic">not</i>
going to include that’. Think of Watson and Crick’s tin plate and wire
model of DNA. It was very important for them to represent, in their
model, the length of the four nitrogen-containing nucleobases (cytosine
(C), guanine (G), adenine (A), and thymine (T)), but not their internal
molecular structure. That is because they were trying to reason about
how these four nucleobases could fit together like a puzzle. So they
used a (3D) puzzle-piece–like representational toolkit to build the
model and to help them do that reasoning.</p><p class="p">This
brings us to our third extremely important feature of many models.
Their criterion of adequacy is most often not that they are ‘true’ to
the world. It is not an important criticism of a subway map that the
Broadway line ‘isn’t really orange’, or that the map doesn’t show that
some subway lines cross bodies of water by going under them in tunnels
while others go over them on bridges. Yet it <i class="italic">would</i>
be an important criticism of a subway map if it were to represent two
nearby stations by the exact same dot on the map. After all, this would
make users think they could change lines at that stop without leaving
the system, and avoiding this kind of mistake is what subway maps are
supposed to facilitate. Part of a subway map’s intended purpose is to
help users make accurate inferences about where and how to change subway
lines. With models, as with maps, the criterion of adequacy is that
they are good enough for the purposes we intend to use them for.
Sometimes meeting certain purposes requires that the representational
relationship between the model (or map) and the world is verisimilitude.
But often it does not. And adequacy for purpose is the telos of a model
and a map, not truth.</p><p class="p"><span class="page-marker"></span>Whether
or not this fact about models, along with the fact that models very
often involve deliberate distortions, poses a threat to scientific
realism is a topic of much philosophical debate. The threat might arise
(according to the debate) if one assumes, as many philosophers do, that
science is fundamentally a model-based activity (most famously Nancy
Cartwright (<a class="xref bibr" data-sectionid="#REFe-r-023" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-023"><span class="show-for-sr">Reference Cartwright</span>Cartwright 1983</a>, <a class="xref bibr" data-sectionid="#REFe-r-024" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-024"><span class="show-for-sr">Reference Cartwright</span>1989</a>, <a class="xref bibr" data-sectionid="#REFe-r-025" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-025"><span class="show-for-sr">Reference Cartwright</span>2019</a>), Ronald Giere (<a class="xref bibr" data-sectionid="#REFe-r-043" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-043"><span class="show-for-sr">Reference Giere</span>Giere 1988</a>, <a class="xref bibr" data-sectionid="#REFe-r-044" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-044"><span class="show-for-sr">Reference Giere</span>1999</a>, <a class="xref bibr" data-sectionid="#REFe-r-045" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-045"><span class="show-for-sr">Reference Giere</span>2006</a>; <a class="xref bibr" data-sectionid="#REFe-r-046" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-046"><span class="show-for-sr">Reference Giere, Bickle and Mauldin</span>Giere, Bickle, and Mauldin 1979</a>), and Margaret Morrison (<a class="xref bibr" data-sectionid="#REFe-r-084" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-084"><span class="show-for-sr">Reference Morgan and Morrison</span>Morgan and Morrison 1999</a>; <a class="xref bibr" data-sectionid="#REFe-r-084" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-084"><span class="show-for-sr">Reference Morgan and Morrison</span>Morrison 1999</a>, <a class="xref bibr" data-sectionid="#REFe-r-029" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-029"><span class="show-for-sr">Reference Douglas</span>2000</a>, <a class="xref bibr" data-sectionid="#REFe-r-036" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-036"><span class="show-for-sr">Reference Ferguson, Cummings and Cauchemez</span>2005</a>, <a class="xref bibr" data-sectionid="#REFe-r-088" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-088"><span class="show-for-sr">Reference Morrison, Eigner, Leonelli and de Regt</span>2009</a>)).<a class="xref footnote" data-sectionid="#FN-fn-1" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#FN-fn-1"><span class="show-for-sr">Footnote </span><sup class="sup">1</sup></a></p><div class="sec" data-magellan-destination="B-sec-2" id="B-sec-2"><h4 class="B title"><span class="label" style="color: #660000;">1.2</span><span style="color: #660000;"> Are There ‘Types’ of Models? </span></h4><p class="p">Given all of the above, it is not surprising that philosophers have made various attempts to classify models into <i class="italic">types</i>.
Some of these classification attempts have corresponded to key
questions like ‘How does the model represent?’ and ‘What is the model’s
cognitive function?’. Partly because it is a closely related question,
and partly because philosophers are always fascinated by ontological
questions, the question ‘What kind of entity is a model?’ often also
plays a central role in classifications of models. We will not explore
here all the philosophical attempts to classify models (see, especially,
<a class="xref bibr" data-sectionid="#REFe-r-040" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-040"><span class="show-for-sr">Reference Frigg and Hartmann</span>Frigg and Hartmann (2012)</a> and <a class="xref bibr" data-sectionid="#REFe-r-115" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-115"><span class="show-for-sr">Reference Weisberg</span>Weisberg (2013)</a>
if this is of interest). However, the following rough division of
models into four categories will be helpful in tying together some of
the central issues that concern us in this Element. We should emphasize
that not everyone will find this categorization scheme adequate,
especially readers who are concerned with understanding the most
heterogeneous lists of models.</p><div class="sec" data-magellan-destination="C-sec-1" id="C-sec-1"><h3 class="C title" style="text-align: left;"><span class="label" style="color: #660000;">1.2.1</span><span style="color: #660000;"> Abstract/Mental Models </span></h3><p class="p" style="text-align: justify;">Consider
our very first example of something scientists call a model: a picture
of a cell in a biology textbook. This what Stephen <a class="xref bibr" data-sectionid="#REFe-r-031" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-031"><span class="show-for-sr">Reference Downes</span>Downes (1992)</a>
has called an ‘idealized’ exemplar. As Downes notes, textbooks will
present a schematized cell that contains items of interest: in a botany
textbook the schematized cell will contain chloroplasts and an outer
cell wall, but in a zoology textbook the schematized cell will not
include those things (<a class="xref bibr" data-sectionid="#REFe-r-031" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-031"><span class="show-for-sr">Reference Downes</span>1992</a>,
145). As Downes puts it, ‘the cell is a model in a large group of
interrelated models that enable us to understand the operations of all
cells. The model is not a nerve cell, nor is it a muscle cell, nor a
pancreatic cell, it stands for all of these’ (<a class="xref bibr" data-sectionid="#REFe-r-031" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-031"><span class="show-for-sr">Reference Downes</span>1992</a>, 145). As we will see, abstract/mental models have much in common with our next category <span class="page-marker"></span>of models, <i class="italic">concrete models</i>.
However, one important difference should be emphasized. Making
inferences from abstract/mental models requires model users to have an
implicit understanding of the target system that allows them to do
something akin to mentally simulating its behaviour. Unlike the concrete
models we describe next, abstract/mental models, like a simple drawing
of a cell, <i class="italic">do not mechanically generate their own behaviour</i>.</p></div><div class="sec" data-magellan-destination="C-sec-2" id="C-sec-2"><h5 class="C title"><span class="label">1.2.2</span> Concrete Models </h5><p class="p">Some
examples of concrete models from our introductory list are the San
Francisco Bay model, the solar system as a model of the atom, and the
laboratory rat as a model of biological phenomena in humans. What seems
to be special about concrete models is that they come with their own
dynamical behaviour <i class="italic">and</i> they represent and
support dynamical inferences by purporting to be a causal duplicate of
the target system. For example, the San Francisco Bay model is a
concrete thing that mechanically generates its own behaviour: water
literally sloshes in the model, and when it sloshes in a particular way
in the model, the user infers that water will slosh similarly in the
real San Francisco Bay. While concrete models are not the only type of
model that is meant to license dynamical inferences about their target
systems, they are the only type of model that does this by presenting
actual behaviour. As a result of this built-in behaviour, users of a
concrete model do not need to know how to reason about how the system
should be expected to evolve – rather, a concrete model demonstrates
that evolution. For example, a schematized cell in a biology textbook
(an abstract/mental model) does not demonstrate to us how mitochondria
behave in cells, but a laboratory rat (a concrete model) might very well
be used to demonstrate this behaviour.</p><p class="p">When considering concrete models, a sometimes useful distinction is that between analogical models (<a class="xref bibr" data-sectionid="#REFe-r-007" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-007"><span class="show-for-sr">Reference Bailer-Jones</span>Bailer-Jones 2002</a>, <a class="xref bibr" data-sectionid="#REFe-r-008" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-008"><span class="show-for-sr">Reference Bailer-Jones</span>2009</a>; <a class="xref bibr" data-sectionid="#REFe-r-007" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-007"><span class="show-for-sr">Reference Bailer-Jones</span>Bailer-Jones and Bailer-Jones 2002</a>; <a class="xref bibr" data-sectionid="#REFe-r-057" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-057"><span class="show-for-sr">Reference Hesse</span>Hesse 1963</a>, <a class="xref bibr" data-sectionid="#REFe-r-058" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-058"><span class="show-for-sr">Reference Hesse and Edwards</span>1967</a>, <a class="xref bibr" data-sectionid="#REFe-r-059" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-059"><span class="show-for-sr">Reference Hesse</span>1974</a>) and scale models (<a class="xref bibr" data-sectionid="#REFe-r-014" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-014"><span class="show-for-sr">Reference Black</span>Black 1962</a>; <a class="xref bibr" data-sectionid="#REFe-r-102" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-102"><span class="show-for-sr">Reference Sterrett</span>Sterrett 2006</a>, <a class="xref bibr" data-sectionid="#REFe-r-103" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-103"><span class="show-for-sr">Reference Sterrett, Michelfelder and Doorn</span>2021</a>).
Both analogical models and scale models are concrete, but an analogical
model is found (e.g., the solar system as a model of the atom) and a
scale model is constructed (e.g., the San Francisco Bay model). While
inferences from model to target are probably the most straightforward in
the case of scale models, this does not necessarily translate into
reliability. It is very easy to see what the San Francisco Bay model
says about what will happen in the real San Francisco Bay. However, the
inference is only as reliable as the assumption that one is a causal
duplicate of the other. In fact, this is almost certainly not true in
this case, because fluids have scale-dependent features. The San
Francisco Bay model was mostly used for rhetorical purposes (more on the
use of models for rhetorical purposes in <a class="xref sec" data-sectionid="#A-sec-5" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#A-sec-5">Section 3</a>).</p></div><div class="sec" data-magellan-destination="C-sec-3" id="C-sec-3"><h5 class="C title"><span class="label" style="color: #660000;">1.2.3</span><span style="color: #660000;"> Data Models </span></h5><p class="p">An
example of a data model from our introductory list is a reconstructed
record of the Earth’s temperature in past geological periods that is
developed using proxy data. A data model is essentially a summary of
information – a <i class="italic">corrected, normalized, systematized</i>, and <i class="italic">idealized</i>
summary – that scientists believe is relevant to their reconstruction
project and that they have collected from various data sources. For
example, scientists may start by summarizing variations in the ratio of
different isotopes of oxygen in deep ice cores and in the fossilized
shells of tiny animals (<a class="xref bibr" data-sectionid="#REFe-r-090" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-090"><span class="show-for-sr">Reference Parker</span>Parker 2018</a>)
and gradually incorporate this data model into another to draw
inferences about the Earth’s temperature in the past. Examples of other
familiar data models come from randomized controlled trials and
observational studies in the health sciences: for example, when a new
drug is developed, researchers will routinely collect selected pieces of
information from people who are taking and those who are not taking the
drug and summarize it in the form of a data model (e.g., descriptive
statistical models of clinical outcomes). Such data models often become
useful sources of clinical information that are perceived to be relevant
to and can be incorporated into other research projects, including
computational models (described in the <a class="xref sec" data-sectionid="#C-sec-4" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#C-sec-4">next subsection</a>).
For example, a randomized controlled trial may find that patients who
receive a new asthma drug have an annual rate of asthma exacerbation of
0.11 (95% CI 0.10, 0.13), while patients who receive an existing asthma
drug have an annual rate of exacerbation of 0.12 (95% CI 0.10, 0.14).
This summary of clinical information may then be incorporated as a <i class="italic">parameter</i> in the sort of model that is used to explore the cost-effectiveness of a new medication (e.g., <a class="xref bibr" data-sectionid="#REFe-r-039" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-039"><span class="show-for-sr">Reference FitzGerald, Arnetorp and Smare</span>FitzGerald et al. 2020</a>).
Importantly, data models also play a central role in evaluating
computational models. That is, the results of computational models will
often be directly compared to data models as a means of assessing
whether their results are consistent with our existing knowledge about
the world. Although our focus in the later sections of this Element is
not on data models themselves, they are a key component of the models we
focus on. In this context, the important thing to keep in mind is that
data and data models ‘are representations that are products of a process
of inquiry’ (<a class="xref bibr" data-sectionid="#REFe-r-016" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-016"><span class="show-for-sr">Reference Bokulich and Parker</span>Bokulich and Parker 2021</a>, 31). Like the other models we discuss, data models involve representational choices, and our objective is for them to be <i class="italic">adequate for purpose</i>, not true or false.</p></div><div class="sec" data-magellan-destination="C-sec-4" id="C-sec-4"><h5 class="C title"><span class="label" style="color: #660000;">1.2.4</span><span style="color: #660000;"> Mathematical (including Computational) Models </span></h5><p class="p">At
a high level, the purpose of mathematical models is to be able to fit
together mathematical relationships that we think describe the world and
apply them to a target system. Those mathematical relationships can be
bits of theory, law, <span class="page-marker"></span>mathematical
regularity, rules of behaviour, or the product of our own human
reasoning. We then use the mathematical model to reason about that
target system and help us better understand it. Think of a weather
system, a hurricane, traffic patterns, predator–prey relations in an
ecosystem, or the spread of disease in a human population. In a weather
model, we fit together laws of thermodynamics with laws that govern the
dynamical flow of parcels of air in the atmosphere. In a hurricane
model, we treat the atmosphere as a fluid that we divide into parcels
and use basic primitive laws of motion as well as thermodynamic laws of
gas dynamics to calculate how those parcels of air will move around. In a
traffic model, we give each car (with its driver) a set of rules for
when it will speed up, slow down, or stop depending on what it sees in
its environment. In a predator–prey model, we fit together our best
assumptions about the rate at which predators kill prey with the rate at
which predators die when they fail to capture prey. At the end of the
day, the goal is to integrate various salient bits of theory and other
mathematical regularities that we have some trust in so that they can be
applied to draw out inferences about the <i class="italic">target system</i> (the hurricane or the traffic jam) by reasoning with the model (<a class="xref bibr" data-sectionid="#REFe-r-084" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-084"><span class="show-for-sr">Reference Morgan and Morrison</span>Morgan and Morrison 1999</a>, chap. 1; <a class="xref bibr" data-sectionid="#REFe-r-117" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-117"><span class="show-for-sr">Reference Winsberg</span>Winsberg 2010</a>).</p><p class="p">Computational
models can be understood as a subset of mathematical models. A
mathematical model becomes computational when the bits of math in the
model become too analytically intractable to draw the needed inferences
using pencil and paper. Often this is because the model involves
differential equations that can’t be solved analytically (<a class="xref bibr" data-sectionid="#REFe-r-117" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-117"><span class="show-for-sr">Reference Winsberg</span>Winsberg 2010</a>).
But it can also be because the mathematical model is more about rules
of behaviour than it is about solvable or unsolvable equations.</p><p class="p">Earlier
we pointed out that abstract/mental models are quite different from
concrete models in that the latter mechanically generate their own
behaviour while the former require model users to effectively mentally
simulate the behaviour of the target system. As a result, what behaviour
abstract/mental models will predict depends quite a bit on what the
model user brings to the task. Mathematical models, interestingly,
straddle this divide. If you have a basic pencil and paper model with
which you can make simple calculations, then the model just does what it
does, not unlike a concrete model. However, if the mathematical model
needs to be turned into a computational model, then how that gets
implemented by its builder/user will often have a significant effect on
what behaviour it exhibits.</p></div></div><div class="sec" data-magellan-destination="B-sec-3" id="B-sec-3"><h4 class="B title"><span class="label" style="color: #660000;">1.3</span><span style="color: #660000;"> Mathematical and Computational Models: A Closer Look </span></h4><p class="p">In this subsection, we delve in greater detail into mathematical and computational models; <a class="xref sec" data-sectionid="#A-sec-4" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#A-sec-4">Sections 2</a>, <a class="xref sec" data-sectionid="#A-sec-5" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#A-sec-5">3</a>, and <a class="xref sec" data-sectionid="#A-sec-6" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#A-sec-6">4</a> will focus on philosophical issues connected specifically with these models. Our main goal in this subsection is to establish <span class="page-marker"></span>that mathematical models can vary along at least five overlapping continua – <i class="italic">idealization</i>, <i class="italic">articulation</i>, <i class="italic">credentials</i>, <i class="italic">sensitivity</i>, and <i class="italic">skill</i> –and to explain how we will use these terms in the remainder of this Element.</p><div class="sec" data-magellan-destination="C-sec-5" id="C-sec-5"><h5 class="C title"><span class="label">1.3.1</span> Idealization </h5><p class="p">It
has become popular in recent philosophy of science to call certain
kinds of models ‘idealized’ models, and to divide so-called idealization
into ‘Aristotelian idealization’ (<a class="xref bibr" data-sectionid="#REFe-r-083" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-083"><span class="show-for-sr">Reference McMullin</span>McMullin 1985</a>) and ‘Galilean idealization’ (<a class="xref bibr" data-sectionid="#REFe-r-024" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-024"><span class="show-for-sr">Reference Cartwright</span>Cartwright 1989</a>).
On this account, Aristotelian idealization consists of ‘stripping
away’, in our imagination, all properties from a concrete object that we
believe are not relevant to the problem at hand. This allows us to
focus on a limited set of properties in isolation. An example is a
classical mechanics model of the planetary system, which describes the
planets as objects only having shape and mass and disregards all other
properties. Galilean idealizations, on the other hand, are ones that
involve deliberate <i class="italic">distortions</i>. Physicists build
models consisting of point masses moving on frictionless planes,
economists assume that agents are omniscient, biologists study isolated
populations, and so on. It was characteristic of Galileo’s approach to
science to use simplifications of this sort whenever a situation was too
complicated to tackle (<a class="xref bibr" data-sectionid="#REFe-r-040" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-040"><span class="show-for-sr">Reference Frigg and Hartmann</span>Frigg and Hartmann 2012</a>).
In mathematical models, Aristotelian and Galilean idealization usually
work in harmony. Indeed, the two generally go hand in hand for good
reason: so-called Galilean idealization usually only works insofar as
some degree of ‘Aristotelian’ reasoning is in play. If we do too much
Galilean idealization without ensuring that this idealization doesn’t
affect what is ‘relevant’ to us, we will get into trouble. Making the
two kinds of idealization work in harmony is part of the way we ensure
that our models are adequate for purpose. The takeaway point is that
mathematical models (including computational models) can vary in terms
of how idealized they are, both in terms of the degree to which they
simplify reality (i.e., exclude elements of the target system from the
representation) and distort it (i.e., deliberately change elements of
the target system in the representation).</p></div><div class="sec" data-magellan-destination="C-sec-6" id="C-sec-6"><h5 class="C title"><span class="label">1.3.2</span> Articulation </h5><p class="p">Earlier,
we drew attention to the fact that mathematical models seem to come in
two varieties. Sometimes, a mathematical model invites us to use simple
paper and pencil methods to draw out inferences about the world. Other
times, a mathematical model needs to be augmented with additional
reasoning, including reasoning about which computational methods can and
will be used to implement it – and, as we said, how a computational
model gets implemented will often have a significant effect on what
behaviour it exhibits. Consider the case of a computational model <span class="page-marker"></span>designed
to simulate fluid flows that contain significant shock discontinuities.
The existence of shocks makes it difficult to turn the differential
equations of fluid dynamics into a step-by-step algorithm that a
computer can calculate without errors compounding and blowing up. A host
of different strategies, including the so-called piecewise parabolic
method discussed in <a class="xref bibr" data-sectionid="#REFe-r-117" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-117"><span class="show-for-sr">Reference Winsberg</span>Winsberg (2010</a>,
46), is an extreme case of a mathematical model needing substantial
augmentation in order to be implemented successfully, and different
implementations could lead to substantially different results. A useful
piece of vocabulary to help understand the degree to which a
mathematical model needs to be supplemented with additional reasoning in
order to successfully calculate with it is <i class="italic">articulation</i>.
We can say that the simplest paper and pencil model will not require
any articulation at all, while any computational model will require at
least some degree of articulation; the more complex the computational
model, the more articulation it will require. A model comprising
differential equations of fluid dynamics that we hope will enable us to
calculate the behaviour of shocks will require enormous articulation.
Only a model that requires no articulation will just ‘do what it does’,
that is, generate its own behaviour like a concrete model (see <a class="xref sec" data-sectionid="#B-sec-8" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#B-sec-8">Section 2.4</a>).
Note that it may be tempting to say that once we have finished
developing a computational model, the model just ‘does what it does’.
However, this is only really true if we zero in on a specific version of
the model, written with a specific set of instructions, run on a
specific piece of hardware, and compiled by a specific compiler.
Otherwise, the ‘same’ computational model (if it is complex enough) can
very easily exhibit substantially different behaviour. It is therefore
very useful to understand mathematical and computational models with
reference to the degree of articulation they require.</p></div><div class="sec" data-magellan-destination="C-sec-7" id="C-sec-7"><h5 class="C title"><span class="label" style="color: #783f04;">1.3.3</span><span style="color: #783f04;"> Sensitivity </span></h5><p class="p">In many contexts, a model’s <i class="italic">sensitivity</i>
refers to how sensitive its output is to choices of parameter values.
More generally, it could refer to how sensitive the model’s output is to
methodological choices of any kind. It is important to distinguish a
model’s sensitivity to choices of parameter values and the sensitivity
that the system it models exhibits to the initial value of its
variables. Here we are concerned with the former.</p><p class="p">What
is the difference between a variable and a parameter? A parameter value
is the value of some measurable quantity associated with the system that
stays fixed throughout the life of the system over the timescale of the
model, while a variable, obviously, varies. A variable for a model is
thus both an input for a model (the value the variable takes at an
initial time) and an output (the value the variable takes at all
subsequent times – including, of course, at the final time of a model
run). A parameter is simply an input. So, for example, in an <span class="page-marker"></span>epidemiological
model, it might be that the reproductive rate of the virus is a
parameter, and the value for the number of people infected at any one
time is a variable.</p><p class="p">When building mathematical models,
there can be varying levels of uncertainty about what to represent in
the model and how to represent it. In some cases, there is a wealth of
well-established background knowledge about the target system, which
functions to inform and even <i class="italic">constrain</i>
representational decisions. For example, when modellers build
computational models of the motions of the planets in the solar system,
modellers’ representational decisions are constrained by the
well-established laws of celestial mechanics. However, in other cases,
background knowledge about a target system is lacking: there is far more
uncertainty around what should be represented in the model and how. In
these cases, we say that representational decisions are <i class="italic">unconstrained</i>.
When a model is built under these conditions, it is important for
modellers to explore whether and how model results change when they make
different representational decisions. In other words, it is important
to explore how <i class="italic">sensitive</i> model results are to
changes in representational decisions. If the value of X that a model
calculates is highly sensitive to representational decisions, and those
decisions are not highly constrained, we have reasons not to trust the
model to give us precise predictions of the value of X.</p></div><div class="sec" data-magellan-destination="C-sec-8" id="C-sec-8"><h5 class="C title"><span class="label" style="color: #783f04;">1.3.4</span><span style="color: #783f04;"> Credentials </span></h5><p class="p">Roughly
speaking, a model’s credentials correspond to how much trust we should
put in it: the degree to which we should expect the model not to let us
down, but rather to prove to be a successful way of understanding how
the world works. In the case of mathematical models, model credentials
are intrinsically linked to the <i class="italic">ancestries</i> of
whatever bits of math have gone into making them. After all, bits of
math can come from almost any source we use to gain knowledge about the
world: they can come from theory, from patterns we see in data that we
think are robust, or just basic assumptions we think are plausible (for
whatever reason). If the bits of math that go into a model come from
theory, this will help to give the model credentials (as we described in
<a class="xref sec" data-sectionid="#B-sec-1" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#B-sec-1">Section 1.1</a>, we reserve the term ‘theory’ for a <i class="italic">well-credentialled</i>
way of understanding the world, one that enjoys not only wide
recognition but a history of success). If the bits of math that go into
making a mathematical model come, not from theory but from <i class="italic">models of data</i>,
then those bits of math will only be as good and as widely applicable
as those models of data – the model’s credentials are linked to the data
models. And if those bits of math come not from theory, not from data
models, but from human intuitions of what is plausible, then those bits
of math <span class="page-marker"></span>will only be as reliable as
human intuitions are. A model whose credentials are intrinsically linked
to human intuitions is one we should put less trust in.</p><p class="p">To
give an example, imagine we are building a climate model and we need a
bit of math that calculates how much infrared radiation the Earth
radiates back as it absorbs ultraviolet radiation from the Sun. To find
one, we can reach for the Stefan–Boltzmann law, which is a direct
application of Planck’s law, which is ultimately grounded in the
well-established theory of quantum mechanics. Putting a mathematical
relationship like that into our climate model is unlikely to let us
down. On the other hand, imagine we need a bit of math that calculates
how many clouds and of what kind will form in various parcels of the
atmosphere. Unfortunately, it is unlikely that we will be able use basic
physics to calculate this: in general, climate models are too
coarse-grained to ‘resolve’ cloud formation (<a class="xref bibr" data-sectionid="#REFe-r-117" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-117"><span class="show-for-sr">Reference Winsberg</span>Winsberg 2010</a>, <a class="xref bibr" data-sectionid="#REFe-r-119" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-119"><span class="show-for-sr">Reference Winsberg</span>2018</a>). Instead, we will need to come up with a <i class="italic">subgrid model</i>
of cloud formation. The goal of a subgrid model is to dictate a
function that will tell us how much cloud structure there is in each
grid cell of the simulation as a function of the temperature, humidity,
pressure, and other variables in the grid cell. Because subgrid models
often require a number of parameter values to specify them, they are
often called <i class="italic">parameterizations</i>. These subgrid
models have complex ancestries: they are derived from laboratory
experiment, field observation, and, more recently, from the output of
machine learning algorithms. None of these sources will have the same
credentials as our piece of math that calculates infrared radiation.
Furthermore, it is not uncommon in climate models for the
parameterization of cloud formation to be deliberately wrong – so as to
offset other errors that climate models are known to have (<a class="xref bibr" data-sectionid="#REFe-r-080" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-080"><span class="show-for-sr">Reference Mauritsen, Stevens and Roeckner</span>Mauritsen et al. 2012</a>).
This highlights an important fact about representational decisions: the
best representational decision is the one that is most adequate for
purpose, not necessarily the one that is true to the world. The ‘best’
parameterization of cloud formation in a climate model might not be the
one that most accurately depicts cloud formation, but the one that, in
conjunction with all the other representational decisions, makes the
model most adequate for purpose (that is, perhaps, the one that tells us
the truest things about what the climate will be like at the end of the
century, if that is our model’s purpose).</p><p class="p">To the extent
that model constructions depend on human judgement and ability – rather
than, for example, being determined by theory – our appraisal of the
trustworthiness of a model might depend, at least in part, on the people
or groups of people who built the model. Different researchers and
groups of researchers have past track records of success, institutional
credentials, and so on, and there is no reason that these elements
should not affect the degree of trust we can rationally put in their
work.</p></div><div class="sec" data-magellan-destination="C-sec-9" id="C-sec-9"><h5 class="C title"><span class="label">1.3.5</span> Skill </h5><p class="p">
Mathematical models can help us to reason about the world in myriad
ways and can be put to almost infinitely many purposes (as we said,
nothing technically stops us from using a model as an art installation).
However, models do vary in their degree of adequacy for different
purposes. One way of understanding model adequacy for purpose is in
terms of model <i class="italic">skill</i> (<a class="xref bibr" data-sectionid="#REFe-r-119" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-119"><span class="show-for-sr">Reference Winsberg</span>Winsberg 2018</a>,
chap. 10). Skill refers to a model’s adequacy for specific types of
epistemic purposes. Three examples of epistemic purposes for which
mathematical models may be more or less <i class="italic">skilled</i> are the following:
</p><ol class="list order nomark"><li class="list-item"><p class="p"><span class="label">(1)</span> <i class="italic">Prediction:</i> mathematical models can help us to know what a target system will do in the real world at some particular point in time.</p></li><li class="list-item"><p class="p"><span class="label">(2)</span> <i class="italic">Projection:</i>
mathematical models can help us to estimate what a target system would
likely do under different possible counterfactual scenarios, especially
under various possible human interventions.</p></li><li class="list-item"><p class="p"><span class="label">(3)</span> <i class="italic">Causal inference:</i> mathematical models can help us to learn <i class="italic">what causes what</i>
in a target system. If a model of the climate without increased carbon
concentration does not exhibit the warming exhibited by both the real
world and models with increased carbon concentration, then we might
plausibly infer that the carbon caused the warming (especially if that
warming exhibits the same ‘fingerprints’ in both cases).</p></li></ol><p class="p continuation">
As we will see in the <a class="xref sec" data-sectionid="#B-sec-4" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#B-sec-4">next section</a>,
sometimes model skill can be measured using specific types of empirical
tests. However, at other times no such empirical tests are available,
in which case model users must judge a model’s skill using other means,
often taking into consideration its levels of idealization,
articulation, and credentials, among other things. In any case, model
skill should be understood on a continuum: that is, when assessing a
model for the purpose of prediction, projection, or causal inference, we
speak of it being <i class="italic">more or less</i> skilled.</p></div></div><div class="sec" data-magellan-destination="B-sec-4" id="B-sec-4"><h4 class="B title"><span class="label" style="color: #783f04;">1.4</span><span style="color: #783f04;"> Conclusion </span></h4><p class="p" style="text-align: justify;"><span style="color: #274e13;">En esta sección introductoria hemos examinado una serie de entidades de representación de la ciencia a las que se suele denominar "modelos". Hemos observado que se trata de una colección muy heterogénea de entidades, pero que pueden agruparse en cuatro categorías: modelos abstractos/mentales, modelos concretos, modelos de datos y modelos matemáticos/computacionales. También observamos que una cosa que tienen en común estas entidades es que, aunque todas son representacionales (es decir, todas describen el mundo de una forma u otra), pocas de ellas son aptas para la verdad, es decir, el tipo de cosas que decimos que son "verdaderas". Más bien, son adecuados (o no) para un propósito u otro, aunque a menudo el propósito para el que esperamos que sean adecuados incluye el de inferir afirmaciones verdaderas sobre el mundo. Los modelos en sí mismos no son aptos para la verdad, pero a menudo las inferencias que extraemos de ellos sí lo son, y la adecuación para un fin a menudo significa "este modelo es adecuado para inferir afirmaciones sobre el mundo que es probable (suficientemente) que sean ciertas con un alto (suficiente) grado de precisión en un amplio (suficiente) dominio de aplicación". (Habrá mucho más sobre esto en la próxima sección).</span><br /></p><p class="p">We
next decided to zero in on mathematical and computational models. We
saw that models like these are especially useful for the tasks of
prediction, projection, and causal inference, and that this, in turn,
makes them especially useful and important in guiding decision-making.
When this is the case, it’s especially helpful to talk about the degree
of <i class="italic">skill</i> that models of this kind have for
carrying out these tasks. Skill, we said, is adequacy for a specific
kind of quantitative purpose, and evaluating the skill of a model for
various purposes is complex and motley – but it often involves looking
at the credentials of the model, and the degree to which it is
idealized, articulated, and sensitive to the representational choices
that we made in creating it.</p></div><div class="sec" data-magellan-destination="A-sec-4" id="A-sec-4"><h3 class="A title"><span class="label" style="color: #783f04;">2</span><span style="color: #783f04;"> Adequacy for Purpose </span></h3><div class="sec" data-magellan-destination="B-sec-5" id="B-sec-5"><h4 class="B title"><span class="label">2.1</span> Introduction </h4><p class="p">In <a class="xref sec" data-sectionid="#A-sec-3" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#A-sec-3">Section 1</a>,
we emphasized that our objective is not usually that a model be true,
but rather adequate for purpose. This naturally leads to the question:
‘what does it <i class="italic">mean</i> for a model to be adequate
for purpose?’. This simple question has a fairly straightforward answer:
the model users simply need to trust that they can use the model for
whatever tasks they intend to use it for. However, this simple question
invites far trickier ones. For example, ‘What are all the purposes to
which models can be put?’. In fact, models can have myriad purposes:
they can be used to make predictions or projections of various kinds, to
draw causal inferences, to explain behaviour, to convey things
pedagogically, or for something else entirely. Furthermore, when we
specify a model’s purpose, we usually do so relative to a particular <i class="italic">standard of accuracy</i>.
In other words, we may count a model as adequate for purpose if it gets
things right a certain amount of the time, but no less: our model’s
purpose, then, is cemented to our standard of accuracy. We also tend to
specify a model’s purpose relative to a particular <i class="italic">domain</i>.
We may trust a model to predict the weather in one geographical region,
but not in another, for example. Because of this, a model’s purpose may
end up being expressed in a rather complicated sort of compound
statement: we may intend a model to assist reasoning in <i class="italic">x</i> way, to <i class="italic">y</i> degree of accuracy, and across <i class="italic">z</i>
domain of targets. In light of this complexity, articulating all the
purposes to which models can be put is a lofty project (and we will not
attempt it here). The best we can do is provide <span class="page-marker"></span>illustrative examples, which can help us gain a good understanding of a given model’s purpose in context.</p><p class="p">An even trickier epistemological question is: ‘how do we <i class="italic">decide</i>
when a model is adequate for a specified purpose?’. This question
defies a general answer – and we should be very clear that the question
of what ‘adequacy for purpose’ <i class="italic">means</i> is a
different question from how we assess it. In fact, assessing a model’s
adequacy for purpose requires a good understanding of the model’s
purpose in context, as well as intimate knowledge of several model
attributes, including its levels of idealization, articulation,
credentials, and sensitivity, among other things. For example, if using a
model requires that it be seeded with initial conditions that reflect
the present (or past) state of the world, then our assessment might also
depend on the confidence we have in the data models that we treat as
initial conditions.</p><p class="p" style="text-align: justify;">In this section, we explore the
topic of model adequacy for purpose by taking a close look at three
different models that are used in the Earth and atmospheric sciences: a <i class="italic">zero-dimensional energy balance model</i>, a <i class="italic">weather forecasting model</i>, and a <i class="italic">global circulation model of the atmosphere</i>.
As we will see, each of these models is used for a different purpose in
a different context, and each has its own special combination of
attributes. The first of these models, in particular, is used for what
we call an <i class="italic">idealized purpose</i>, that is, a purpose in the context of Aristotelian idealization. The second and third models, on the other hand, are used for <i class="italic">non-idealized purposes</i>, which are the same types of epistemic purposes that we associate with model <i class="italic">skill</i> (<a class="xref sec" data-sectionid="#C-sec-9" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#C-sec-9">Section 1.3.5</a>).
The measurement of model skill (or ‘adequacy for a non-idealized
purpose’) raises special epistemological issues in different contexts:
in some cases, model skill can be measured operationally; in others, it
cannot. In light of these complexities, our discussion of assessing
adequacy for purpose proceeds differently for each of the models we
explore in this section.</p></div><div class="sec" data-magellan-destination="B-sec-6" id="B-sec-6"><h4 class="B title"><span class="label">2.2</span> A Zero-Dimensional Energy Balance Model </h4><p class="p">One very basic epistemic purpose to which models can be put is <i class="italic">explanation</i>:
models can help us to understand why a target system behaves in the way
it does. Consider a very interesting phenomenon we might like to
explain: why does Earth have an equilibrium temperature, rather than
simply heating up indefinitely and burning up under the Sun? To help
explain this, we can use a simple mathematical model called a <i class="italic">zero-dimensional energy balance model</i> (ZDEBM) (<a class="xref bibr" data-sectionid="#REFe-r-119" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-119"><span class="show-for-sr">Reference Winsberg</span>Winsberg 2018</a>). This simple model is described as <i class="italic">zero-dimensional</i>
because it does not allow for any variation in time, nor any variation
in space. It simply treats the Earth as the surface of a sphere, as it <span class="page-marker"></span>would look to you if you were looking at it from the surface of the Sun, which is really just a disc. The term <i class="italic">energy balance</i>
in the model’s name hints at the useful explanation it provides, which
is that Earth reaches an equilibrium temperature thanks to a balance
between its incoming and outgoing energy.</p><p class="p">As it allows no variation in time and no variation in space, we can already tell that a ZDEBM is a highly <i class="italic">idealized</i>
model. The model strips away properties that are not relevant to the
problem at hand, which is to understand why planets achieve equilibrium
temperatures. It also involves deliberate distortions, as we will see.
However, the model is simple enough that we can reason with it using
pencil and paper: it does not require articulation.</p><p class="p"> The
bits of theory and mathematical relationships that fit together in the
model come from solar physics, simple optics, and quantum mechanics,
which are all well-credentialled sources. Here is how they work together
in the model:
</p><ul class="list bullet"><li class="list-item"><p class="p"> Solar physics tells us how much incoming radiation there is.</p></li><li class="list-item"><p class="p">
Simple optics tells us that some of that radiation gets reflected into
space. We call the rate at which the Earth reflects solar radiation its
‘albedo’.</p></li><li class="list-item"><p class="p"> The
Stefan–Boltzmann law of black body radiation tells us how much radiation
gets re-radiated back into space. The hotter the disc gets, the more
radiation gets sent back.</p></li></ul><p class="p continuation">
If we assume that all of these must balance out, we can calculate a target temperature in the following way.</p><p class="p"> The only sources of incoming and outgoing energy are radiation, which we can measure in watts per square metre (Wm<sup class="sup">−2</sup>). So we have incoming radiative energy, E<sub class="sub">in</sub>, and outgoing radiative energy, E<sub class="sub">out</sub>. And since we want an equilibrium model, we set
</p><div class="disp-formula" data-magellan-destination="EQN-df-1" data-mathjax-status="texmath alt-graphic" id="EQN-df-1"><span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"><span class="MathJax_Preview" color="inherit"></span><span class="MathJax" data-mathml="<math class="math" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msub><mtext>E</mtext><mrow><mtext>in</mtext></mrow></msub><mo>=</mo><msub><mtext>E</mtext><mrow><mtext>out</mtext></mrow></msub><mo>.</mo></mrow></math>" id="MathJax-Element-1-Frame" role="presentation" style="position: relative;" tabindex="0"><nobr aria-hidden="true"><span class="math math" id="MathJax-Span-1" style="display: inline-block; width: 5.916em;"><span style="display: inline-block; font-size: 124%; height: 0px; position: relative; width: 4.749em;"><span style="clip: rect(1.515em, 1004.67em, 2.622em, -1000em); left: 0em; position: absolute; top: -2.33em;"><span class="mrow" id="MathJax-Span-2"><span class="mrow" id="MathJax-Span-3"><span class="msub" id="MathJax-Span-4"><span style="display: inline-block; height: 0px; position: relative; width: 1.346em;"><span style="clip: rect(3.173em, 1000.65em, 4.122em, -1000em); left: 0em; position: absolute; top: -3.987em;"><span class="mtext" id="MathJax-Span-5" style="font-family: MathJax_Main;">E</span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span><span style="left: 0.681em; position: absolute; top: -3.837em;"><span class="mrow" id="MathJax-Span-6"><span class="mtext" id="MathJax-Span-7" style="font-family: MathJax_Main; font-size: 70.7%;">in</span></span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span></span></span><span class="mo" id="MathJax-Span-8" style="font-family: MathJax_Main; padding-left: 0.278em;">=</span><span class="msub" id="MathJax-Span-9" style="padding-left: 0.278em;"><span style="display: inline-block; height: 0px; position: relative; width: 1.778em;"><span style="clip: rect(3.173em, 1000.65em, 4.122em, -1000em); left: 0em; position: absolute; top: -3.987em;"><span class="mtext" id="MathJax-Span-10" style="font-family: MathJax_Main;">E</span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span><span style="left: 0.681em; position: absolute; top: -3.837em;"><span class="mrow" id="MathJax-Span-11"><span class="mtext" id="MathJax-Span-12" style="font-family: MathJax_Main; font-size: 70.7%;">out</span></span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span></span></span><span class="mo" id="MathJax-Span-13" style="font-family: MathJax_Main;">.</span></span></span><span style="display: inline-block; height: 2.33em; width: 0px;"></span></span></span><span style="border-left: 0px solid; display: inline-block; height: 1.15em; overflow: hidden; vertical-align: -0.251em; width: 0px;"></span></span></nobr></span></span></span></span></div></div></div><div class="disp-formula" data-magellan-destination="EQN-df-1" data-mathjax-status="texmath alt-graphic" id="EQN-df-1"><span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"></span></span></span><span class="label">(1)</span>
</div><p class="p continuation">
We call the energy per square metre that the Sun delivers to the Earth the ‘Incident solar radiation’, S<sub class="sub">0</sub>.</p><p class="p">Since the Earth presents a disc-shaped face to the Sun, it has area <span class="inline-formula" data-magellan-destination="EQNt-inf-1" data-mathjax-status="texmath alt-graphic" id="EQNt-inf-1">
<span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"><span class="MathJax_Preview" color="inherit"></span><span class="MathJax" data-mathml="<math class="math" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>&#x3C0;</mi><msup><mi>R</mi><mn>2</mn></msup></mrow></math>" id="MathJax-Element-2-Frame" role="presentation" style="position: relative;" tabindex="0"><nobr aria-hidden="true"><span class="math math" id="MathJax-Span-14" style="display: inline-block; width: 2.198em;"><span style="display: inline-block; font-size: 124%; height: 0px; position: relative; width: 1.747em;"><span style="clip: rect(1.451em, 1001.75em, 2.575em, -1000em); left: 0em; position: absolute; top: -2.419em;"><span class="mrow" id="MathJax-Span-15"><span class="mrow" id="MathJax-Span-16"><span class="mi" id="MathJax-Span-17" style="font-family: MathJax_Math; font-style: italic;">π<span style="display: inline-block; height: 1px; overflow: hidden; width: 0.003em;"></span></span><span class="msup" id="MathJax-Span-18"><span style="display: inline-block; height: 0px; position: relative; width: 1.188em;"><span style="clip: rect(3.17em, 1000.76em, 4.143em, -1000em); left: 0em; position: absolute; top: -3.987em;"><span class="mi" id="MathJax-Span-19" style="font-family: MathJax_Math; font-style: italic;">R</span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span><span style="left: 0.759em; position: absolute; top: -4.35em;"><span class="mn" id="MathJax-Span-20" style="font-family: MathJax_Main; font-size: 70.7%;">2</span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span></span></span></span></span><span style="display: inline-block; height: 2.419em; width: 0px;"></span></span></span><span style="border-left: 0px solid; display: inline-block; height: 1.171em; overflow: hidden; vertical-align: -0.082em; width: 0px;"></span></span></nobr></span></span></span></span></span></p><span class="inline-formula" data-magellan-destination="EQNt-inf-1" data-mathjax-status="texmath alt-graphic" id="EQNt-inf-1"><span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"></span></span></span>
</span>, so it receives <span class="inline-formula" data-magellan-destination="EQNt-inf-2" data-mathjax-status="texmath alt-graphic" id="EQNt-inf-2">
<span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"><span class="MathJax_Preview" color="inherit"></span><span class="MathJax" data-mathml="<math class="math" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>&#x3C0;</mi><msup><mi>R</mi><mn>2</mn></msup><msub><mi>S</mi><mn>0</mn></msub></mrow></math>" id="MathJax-Element-3-Frame" role="presentation" style="position: relative;" tabindex="0"><nobr aria-hidden="true"><span class="math math" id="MathJax-Span-21" style="display: inline-block; width: 3.497em;"><span style="display: inline-block; font-size: 124%; height: 0px; position: relative; width: 2.823em;"><span style="clip: rect(1.451em, 1002.82em, 2.719em, -1000em); left: 0em; position: absolute; top: -2.419em;"><span class="mrow" id="MathJax-Span-22"><span class="mrow" id="MathJax-Span-23"><span class="mi" id="MathJax-Span-24" style="font-family: MathJax_Math; font-style: italic;">π<span style="display: inline-block; height: 1px; overflow: hidden; width: 0.003em;"></span></span><span class="msup" id="MathJax-Span-25"><span style="display: inline-block; height: 0px; position: relative; width: 1.188em;"><span style="clip: rect(3.17em, 1000.76em, 4.143em, -1000em); left: 0em; position: absolute; top: -3.987em;"><span class="mi" id="MathJax-Span-26" style="font-family: MathJax_Math; font-style: italic;">R</span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span><span style="left: 0.759em; position: absolute; top: -4.35em;"><span class="mn" id="MathJax-Span-27" style="font-family: MathJax_Main; font-size: 70.7%;">2</span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span></span></span><span class="msub" id="MathJax-Span-28"><span style="display: inline-block; height: 0px; position: relative; width: 1.042em;"><span style="clip: rect(3.148em, 1000.65em, 4.144em, -1000em); left: 0em; position: absolute; top: -3.987em;"><span class="mi" id="MathJax-Span-29" style="font-family: MathJax_Math; font-style: italic;">S<span style="display: inline-block; height: 1px; overflow: hidden; width: 0.032em;"></span></span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span><span style="left: 0.613em; position: absolute; top: -3.837em;"><span class="mn" id="MathJax-Span-30" style="font-family: MathJax_Main; font-size: 70.7%;">0</span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span></span></span></span></span><span style="display: inline-block; height: 2.419em; width: 0px;"></span></span></span><span style="border-left: 0px solid; display: inline-block; height: 1.35em; overflow: hidden; vertical-align: -0.261em; width: 0px;"></span></span></nobr></span></span></span></span></span><p class="p"><span class="inline-formula" data-magellan-destination="EQNt-inf-2" data-mathjax-status="texmath alt-graphic" id="EQNt-inf-2"><span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"></span></span></span>
</span> in incoming radiation.</p><p class="p"> We assume that some
fraction of that (which we call the albedo) is reflected back into space
and that the remainder (1-α) (the ‘co-albedo’) is absorbed. We now have
a formula for E<sub class="sub">in</sub>:
</p><div class="disp-formula" data-magellan-destination="EQN-df-2" data-mathjax-status="texmath alt-graphic" id="EQN-df-2"><span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"><span class="MathJax_Preview" color="inherit"></span><span class="MathJax" data-mathml="<math class="math" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msub><mi>E</mi><mrow><mi>i</mi><mi>n</mi></mrow></msub><mo>=</mo><mrow><mo>(</mo><mrow><mn>1</mn><mo>&#x2212;</mo><mi>&#x3B1;</mi></mrow><mo>)</mo></mrow><mi>&#x3C0;</mi><msup><mi>R</mi><mn>2</mn></msup><msub><mi>S</mi><mn>0</mn></msub><mtext></mtext></mrow></math>" id="MathJax-Element-4-Frame" role="presentation" style="position: relative;" tabindex="0"><nobr aria-hidden="true"><span class="math math" id="MathJax-Span-31" style="display: inline-block; width: 11.069em;"><span style="display: inline-block; font-size: 124%; height: 0px; position: relative; width: 8.916em;"><span style="clip: rect(1.451em, 1008.92em, 2.804em, -1000em); left: 0em; position: absolute; top: -2.419em;"><span class="mrow" id="MathJax-Span-32"><span class="mrow" id="MathJax-Span-33"><span class="msub" id="MathJax-Span-34"><span style="display: inline-block; height: 0px; position: relative; width: 1.481em;"><span style="clip: rect(3.173em, 1000.76em, 4.122em, -1000em); left: 0em; position: absolute; top: -3.987em;"><span class="mi" id="MathJax-Span-35" style="font-family: MathJax_Math; font-style: italic;">E<span style="display: inline-block; height: 1px; overflow: hidden; width: 0.026em;"></span></span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span><span style="left: 0.738em; position: absolute; top: -3.837em;"><span class="mrow" id="MathJax-Span-36"><span class="mi" id="MathJax-Span-37" style="font-family: MathJax_Math; font-size: 70.7%; font-style: italic;">i</span><span class="mi" id="MathJax-Span-38" style="font-family: MathJax_Math; font-size: 70.7%; font-style: italic;">n</span></span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span></span></span><span class="mo" id="MathJax-Span-39" style="font-family: MathJax_Main; padding-left: 0.278em;">=</span><span class="mrow" id="MathJax-Span-40" style="padding-left: 0.278em;"><span class="mo" id="MathJax-Span-41" style="font-family: MathJax_Main;">(</span><span class="mrow" id="MathJax-Span-42"><span class="mn" id="MathJax-Span-43" style="font-family: MathJax_Main;">1</span><span class="mo" id="MathJax-Span-44" style="font-family: MathJax_Main; padding-left: 0.222em;">−</span><span class="mi" id="MathJax-Span-45" style="font-family: MathJax_Math; font-style: italic; padding-left: 0.222em;">α</span></span><span class="mo" id="MathJax-Span-46" style="font-family: MathJax_Main;">)</span></span><span class="mi" id="MathJax-Span-47" style="font-family: MathJax_Math; font-style: italic; padding-left: 0.167em;">π<span style="display: inline-block; height: 1px; overflow: hidden; width: 0.003em;"></span></span><span class="msup" id="MathJax-Span-48"><span style="display: inline-block; height: 0px; position: relative; width: 1.188em;"><span style="clip: rect(3.17em, 1000.76em, 4.143em, -1000em); left: 0em; position: absolute; top: -3.987em;"><span class="mi" id="MathJax-Span-49" style="font-family: MathJax_Math; font-style: italic;">R</span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span><span style="left: 0.759em; position: absolute; top: -4.35em;"><span class="mn" id="MathJax-Span-50" style="font-family: MathJax_Main; font-size: 70.7%;">2</span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span></span></span><span class="msub" id="MathJax-Span-51"><span style="display: inline-block; height: 0px; position: relative; width: 1.042em;"><span style="clip: rect(3.148em, 1000.65em, 4.144em, -1000em); left: 0em; position: absolute; top: -3.987em;"><span class="mi" id="MathJax-Span-52" style="font-family: MathJax_Math; font-style: italic;">S<span style="display: inline-block; height: 1px; overflow: hidden; width: 0.032em;"></span></span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span><span style="left: 0.613em; position: absolute; top: -3.837em;"><span class="mn" id="MathJax-Span-53" style="font-family: MathJax_Main; font-size: 70.7%;">0</span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span></span></span><span class="mtext" id="MathJax-Span-54"></span></span></span><span style="display: inline-block; height: 2.419em; width: 0px;"></span></span></span><span style="border-left: 0px solid; display: inline-block; height: 1.455em; overflow: hidden; vertical-align: -0.366em; width: 0px;"></span></span></nobr></span></span></span></span></div><div class="disp-formula" data-magellan-destination="EQN-df-2" data-mathjax-status="texmath alt-graphic" id="EQN-df-2"><span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"></span></span></span><span class="label">(2)</span>
</div><p class="p continuation">
To model outgoing energy, the model treats the Earth as a simple,
spherical black body that obeys the Stefan–Boltzmann law, which says
that a black body <span class="page-marker"></span>will radiate away
heat in proportion to the fourth power of the temperature (T, in degrees
Kelvin), with the constant of proportionality, <span class="inline-formula" data-magellan-destination="EQNt-inf-3" data-mathjax-status="texmath alt-graphic" id="EQNt-inf-3">
<span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"><span class="MathJax_Preview" color="inherit"></span><span class="MathJax" data-mathml="<math class="math" xmlns="http://www.w3.org/1998/Math/MathML"><mi>&#x3C3;</mi></math>" id="MathJax-Element-5-Frame" role="presentation" style="position: relative;" tabindex="0"><nobr aria-hidden="true"><span class="math math" id="MathJax-Span-55" style="display: inline-block; width: 0.719em;"><span style="display: inline-block; font-size: 124%; height: 0px; position: relative; width: 0.582em;"><span style="clip: rect(1.854em, 1000.58em, 2.565em, -1000em); left: 0em; position: absolute; top: -2.419em;"><span class="mrow" id="MathJax-Span-56"><span class="mi" id="MathJax-Span-57" style="font-family: MathJax_Math; font-style: italic;">σ<span style="display: inline-block; height: 1px; overflow: hidden; width: 0.001em;"></span></span></span><span style="display: inline-block; height: 2.419em; width: 0px;"></span></span></span><span style="border-left: 0px solid; display: inline-block; height: 0.659em; overflow: hidden; vertical-align: -0.069em; width: 0px;"></span></span></nobr></span></span></span></span></span></p><span class="inline-formula" data-magellan-destination="EQNt-inf-3" data-mathjax-status="texmath alt-graphic" id="EQNt-inf-3"><span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"></span></span></span>
</span>, called the Stefan–Boltzmann constant. This gives us an equation for <span class="inline-formula" data-magellan-destination="EQNt-inf-4" data-mathjax-status="texmath alt-graphic" id="EQNt-inf-4">
<span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"><span class="MathJax_Preview" color="inherit"></span><span class="MathJax" data-mathml="<math class="math" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msub><mi>E</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub></mrow></math>" id="MathJax-Element-6-Frame" role="presentation" style="position: relative;" tabindex="0"><nobr aria-hidden="true"><span class="math math" id="MathJax-Span-58" style="display: inline-block; width: 2.287em;"><span style="display: inline-block; font-size: 124%; height: 0px; position: relative; width: 1.837em;"><span style="clip: rect(1.515em, 1001.84em, 2.622em, -1000em); left: 0em; position: absolute; top: -2.33em;"><span class="mrow" id="MathJax-Span-59"><span class="mrow" id="MathJax-Span-60"><span class="msub" id="MathJax-Span-61"><span style="display: inline-block; height: 0px; position: relative; width: 1.816em;"><span style="clip: rect(3.173em, 1000.76em, 4.122em, -1000em); left: 0em; position: absolute; top: -3.987em;"><span class="mi" id="MathJax-Span-62" style="font-family: MathJax_Math; font-style: italic;">E<span style="display: inline-block; height: 1px; overflow: hidden; width: 0.026em;"></span></span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span><span style="left: 0.738em; position: absolute; top: -3.837em;"><span class="mrow" id="MathJax-Span-63"><span class="mi" id="MathJax-Span-64" style="font-family: MathJax_Math; font-size: 70.7%; font-style: italic;">o</span><span class="mi" id="MathJax-Span-65" style="font-family: MathJax_Math; font-size: 70.7%; font-style: italic;">u</span><span class="mi" id="MathJax-Span-66" style="font-family: MathJax_Math; font-size: 70.7%; font-style: italic;">t</span></span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span></span></span></span></span><span style="display: inline-block; height: 2.33em; width: 0px;"></span></span></span><span style="border-left: 0px solid; display: inline-block; height: 1.15em; overflow: hidden; vertical-align: -0.251em; width: 0px;"></span></span></nobr></span></span></span></span></span><p class="p continuation"><span class="inline-formula" data-magellan-destination="EQNt-inf-4" data-mathjax-status="texmath alt-graphic" id="EQNt-inf-4"><span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"></span></span></span>
</span>:
</p><div class="disp-formula" data-magellan-destination="EQN-df-3" data-mathjax-status="texmath alt-graphic" id="EQN-df-3"><span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"><span class="MathJax_Preview" color="inherit"></span><span class="MathJax" data-mathml="<math class="math" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msub><mi>E</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo>=</mo><mn>4</mn><mi>&#x3C0;</mi><msup><mi>R</mi><mn>2</mn></msup><mi>&#x3C3;</mi><mi>T</mi></mrow></math>" id="MathJax-Element-7-Frame" role="presentation" style="position: relative;" tabindex="0"><nobr aria-hidden="true"><span class="math math" id="MathJax-Span-67" style="display: inline-block; width: 8.291em;"><span style="display: inline-block; font-size: 124%; height: 0px; position: relative; width: 6.676em;"><span style="clip: rect(1.451em, 1006.68em, 2.712em, -1000em); left: 0em; position: absolute; top: -2.419em;"><span class="mrow" id="MathJax-Span-68"><span class="mrow" id="MathJax-Span-69"><span class="msub" id="MathJax-Span-70"><span style="display: inline-block; height: 0px; position: relative; width: 1.816em;"><span style="clip: rect(3.173em, 1000.76em, 4.122em, -1000em); left: 0em; position: absolute; top: -3.987em;"><span class="mi" id="MathJax-Span-71" style="font-family: MathJax_Math; font-style: italic;">E<span style="display: inline-block; height: 1px; overflow: hidden; width: 0.026em;"></span></span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span><span style="left: 0.738em; position: absolute; top: -3.837em;"><span class="mrow" id="MathJax-Span-72"><span class="mi" id="MathJax-Span-73" style="font-family: MathJax_Math; font-size: 70.7%; font-style: italic;">o</span><span class="mi" id="MathJax-Span-74" style="font-family: MathJax_Math; font-size: 70.7%; font-style: italic;">u</span><span class="mi" id="MathJax-Span-75" style="font-family: MathJax_Math; font-size: 70.7%; font-style: italic;">t</span></span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span></span></span><span class="mo" id="MathJax-Span-76" style="font-family: MathJax_Main; padding-left: 0.278em;">=</span><span class="mn" id="MathJax-Span-77" style="font-family: MathJax_Main; padding-left: 0.278em;">4</span><span class="mi" id="MathJax-Span-78" style="font-family: MathJax_Math; font-style: italic;">π<span style="display: inline-block; height: 1px; overflow: hidden; width: 0.003em;"></span></span><span class="msup" id="MathJax-Span-79"><span style="display: inline-block; height: 0px; position: relative; width: 1.188em;"><span style="clip: rect(3.17em, 1000.76em, 4.143em, -1000em); left: 0em; position: absolute; top: -3.987em;"><span class="mi" id="MathJax-Span-80" style="font-family: MathJax_Math; font-style: italic;">R</span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span><span style="left: 0.759em; position: absolute; top: -4.35em;"><span class="mn" id="MathJax-Span-81" style="font-family: MathJax_Main; font-size: 70.7%;">2</span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span></span></span><span class="mi" id="MathJax-Span-82" style="font-family: MathJax_Math; font-style: italic;">σ<span style="display: inline-block; height: 1px; overflow: hidden; width: 0.001em;"></span></span><span class="mi" id="MathJax-Span-83" style="font-family: MathJax_Math; font-style: italic;">T<span style="display: inline-block; height: 1px; overflow: hidden; width: 0.12em;"></span></span></span></span><span style="display: inline-block; height: 2.419em; width: 0px;"></span></span></span><span style="border-left: 0px solid; display: inline-block; height: 1.341em; overflow: hidden; vertical-align: -0.251em; width: 0px;"></span></span></nobr></span></span></span></span></div><div class="disp-formula" data-magellan-destination="EQN-df-3" data-mathjax-status="texmath alt-graphic" id="EQN-df-3"><span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"></span></span></span><span class="label">(3)</span>
</div><p class="p continuation">
(<span class="inline-formula" data-magellan-destination="EQNt-inf-5" data-mathjax-status="texmath alt-graphic" id="EQNt-inf-5">
<span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"><span class="MathJax_Preview" color="inherit"></span><span class="MathJax" data-mathml="<math class="math" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mn>4</mn><mi>&#x3C0;</mi><msup><mi>R</mi><mn>2</mn></msup></mrow></math>" id="MathJax-Element-8-Frame" role="presentation" style="position: relative;" tabindex="0"><nobr aria-hidden="true"><span class="math math" id="MathJax-Span-84" style="display: inline-block; width: 2.78em;"><span style="display: inline-block; font-size: 124%; height: 0px; position: relative; width: 2.24em;"><span style="clip: rect(1.451em, 1002.24em, 2.575em, -1000em); left: 0em; position: absolute; top: -2.419em;"><span class="mrow" id="MathJax-Span-85"><span class="mrow" id="MathJax-Span-86"><span class="mn" id="MathJax-Span-87" style="font-family: MathJax_Main;">4</span><span class="mi" id="MathJax-Span-88" style="font-family: MathJax_Math; font-style: italic;">π<span style="display: inline-block; height: 1px; overflow: hidden; width: 0.003em;"></span></span><span class="msup" id="MathJax-Span-89"><span style="display: inline-block; height: 0px; position: relative; width: 1.188em;"><span style="clip: rect(3.17em, 1000.76em, 4.143em, -1000em); left: 0em; position: absolute; top: -3.987em;"><span class="mi" id="MathJax-Span-90" style="font-family: MathJax_Math; font-style: italic;">R</span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span><span style="left: 0.759em; position: absolute; top: -4.35em;"><span class="mn" id="MathJax-Span-91" style="font-family: MathJax_Main; font-size: 70.7%;">2</span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span></span></span></span></span><span style="display: inline-block; height: 2.419em; width: 0px;"></span></span></span><span style="border-left: 0px solid; display: inline-block; height: 1.171em; overflow: hidden; vertical-align: -0.082em; width: 0px;"></span></span></nobr></span></span></span></span></span></p><p class="p continuation"><span class="inline-formula" data-magellan-destination="EQNt-inf-5" data-mathjax-status="texmath alt-graphic" id="EQNt-inf-5"><span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"></span></span></span>
</span> being the surface area of a sphere)</p><p class="p"> Combining <a class="xref disp-formula" data-sectionid="#EQN-df-1" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#EQN-df-1">equations 1</a>, <a class="xref disp-formula" data-sectionid="#EQN-df-2" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#EQN-df-2">2</a>, and <a class="xref disp-formula" data-sectionid="#EQN-df-3" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#EQN-df-3">3</a>, and solving for T we get:
</p><div class="disp-formula" data-magellan-destination="EQN-df-4" data-mathjax-status="texmath alt-graphic" id="EQN-df-4"><span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"><span class="MathJax_Preview" color="inherit"></span><span class="MathJax" data-mathml="<math class="math" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>T</mi><mo>=</mo><msup><mrow><mrow><mo>(</mo><mrow><mfrac><mrow><mrow><mo>(</mo><mrow><mn>1</mn><mo>&#x2212;</mo><mi>&#x3B1;</mi></mrow><mo>)</mo></mrow><msub><mi>S</mi><mn>0</mn></msub></mrow><mrow><mn>4</mn><mi>&#x3C3;</mi></mrow></mfrac></mrow><mo>)</mo></mrow></mrow><mrow><mfrac><mn>1</mn><mn>4</mn></mfrac></mrow></msup></mrow></math>" id="MathJax-Element-9-Frame" role="presentation" style="position: relative;" tabindex="0"><nobr aria-hidden="true"><span class="math math" id="MathJax-Span-92" style="display: inline-block; width: 8.56em;"><span style="display: inline-block; font-size: 124%; height: 0px; position: relative; width: 6.9em;"><span style="clip: rect(0.755em, 1006.9em, 3.203em, -1000em); left: 0em; position: absolute; top: -2.419em;"><span class="mrow" id="MathJax-Span-93"><span class="mrow" id="MathJax-Span-94"><span class="mi" id="MathJax-Span-95" style="font-family: MathJax_Math; font-style: italic;">T<span style="display: inline-block; height: 1px; overflow: hidden; width: 0.12em;"></span></span><span class="mo" id="MathJax-Span-96" style="font-family: MathJax_Main; padding-left: 0.278em;">=</span><span class="msup" id="MathJax-Span-97" style="padding-left: 0.278em;"><span style="display: inline-block; height: 0px; position: relative; width: 4.882em;"><span style="clip: rect(2.704em, 1004.02em, 4.771em, -1000em); left: 0em; position: absolute; top: -3.987em;"><span class="mrow" id="MathJax-Span-98"><span class="mrow" id="MathJax-Span-99"><span class="mo" id="MathJax-Span-100" style="vertical-align: 0em;"><span style="font-family: MathJax_Size2;">(</span></span><span class="mrow" id="MathJax-Span-101"><span class="mfrac" id="MathJax-Span-102"><span style="display: inline-block; height: 0px; margin-left: 0.12em; margin-right: 0.12em; position: relative; width: 2.763em;"><span style="clip: rect(3.323em, 1002.64em, 4.299em, -1000em); left: 50%; margin-left: -1.321em; position: absolute; top: -4.558em;"><span class="mrow" id="MathJax-Span-103"><span class="mrow" id="MathJax-Span-104"><span class="mo" id="MathJax-Span-105" style="font-family: MathJax_Main; font-size: 70.7%;">(</span><span class="mrow" id="MathJax-Span-106"><span class="mn" id="MathJax-Span-107" style="font-family: MathJax_Main; font-size: 70.7%;">1</span><span class="mo" id="MathJax-Span-108" style="font-family: MathJax_Main; font-size: 70.7%;">−</span><span class="mi" id="MathJax-Span-109" style="font-family: MathJax_Math; font-size: 70.7%; font-style: italic;">α</span></span><span class="mo" id="MathJax-Span-110" style="font-family: MathJax_Main; font-size: 70.7%;">)</span></span><span class="msub" id="MathJax-Span-111"><span style="display: inline-block; height: 0px; position: relative; width: 0.736em;"><span style="clip: rect(3.355em, 1000.46em, 4.137em, -1000em); left: 0em; position: absolute; top: -3.987em;"><span class="mi" id="MathJax-Span-112" style="font-family: MathJax_Math; font-size: 70.7%; font-style: italic;">S<span style="display: inline-block; height: 1px; overflow: hidden; width: 0.023em;"></span></span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span><span style="left: 0.433em; position: absolute; top: -3.881em;"><span class="mn" id="MathJax-Span-113" style="font-family: MathJax_Main; font-size: 50%;">0</span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span></span></span></span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span><span style="clip: rect(3.374em, 1000.76em, 4.13em, -1000em); left: 50%; margin-left: -0.379em; position: absolute; top: -3.615em;"><span class="mrow" id="MathJax-Span-114"><span class="mn" id="MathJax-Span-115" style="font-family: MathJax_Main; font-size: 70.7%;">4</span><span class="mi" id="MathJax-Span-116" style="font-family: MathJax_Math; font-size: 70.7%; font-style: italic;">σ<span style="display: inline-block; height: 1px; overflow: hidden; width: 0.001em;"></span></span></span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span><span style="clip: rect(0.881em, 1002.76em, 1.21em, -1000em); left: 0em; position: absolute; top: -1.295em;"><span style="border-top: 1.3px solid; display: inline-block; height: 0px; overflow: hidden; vertical-align: 0em; width: 2.763em;"></span><span style="display: inline-block; height: 1.075em; width: 0px;"></span></span></span></span></span><span class="mo" id="MathJax-Span-117" style="vertical-align: 0em;"><span style="font-family: MathJax_Size2;">)</span></span></span></span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span><span style="left: 4.197em; position: absolute; top: -4.864em;"><span class="mrow" id="MathJax-Span-118"><span class="mfrac" id="MathJax-Span-119"><span style="display: inline-block; height: 0px; margin-left: 0.12em; margin-right: 0.12em; position: relative; width: 0.37em;"><span style="clip: rect(3.52em, 1000.21em, 4.122em, -1000em); left: 50%; margin-left: -0.125em; position: absolute; top: -4.308em;"><span class="mn" id="MathJax-Span-120" style="font-family: MathJax_Main; font-size: 50%;">1</span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span><span style="clip: rect(3.515em, 1000.24em, 4.122em, -1000em); left: 50%; margin-left: -0.125em; position: absolute; top: -3.682em;"><span class="mn" id="MathJax-Span-121" style="font-family: MathJax_Main; font-size: 50%;">4</span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span><span style="clip: rect(0.881em, 1000.37em, 1.21em, -1000em); left: 0em; position: absolute; top: -1.222em;"><span style="border-top: 1.3px solid; display: inline-block; height: 0px; overflow: hidden; vertical-align: 0em; width: 0.37em;"></span><span style="display: inline-block; height: 1.075em; width: 0px;"></span></span></span></span></span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span></span></span></span></span><span style="display: inline-block; height: 2.419em; width: 0px;"></span></span></span><span style="border-left: 0px solid; display: inline-block; height: 2.814em; overflow: hidden; vertical-align: -0.861em; width: 0px;"></span></span></nobr></span></span></span></span></div><div class="disp-formula" data-magellan-destination="EQN-df-4" data-mathjax-status="texmath alt-graphic" id="EQN-df-4"><span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"></span></span></span><span class="label">(4)</span>
</div><p class="p continuation">
Since <span class="inline-formula" data-magellan-destination="EQNt-inf-6" data-mathjax-status="texmath alt-graphic" id="EQNt-inf-6">
<span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"><span class="MathJax_Preview" color="inherit"></span><span class="MathJax" data-mathml="<math class="math" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msub><mi>S</mi><mn>0</mn></msub><mo>=</mo><mn>1</mn><mo>,</mo><mn>368</mn><mo>&#xA0;</mo><mi>W</mi><msup><mi>m</mi><mrow><mo>&#x2212;</mo><mn>2</mn></mrow></msup></mrow></math>" id="MathJax-Element-10-Frame" role="presentation" style="position: relative;" tabindex="0"><nobr aria-hidden="true"><span class="math math" id="MathJax-Span-122" style="display: inline-block; width: 9.904em;"><span style="display: inline-block; font-size: 124%; height: 0px; position: relative; width: 7.975em;"><span style="clip: rect(1.451em, 1007.98em, 2.748em, -1000em); left: 0em; position: absolute; top: -2.419em;"><span class="mrow" id="MathJax-Span-123"><span class="mrow" id="MathJax-Span-124"><span class="msub" id="MathJax-Span-125"><span style="display: inline-block; height: 0px; position: relative; width: 1.042em;"><span style="clip: rect(3.148em, 1000.65em, 4.144em, -1000em); left: 0em; position: absolute; top: -3.987em;"><span class="mi" id="MathJax-Span-126" style="font-family: MathJax_Math; font-style: italic;">S<span style="display: inline-block; height: 1px; overflow: hidden; width: 0.032em;"></span></span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span><span style="left: 0.613em; position: absolute; top: -3.837em;"><span class="mn" id="MathJax-Span-127" style="font-family: MathJax_Main; font-size: 70.7%;">0</span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span></span></span><span class="mo" id="MathJax-Span-128" style="font-family: MathJax_Main; padding-left: 0.278em;">=</span><span class="mn" id="MathJax-Span-129" style="font-family: MathJax_Main; padding-left: 0.278em;">1</span><span class="mo" id="MathJax-Span-130" style="font-family: MathJax_Main;">,</span><span class="mn" id="MathJax-Span-131" style="font-family: MathJax_Main; padding-left: 0.167em;">368</span><span class="mo" id="MathJax-Span-132" style="font-family: MathJax_Main;"> </span><span class="mi" id="MathJax-Span-133" style="font-family: MathJax_Math; font-style: italic;">W<span style="display: inline-block; height: 1px; overflow: hidden; width: 0.104em;"></span></span><span class="msup" id="MathJax-Span-134"><span style="display: inline-block; height: 0px; position: relative; width: 1.857em;"><span style="clip: rect(3.411em, 1000.86em, 4.133em, -1000em); left: 0em; position: absolute; top: -3.987em;"><span class="mi" id="MathJax-Span-135" style="font-family: MathJax_Math; font-style: italic;">m</span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span><span style="left: 0.878em; position: absolute; top: -4.35em;"><span class="mrow" id="MathJax-Span-136"><span class="mo" id="MathJax-Span-137" style="font-family: MathJax_Main; font-size: 70.7%;">−</span><span class="mn" id="MathJax-Span-138" style="font-family: MathJax_Main; font-size: 70.7%;">2</span></span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span></span></span></span></span><span style="display: inline-block; height: 2.419em; width: 0px;"></span></span></span><span style="border-left: 0px solid; display: inline-block; height: 1.386em; overflow: hidden; vertical-align: -0.296em; width: 0px;"></span></span></nobr></span></span></span></span></span></p><span class="inline-formula" data-magellan-destination="EQNt-inf-6" data-mathjax-status="texmath alt-graphic" id="EQNt-inf-6"><span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"></span></span></span>
</span> and <span class="inline-formula" data-magellan-destination="EQNt-inf-7" data-mathjax-status="texmath alt-graphic" id="EQNt-inf-7">
<span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"><span class="MathJax_Preview" color="inherit"></span><span class="MathJax" data-mathml="<math class="math" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>&#x3C3;</mi><mo>=</mo><mn>5.67</mn><mo>&#xD7;</mo><msup><mrow><mn>10</mn></mrow><mrow><mo>&#x2212;</mo><mn>8</mn></mrow></msup><mo>&#xA0;</mo><mi>W</mi><msup><mi>m</mi><mrow><mo>&#x2212;</mo><mn>2</mn></mrow></msup><msup><mi>K</mi><mrow><mo>&#x2212;</mo><mn>4</mn></mrow></msup></mrow></math>" id="MathJax-Element-11-Frame" role="presentation" style="position: relative;" tabindex="0"><nobr aria-hidden="true"><span class="math math" id="MathJax-Span-139" style="display: inline-block; width: 14.832em;"><span style="display: inline-block; font-size: 124%; height: 0px; position: relative; width: 11.962em;"><span style="clip: rect(1.421em, 1011.96em, 2.576em, -1000em); left: 0em; position: absolute; top: -2.419em;"><span class="mrow" id="MathJax-Span-140"><span class="mrow" id="MathJax-Span-141"><span class="mi" id="MathJax-Span-142" style="font-family: MathJax_Math; font-style: italic;">σ<span style="display: inline-block; height: 1px; overflow: hidden; width: 0.001em;"></span></span><span class="mo" id="MathJax-Span-143" style="font-family: MathJax_Main; padding-left: 0.278em;">=</span><span class="mn" id="MathJax-Span-144" style="font-family: MathJax_Main; padding-left: 0.278em;">5.67</span><span class="mo" id="MathJax-Span-145" style="font-family: MathJax_Main; padding-left: 0.222em;">×</span><span class="msup" id="MathJax-Span-146" style="padding-left: 0.222em;"><span style="display: inline-block; height: 0px; position: relative; width: 1.979em;"><span style="clip: rect(3.187em, 1000.96em, 4.144em, -1000em); left: 0em; position: absolute; top: -3.987em;"><span class="mrow" id="MathJax-Span-147"><span class="mn" id="MathJax-Span-148" style="font-family: MathJax_Main;">10</span></span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span><span style="left: 1em; position: absolute; top: -4.38em;"><span class="mrow" id="MathJax-Span-149"><span class="mo" id="MathJax-Span-150" style="font-family: MathJax_Main; font-size: 70.7%;">−</span><span class="mn" id="MathJax-Span-151" style="font-family: MathJax_Main; font-size: 70.7%;">8</span></span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span></span></span><span class="mo" id="MathJax-Span-152" style="font-family: MathJax_Main;"> </span><span class="mi" id="MathJax-Span-153" style="font-family: MathJax_Math; font-style: italic;">W<span style="display: inline-block; height: 1px; overflow: hidden; width: 0.104em;"></span></span><span class="msup" id="MathJax-Span-154"><span style="display: inline-block; height: 0px; position: relative; width: 1.857em;"><span style="clip: rect(3.411em, 1000.86em, 4.133em, -1000em); left: 0em; position: absolute; top: -3.987em;"><span class="mi" id="MathJax-Span-155" style="font-family: MathJax_Math; font-style: italic;">m</span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span><span style="left: 0.878em; position: absolute; top: -4.35em;"><span class="mrow" id="MathJax-Span-156"><span class="mo" id="MathJax-Span-157" style="font-family: MathJax_Main; font-size: 70.7%;">−</span><span class="mn" id="MathJax-Span-158" style="font-family: MathJax_Main; font-size: 70.7%;">2</span></span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span></span></span><span class="msup" id="MathJax-Span-159"><span style="display: inline-block; height: 0px; position: relative; width: 1.93em;"><span style="clip: rect(3.17em, 1000.89em, 4.122em, -1000em); left: 0em; position: absolute; top: -3.987em;"><span class="mi" id="MathJax-Span-160" style="font-family: MathJax_Math; font-style: italic;">K<span style="display: inline-block; height: 1px; overflow: hidden; width: 0.04em;"></span></span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span><span style="left: 0.951em; position: absolute; top: -4.35em;"><span class="mrow" id="MathJax-Span-161"><span class="mo" id="MathJax-Span-162" style="font-family: MathJax_Main; font-size: 70.7%;">−</span><span class="mn" id="MathJax-Span-163" style="font-family: MathJax_Main; font-size: 70.7%;">4</span></span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span></span></span></span></span><span style="display: inline-block; height: 2.419em; width: 0px;"></span></span></span><span style="border-left: 0px solid; display: inline-block; height: 1.21em; overflow: hidden; vertical-align: -0.083em; width: 0px;"></span></span></nobr></span></span></span></span></span><p class="p continuation"><span class="inline-formula" data-magellan-destination="EQNt-inf-7" data-mathjax-status="texmath alt-graphic" id="EQNt-inf-7"><span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"></span></span></span>
</span></p><p class="p">we calculate that <span class="inline-formula" data-magellan-destination="EQNt-inf-8" data-mathjax-status="texmath alt-graphic" id="EQNt-inf-8">
<span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"><span class="MathJax_Preview" color="inherit"></span><span class="MathJax" data-mathml="<math class="math" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mtext>T=254</mtext><mtext>.8&#xA0;K&#xA0;or&#xA0;&#x2212;18</mtext><mtext>.3&#xB0;C</mtext><mtext>.</mtext></mrow></math>" id="MathJax-Element-12-Frame" role="presentation" style="position: relative;" tabindex="0"><nobr aria-hidden="true"><span class="math math" id="MathJax-Span-164" style="display: inline-block; width: 12.726em;"><span style="display: inline-block; font-size: 124%; height: 0px; position: relative; width: 10.26em;"><span style="clip: rect(1.48em, 1010.18em, 2.546em, -1000em); left: 0em; position: absolute; top: -2.33em;"><span class="mrow" id="MathJax-Span-165"><span class="mrow" id="MathJax-Span-166"><span class="mtext" id="MathJax-Span-167" style="font-family: MathJax_Main;">T=254</span><span class="mtext" id="MathJax-Span-168" style="font-family: MathJax_Main;">.8 K or −18</span><span class="mtext" id="MathJax-Span-169" style="font-family: MathJax_Main;">.3°C</span><span class="mtext" id="MathJax-Span-170" style="font-family: MathJax_Main;">.</span></span></span><span style="display: inline-block; height: 2.33em; width: 0px;"></span></span></span><span style="border-left: 0px solid; display: inline-block; height: 1.099em; overflow: hidden; vertical-align: -0.157em; width: 0px;"></span></span></nobr></span></span></span></span></span></p><div class="sec" data-magellan-destination="A-sec-4" id="A-sec-4"><div class="sec" data-magellan-destination="B-sec-6" id="B-sec-6"><p class="p"><span class="inline-formula" data-magellan-destination="EQNt-inf-8" data-mathjax-status="texmath alt-graphic" id="EQNt-inf-8"><span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"></span></span></span>
</span></p><p class="p">We can call this the Earth’s <i class="italic">effective</i> temperature. Note that this is not equal to the Earth’s <i class="italic">actual</i>
temperature, which is more like 15°C: if our goal was to find the
Earth’s actual temperature, rather than its effective temperature, we
would need to alter the model to make it account for the Earth’s
emissivity.<a class="xref footnote" data-sectionid="#FN-fn-2" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#FN-fn-2"><span class="show-for-sr">Footnote </span><sup class="sup">2</sup></a> However, the goal of a ZDEBM is not to find Earth’s actual temperature. Rather, the purpose of the model is to explain <i class="italic">why planets have an equilibrium temperature</i>
and identify the dominant causal process. The model gives us a very
good explanation of this phenomenon: the Earth’s ingoing and outgoing
energy balance out.</p><p class="p" style="text-align: justify;">The astute reader might be tempted
to interject the following: ‘I get that a ZDEBM is being used for
explanation rather than prediction, but I still don’t understand how a
model that gets Earth’s equilibrium temperature so wrong could be
adequate for any purpose that has to do with the Earth’s temperature.’
The answer here has to do with Aristotelian idealization. Recall that
Aristotelian idealization is when we strip away, in our imagination,
factors that we deem unimportant to the system of interest. In such
cases, it is helpful, following Martin Jones (2005) and Peter
Godfrey-Smith (2009), to think of models that engage in Aristotelian
idealization as telling us literally true things about abstract systems.
So, on this conception, the ZDEBM is a perfectly accurate model of a
perfectly spherical black body immersed for an arbitrarily long time in a
uniform field of high frequency radiation. Why should we care about
that if we are thinking about the Earth, which is, after all, not a
perfectly simple black body, and so on? The reason is that we think that
in understanding how such <span class="page-marker"></span>a simple,
idealized, imaginary system behaves, we get insight both into why the
Earth has an equilibrium temperature and regarding one of the most
fundamental causal processes involved in determining what it is. In many
cases, radical idealization of the kind we see in a ZDEBM is a red flag
that a model is unlikely to be adequate for purpose. However, in other
cases, we understand the purpose of the model in terms of Aristotelian
idealization, and in terms of getting at only <i class="italic">one</i>
of many possible causal processes that are behind a phenomenon we want
to understand. In those cases, it is easy to see why radical
idealization is not an impediment to adequacy for purpose.</p></div><div class="sec" data-magellan-destination="B-sec-7" id="B-sec-7"><h4 class="B title"><span class="label">2.3</span> Weather Forecasting Model </h4><p class="p">The purpose of weather forecasting models is to make spatially and temporally fine-grained <i class="italic">predictions</i> about states of the atmosphere over a short time horizon – in other words, to tell us what weather conditions will <i class="italic">actually occur</i> at a particular time and place in the near future. Compared to the ZDEBM described in <a class="xref sec" data-sectionid="#B-sec-6" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#B-sec-6">Section 2.2</a>,
weather forecasting models are far less idealized. In fact, these
models are based on a comparatively high degree of fidelity to our
understanding of causal relationships in weather systems.</p><p class="p">Weather
models incorporate both initial conditions and the so-called primitive
equations. The primitive equations are a set of non-linear partial
differential equations grounded in the well-credentialled theories of
fluid mechanics and thermodynamics. Initial conditions are established
through a data-driven process: direct measurements of a large number of
observable variables that describe the atmosphere and its surroundings
in a specified region, such as temperature, precipitation, humidity,
atmospheric pressure, wind, and cloud cover. Along with direct
measurements of these variables at weather stations, data on initial
conditions come from satellite data, aircraft observations, weather
balloons, and stream gauges. This measurement process results in a
wealth of data, which are often subjected to various smoothing,
interpolation, and error correction methods that turn them into
standardized datasets (i.e., models of data). These standardized
datasets are treated as initial conditions.<a class="xref footnote" data-sectionid="#FN-fn-3" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#FN-fn-3"><span class="show-for-sr">Footnote </span><sup class="sup">3</sup></a></p><p class="p">The
primitive equations are only solvable analytically (i.e., with paper
and pencil) in very simple and unrealistic scenarios, which means they
have to be turned into equations that can be calculated time-step by
time-step on a computer if we actually want to predict the weather. This
means weather forecasting models require articulation. Articulation of
the equations of this model consists in transforming them into a form
that is discrete in both space and time. (In a weather model, the
spatial grid size is roughly 10 kilometres and the time-step is a few
minutes.) <span class="page-marker"></span>The discretized version of
the equations is supplemented with parameterizations that try to
estimate what is happening inside these relatively large grid cells and
is therefore lost to the primitive equations. Once the computer model
completes these calculations for one time-step, the model can create
something that looks just like the initial dataset. This new ‘dataset’
is fed back into the model, which then runs for another time-step. The
model can be used to visualize these data to see what the atmosphere
will look like over time. We can also combine (say, by averaging) the
forecasts of ensembles of models that rely on different articulations or
on different datasets by way of initial conditions. Often the ensemble
averages perform better than any single model.</p><p class="p">An
epistemologically significant thing about weather models is that we can
measure their forecasting skill (i.e., their adequacy for <i class="italic">prediction</i>)
fairly objectively with various operationalized metrics. If we use a
weather model to make a large number of forecasts and then compare those
to what actually happens, we can define measures of forecasting skill
like mean absolute error (MAE), bias, and Briar score. These measures
tell us, respectively, things like how far the predictions are from what
we measured, whether they are systematically wrong in one direction or
another, and whether the probabilities they generate match the observed
frequencies in the world.<a class="xref footnote" data-sectionid="#FN-fn-4" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#FN-fn-4"><span class="show-for-sr">Footnote </span><sup class="sup">4</sup></a>
This enables us to see how much progress is being made in improving a
weather model’s forecasting skill over time. The Briar score is
especially important because it lends a natural understanding to the
probabilities that weather forecasts give us, since they are evaluated
with respect to observed frequencies.</p><p class="p">In summary,
weather models wear their adequacy for purpose on their sleeves: because
they are used to predict phenomena that we soon come to observe in
great detail, we can measure their skill in a relatively straightforward
manner. On the flipside, we can see very easily what weather models are
<i class="italic">not</i> skilled at: as everyone knows, it is
impossible to predict whether it will rain on any particular day six
months from now. As we will see in <a class="xref sec" data-sectionid="#B-sec-8" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#B-sec-8">Section 2.4</a>,
model skill cannot always be measured so straightforwardly as it can in
the context of weather models. This raises special epistemological
issues, notably in the area of climate science.</p></div><div class="sec" data-magellan-destination="B-sec-8" id="B-sec-8"><h4 class="B title"><span class="label" style="color: #783f04;">2.4</span><span style="color: #783f04;"> Climate Models </span></h4><p class="p" style="text-align: justify;">The
dynamics of the atmosphere are chaotic. This means that small errors in
initial conditions grow very fast when we try to predict the future
state of the atmosphere, which explains why predicting the weather more
than about 10 days <span class="page-marker"></span>into the future is nearly impossible for practical purposes. In contrast to ‘weather’, the very concept of <i class="italic">climate</i>
is tied to a long time horizon: when we speak of a region’s climate, we
are referring to big-picture trends in the average values of weather
variables over periods of at least 30 years. At a high level, then, the
purpose of climate models is to produce coarse-grained summaries of
weather variables, often in the form of global or continental averages
or degrees of variation, and to use this knowledge to help make
projections of future climate conditions. To do this, climate models
generally do not take into account present weather conditions. (After
all, these contribute only to the climate system’s internal variability,
which is not the target of interest in climate modelling. Worse still,
the information contained in a weather dataset is mostly lost after
about 10 days of forecasting.) What climate models <i class="italic">do</i> take into account are the possible effects of various <i class="italic">external forcings</i>: things like ozone depletion, CO<sub class="sub">2</sub>
emissions, other greenhouse gases, deforestation, and so on. These are
forces that are external to the climate system, but which could push it
beyond its normal range of variation – and therefore affect projections
of future climate conditions. Of course, there is much uncertainty
around these forcings themselves: future CO<sub class="sub">2</sub>
emissions, for example, depend on numerous unpredictable factors,
including (but not limited to) future energy policies and practices. As a
result, climate models often consider a range of plausible future
scenarios for external forcings, producing projections that correspond
to these counterfactual scenarios.</p><p class="p">Climate models of the
most advanced kind include not only representations of the Earth’s
atmosphere, but of its hydrosphere (including rivers and seas),
cryosphere (ice caps and sheets), land surfaces, and biosphere, as well
as the many complex interactions between these systems. Our purposes for
such models are almost as complex as the models themselves: we use them
to estimate climate sensitivity; to attribute human activities as the
cause of observed warming; to project regional changes to the climate
under various forcing scenarios; to project sea-level rise; to find
possible ‘tipping points’ in the climate (a critical threshold, the
crossing of which leads to dramatic and irreversible changes), and more.
While these are some of the many <i class="italic">skills</i> we hope
our climate models will have, we are not quite done spelling out the
purposes of climate models. In practice, we have to specify model
purposes relative to the degree of accuracy we expect, the degree of
confidence we want to have in an answer before we use it, and so on. In
climate modelling generally, we face certain obstacles that we must
mitigate by <i class="italic">lowering</i> our standards of adequacy:
the chaotic nature of the atmosphere and the long timescales that are of
interest to us, for example, mean we must limit what we expect from our
models. At best, we hope that climate models will give us averages on
timescales of two to three decades. To mitigate other sources of error,
we temper our <span class="page-marker"></span>expectations regarding
the degree of accuracy we expect from climate models, asking only for
relatively wide uncertainty bands, rather than point forecasts.</p><p class="p">An
example of an advanced climate model is the Geophysical Fluid Dynamics
Laboratory Earth System Model (GFL ESM 4.1). This complex model actually
consists of several modules, each corresponding to a different aspect
of the climate system. In order to be computationally manageable, all of
these modules will necessarily have a much lower degree of fidelity to
physical theory than weather prediction models. To show what we mean by
this, we can look at just one of the modules in the GFL ESM 4.1, the AM
4.0. The AM 4.0 is an example of a general circulation model of the
atmosphere (AGCM), which <a class="xref bibr" data-sectionid="#REFe-r-119" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-119"><span class="show-for-sr">Reference Winsberg</span>Winsberg (2018</a>,
44) describes as a ‘flagship’ climate model. It models the Earth’s
atmosphere, ‘at approximately 1° of resolution with 49 levels of
comprehensive, interactive chemistry and aerosols (including aerosol
indirect effect) from precursor emissions’ (<a class="xref bibr" data-sectionid="#REFe-r-005" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-005"><span class="show-for-sr">Reference Alvich</span>Alvich n.d</a>.a.).
By examining how an AGCM specifically is developed, we can get a good
idea of how climate models are developed more generally, including the
role of <i class="italic">equations</i>, <i class="italic">discretization</i>, <i class="italic">parameterization</i>, and <i class="italic">tuning</i>.<a class="xref footnote" data-sectionid="#FN-fn-5" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#FN-fn-5"><span class="show-for-sr">Footnote </span><sup class="sup">5</sup></a></p><p class="p">In
principle, the core behaviour of the Earth’s atmosphere can be modelled
with pencil and paper and three simple equations: (1) Newton’s laws of
motion as they apply to parcels of fluid; (2) the conservation of mass;
and (3) a simple thermodynamic equation that allows us to calculate the
heating effect on each parcel of air via a parameterized value of the
radiation from the Sun. Unfortunately, this results in a coupled set of
non-linear partial differential equations for which we have no
closed-form solution. However, if we want a <i class="italic">numerical approximation</i> of how the atmosphere should behave, we can develop a computer simulation model like an AGCM.</p><p class="p">In
an AGCM, we transform continuous differential equations into discrete
difference equations that approximate them, then we use a computer to
solve those equations. At heart, an AGCM is a more coarse-grained
version of a weather model. It is more coarse-grained because it has to
be run for much longer times. And, of course, because it is more
coarse-grained, though its primitive equations might be very close
cousins of the weather model’s, the degree of articulation it requires
is dramatically greater.</p><p class="p">Ultimately, an AGCM consists of
a three-dimensional grid of cells, with each cell exchanging radiation,
heat, moisture, momentum, and mass with its neighbours (<a class="xref bibr" data-sectionid="#REFe-r-104" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-104"><span class="show-for-sr">Reference Stone, Knutti, Fung, Lopez and New</span>Stone and Knutti 2010</a>).
In an AGCM, anything that happens below the level of the chosen grid
size (i.e., within a grid box) cannot be calculated using the
fundamental equations that have been discretized at that grid size.
Instead, <span class="page-marker"></span>any interactions in the system
that occur within a grid box need to be treated with a subgrid
parameterization. Parameterization refers to the method of using simple
mathematical descriptions, that is, equations with parameters, to
replace processes that are too small-scale or complex to be physically
represented in the discretized model. In climate modelling, parameters
are often referred to as ‘non-physical’, because there are no
corresponding values in nature. In developing an AGCM, subgrid
parameterizations are chosen and refined based on individual
performance; the best value for a parameter in an AGCM is generally an
artefact of the computation scheme. Despite being grounded in physical
theory, the more a computational model relies on parameterization, the
less fidelity to theory the model will exhibit.</p><p class="p" style="text-align: justify;">The last step in developing an AGCM is <i class="italic">tuning</i> (<a class="xref bibr" data-sectionid="#REFe-r-080" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-080"><span class="show-for-sr">Reference Mauritsen, Stevens and Roeckner</span>Mauritsen et al. 2012</a>).
As we use it here, this term refers to the process of adjusting a few
key parameter values in order to ensure that the model’s <i class="italic">overall</i> behaviour is acceptable. For example, recall from our discussion of the ZDEBM (<a class="xref sec" data-sectionid="#B-sec-6" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#B-sec-6">Section 2.2</a>)
that global warming/cooling will occur whenever there is an imbalance
between the amount of solar radiation coming into Earth and the amount
of emitted radiation leaving at the top of the atmosphere (TOA). In
general, a priority in climate model development is to ensure that the
model produces the expected results for TOA energy balance. Model tuning
can also be used to ensure that other important model results match
expectations (e.g., the general features of atmospheric circulation,
observed global mean temperature, tropical variability, and sea-ice
seasonality). Understanding how a climate model is tuned is key to
understanding how we assess its adequacy for purpose.</p><p class="p" style="text-align: justify;">With
that preamble in mind, what do we look to when we evaluate whether
climate models are adequate for our purposes? As with a weather model,
we can evaluate a climate model on various metrics of observable skill,
such as bias (see <a class="xref sec" data-sectionid="#B-sec-7" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#B-sec-7">Section 2.3</a>).
But unlike a weather model, measured skill cannot be the be-all and
end-all of climate model appraisal, for a number of reasons. First of
all, we hope to use our climate models to forecast how the climate will
evolve under conditions we have never seen before. The fact that a
climate model exhibits skill under the conditions we are now
experiencing is no guarantee that it will do so under future forcings.
Even for present conditions, assessing the skill of a climate model is
more difficult than assessing the skill of a weather model because we
don’t have nearly as much relevant data. We can evaluate the skill of a
weather model nearly every day, but we can only evaluate a climate model
roughly a decade at a time, and we only have a handful of decades of
good data to compare against – with even fewer than that having occurred
after the first climate models came into service.</p><p class="p">Furthermore,
insofar as we want to get probabilities out of a climate model, we
can’t possibly measure anything like a Briar score because we don’t have
<span class="page-marker"></span>multiple trials of the same set-ups. A
weather forecaster makes predictions under similar situations multiple
times, so if it predicts a 70% chance of rain under those conditions, we
can check to see whether rain occurs 70% of the time. But nothing
comparable happens in climate, so the probabilities we infer from
climate models are more difficult to interpret and understand.</p><p class="p">Perhaps
most importantly, we want to use climate models to tell us what will
happen under various different counterfactual scenarios; for example,
those defined by possible emissions pathways. This is the only way we
can use a climate model to help us decide if the expense of pursuing a
particular scenario – for example, an emissions pathway associated with a
more demanding policy change mandating less fossil fuel use – is worth
the cost. And to be effective at doing this, we have to be able to trust
a climate model to tell us not only how the world will actually behave,
but also how it <i class="italic">would have behaved</i> under emissions pathways that never came to be.</p><p class="p" style="text-align: justify;">As a result, much more than in the case of weather models, we have to look at the <i class="italic">internal qualities</i>
of a climate model very carefully in order to appraise whether it is
adequate for our purposes. What qualities do we look at? There’s a long
list, beginning with the model’s fidelity to well-established theory and
the mathematical arguments for the trustworthiness of steps we take in
articulating our basic model into its computational form. We also
perform sensitivity analyses: we can check to see what degree of
sensitivity our models display to choices of parameters about which we
have substantial uncertainty. If a climate model needs a mathematical
function to estimate a particular subgrid parameter (e.g., cloud
formation), and we are uncertain what the best form of that function is,
we can explore how sensitive the model is to different values that we
might pick. If the model is highly sensitive to the value we choose, and
we think that several different values are equally reasonable, we can
temper our expectations concerning the accuracy of our projections by
widening our confidence intervals.</p><p class="p">Another strategy we
use is ensemble studies: different models not only have different
parameter values, but they have different model structures. We can try
to do for model structure the same things we do for parameter values,
though the situation here gets a bit more complex. The basic idea is
that climate research centres around the world produce a variety of
different models. Some of these models differ from each other in ways
that are deeper than simply having different values for parameters
plugged in. We describe such models as having different ‘model
structures’. The idea is that by looking at which results come out the
same in all members of the ensemble we might be able to learn something
about whether we can trust those results. This is all far from
straightforward, however, and a large literature exists on how to manage
this source of knowledge (<a class="xref bibr" data-sectionid="#REFe-r-001" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-001"><span class="show-for-sr">Reference Abramowitz, Herger and Gutmann</span>Abramowitz et al. 2019</a>). There are other more bespoke <span class="page-marker"></span>techniques
that climate scientists turn to, including trying to achieve process
understanding and employing emergent constraints (see <a class="xref bibr" data-sectionid="#REFe-r-066" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-066"><span class="show-for-sr">Reference Knutti, Lloyd and Winsberg</span>Knutti (2018</a>) and <a class="xref bibr" data-sectionid="#REFe-r-119" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-119"><span class="show-for-sr">Reference Winsberg</span>Winsberg (2018</a>, chap. 12) for more details).</p><p class="p" style="text-align: justify;">And of course, we are free to declare that the models are <i class="italic">not</i>
adequate for some of the purposes we hoped they would be. Indeed, there
is some controversy about how adequate climate models are for
forecasting the melting of land ice, projecting regional conditions like
precipitation levels, and foreseeing the likely impacts on major
climatic structures, such as the thermohaline circulation and the El
Niño–Southern Oscillation cycle. Alternatively, we can declare that our
models are adequate for certain purposes only in conjunction with other
lines of evidence. For example, many climate scientists would say that
climate models are adequate for the purpose of informing, <i class="italic">along with other sources of evidence</i>, an estimate of equilibrium climate sensitivity (ECS), the amount we expect the world to warm under a doubling of CO<sub class="sub">2</sub> in an uncertainty band that is about 3<b class="bold">°</b>C wide. But some would hesitate to claim that, <i class="italic">on their own</i>, climate models would be adequate for that purpose.</p></div><div class="sec" data-magellan-destination="B-sec-9" id="B-sec-9"><h4 class="B title"><span class="label" style="color: #783f04;">2.5</span><span style="color: #783f04;"> Conclusion </span></h4><p class="p" style="text-align: justify;"><span style="color: #274e13;">En esta sección hemos examinado detenidamente tres tipos diferentes de modelos y los hemos utilizado para ilustrar tres formas muy distintas de evaluar la adecuación de un modelo a su finalidad. Aunque no pretende ser una taxonomía completa y exhaustiva de tales métodos de evaluación, sí ilustra lo variados que son. En el primer caso, la adecuación a la finalidad se defiende limitando la finalidad prevista del modelo a dar explicaciones extremadamente idealizadas de los fenómenos. En el segundo, se argumenta poniendo a prueba repetidamente las predicciones del modelo, puntuándolo según una serie de métricas de destreza y, a continuación, circunscribiendo cuidadosamente nuestras atribuciones de destreza para el modelo a los tipos de predicciones o pronósticos en los que el modelo obtiene una buena puntuación. En este último caso, la situación es totalmente abigarrada y casi imposible de analizar en su totalidad. Cuando se trata de un modelo complejo de proyección sobre el mundo real como un modelo climático -un modelo muy idealizado y articulado que pretende hacer afirmaciones contrafácticas sobre el futuro-, determinar la adecuación al propósito exige examinar una amplia gama de características del modelo. También exige ser muy cuidadoso y circunscribirse a los fines para los que consideramos que el modelo es adecuado.</span><br /></p><p class="p"></p></div></div><div class="sec" data-magellan-destination="A-sec-5" id="A-sec-5"><h3 class="A title"><span class="label" style="color: #783f04;">3</span><span style="color: #783f04;"> Inadequacy for Purpose </span></h3><div class="sec" data-magellan-destination="B-sec-10" id="B-sec-10"><h4 class="B title"><span class="label">3.1</span> Introduction </h4><div style="text-align: justify;"><span style="color: #274e13;">El 1 de mayo de 2020, el biólogo evolutivo y experto en modelización Carl Bergstom y la bioestadística Natalie Dean publicaron un artículo de opinión en el New York Times (Referencia Bergstrom y DeanBergstrom y Dean 2020). Escrito al comienzo de la pandemia de Covid-19, su título contenía una advertencia: "Lo que no dicen los defensores de la inmunidad "natural" del rebaño: intente alcanzarla sin una vacuna y morirán millones". En el artículo, Bergstrom y Dean calculaban un valor denominado "umbral de inmunidad de rebaño", que, como explicaban, es un concepto que suele describirse en el contexto de una vacuna. Cuando hay suficientes personas vacunadas", escribieron,un agente patógeno no puede propagarse fácilmente entre la población. Si uno se infecta de sarampión pero todas las personas con las que interactúa han sido vacunadas, la transmisión se detendrá en seco. Los niveles de vacunación deben mantenerse por encima de un umbral que depende de la transmisibilidad del patógeno. Todavía no sabemos exactamente cómo de transmisible es el coronavirus, pero digamos que cada persona infecta a una media de otras tres. Eso significaría que casi dos tercios de la población tendrían que ser inmunes para conferir inmunidad de rebaño.</span><br /></div></div><div class="sec" data-magellan-destination="B-sec-10" id="B-sec-10"><br /><br /><div class="disp-quote"><div class="attrib">(<a class="xref bibr" data-sectionid="#REFe-r-012" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-012"><span class="show-for-sr">Reference Bergstrom and Dean</span>Bergstrom and Dean 2020</a>)</div></div><p class="p continuation">
In the absence of a vaccine (as was the case in May 2020), Bergstrom and
Dean stressed, immunity to the virus would only be achieved through
infection and this would lead to a lot of deaths. More deaths, even,
than we might think. After all, new infections would continue for some
time even after the herd immunity threshold was reached, an
epidemiological phenomenon called <i class="italic">overshoot</i>.</p><p class="p">In
this section, we examine Bergstrom and Dean’s op-ed, alongside related
arguments on social media, in order to build on our discussion of model
adequacy for purpose and introduce the topic of values in modelling. As
we will show, although Bergstrom and Dean’s op-ed explains
epidemiological phenomena that a simple model can help us understand, it
also draws conclusions that go far beyond what that model can tell us.
This case study raises a broader question: what is the significance of
building and using models for purposes for which they are not adequate?</p></div><div class="sec" data-magellan-destination="B-sec-11" id="B-sec-11"><h4 class="B title"><span class="label" style="color: #783f04;">3.2</span><span style="color: #783f04;"> SIR Models: From Overshoot to Lockdown? </span></h4><p class="p"> In their op-ed, <a class="xref bibr" data-sectionid="#REFe-r-011" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-011"><span class="show-for-sr">Reference Bergstrom</span>Bergstrom and Dean (2020</a>)
focus first on estimating the herd immunity threshold in the context of
Covid-19 and explaining the concept of overshoot. The origin of these
concepts is a simple mathematical model called an ‘SIR’ model
(‘Susceptible, Infected, Removed’) (<a class="xref bibr" data-sectionid="#REFe-r-018" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-018"><span class="show-for-sr">Reference Britton</span>Britton 2010</a>; <a class="xref bibr" data-sectionid="#REFe-r-049" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-049"><span class="show-for-sr">Reference Handel, Longini and Antia</span>Handel, Longini, and Antia 2007</a>).
This highly idealized model represents the dynamics between people in
the context of an infectious disease, with each of three groups
represented in its own ‘compartment’:
</p><ul class="list bullet"><li class="list-item"><p class="p"> <b class="bold">Susceptible</b>: The number of people who have not yet been infected with a pathogen and are therefore vulnerable to infection.</p></li><li class="list-item"><p class="p"> <b class="bold">Infectious</b>: The number of people who are infected and capable of infecting others.</p></li><li class="list-item"><p class="p"> <span class="page-marker"></span><b class="bold">Removed:</b>
The number of people who are no longer capable of infecting others or
being infected (e.g., because they have recovered from the disease or
died). This compartment may also be called ‘recovered’ or ‘resistant’.</p></li></ul><p class="p continuation">
In a SIR model, when a susceptible individual and an infectious
individual come into ‘infectious contact’, the susceptible person
becomes infected and moves from the S compartment to the I compartment
(or, in some models, does so with some probability). Over time,
infectious individuals also move from the ‘Infectious’ to the ‘Removed’
compartment. There are a number of different ways of giving this kind of
model its dynamics – that is, of providing the mathematics of how the
system will evolve over time such that people will gradually move from
the S to the I to the R compartment. The most standard form of the
dynamics looks like this:</p><p class="p"> The transition rate between S and I is given by:
</p><div class="disp-formula" data-magellan-destination="EQN-df-5" data-mathjax-status="texmath alt-graphic" id="EQN-df-5"><span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"><span class="MathJax_Preview" color="inherit"></span><span class="MathJax" data-mathml="<math class="math" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mrow><mrow><mi>d</mi><mi>S</mi></mrow><mo>/</mo><mrow><mi>d</mi><mi>t</mi></mrow></mrow><mo>=</mo><mo>&#x2212;</mo><mi>&#x3B2;</mi><mi>S</mi><mi>I</mi></mrow></math>" id="MathJax-Element-13-Frame" role="presentation" style="position: relative;" tabindex="0"><nobr aria-hidden="true"><span class="math math" id="MathJax-Span-171" style="display: inline-block; width: 7.977em;"><span style="display: inline-block; font-size: 124%; height: 0px; position: relative; width: 6.407em;"><span style="clip: rect(1.535em, 1006.41em, 2.804em, -1000em); left: 0em; position: absolute; top: -2.419em;"><span class="mrow" id="MathJax-Span-172"><span class="mrow" id="MathJax-Span-173"><span class="mrow" id="MathJax-Span-174"><span class="mrow" id="MathJax-Span-175"><span class="mi" id="MathJax-Span-176" style="font-family: MathJax_Math; font-style: italic;">d<span style="display: inline-block; height: 1px; overflow: hidden; width: 0.003em;"></span></span><span class="mi" id="MathJax-Span-177" style="font-family: MathJax_Math; font-style: italic;">S<span style="display: inline-block; height: 1px; overflow: hidden; width: 0.032em;"></span></span></span><span class="mo" id="MathJax-Span-178" style="font-family: MathJax_Main;">/</span><span class="mrow" id="MathJax-Span-179"><span class="mi" id="MathJax-Span-180" style="font-family: MathJax_Math; font-style: italic;">d<span style="display: inline-block; height: 1px; overflow: hidden; width: 0.003em;"></span></span><span class="mi" id="MathJax-Span-181" style="font-family: MathJax_Math; font-style: italic;">t</span></span></span><span class="mo" id="MathJax-Span-182" style="font-family: MathJax_Main; padding-left: 0.278em;">=</span><span class="mo" id="MathJax-Span-183" style="font-family: MathJax_Main; padding-left: 0.278em;">−</span><span class="mi" id="MathJax-Span-184" style="font-family: MathJax_Math; font-style: italic;">β<span style="display: inline-block; height: 1px; overflow: hidden; width: 0.007em;"></span></span><span class="mi" id="MathJax-Span-185" style="font-family: MathJax_Math; font-style: italic;">S<span style="display: inline-block; height: 1px; overflow: hidden; width: 0.032em;"></span></span><span class="mi" id="MathJax-Span-186" style="font-family: MathJax_Math; font-style: italic;">I<span style="display: inline-block; height: 1px; overflow: hidden; width: 0.064em;"></span></span></span></span><span style="display: inline-block; height: 2.419em; width: 0px;"></span></span></span><span style="border-left: 0px solid; display: inline-block; height: 1.351em; overflow: hidden; vertical-align: -0.366em; width: 0px;"></span></span></nobr></span></span></span></span></div></div></div><div class="disp-formula" data-magellan-destination="EQN-df-5" data-mathjax-status="texmath alt-graphic" id="EQN-df-5"><span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"></span></span></span><span class="label">(1)</span>
</div><p class="p continuation">
where</p><p class="p">β is the average rate of potentially infecting
contacts a single person has per unit time multiplied by the probability
that actual infection would occur between an infected person and a
susceptible person.</p><p class="p"> And the transition rate between I and R is given by:
</p><div class="disp-formula" data-magellan-destination="EQN-df-6" data-mathjax-status="texmath alt-graphic" id="EQN-df-6"><span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"><span class="MathJax_Preview" color="inherit"></span><span class="MathJax" data-mathml="<math class="math" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mrow><mrow><mtext>dR</mtext></mrow><mo>/</mo><mrow><mtext>dt</mtext></mrow></mrow><mo>=</mo><mo>&#x2212;</mo><mtext>&#x3B3;I</mtext></mrow></math>" id="MathJax-Element-14-Frame" role="presentation" style="position: relative;" tabindex="0"><nobr aria-hidden="true"><span class="math math" id="MathJax-Span-187" style="display: inline-block; width: 7.126em;"><span style="display: inline-block; font-size: 124%; height: 0px; position: relative; width: 5.735em;"><span style="clip: rect(1.535em, 1005.71em, 2.804em, -1000em); left: 0em; position: absolute; top: -2.419em;"><span class="mrow" id="MathJax-Span-188"><span class="mrow" id="MathJax-Span-189"><span class="mrow" id="MathJax-Span-190"><span class="mrow" id="MathJax-Span-191"><span class="mtext" id="MathJax-Span-192" style="font-family: MathJax_Main;">dR</span></span><span class="mo" id="MathJax-Span-193" style="font-family: MathJax_Main;">/</span><span class="mrow" id="MathJax-Span-194"><span class="mtext" id="MathJax-Span-195" style="font-family: MathJax_Main;">dt</span></span></span><span class="mo" id="MathJax-Span-196" style="font-family: MathJax_Main; padding-left: 0.278em;">=</span><span class="mo" id="MathJax-Span-197" style="font-family: MathJax_Main; padding-left: 0.278em;">−</span><span class="mtext" id="MathJax-Span-198" style="font-family: MathJax_Math; font-style: italic;">γ<span style="font-family: MathJax_Main; font-style: normal; font-weight: normal;">I</span></span></span></span><span style="display: inline-block; height: 2.419em; width: 0px;"></span></span></span><span style="border-left: 0px solid; display: inline-block; height: 1.351em; overflow: hidden; vertical-align: -0.366em; width: 0px;"></span></span></nobr></span></span></span></span></div><div class="disp-formula" data-magellan-destination="EQN-df-6" data-mathjax-status="texmath alt-graphic" id="EQN-df-6"><span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"></span></span></span><span class="label">(2)</span>
</div><p class="p continuation">
where</p><p class="p">γ is the fraction of infected people who recover per unit time.</p><p class="p">In
a SIR model, we begin by assuming that mixing in human populations is
homogeneous: that is, every individual potentially encounters every
other individual and, <i class="italic">ceteris paribus</i>, every infected individual will infect a given number of random new people. This number is parameterized with the parameter R<sub class="sub">0</sub>. In other words, at the very beginning of the epidemic (time 0) we expect every infected individual to infect R<sub class="sub">0</sub> many new individuals (e.g., <span class="inline-formula" data-magellan-destination="EQNt-inf-9" data-mathjax-status="texmath alt-graphic" id="EQNt-inf-9">
<span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"><span class="MathJax_Preview" color="inherit"></span><span class="MathJax" data-mathml="<math class="math" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msub><mtext>R</mtext><mtext>0</mtext></msub><mo>=</mo><mtext>5</mtext></mrow></math>" id="MathJax-Element-15-Frame" role="presentation" style="position: relative;" tabindex="0"><nobr aria-hidden="true"><span class="math math" id="MathJax-Span-199" style="display: inline-block; width: 3.721em;"><span style="display: inline-block; font-size: 124%; height: 0px; position: relative; width: 3.002em;"><span style="clip: rect(1.512em, 1002.95em, 2.63em, -1000em); left: 0em; position: absolute; top: -2.33em;"><span class="mrow" id="MathJax-Span-200"><span class="mrow" id="MathJax-Span-201"><span class="msub" id="MathJax-Span-202"><span style="display: inline-block; height: 0px; position: relative; width: 1.165em;"><span style="clip: rect(3.17em, 1000.73em, 4.144em, -1000em); left: 0em; position: absolute; top: -3.987em;"><span class="mtext" id="MathJax-Span-203" style="font-family: MathJax_Main;">R</span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span><span style="left: 0.736em; position: absolute; top: -3.837em;"><span class="mtext" id="MathJax-Span-204" style="font-family: MathJax_Main; font-size: 70.7%;">0</span><span style="display: inline-block; height: 3.987em; width: 0px;"></span></span></span></span><span class="mo" id="MathJax-Span-205" style="font-family: MathJax_Main; padding-left: 0.278em;">=</span><span class="mtext" id="MathJax-Span-206" style="font-family: MathJax_Main; padding-left: 0.278em;">5</span></span></span><span style="display: inline-block; height: 2.33em; width: 0px;"></span></span></span><span style="border-left: 0px solid; display: inline-block; height: 1.163em; overflow: hidden; vertical-align: -0.261em; width: 0px;"></span></span></nobr></span></span></span></span></span></p><p class="p"><span class="inline-formula" data-magellan-destination="EQNt-inf-9" data-mathjax-status="texmath alt-graphic" id="EQNt-inf-9"><span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"></span></span></span>
</span>). (That is, when Bergstrom and Dean say, ‘We don’t yet know
exactly how transmissible the coronavirus is, but say each person
infects an average of three others’, they are imagining an R<sub class="sub">0</sub> of 3.)</p><p class="p">As
time goes on, a SIR model shows, the number of infected individuals
will increase and the number of susceptible individuals will decrease –
meaning each infected individual has fewer potential people to infect.
We can call the proportion of the population that is susceptible, at
time t, ‘S<sub class="sub">t</sub>’ and the ‘effective reproduction number’, at time t, ‘R<sub class="sub">t</sub>’, which is equal to R<sub class="sub">0</sub>*S<sub class="sub">t</sub>. In simple terms, if, at the beginning of an epidemic, we expected each infected person to infect 5 people (i.e.,), but now only 80% or 0.8 of the population is susceptible, then we will expect each infected person to infect only new people.</p><div class="sec" data-magellan-destination="B-sec-11" id="B-sec-11"><p class="p">Once R<sub class="sub">t</sub> falls
below one, each new infected person will infect less than one new
person in turn –at that point, the epidemic will wane. In fact, R<sub class="sub">t</sub> will fall below one when S reaches a threshold of S<sub class="sub">threshold</sub> = 1/R<sub class="sub">0</sub>, which is when 1-S = 1–1/R<sub class="sub">0</sub>. And 1-S is simply the number of people who have already either been infected or vaccinated. So, in this simple model, 1/(1-R<sub class="sub">0</sub>) is what is considered the ‘herd immunity threshold’.</p><p class="p">A
simple SIR model can also show that the rate of new infections doesn’t
immediately grind to a halt once the herd immunity threshold is reached.
Rather, the first derivative of the rate of new infections turns
negative for the first time, representing a rate of decay rather than a
rate of growth. In fact, the number of susceptibles can fall well below
the threshold S<sub class="sub">threshold</sub>. This additional depletion of susceptibles is what is referred to as ‘overshoot’ (<a class="xref bibr" data-sectionid="#REFe-r-049" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-049"><span class="show-for-sr">Reference Handel, Longini and Antia</span>Handel, Longini, and Antia 2007</a>).</p><p class="p">In much the same way that we can use a highly idealized ZDEBM to calculate the effective temperature of the Earth (see <a class="xref sec" data-sectionid="#A-sec-4" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#A-sec-4">Section 2</a>),
we can use a simple SIR model to explain why certain infectious disease
epidemics will eventually wane and why new infections do not stop
abruptly when the herd immunity threshold is reached. However, it is
important to emphasize that a SIR model is a ‘very strong
oversimplification’ of any real infectious disease outbreak (<a class="xref bibr" data-sectionid="#REFe-r-049" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-049"><span class="show-for-sr">Reference Handel, Longini and Antia</span>Handel, Longini, and Antia 2007</a>).
In fact, real outbreaks involve stochasticity, take place among
populations with heterogeneous clinical characteristics and social
contact networks, and have many other complicating features, such as the
potential for pathogens to exhibit seasonality (<a class="xref bibr" data-sectionid="#REFe-r-026" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-026"><span class="show-for-sr">Reference Chikina and Pegden</span>Chikina and Pegden 2020</a>; <a class="xref bibr" data-sectionid="#REFe-r-027" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-027"><span class="show-for-sr">Reference Choi, Tuel and Eltahir</span>Choi, Tuel, and Eltahir 2021</a>; <a class="xref bibr" data-sectionid="#REFe-r-049" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-049"><span class="show-for-sr">Reference Handel, Longini and Antia</span>Handel, Longini, and Antia 2007</a>; <a class="xref bibr" data-sectionid="#REFe-r-096" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-096"><span class="show-for-sr">Reference Rucinski, Binnicker, Thomas and Patel</span>Rucinski et al. 2020</a>).</p><p class="p">The
fact that none of this complexity is represented in simple SIR models
limits the range of purposes to which these models can be put. To
mitigate this, modellers will often build additional features onto
simple SIR models. For example, modellers will often add an ‘exposed’ or
‘E’ group, representing the group of people who have been exposed to an
infectious pathogen, but who are in a latency period and not quite
ready to infect new people. In such a model, sometimes called a ‘SEIR’
model, people will transition from susceptible to exposed before finally
becoming infected. A SEIR model with a substantial latency period will
already exhibit less overshoot than a simple SIR model. In the most
complex adaptations to SIR models, modellers aim to represent the fact
that real populations display a complex network structure and
heterogeneous mixing, which greatly influences infectious disease
dynamics, including the herd immunity threshold (<a class="xref bibr" data-sectionid="#REFe-r-019" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-019"><span class="show-for-sr">Reference Britton, Ball and Trapman</span>Britton, Ball, and Trapman 2020</a>).</p><p class="p"><span class="page-marker"></span>In their op-ed, <a class="xref bibr" data-sectionid="#REFe-r-011" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-011"><span class="show-for-sr">Reference Bergstrom</span>Bergstrom and Dean (2020</a>)
do not mention SIR models directly, but these models are the source of
the simple method they use to estimate the herd immunity threshold and
establish the risk of overshoot. Importantly, a simple SIR model allows
us to calculate the herd immunity threshold in much the same way a ZDEBM
allows us to calculate the effective (but not <i class="italic">actual</i>)
temperature of the Earth. Just as a ZDEBM does not account for the
Earth’s emissivity, a simple SIR model does not account for things like
stochasticity, seasonality, and heterogeneous population mixing: hence,
the results of each model come with a strong <i class="italic">ceteris paribus</i>
clause. Neither is adequate for making real-world projections of
quantitative outcomes under counterfactual scenarios, or for
establishing conclusions like Bergstrom and Dean’s in their op-ed.</p><p class="p">In particular, <a class="xref bibr" data-sectionid="#REFe-r-011" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-011"><span class="show-for-sr">Reference Bergstrom</span>Bergstrom and Dean (2020</a>)
assume that once an infectious disease outbreak has begun, in the
absence of external control measures, it will continue unabated until
the herd immunity threshold is reached, and even beyond. This assumption
is represented in the figure that accompanies the op-ed, which depicts a
logistic increase in infections. The figure even includes a timescale
in days and a representation of the percentage of total infections that
would be due to overshoot, though SIR models are not adequate for the
purpose of predicting these quantitative outcomes. Again, the many
factors that complicate epidemics but are not represented in SIR models
make it impossible for us to use simple SIR models for these purposes.
Nothing like what the figure predicts happened anywhere in the world,
because a simple SIR model was nowhere near adequate for predicting the
spread of SARS-CoV-2. <a class="xref bibr" data-sectionid="#REFe-r-012" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-012"><span class="show-for-sr">Reference Bergstrom and Dean</span>Bergstrom and Dean’s (2020</a>) claim that ‘If the pandemic went uncontrolled in the United States, it <i class="italic">could</i>
[our emphasis] continue for months after herd immunity was reached,
infecting many more millions in the process’ is similar to the claim
that the Earth <i class="italic">could</i> have stopped warming at -15<b class="bold">°</b>C.
Insofar as these are claims that could be taken to be true, they are
simply claims about how a world would behave under highly unrealistic <i class="italic">ceteris paribus</i>
conditions. Otherwise, a claim like ‘it could continue for months’ is
an empty modal with seemingly only rhetorical significance.</p><p class="p"> Following their discussion of herd immunity and overshoot, <a class="xref bibr" data-sectionid="#REFe-r-011" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-011"><span class="show-for-sr">Reference Bergstrom</span>Bergstrom and Dean (2020</a>)
went on to make remarks about pandemic mitigation. They noted that some
countries had attempted strategies intended to safely build up
population immunity to Covid-19 without a vaccine. For example, they
noted, Sweden had advised older people and other special groups to
self-quarantine, but kept many schools, restaurants, and bars open – and
many commentators had suggested that similar policies would be good for
poorer countries like India. But given the fatality rate of Covid-19, <a class="xref bibr" data-sectionid="#REFe-r-011" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-011"><span class="show-for-sr">Reference Bergstrom</span>Bergstrom and Dean (2020</a>) <span class="page-marker"></span>stressed, ‘<i class="italic">there would be no way to do this without huge numbers of casualties</i>’
(italics added). On these grounds, they argued that it was too early to
proceed as if most people would inevitably become infected, and that we
should not trust our ability to achieve a ‘controlled burn’ of the
pandemic. Instead, <a class="xref bibr" data-sectionid="#REFe-r-011" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-011"><span class="show-for-sr">Reference Bergstrom</span>Bergstrom and Dean (2020</a>)
emphasized that aggressive control and containment could be used to
reduce strain on the healthcare system and to buy the scientific
community time to develop treatments, vaccines, and so on. (It is, of
course, noteworthy that although Sweden continued the policy discussed
by Bergstrom and Dean, its number of casualties was not an outlier
relative to European countries that pursued the aggressive mitigation
strategies they had in mind.) These arguments echoed one that Bergstrom
had made on social media the month before, in which he drew on a SEIR
model to support the idea of instituting a 30-day ‘lockdown’ at the peak
of an epidemic:
</p><div class="disp-quote"><p class="p">If one can break the momentum
that is driving the epidemic beyond the herd immunity threshold, one can
reduce that overshoot substantially. Below, I’ve modelled a 30 day
period of social distancing around the epidemic peak, that drops R0 from
2.5 to 0.3. This 30 day lockdown period is far less onerous than that
required to hold the virus in check until a vaccine is available but
reduces the total fraction infected from 90% to 70% by eliminating much
of the overshoot. In my view, this is not a substitute for aggressive
control and containment, because 60% of the population still becomes
infected. In India, for example, this would cost about 19 million lives
with a 2% infection fatality rate. But the 30 day lockdown would save
>5 million.</p><div class="attrib">(Bergstrom on Twitter, 19 April <a class="xref bibr" data-sectionid="#REFe-r-012" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-012"><span class="show-for-sr">Reference Bergstrom and Dean</span>2020</a>; cited in <a class="xref bibr" data-sectionid="#REFe-r-120" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-120"><span class="show-for-sr">Reference Winsberg</span>Winsberg 2022</a>)</div></div><p class="p continuation">
This argument turns on several precise quantitative claims, including a
specific reduction in infections that could be achieved with a 30-day
lockdown. Yet such precise quantitative claims go beyond what a simple
SEIR model can tell us, as we have shown, and are highly sensitive to
inputs like the infection fatality rate, here assumed to be 2%, though
assumed to be 0.5%–1% in <a class="xref bibr" data-sectionid="#REFe-r-012" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-012"><span class="show-for-sr">Reference Bergstrom and Dean</span>Bergstrom and Dean’s (2020</a>)
op-ep. (The true global average of this value is controversial, but is
probably much closer to the lower end of Bergstrom and Dean’s estimate,
if not slightly below it, and substantially lower than the 2% estimate
Bergstrom used in his social media thread.<a class="xref footnote" data-sectionid="#FN-fn-6" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#FN-fn-6"><span class="show-for-sr">Footnote </span><sup class="sup">6</sup></a>)
They are also highly sensitive to whether or not the model contains the
kinds of mechanisms that seem to make respiratory viral pathogens come
in waves, even in the absence of human interventions. In the remainder
of this section, we discuss the social and ethical significance of
building and using scientific models for purposes for which they are not
adequate, placing this in the context of philosophy’s values in science
literature. <span class="page-marker"></span>We also discuss what sorts
of considerations are important when using scientific models to inform
public policy decisions, such as implementing the public health measures
that <a class="xref bibr" data-sectionid="#REFe-r-011" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-011"><span class="show-for-sr">Reference Bergstrom</span>Bergstrom and Dean (2020</a>) endorsed.</p></div><div class="sec" data-magellan-destination="B-sec-12" id="B-sec-12"><h4 class="B title"><span class="label" style="color: #783f04;">3.3</span><span style="color: #783f04;"> Two Kinds of Risk </span></h4><p class="p">As we established in <a class="xref sec" data-sectionid="#A-sec-3" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#A-sec-3">Section 1</a>,
models are not perfectly complete and entirely accurate representations
of target systems. By necessity, modelling involves representational
decisions – overlapping decisions about <i class="italic">what to represent</i> and <i class="italic">how to represent it</i> – which are driven by the purpose of the model (<a class="xref bibr" data-sectionid="#REFe-r-120" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-120"><span class="show-for-sr">Reference Winsberg</span>Harvard and Winsberg 2022</a>).
For example, as we have seen, it is not always necessary to represent
every complicating factor in a model of an infectious disease epidemic.
If a model’s purpose is to explain why some infectious disease epidemics
exhibit a herd immunity threshold, the simplest SIR model will be
adequate for this purpose. However, if a model’s purpose is to make a
quantitative prediction regarding the number of infections that will be
observed over a specific time period under various human interventions,
the simplest SIR model will not do. If our goal was to build a model
adequate for this purpose, we would have to represent many, if not all,
of the complicating factors discussed in <a class="xref sec" data-sectionid="#B-sec-6" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#B-sec-6">Section 2.2</a>
(and possibly more). Indeed, it is possible that even the very best
infectious disease model we could build would only be adequate for the
purpose of making quantitative predictions if we defined that purpose
conservatively, such that we accepted predictions with very large
margins of error. In any case, our decision concerning <i class="italic">what</i> factors to represent in the model would be informed, at least in part, by how <i class="italic">accurate</i> we wanted our quantitative predictions to be, which we can think of as part of our model’s purpose (see <a class="xref sec" data-sectionid="#A-sec-4" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#A-sec-4">Section 2</a>).
And whatever complicating factors we decide to represent – say, for
example, heterogeneity in social contact rates – will present us with
further decisions regarding <i class="italic">how</i> to represent
them. For example, deciding to include heterogeneity in social contact
rates means we will have to decide which source of data or evidence is
adequate to represent this phenomenon.</p><p class="p">The purpose of this subsection is to reflect on the ways in which representational decisions in modelling are <i class="italic">value-laden</i>.<a class="xref footnote" data-sectionid="#FN-fn-7" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#FN-fn-7"><span class="show-for-sr">Footnote </span><sup class="sup">7</sup></a>
There are at least three ways for us to disentangle and appreciate
this. First, representational decisions correspond to the purpose of the
model and <i class="italic">purposes</i> are value-laden. Second, even with a set purpose, judgements about what is <i class="italic">adequate</i> for that purpose are <span class="page-marker"></span>value-laden,
especially when model skill cannot be measured operationally. Third,
representational decisions influence model results, which affect our
‘inferential decisions’ concerning which facts to endorse at the stage
of model interpretation, the moment at which we use the model to infer
facts about the world (<a class="xref bibr" data-sectionid="#REFe-r-055" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-055"><span class="show-for-sr">Reference Harvard, Winsberg, Symons and Adibi</span>Harvard et al. 2021</a>). This means representational decisions influence our risk of endorsing a false fact, which is what we call <i class="italic">inductive risk</i> (<a class="xref bibr" data-sectionid="#REFe-r-120" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-120"><span class="show-for-sr">Reference Winsberg</span>Harvard and Winsberg 2022</a>).<a class="xref footnote" data-sectionid="#FN-fn-8" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#FN-fn-8"><span class="show-for-sr">Footnote </span><sup class="sup">8</sup></a></p><p class="p">Inductive
risk highlights the role of social and ethical values in model
interpretation, since we put these in play when deciding what facts to
endorse as true, taking into account the possible harms of endorsing a
false fact; if we evaluate those harms as serious, our ethical values
tell us to demand a higher standard of evidence. However, our values
come into play in modelling well before model interpretation, and
value-laden representational decisions are distinct from decisions
around which facts to endorse – after all, representational decisions
are generally about what is adequate for purpose, not about what is
true. In modelling, then, it is useful to think in terms of two types of
risk: while inductive risk is the risk of endorsing a false fact, <i class="italic">representational risk</i>
is the risk of making a representational decision that is inadequate
for purpose. These two types of risk are not unrelated because an
inadequate representational decision will sometimes lead to a downstream
endorsement of a false fact. Yet, (1) <i class="italic">it needn’t</i> lead to this and (2) it can also lead to <i class="italic">other harms</i>
that are distinct from a false conclusion. These distinct harms include
lamentably incomplete scientific results, irrelevant or distracting
results, and even pernicious and unjust gaps in scientific knowledge (<a class="xref bibr" data-sectionid="#REFe-r-120" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-120"><span class="show-for-sr">Reference Winsberg</span>Harvard and Winsberg 2022</a>).
The two types of risk therefore encourage us to distinguish between the
influence of values in model development/selection (i.e., in managing
representational risk) and in model interpretation (i.e., in managing
inductive risk), respectively.</p><p class="p">To help appreciate the role of values in modelling, we can think about the risks that <a class="xref bibr" data-sectionid="#REFe-r-011" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-011"><span class="show-for-sr">Reference Bergstrom</span>Bergstrom and Dean (2020</a>) and Bergstrom (on Twitter, 19 April <a class="xref bibr" data-sectionid="#REFe-r-012" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-012"><span class="show-for-sr">Reference Bergstrom and Dean</span>2020</a>)
were running in their op-ed and social media threads, respectively. In
both cases, Bergstrom and Dean were making representational decisions
(i.e., to use simple SIR and SEIR models, respectively, which are
adequate for some purposes, but not for others, as well as to choose
certain parameters for the models) and inferential decisions (i.e.,
decisions about what facts to endorse as true). This means we can use
both the representational risk and inductive risk ‘lenses’ to analyse
what social and ethical considerations would have been relevant to
Bergstrom and Dean’s decisions.</p><p class="p">Let’s start with inductive risk, by identifying some ‘facts’ that <a class="xref bibr" data-sectionid="#REFe-r-011" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-011"><span class="show-for-sr">Reference Bergstrom</span>Bergstrom and Dean (2020</a>) and Bergstrom (on Twitter, 19 April <a class="xref bibr" data-sectionid="#REFe-r-012" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-012"><span class="show-for-sr">Reference Bergstrom and Dean</span>2020</a>)
endorsed. We might want to consider the claim that a 30-day lockdown
period ‘reduces the total fraction infected from 90% to 70% by
eliminating much of the overshoot’ and that in India this ‘would save
>5 million’ lives assuming a 2% fatality rate (Bergstrom on Twitter,
19 April <a class="xref bibr" data-sectionid="#REFe-r-012" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-012"><span class="show-for-sr">Reference Bergstrom and Dean</span>2020</a>).
To analyse the inductive risk here, we must consider the possible harms
of endorsing this ‘fact’ (which turned out to be far from true). Thus,
we should keep in mind that the claim is both highly precise and
explicitly made in the context of endorsing a public health intervention
that is known to present potential harms as well as potential benefits,
as most health interventions do. In health policy-making and
decision-making, the conventional approach is to quantify both potential
harms and benefits and evaluate whether the intervention would be net
beneficial. How accurately potential harms and benefits are quantified
is therefore of great importance. If, for example, a 30-day lockdown
(which affects all or most of the population) were to reduce the total
fraction infected from 40% to 30%, or from 30% to 20%, rather than from
90% to 70%, this may flip the results of a harm–benefit analysis (i.e.,
change the policy recommendation). Deciding to endorse the fact that a
30-day lockdown period ‘<i class="italic">reduces</i> the total fraction infected from 90% to 70%’ (italics added) therefore <i class="italic">specifically</i>
involves ethical considerations. In particular, it involves considering
whether a simple SEIR model is adequate to provide evidence strong
enough to endorse this fact, or whether the seriousness of a factual
error in this context means other evidence is required.</p><p class="p">Instead
of focusing on Bergstrom’s decision to endorse a fact, we can focus on
his decision to use a simple SEIR model to: (1) predict the impact of a
30-day lockdown on total Covid-19 infections over 140 days, and (2)
inform the decision to implement a 30-day lockdown in India. As we
interpret it, these are two purposes to which Bergstrom put his SEIR
model. In this context, the relevant risk is <i class="italic">representational risk</i>,
the risk that a simple SEIR model will be inadequate for these
purposes. With respect to purpose (1), representational risk is high
because the model’s precise quantitative predictions depend on highly <i class="italic">unconstrained</i>
‘what to represent’ and ‘how to represent’ decisions. Specifically, the
model excludes information about many factors that complicate epidemics
and incorporates both a highly controversial estimate of the Covid-19
infection fatality rate (i.e., 2%) and a highly <i class="italic">idealized</i>
assumption about epidemic growth (i.e., in the absence of
interventions, Covid-19 infections would follow logistic growth all the
way to the herd immunity threshold and then continue into overshoot over
a very short period of time – with 60% of people infected on the order
of 60 days). Perhaps the most salient harm that could result if the SEIR
model were inadequate for purpose (1) is that its <span class="page-marker"></span>inaccurate
results would mislead people. In this context, representational risk
and inductive risk seem to be entangled: both link to the harm of
endorsing a false fact. However, with respect to purpose (2),
representational risk links to a distinct harm, as we will see.</p><p class="p">We
interpreted purpose (2) of Bergstrom’s SEIR model in terms of
decision-making: the purpose of informing the decision to implement a
30-day lockdown in India. In the decision-making context, it is not only
important that a model’s quantitative predictions are accurate, but
that it represents all of the information that is relevant to the
decision. Importantly, what information is relevant to a decision is a
highly value-laden question: we can imagine, for example, that some
decision-makers would desire only information about the effect of a
30-day lockdown on total number of Covid-19 infections, but that other
decision-makers would desire information about the effect of the
lockdown on food insecurity, educational outcomes, all-cause mortality,
and so on. In light of this, we can see that whether or not Bergstrom’s
SEIR model is adequate for purpose (2) depends directly on the <i class="italic">model users</i>
and their moral values around how decisions about public health
interventions should be made. If model users desire only information
about the effect of a 30-day lockdown on total number of Covid-19
infections, Bergstrom’s SEIR model would be adequate for the purpose of
decision-making if its quantitative predictions were accurate. However,
if model users desire information about the effect of a lockdown on food
insecurity, educational outcomes, and all-cause mortality in order to
inform their decision, then Bergstrom’s SEIR model would not be adequate
for purpose, regardless of how accurate it were at predicting Covid-19
deaths. In this context, the harm at stake is best understood as <i class="italic">incomplete</i>
results rather than factually incorrect results. After all, even if the
model’s quantitative predictions were precisely accurate, the model
could still be inadequate for purpose if model users’ decision-making
approach demanded information on all the potential harms of lockdown.
Depending on our moral values, and perhaps our knowledge about the
ultimate effects of lockdown in India (<a class="xref bibr" data-sectionid="#REFe-r-099" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-099"><span class="show-for-sr">Reference Shanker and Raghavan</span>Shanker and Raghavan 2021</a>), we may consider such incompleteness in model results to be more or less harmful, somewhere between lamentable and unjust.</p><div class="sec" data-magellan-destination="B-sec-13" id="B-sec-13"><h4 class="B title"><span class="label" style="color: #783f04;">3.4</span><span style="color: #783f04;"> Models for Public Decisions </span></h4><p class="p" style="text-align: justify;"><span style="color: #274e13;">En la última subsección hemos subrayado que nuestros valores sociales y éticos entran en juego (1) a la hora de decidir qué representar en un modelo y cómo representarlo; y (2) a la hora de decidir qué hechos respaldar a partir de los resultados de un modelo. También destacamos que los fines de los modelos pueden explicarse a distintos niveles: a veces, un modelo puede entenderse con fines de predicción cuantitativa a un nivel, pero con fines de toma de decisiones públicas a un nivel superior. Los modelos que pueden utilizarse para fundamentar decisiones públicas plantean consideraciones morales especiales, entre otras cosas porque su adecuación al propósito depende directamente de los usuarios de los modelos y de sus valores morales sobre cómo deben tomarse las decisiones públicas; sin embargo, los modelizadores y los usuarios de los modelos no siempre están directamente conectados. A veces, los modelizadores trabajan directamente con los usuarios de los modelos y construyen modelos que se basan explícitamente en los valores de estos últimos en torno a la toma de decisiones. Otras veces, los modelizadores no tienen relación directa con los usuarios de los modelos, sino que los construyen de acuerdo con sus propios valores en torno a la toma de decisiones. En estas condiciones, los modelos pueden utilizarse como recursos retóricos, lo que plantea otras consideraciones morales. En este subapartado esbozamos sólo algunos de los posibles enfoques de la toma de decisiones públicas y establecemos que los modelos deben construirse de forma muy diferente para que sean adecuados para apoyar cada uno de ellos.</span><br /></p><p class="p">Consider,
for example, three possible approaches to deciding whether to implement
a 30-day lockdown in India. Following the first, the decision depends
uniquely on whether a 30-lockdown is likely to prevent Covid-19
infections; we ignore all other considerations. Following the second, we
still ignore all other considerations, but now we require that a 30-day
lockdown is likely to prevent a <i class="italic">certain minimum number</i>
of Covid-19 infections. And following the third, the decision depends
on whether a 30-lockdown is likely to be net beneficial, taking into
account a broad range of harms and benefits. The first of these
approaches assumes a duty to prevent Covid-19 infections, period, and
the second assumes a duty to prevent a certain number of Covid-19
infections, if possible (cf. <a class="xref bibr" data-sectionid="#REFe-r-052" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-052"><span class="show-for-sr">Reference Harvard and Winsberg</span>Harvard and Winsberg 2021</a>).The
third does not assume any straightforward, singular duties of these
kinds, but rather assumes that decisions should be net beneficial from
some perspective. To inform the first decision-making approach, it would
not be necessary to build a model capable of quantitative prediction
(indeed, it may not be necessary or appropriate to build a model at
all). However, to inform the other two decision-making approaches, a
model would have to be adequate for quantitative prediction. Both of
these decision-making approaches, then, raise a further value-laden
question, which pertains to the existence of <i class="italic">uncertainty</i>
surrounding quantitative predictions. Such uncertainty is particularly
inevitable when modelling complex, non-linear systems like epidemics.
Because there is generally a good deal of uncertainty around the harms
and benefits of health interventions, conventional approaches to health
modelling for decision-making typically combine something like a
cost–benefit analysis with uncertainty analysis (<a class="xref bibr" data-sectionid="#REFe-r-017" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-017"><span class="show-for-sr">Reference Briggs, Sculpher and Claxton</span>Briggs, Sculpher, and Claxton 2006</a>).</p><p class="p"><span class="page-marker"></span>As we saw, the SIR and SEIR models that <a class="xref bibr" data-sectionid="#REFe-r-011" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-011"><span class="show-for-sr">Reference Bergstrom</span>Bergstrom and Dean (2020</a>) and Bergstrom (on Twitter, 19 April <a class="xref bibr" data-sectionid="#REFe-r-011" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-011"><span class="show-for-sr">Reference Bergstrom</span>2020</a>)
presented in their op-ed and social media threads, respectively, do not
represent potential harms associated with social distancing
policies/lockdowns or estimate the uncertainty around their predictions.
As a result, their models are adequate only for certain model users:
those who consider only the number of Covid-19 infections averted to be
relevant to the decision and who are unconcerned by uncertainty. It is
also apparent that Bergstrom and Dean’s models were built by them
independently, rather than in collaboration with model users, such as
those responsible for deciding whether or not to implement a 30-day
lockdown in India. As a result, there is some risk that Bergstrom and
Dean’s models could be perceived as having a rhetorical purpose – for
example, the purpose of persuading people to implement a 30-day lockdown
in India. To see why there would be a risk of such a rhetorical purpose
being perceived, it is useful to think about what the dialectical
situation was (at least for some people) regarding lockdowns generally.
In early 2020, some people were arguing that lockdowns would cause more
harm than good, because of their negative impact on the economy,
education, and so on. However, this view was not very popular among
public health professionals (and perhaps the typical <i class="italic">New York Times</i>
reader), at least not with respect to the developed world. The thought
was that lockdowns would surely achieve more good via saved lives than
they would cause harm via decreased general welfare. Many people were
willing to conclude this simply on the basis of their moral intuitions.</p><p class="p" style="text-align: justify;">However,
it was sometimes acknowledged that the calculus could be considerably
different in developing economies like India and South Africa, due to
the fact that overall welfare is much closer to dangerously low levels
for a large majority of people (<a class="xref bibr" data-sectionid="#REFe-r-020" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-020"><span class="show-for-sr">Reference Broadbent</span>Broadbent 2020</a>; <a class="xref bibr" data-sectionid="#REFe-r-021" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-021"><span class="show-for-sr">Reference Broadbent and Streicher</span>Broadbent and Streicher 2022</a>).
Bergstrom himself effectively acknowledged this: ‘Every possible
measure should be taken to prevent this [unprecedented humanitarian
disaster] from happening. Yet in some countries this may be unavoidable.
Some nations may simply lack the economic resources, technological
capacity, and political will to contain the virus until a vaccine can be
developed.’ (Bergstrom on Twitter, 19 April <a class="xref bibr" data-sectionid="#REFe-r-011" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-011"><span class="show-for-sr">Reference Bergstrom</span>2020</a>; cited in <a class="xref bibr" data-sectionid="#REFe-r-120" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-120"><span class="show-for-sr">Reference Winsberg</span>Winsberg 2022</a>).
Indeed, it was reasonable to worry that even if developing countries
could sustain lockdowns as long as developed countries (a dubious
proposition), vaccines would be rolled out very slowly in developing
countries anyway. Thus, Bergstrom was aware that the central question
regarding lockdowns in developing countries was not ‘Should they
lockdown until a vaccine is developed?’, but rather ‘Should they try a
relatively short lockdown?’. One prima facie reasonable response to this
would be to point out that a lockdown that lasts only a tenth or less
of the time <span class="page-marker"></span>needed to make a vaccine
available would be pointlessly harmful, so it was clear that a special
argument would be needed for the case of the developing world. This is
what Bergstrom’s SEIR model appears to provide in representing the
phenomenon of ‘overshoot’, along with a very high infection fatality
rate of 2% (a rate 2 to 4 times higher than the one he and Dean used in
the <i class="italic">New York Times</i> piece (<a class="xref bibr" data-sectionid="#REFe-r-012" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-012"><span class="show-for-sr">Reference Bergstrom and Dean</span>Bergstrom and Dean 2020</a>)).
In other words, it could be perceived that Bergstrom’s SEIR model was
serving the rhetorical purpose of presenting a quantitative prediction
so nuclear that it would obviate the need for more a complex
cost–benefit model.</p></div><div class="sec" data-magellan-destination="B-sec-14" id="B-sec-14"><h4 class="B title"><span class="label" style="color: #660000;">3.5</span><span style="color: #660000;"> Conclusion </span></h4><div style="text-align: justify;"><span style="color: #274e13;">En esta sección, hemos examinado detenidamente una aplicación de un modelo SIR/SEIR, tanto para ilustrar las formas en que los modelos pueden utilizarse para fines para los que no son adecuados como para vincular esto al hecho de que la construcción y el uso de modelos invocan valores sociales y éticos. Demostramos que existen dos riesgos asociados a la construcción y el uso de un modelo para un fin específico: el riesgo asociado a extraer inferencias falsas del modelo (riesgo inductivo) y el riesgo asociado a tomar decisiones de representación inadecuadas para el fin perseguido (riesgo de representación). Ambos riesgos son morales. En el primer caso, el riesgo es moral porque cuando utilizamos un modelo para inferir una conclusión y aprobamos esa conclusión como un hecho, pueden producirse daños y beneficios. Esto significa que la decisión de respaldar o no un hecho requiere una reflexión sobre los perjuicios de respaldar el hecho cuando es falso y sobre los perjuicios de no respaldar el hecho cuando es verdadero. En el segundo caso, el riesgo es moral porque tomar decisiones de representación inadecuadas para el propósito no sólo puede aumentar el riesgo inductivo, sino también provocar daños distintos de una conclusión falsa.</span></div></div><div class="sec" data-magellan-destination="B-sec-14" id="B-sec-14" style="text-align: justify;"><span style="color: #274e13;"> </span></div><div class="sec" data-magellan-destination="B-sec-14" id="B-sec-14" style="text-align: justify;"><span style="color: #274e13;">También sugerimos, al final de esta sección, que el modelo de referencia de Bergstrom y DeanBergstrom y Dean (2020) podría percibirse como que ha servido a un propósito retórico. En particular, señalamos que el modelo podría percibirse como utilizado para presentar un argumento especial a favor de los cierres en el caso del mundo en desarrollo. Así pues, parece que nos hemos encontrado con un nuevo propósito de los modelos, que va más allá de los propósitos que exploramos en la Sección 2: los modelos pueden utilizarse retóricamente para promover argumentos a favor de las conclusiones que los creadores del modelo quieren promover. Esto apunta a una controversia actual en la literatura sobre los valores en la modelización: ¿cuándo es legítimo utilizar un modelo con fines retóricos? </span></div><div class="sec" data-magellan-destination="B-sec-14" id="B-sec-14" style="text-align: justify;"><span style="color: #274e13;"> </span></div><div class="sec" data-magellan-destination="B-sec-14" id="B-sec-14" style="text-align: justify;"><span style="color: #274e13;">Una forma de concebir el propósito retórico de los modelos es considerar que el propósito de un modelo es su "performatividad" o impacto "performativo" (White, Khosrowi y Frisch-Basshuysen et al. 2021). El impacto performativo de un modelo es el grado en que anima a las personas a cambiar su comportamiento y/o obliga a los responsables políticos a adoptar diversas políticas. Que los modelos pueden tener el propósito de la performatividad se ha discutido en la literatura epidemiológica: Biggs y LittlejohnBiggs y Littlejohn (2021), por ejemplo, señalan que "[l]as proyecciones iniciales [de un modelo Covid-19] incluían los peores escenarios posibles que nunca ocurrirían como medio para incitar a los líderes a la acción" (92), y Ioannidis, Cripps y TannerIoannidis, Cripps y Tanner (2022) especulan con que "[d]e hecho, las predicciones erróneas pueden incluso haber sido útiles. Una predicción errónea del fin del mundo puede incentivar a la gente a mejorar su higiene personal". Algunos filósofos han sugerido que, incluso si un modelo hace predicciones o proyecciones muy pobres, podríamos querer, en algunas condiciones, considerar el impacto performativo de un modelo como una virtud potencial (Referencia White, Khosrowi y FrischBasshuysen et al. 2021). Designar el impacto performativo de un modelo como una virtud bajo algunas condiciones equivale a sancionar el uso de un modelo con fines retóricos bajo esas condiciones (Referencia Winsberg y HarvardWinsberg y Harvard 2022). </span><br /></div><div class="sec" data-magellan-destination="B-sec-14" id="B-sec-14"><br /><p class="p"></p><p class="p" style="text-align: justify;">Mientras que algunos autores han sugerido que el uso de modelos con fines retóricos es a veces aceptable, otros han argumentado que esta práctica debería evitarse estrictamente. Aunque puede existir la tentación de animar a las personas o a los responsables políticos a adoptar determinados comportamientos, Winsberg y HarvardWinsberg y Harvard (2022) afirman que los creadores de modelos no deberían tratar de hacerlo incorporando a los modelos los peores escenarios u otros tipos específicos de predicciones. Una de las razones es que los cambios de comportamiento suelen tener costes, tanto financieros como otros efectos no deseados, que pueden ser difíciles de prever y aún más de cuantificar. De hecho, una de las funciones más importantes de la modelización orientada a las políticas es facilitar un análisis coste-beneficio, que fomente una reflexión cuidadosa sobre los posibles efectos secundarios del cambio de comportamiento, los métodos disponibles para cuantificar esos efectos y la inevitable incertidumbre que rodeará a los resultados. En otras palabras, una de las funciones básicas de los modelos es ayudarnos a determinar con mayor precisión y fiabilidad qué comportamientos son realmente los mejores que podemos adoptar, dados los valores que atribuimos a los distintos resultados potenciales. Debe quedar claro, por tanto, que utilizar modelos para promover un determinado curso de acción desde el principio va en contra de esa función central, y nos pone en riesgo de adoptar comportamientos que en realidad no concuerdan con nuestros valores</p><p class="p" style="text-align: justify;">Debe quedar claro, por tanto, que utilizar modelos para promover un determinado curso de acción desde el principio va en contra de esa función central, y nos pone en riesgo de adoptar comportamientos que en realidad no concuerdan con nuestros valores. Además, como afirman Winsberg y HarvardWinsberg y Harvard (2022), utilizar modelos con fines retóricos corre el riesgo de socavar la credibilidad de la ciencia, así como el derecho del público a tener políticas que reflejen sus propios valores (no sólo los de los creadores de modelos). La cuestión de si es aceptable que los modelos se utilicen con fines retóricos sigue siendo un tema de debate en la literatura sobre los valores en la modelización. En la próxima sección, analizaremos las consideraciones a favor de la afirmación de que el público merece opinar sobre cómo deben desarrollarse y utilizarse los modelos para informar las políticas públicas.<br /></p></div><div class="sec" data-magellan-destination="A-sec-6" id="A-sec-6"><h3 class="A title"><span class="label" style="color: #783f04;">4</span><span style="color: #783f04;"> Models and Values </span></h3><div class="sec" data-magellan-destination="B-sec-15" id="B-sec-15"><h4 class="B title"><span class="label">4.1</span> Introduction </h4><p class="p">In <a class="xref sec" data-sectionid="#A-sec-5" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#A-sec-5">Section 3</a>,
we introduced the topic of values in modelling by establishing that
scientific models give rise to two kinds of risk. First, during model
development, there is representational risk: the risk that modellers
will make representational decisions (i.e., decisions about <i class="italic">what to represent</i> and <i class="italic">how to represent it</i>)
that will be inadequate for the intended purpose of the model. Second,
during model interpretation, there is inductive risk: the risk that
model users will make inferential decisions (i.e., decisions about <i class="italic">what to endorse as a fact</i>)
that will be erroneous. Because social and ethical values come into
play in deciding how to manage these risks, we said that the processes
of model development and interpretation are value-laden. In this
section, we revisit this topic in more detail, showing how we can follow
<a class="xref bibr" data-sectionid="#REFe-r-120" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-120"><span class="show-for-sr">Reference Winsberg</span>Harvard and Winsberg (2022)</a> and <a class="xref bibr" data-sectionid="#REFe-r-055" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-055"><span class="show-for-sr">Reference Harvard, Winsberg, Symons and Adibi</span>Harvard et al. (2021)</a>
in developing a basic framework to identify and understand value
judgements in modelling generally. We then apply this framework to the
Imperial College London (ICL) ‘CovidSim’ model, a well-known model used
to estimate the impact of non-pharmaceutical interventions (NPIs) on
Covid-19 mortality and healthcare demand (<a class="xref bibr" data-sectionid="#REFe-r-038" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-038"><span class="show-for-sr">Reference Ferguson, Laydon and Nedjati Gilani</span>Ferguson et al. 2020</a>).
Building on this detailed account of value judgements in a single
model, we end our discussion by considering how model-building can
proceed in an ethical manner, particularly when the goal of modelling is
to inform public policy.</p></div><div class="sec" data-magellan-destination="B-sec-16" id="B-sec-16"><h4 class="B title"><span class="label">4.2</span> A Basic ‘Values in Modelling’ Framework </h4><p class="p">As we discussed in <a class="xref sec" data-sectionid="#A-sec-5" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#A-sec-5">Section 3</a>, representational decisions in modelling take the form of overlapping decisions about <i class="italic">what to represent</i> and <i class="italic">how to represent it</i>. Roughly speaking, we can think of ‘what to represent’ decisions as concerning <i class="italic">what entities to include in and exclude from a model</i> and ‘how to represent’ decisions as about <i class="italic">what methods to use to represent entities chosen for inclusion in a model</i>. But, as <a class="xref bibr" data-sectionid="#REFe-r-055" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-055"><span class="show-for-sr">Reference Harvard, Winsberg, Symons and Adibi</span>Harvard et al. (2021)</a>
acknowledge, these decisions are closely intertwined in practice. For
example, ‘what to represent’ decisions are routinely informed by <i class="italic">how</i> or <i class="italic">whether</i>
the representation of something can be achieved: if modellers would
like to include an entity in a model, but have no data to support doing
so, they may decide to exclude it. In this case, the <i class="italic">how to represent</i> decision affects the <i class="italic">what to represent</i>
decision. Indeed, the distinction between ‘what to represent’ and ‘how
to represent’ is mostly a practical distinction, rather than a
philosophical or theoretical one. Much of what we say in what follows
applies equally to both types of representational decisions, and often
it makes sense to simply speak of ‘representational decisions’ in
general. However, the practical <span class="page-marker"></span>distinction
between ‘what to represent’ and ‘how to represent’ decisions can be
useful for structuring an initial discussion about modelling decisions,
which helps us to see the value-laden (and iterative) character of these
decisions.</p><div class="sec" data-magellan-destination="C-sec-10" id="C-sec-10"><h4 class="C title" style="text-align: left;"><span class="label" style="color: #783f04;">4.2.1</span><span style="color: #783f04;"> What to Represent: A High-Level View </span></h4><p class="p">In their initial discussion of ‘what to represent’ decisions, <a class="xref bibr" data-sectionid="#REFe-r-055" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-055"><span class="show-for-sr">Reference Harvard, Winsberg, Symons and Adibi</span>Harvard et al. (2021)</a>
invite us to think in the big picture: they note that ‘social and
ethical values shape our views about what is necessary or important – or
even acceptable – to reason about using scientific models’. In other
words, the foremost ‘what to represent’ decision in modelling concerns
what topic to study, which is long-recognized to reflect social and
ethical values (<a class="xref bibr" data-sectionid="#REFe-r-075" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-075"><span class="show-for-sr">Reference Longino</span>Longino 1990</a>; <a class="xref bibr" data-sectionid="#REFe-r-114" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-114"><span class="show-for-sr">Reference Weber, Shils and Finch</span>Weber 1949</a>).
At this level, we can recognize that scientists’ decision to build a
model of the Earth’s climate, for example, reflects their judgement that
studying this topic is a good and worthwhile pursuit. We can also
recognize that the decision to build a model of the Earth’s climate
reflects the judgement that to do so is a <i class="italic">priority</i>:
at least in some sense, it is more pressing or important than studying
the climate on Mars (i.e., even if scientists would agree that studying
the climate on Mars is also good and worthwhile, they accept the
opportunity cost in this context and choose Earth over Mars).
Furthermore, building a model of the Earth’s climate reflects the
judgement that doing so is <i class="italic">defensible</i>: that is, there are no ethical reasons <i class="italic">not</i>
to model the Earth’s climate, as there might be not to engage in
certain other research practices (e.g., experimental testing on
animals).</p><p class="p">We can characterize the notion of ‘values’ in
the loose and general way we do here, appealing to what’s considered
‘important’ or ‘worthwhile’ or ‘a priority’ or ‘defensible’, or we can
tighten the notion up with a more abstract framework coming from
decision theory, defining ‘values’ in the way that decision theorists
define ‘preferences over prospects’ – that is, in terms of what states
of affairs in the world an agent prefers to have obtain over another.
For example, to say that an agent thinks ‘building a model of the
Earth’s climate is a good and worthwhile pursuit’ means the agent
prefers the state of affairs in a world where the model is built (and
the time and money expended to do so) to the state of affairs in a world
where the model is not built. To say that ‘building a model of the
Earth’s climate is more pressing or important than studying the climate
on Mars’ means the agent prefers the state of affairs where a model of
the Earth’s climate exists and a model of Mars’ climate doesn’t, over
the opposite state of affairs. To say that ‘building a model of the
Earth’s climate is <i class="italic">defensible</i>’ might mean
something like ‘a reasonably large number of reasonable people prefer
the state of the world where the model exists to the one where it
doesn’t’. In what follows, we will sometimes use the more concrete
language of the form ‘X believes Y is <span class="page-marker"></span>a
worthwhile project’ or ‘X thinks Y is more important than Z’. But it
will also sometimes be more helpful to speak more abstractly in the
language of decision theory of preferences over prospects.</p><p class="p">In their discussion of big-picture ‘what to represent’ decisions in modelling, what <a class="xref bibr" data-sectionid="#REFe-r-055" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-055"><span class="show-for-sr">Reference Harvard, Winsberg, Symons and Adibi</span>Harvard et al. (2021)</a>
remind us is that not everyone supports building a scientific model for
every purpose: any one of us might object to a model-building project
if its goal were to highlight essential moral differences between men
and women, for example (<a class="xref bibr" data-sectionid="#REFe-r-065" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-065"><span class="show-for-sr">Reference Kitcher</span>Kitcher 2010</a>), or if its inevitable consequence would be to delay a time-sensitive Alpine rescue operation.</p></div><div class="sec" data-magellan-destination="C-sec-11" id="C-sec-11"><h5 class="C title"><span class="label">4.2.2</span> Fine-Grained Representational Decisions </h5><p class="p">But
of course, we can zoom into ‘what to represent’ decisions in modelling
with far more granularity than just looking at the research topic
itself: when we say a model is ‘of the Earth’s climate’, we typically
mean that the model represents multiple attributes of that system in
various different ways. What are some finer-grained ‘what to represent’
decisions? As we discussed in <a class="xref sec" data-sectionid="#A-sec-3" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#A-sec-3">Section 1</a>,
models include both variables and parameters: crudely, a model’s
variables are the quantities it associates with a system that vary in
space and time, while parameters are fixed quantities that help to
specify the mathematical relationships between variables. For example,
in a SEIR model, the value of R0 is a parameter, and the number of
people infected at a given time is a variable.<a class="xref footnote" data-sectionid="#FN-fn-9" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#FN-fn-9"><span class="show-for-sr">Footnote </span><sup class="sup">9</sup></a>
Once we know a model’s variables, we can talk about what the ‘outcome’
of a single run of a model is in terms of the final values that the
variables take. And, last but not least, we can sometimes observe
model-builders having the final values of certain variables as the
outcomes of interest. The whole point of the of the model, from the
point of view of the modellers, is to calculate the final value of these
variables. We could call those the ‘end points’ of the model, but we
should be aware that which outcomes count as end points is not a feature
of the model: it is simply a fact about the model user. And a model
that is built by one person with some set of end points in mind might
end up being used by another user who picks a new and different set of
outcomes as her end points.</p><p class="p">Let’s explore the question
of the sense in which the finer-grained representational decisions
concerning variables, parameters, and outcomes of interest (or ‘end
points’) are value-laden. Recall that what we mean by value-laden here
is that making these decisions will reflect not only the epistemic
judgements of the <span class="page-marker"></span>agents making them,
but their preferences over the possible prospects of making these
decisions. To see why this is so, it is useful to think about how the
modelling process typically goes. Upfront, there is usually a decision
about which variables the final values of which are the end points of
interest. In a climate model, for example, the global mean surface
temperature of the Earth in some future decade, conditional on some
emissions pathway, might be our end point of interest – or, it might be
that researchers are interested in regional values of other variables,
like precipitation or humidity. The fact that choosing an end point of <i class="italic">interest</i> is value-laden should be pretty obvious: people who think American agricultural production is especially <i class="italic">important</i> are apt to be especially <i class="italic">interested</i>
in precipitation values in North America. To put this in the formal
language of decision theory, if I prefer the prospect of having improved
American agricultural production over the prospect of improved European
agricultural production (of some given amount) then I might be more
interested in a model whose primary end point is North American
precipitation.</p><p class="p">Once an end point is chosen, choosing
which other variables and parameters to include is also value-laden, but
in a more subtle way. The process goes like this: having chosen global
mean surface temperature as our end point, we decide which other
variables, and which parameters, are <i class="italic">causally relevant</i>
in a model designed to project global mean surface temperature. This
decision is value-laden both because it is dependent on the end points
of interest, but also because whether, say, a particular variable will
be considered to be ‘causally relevant’ might depend on the degree of
accuracy to which we hope to forecast the value of the end point. And,
of course, whether or not a forecast is considered ‘accurate enough’ is a
value judgement too. Whether a climate model needs to include carbon
sinks and sources, for example, might depend on how accurate we want the
results of the model to be. If I prefer the prospect of having a model
that’s available sooner rather than later and is less costly to build,
but which is slightly less accurate, then I might choose to omit carbon
sinks and sources from the model. If I prefer the prospect of having a
model that takes longer and is more costly to build, but will provide
more accurate information, I might do the work of including carbon sinks
and sources.</p></div><div class="sec" data-magellan-destination="C-sec-12" id="C-sec-12"><h5 class="C title"><span class="label">4.2.3</span> Inferential Decisions </h5><p class="p">As we discussed in <a class="xref sec" data-sectionid="#A-sec-5" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#A-sec-5">Section 3</a>, decisions in the modelling process include not only representational decisions but also <i class="italic">inferential decisions</i>.
That is, at the stage of model interpretation, the goal is generally to
use model results to infer facts about the world. This requires
assessing the ethical consequences of endorsing a fact as true when it
is actually false, of remaining silent, and so <span class="page-marker"></span>on;
so it is a value-laden decision. Furthermore, many facts that we might
decide to infer from model results will embed normative, that is,
value-laden, presuppositions (<a class="xref bibr" data-sectionid="#REFe-r-002" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-002"><span class="show-for-sr">Reference Alexandrova</span>Alexandrova 2017</a>).
Inferential decisions based on model results have a straightforward
moral significance, particularly when these decisions will directly
influence public policy.</p></div></div><div class="sec" data-magellan-destination="B-sec-17" id="B-sec-17"><h4 class="B title"><span class="label">4.3</span> The Ethical Significance of Including and Excluding Variables </h4><p class="p">Of
course, no model is a perfectly accurate and comprehensive depiction of
a target system: all models include some variables and omit others. If a
model does not include certain variables, this could signify one of two
things. First, it might signify that the model-builders deemed that the
variable was neither ethically significant itself nor causally relevant
to an ethically significant outcome. Second, it might signify that the
model-builders determined that, regardless of whether the variable is
ethically significant, it is not the job of the model in question to
inform us about that variable, which is often perfectly reasonable and
unavoidable. Determining what representational decisions say about
model-builders and the outcomes they consider morally significant is a
complex matter, and because it is seldom possible to hear their
perspectives first-hand, some people might say that observers can only
speculate. Nonetheless, it is at least important to consider the <i class="italic">ethical significance</i>
of representational decisions in the context of how model-builders
intend a model to guide policy. Ordinary climate models, for example,
include neither the economic costs of mitigation nor the economic damage
of climate change, whereas so-called integrated assessment models
(IAMs) (see <a class="xref bibr" data-sectionid="#REFe-r-119" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-119"><span class="show-for-sr">Reference Winsberg</span>Winsberg 2018</a>)
do include these. We should not conclude from this that the developers
of ordinary climate models do not deem the economic costs of mitigation
nor the economic damage of climate change to be morally significant, but
rather that they expect their model results to play a less direct role
in policy-making (with knowledge of economic factors coming from another
source) than do the builders of IAMs. On the other hand, when an IAM is
being used to directly inform policy, it is perfectly reasonable to ask
for a discussion to establish whether all of the ethically significant
outcomes were included in the model (<a class="xref bibr" data-sectionid="#REFe-r-119" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-119"><span class="show-for-sr">Reference Winsberg</span>Winsberg (2018)</a> and <a class="xref bibr" data-sectionid="#REFe-r-042" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-042"><span class="show-for-sr">Reference Frisch, Lloyd and Winsberg</span>Frisch (2018</a>) discuss the question of whether or not they do).</p><p class="p">As we noted, ‘what to represent’ decisions are often informed by <i class="italic">how</i> or <i class="italic">whether</i>
the representation of something can be achieved. For example, if there
are no high-quality data on some aspect of a target system, this may be
the reason it is not represented in a model. This raises the question:
do practical barriers negate the social and ethical significance of
building a model that omits certain aspects of a system? Following <a class="xref bibr" data-sectionid="#REFe-r-055" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-055"><span class="show-for-sr">Reference Harvard, Winsberg, Symons and Adibi</span>Harvard et al. (2021)</a>, we say the answer is no, for <i class="italic">at least</i> the following reasons: (1) modellers <i class="italic">could</i> use low-quality data <span class="page-marker"></span>or estimates in order to represent it; (2) modellers <i class="italic">could</i>
decline to build the model on the grounds that there are no adequate
data or estimates on an important aspect of the system, and the model
results would be problematically incomplete without them. Deciding
between these types of alternatives involves not only considering
epistemic values, but weighing social and ethical values (cf. (<a class="xref bibr" data-sectionid="#REFe-r-093" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-093"><span class="show-for-sr">Reference Peschard and van Fraassen</span>Peschard and van Fraassen 2014</a>; <a class="xref bibr" data-sectionid="#REFe-r-120" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-120"><span class="show-for-sr">Reference Winsberg</span>Harvard and Winsberg 2022</a>)).</p><p class="p">In their discussion of ‘how to represent’ decisions, <a class="xref bibr" data-sectionid="#REFe-r-055" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-055"><span class="show-for-sr">Reference Harvard, Winsberg, Symons and Adibi</span>Harvard et al. (2021</a>) focus on the fact that representational decisions often centre around what <i class="italic">inputs</i>
to use for values of parameters in the model: for example, what data
sources should be used to model the rates of cloud formation in a
climate model grid cell, or the probability of hospital admission if
people in various age brackets are infected with a virus. Decisions
around what inputs to use in a model can be challenging to make,
particularly because different data sources generally have different
limitations and higher degrees of adequacy for some purposes than
others. In general, these decisions are value-laden because the choice
to use one input over another will have an effect on model results. In
other words, people with different views around whether it is worse to
overestimate or underestimate the value of a variable, for example, will
be inclined to use different inputs to populate a parameter that
influences that variable, if there is a choice to be made between
inputs.</p></div><div class="sec" data-magellan-destination="B-sec-18" id="B-sec-18"><h4 class="B title"><span class="label" style="color: #783f04;">4.4</span><span style="color: #783f04;"> The Expected Utility of Representational Decisions </span></h4><p class="p">To
put this discussion in the language of decision theory, I might prefer
the expected downstream prospects of making one representational choice
over another. Why would that be? Suppose I think one representational
choice has high probability of overestimating the danger of climate
change and a low probability of underestimating it. And that I think the
other available representational choice will be the opposite. I might
pick the first choice because my expected utility for the first choice
is higher. That is, the utility of overestimating multiplied by its
probability on the first choice, plus (or minus) the utility of
underestimating multiplied by its probability on the first choice, is
higher than all of the above on the second choice.</p><p class="p">
There are two important things to note here. First, notice that in this
slightly more formal presentation, we have moved to the general language
of a representational choice, rather than the language of ‘what inputs
to use for a parameter’. This is because the point here is very general.
Considerations like the above can kick in for all representational
decisions, whether they be choices of what to represent, or how to
represent – including what inputs to use, or what mathematical function
to use, or anything of the sort. Second, in our formal <span class="page-marker"></span>presentation,
we imagine that the person making the representational decision knows
the probabilities and utilities exactly. But this will not generally be
the case. In order for me to choose representation R1 over R2, I need
only know that
</p><div class="disp-formula" data-magellan-destination="EQN-df-7" data-mathjax-status="texmath alt-graphic" id="EQN-df-7">
<span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"><span class="MathJax_Preview" color="inherit"></span><span class="MathJax" data-mathml="<math class="math" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mtext>U</mtext><mn>1</mn><mo>&#xD7;</mo><mtext>P</mtext><mn>1</mn><mo>+</mo><mtext>U</mtext><mn>2</mn><mo>(</mo><mn>1</mn><mo>&#x2212;</mo><mtext>P</mtext><mn>1</mn><mo>)</mo><mo>&gt;</mo><mo>(</mo><mtext>U</mtext><mn>1</mn><mo>&#xD7;</mo><mtext>P</mtext><mn>2</mn><mo>+</mo><mtext>U</mtext><mn>2</mn><mo>(</mo><mn>1</mn><mo>&#x2212;</mo><mtext>P</mtext><mn>2</mn><mo>)</mo></mrow></math>" id="MathJax-Element-18-Frame" role="presentation" style="position: relative;" tabindex="0"><nobr aria-hidden="true"><span class="math math" id="MathJax-Span-222" style="display: inline-block; width: 26.481em;"><span style="display: inline-block; font-size: 124%; height: 0px; position: relative; width: 21.326em;"><span style="clip: rect(1.445em, 1021.23em, 2.714em, -1000em); left: 0em; position: absolute; top: -2.33em;"><span class="mrow" id="MathJax-Span-223"><span class="mrow" id="MathJax-Span-224"><span class="mtext" id="MathJax-Span-225" style="font-family: MathJax_Main;">U</span><span class="mn" id="MathJax-Span-226" style="font-family: MathJax_Main;">1</span><span class="mo" id="MathJax-Span-227" style="font-family: MathJax_Main; padding-left: 0.222em;">×</span><span class="mtext" id="MathJax-Span-228" style="font-family: MathJax_Main; padding-left: 0.222em;">P</span><span class="mn" id="MathJax-Span-229" style="font-family: MathJax_Main;">1</span><span class="mo" id="MathJax-Span-230" style="font-family: MathJax_Main; padding-left: 0.222em;">+</span><span class="mtext" id="MathJax-Span-231" style="font-family: MathJax_Main; padding-left: 0.222em;">U</span><span class="mn" id="MathJax-Span-232" style="font-family: MathJax_Main;">2</span><span class="mo" id="MathJax-Span-233" style="font-family: MathJax_Main;">(</span><span class="mn" id="MathJax-Span-234" style="font-family: MathJax_Main;">1</span><span class="mo" id="MathJax-Span-235" style="font-family: MathJax_Main; padding-left: 0.222em;">−</span><span class="mtext" id="MathJax-Span-236" style="font-family: MathJax_Main; padding-left: 0.222em;">P</span><span class="mn" id="MathJax-Span-237" style="font-family: MathJax_Main;">1</span><span class="mo" id="MathJax-Span-238" style="font-family: MathJax_Main;">)</span><span class="mo" id="MathJax-Span-239" style="font-family: MathJax_Main; padding-left: 0.278em;">></span><span class="mo" id="MathJax-Span-240" style="font-family: MathJax_Main; padding-left: 0.278em;">(</span><span class="mtext" id="MathJax-Span-241" style="font-family: MathJax_Main;">U</span><span class="mn" id="MathJax-Span-242" style="font-family: MathJax_Main;">1</span><span class="mo" id="MathJax-Span-243" style="font-family: MathJax_Main; padding-left: 0.222em;">×</span><span class="mtext" id="MathJax-Span-244" style="font-family: MathJax_Main; padding-left: 0.222em;">P</span><span class="mn" id="MathJax-Span-245" style="font-family: MathJax_Main;">2</span><span class="mo" id="MathJax-Span-246" style="font-family: MathJax_Main; padding-left: 0.222em;">+</span><span class="mtext" id="MathJax-Span-247" style="font-family: MathJax_Main; padding-left: 0.222em;">U</span><span class="mn" id="MathJax-Span-248" style="font-family: MathJax_Main;">2</span><span class="mo" id="MathJax-Span-249" style="font-family: MathJax_Main;">(</span><span class="mn" id="MathJax-Span-250" style="font-family: MathJax_Main;">1</span><span class="mo" id="MathJax-Span-251" style="font-family: MathJax_Main; padding-left: 0.222em;">−</span><span class="mtext" id="MathJax-Span-252" style="font-family: MathJax_Main; padding-left: 0.222em;">P</span><span class="mn" id="MathJax-Span-253" style="font-family: MathJax_Main;">2</span><span class="mo" id="MathJax-Span-254" style="font-family: MathJax_Main;">)</span></span></span><span style="display: inline-block; height: 2.33em; width: 0px;"></span></span></span><span style="border-left: 0px solid; display: inline-block; height: 1.351em; overflow: hidden; vertical-align: -0.366em; width: 0px;"></span></span></nobr></span></span></span></span></div></div></div><div class="sec" data-magellan-destination="B-sec-18" id="B-sec-18"><div class="disp-formula" data-magellan-destination="EQN-df-7" data-mathjax-status="texmath alt-graphic" id="EQN-df-7"><span class="alternatives"><span class="mathjax-tex-wrapper" data-mathjax-type="texmath"><span class="tex-math mathjax-tex-math mathjax-on"></span></span></span>
</div><p class="p continuation">
where U1 and U2 are the utilities I assign to overestimating and
underestimating, respectively, and P1 and P2 are the probabilities of
overestimating on R1 and R2.</p><p class="p">Note that I needn’t know in
virtue of what exact values of P1, P2, U1, and U2 this obtains: I need
only holistically grasp that the inequality obtains for whatever reason.
As representational decisions get more and more complex, intermixing
with other representational decisions, and as outcomes move from being
binary to being continuous and multidimensional, it becomes more and
more likely that model-builders are employing the kind of reasoning we
explain here only in the most implicit sense. To ask modellers, then, to
articulate all of the values and probabilities that drive their
representational decisions would be impossible, in practice, to satisfy
as a general requirement (we discuss this further in <a class="xref sec" data-sectionid="#B-sec-21" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#B-sec-21">Section 4.7</a>).
The degree to which these considerations tend to be implicit and
suppressed will be important to our discussion. We can call this claim,
that model-builders can rarely articulate the full epistemic and
normative considerations that underpin their representational decisions,
the <i class="italic">inarticulability thesis</i> (cf. <a class="xref bibr" data-sectionid="#REFe-r-119" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-119"><span class="show-for-sr">Reference Winsberg</span>Parker and Winsberg 2018</a>; <a class="xref bibr" data-sectionid="#REFe-r-118" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-118"><span class="show-for-sr">Reference Winsberg</span>Winsberg 2012</a>, <a class="xref bibr" data-sectionid="#REFe-r-119" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-119"><span class="show-for-sr">Reference Winsberg</span>2018</a>).</p><p class="p">Returning
to the specific issue of making choices of parameter values: to be
sure, modellers can often perform a sensitivity analysis in order to
explore the effect of using different inputs for different parameters.
However, this generally leaves a value-laden decision to be made around
which inputs will be used for the ‘main’ (or base-case) analysis versus a
sensitivity analysis (keeping in mind that the results of a main
analysis may be the ones emphasized in reports to policy-makers and/or
the media). It is also a value-laden decision whether model users are
better served by a precise estimate or one that fully reflects the range
of uncertainty that comes from our imperfect knowledge of parameter
values. Finally, there are often limits to how many sensitivity analyses
modellers can perform in a timely way.</p></div><div class="sec" data-magellan-destination="B-sec-19" id="B-sec-19"><h4 class="B title"><span class="label" style="color: #783f04;">4.5</span><span style="color: #783f04;"> Case Study: Imperial College London Model </span></h4><div class="sec" data-magellan-destination="C-sec-13" id="C-sec-13"><h5 class="C title"><span class="label">4.5.1</span> Background </h5><p class="p">The ICL model, running on CovidSim, was developed by Neil Ferguson and his team and was based on an earlier influenza model (<a class="xref bibr" data-sectionid="#REFe-r-036" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-036"><span class="show-for-sr">Reference Ferguson, Cummings and Cauchemez</span>Ferguson et al. 2005</a>, <a class="xref bibr" data-sectionid="#REFe-r-037" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-037"><span class="show-for-sr">Reference Ferguson, Cummings and Fraser</span>2006</a>, <a class="xref bibr" data-sectionid="#REFe-r-038" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-038"><span class="show-for-sr">Reference Ferguson, Laydon and Nedjati Gilani</span>2020</a>). The primary purpose of the model was to project the impact of various <span class="page-marker"></span>possible
policy choices on Covid-19 deaths and demand for hospital beds,
including intensive care unit (ICU) beds. Specifically, the report
considers a variety of permutations from a set of possible policy
choices (in addition to a potential ‘do nothing’ policy), consisting of
the following elements, shown in <a class="xref fig" data-sectionid="#FIGm-fig-8" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#FIGm-fig-8">Figure 8</a>.</p><p class="p"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj2wBNacShP4nw9-qKwBnNyXb7EXjzKtytRm2H0z5BXuc386zZ03QimruQIMhznCnI9rI3X5MdYQCQvDdNqP53MVkCyu_2CicIqfkLsjpUiOmabTE-OtgDw60YpKCR4kQ4GHTuSAB1SxqhkScSvPXyKm1LakYhNKoZ9yMi5V-8_v1WdJw7br3CWkhxV8Yg/s751/urn%20cambridge.org%20id%20binary%2020240108085526454-0965%209781009029346%2046821fig8.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="575" data-original-width="751" height="386" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj2wBNacShP4nw9-qKwBnNyXb7EXjzKtytRm2H0z5BXuc386zZ03QimruQIMhznCnI9rI3X5MdYQCQvDdNqP53MVkCyu_2CicIqfkLsjpUiOmabTE-OtgDw60YpKCR4kQ4GHTuSAB1SxqhkScSvPXyKm1LakYhNKoZ9yMi5V-8_v1WdJw7br3CWkhxV8Yg/w504-h386/urn%20cambridge.org%20id%20binary%2020240108085526454-0965%209781009029346%2046821fig8.png" width="504" /></a></div><br /> <!--[if !mso]>
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<p></p><p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">The subsets of these NPIs considered to be ‘mitigation’ strategies that
the model explored were: PC; CI; CI&HQ; CI&SD; CI&HQ&SDO; and
PC&CI&HQ&SDO. All of these strategies are ‘shown’ to result in
massive overwhelm of the healthcare system, as shown in </span><a data-sectionid="#FIGm-fig-9" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#FIGm-fig-9"><span lang="EN-US" style="mso-ansi-language: EN-US;">Figure
9</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">.</span></p>
<section>
<p class="MsoNormal" style="text-align: justify;"><img border="0" class="aop-lazy-load-image graphic" data-img-name="Figure 9" data-large-thumb="urn:cambridge.org:id:binary-alt:20240108085621-52199-largeThumb-png-46821fig9.jpg" data-medium-thumb="urn:cambridge.org:id:binary-alt:20240108085621-58003-mediumThumb-png-46821fig9.jpg" data-optimised-image="urn:cambridge.org:id:binary-alt:20240108085621-27386-optimisedImage-png-46821fig9.jpg" data-small-thumb="urn:cambridge.org:id:binary-alt:20240108085621-17093-smallThumb-png-46821fig9.jpg" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20240108085526454-0965:9781009029346:46821fig9.png?pub-status=live" data-zoomable="false" height="32" src="file:///C:\Users\Usuari\AppData\Local\Temp\msohtmlclip1\01\clip_image001.gif" width="32" /></p>
<p class="p" style="text-align: justify;"><span class="label"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-fareast-font-family: "Times New Roman"; mso-fareast-theme-font: major-fareast;"></span></span></p></section><section>Figure 9<span lang="EN-US" style="mso-ansi-language: EN-US;"> Report 9’s projections for critical
care (ICU) bed requirements under various mitigation and suppression strategies.</span><p></p>
<p class="MsoNormal" style="text-align: justify;"><strong><span lang="EN-US" style="font-family: "Calibri","sans-serif"; mso-ansi-language: EN-US; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: "Times New Roman"; mso-bidi-theme-font: minor-bidi; mso-hansi-theme-font: minor-latin;">Source:</span></strong><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span><a data-sectionid="#REFe-r-038" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-038"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Ferguson, Laydon and Nedjati Gilani</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Ferguson et al. (2020</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">, 8).</span></p>
</section>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">‘Suppression’ is defined as being able to ‘reduce R to close to 1 or
below’ (</span><a data-sectionid="#REFe-r-038" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-038"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Ferguson, Laydon and Nedjati Gilani</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Ferguson et al. 2020</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">, 10). The report cautions that, at
least, ‘[c]ase isolation, general social distancing, and either school and
university closure or home quarantine’ are required to achieve suppression, but
the only ‘suppression’ strategy whose simulation results are presented in the
report is the combination of all four elements (i.e., ‘[h]ome isolation of
cases, household quarantine, school and university closures, and social
distancing of the entire population’). </span><a data-sectionid="#REFe-r-038" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-038"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Ferguson, Laydon and Nedjati Gilani</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Ferguson et al. (2020)</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;"> show the simulation results of
instituting these policies cycling on and off for the 18 months they expected
it to take to achieve a vaccine (assuming a baseline R<sub>0</sub> value of
2.2) (</span><a data-sectionid="#FIGm-fig-10" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#FIGm-fig-10"><span lang="EN-US" style="mso-ansi-language: EN-US;">Figure
10</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">).</span></p>
<section>
<p class="MsoNormal" style="text-align: justify;"><br /></p>
<p class="p" style="text-align: justify;"><span class="label"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-fareast-font-family: "Times New Roman"; mso-fareast-theme-font: major-fareast;"></span></span></p></section><section>Figure 10<span lang="EN-US" style="mso-ansi-language: EN-US;"> Adaptive triggering of suppression
illustrated in Report 9.</span><p></p>
<p class="MsoNormal" style="text-align: justify;"><strong><span lang="EN-US" style="font-family: "Calibri","sans-serif"; mso-ansi-language: EN-US; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: "Times New Roman"; mso-bidi-theme-font: minor-bidi; mso-hansi-theme-font: minor-latin;">Source:</span></strong><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span><a data-sectionid="#REFe-r-038" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-038"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Ferguson, Laydon and Nedjati Gilani</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Ferguson et al. (2020</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">, 12).</span></p>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;"></span></p></section>In building the model, the modellers aimed to steer policy
choices by the UK Government, as well, to a lesser degree, as other governments
around the world, by highlighting the extremely high death toll, and burden on
healthcare systems, that would ensue from pursuing all but the last of those
policy choices (<a data-sectionid="#REFe-r-021" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-021"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Broadbent and Streicher</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Broadbent and Streicher 2022</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">). </span><a data-sectionid="#REFe-r-038" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-038"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Ferguson, Laydon and Nedjati Gilani</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Ferguson et al. (2020</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">, 16) said: </span><p></p>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">We therefore conclude that epidemic suppression is the only viable
strategy at the current time. The social and economic effects of the measures
which are needed to achieve this policy goal will be profound. Many countries
have adopted such measures already, but even those countries at an earlier
stage of their epidemic (such as the United Kingdom) will need to do so
imminently.</span></p>
<h5 style="text-align: justify;"><span class="label"><span lang="EN-US" style="mso-ansi-language: EN-US;">4.5.2</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;"> What Is Represented? Study Topic and End
Points in Context </span></h5>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">Following the basic framework outlined in </span><a data-sectionid="#B-sec-16" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#B-sec-16"><span lang="EN-US" style="mso-ansi-language: EN-US;">Section
4.2</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">, we can begin
to outline some big-picture value judgements that went into building the ICL
model. First and foremost, building this model reflects the judgement that it
is an ethically defensible project: in other words, that it is desirable to
seek more information about the likely effects of certain policy choices on
Covid-19 deaths and hospitalizations. It is important to realize that this
needn’t be taken for granted. It is not hard to imagine people having a set of
values according to which many of the policy choices explored in the model,
particularly those that involved restrictions on various liberties, constitute
violations of fundamental rights and should not even be considered. It is also
not hard to imagine having a set of values, and a set of prior beliefs about
the severity of the virus, on which it would have been morally unacceptable to
delay action even long enough to carry out the modelling project – or to accept
any risk at all that the model would erroneously steer policy-makers away from
drastic suppression measures. It is only according to a certain set of values,
and a certain set of prior beliefs given the state of evidence at the time, and
a certain confidence that a minimally informative model could be built, that it
would appear the right thing to build a model like the ICL model and to
consider using its output to evaluate the costs and benefits of the kinds of
policy choices that are explored in the model.</span></p>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">Next comes representational decisions concerning what the primary end
points of the model ought to be. In the ICL model, the variables taken to be
primary end points were Covid-19 infections, hospitalizations, occupancy of
intensive care hospital beds due to Covid-19, and Covid-19 deaths. Including these
as end points in a pandemic mitigation model signifies an ethical judgement
that these are important outcomes to consider when reasoning about what NPIs to
implement to help slow the pandemic. What about the outcomes that were excluded
from the model? Recall that if a model does <em>not</em> include certain
outcomes this could signify one of two things. The fact that the ICL model did
not project the impact of the possible policy choices explored on things like
educational outcomes and economic output could be interpreted in two ways:
either as suggesting that these outcomes were not ethically significant, or as
suggesting that decision-makers would have to look elsewhere to get this
information.</span></p>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">This highlights the degree to which models must be understood in the context
of the intended purposes of their users. It is <em>not built into the ICL model</em>
whether it is intended to be a tactical model that can be used, directly, to
assess different policy choices, or intended to be used alongside other sources
of information about the effects these choices would have on things other than
Covid-19 deaths, hospitalizations, and so on. But insofar as we can read the
famous ‘Report 9’ (</span><a data-sectionid="#REFe-r-038" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-038"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Ferguson, Laydon and Nedjati Gilani</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Ferguson et al. 2020</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">), which was the primary document
produced with the help of the ICL model, as a policy guidance document,</span><a data-sectionid="#FN-fn-10" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#FN-fn-10"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Footnote </span></span><sup><span lang="EN-US" style="mso-ansi-language: EN-US;">10</span></sup></a><span lang="EN-US" style="mso-ansi-language: EN-US;"> we can read it as suggesting that these
outcomes were relatively ethically insignificant in comparison to the outcomes
that were in fact part of the model.</span></p>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">Here is another way to frame the same point. If you happen to believe
that policy choices that were guided with the help of a model were made without
due consideration of a certain dimension of their consequences, it is difficult
to assign moral responsibility for that moral failure. Responsibility could lie
with the model-builders for failing to include those dimensions among their
model end points, or it could lie with policy-makers for relying on only one
kind of expertise (say, infectious disease epidemiology) when in fact two kinds
of expertise (educational psychology as well as infectious disease
epidemiology) were required. Or responsibility could fall on both parties. But
in the case of Report 9, it seems clear that the ICL group judged that their
model results alone could guide policy, and that their recommended strategy
should be implemented <em>despite its social and economic effects</em>: ‘We
therefore conclude that epidemic suppression is the only viable strategy at the
current time. The social and economic effects of the measures which are needed
to achieve this policy goal will be profound’ (</span><a data-sectionid="#REFe-r-038" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-038"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Ferguson, Laydon and Nedjati Gilani</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Ferguson et al. 2020</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">, 16).</span></p>
<h3 style="text-align: justify;"><span class="label" style="color: #660000;"><span lang="EN-US" style="mso-ansi-language: EN-US;">4.5.3</span></span><span lang="EN-US" style="color: #660000;"> Finer-Grained Representational Decisions </span></h3>
<h6 style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">Other
Variables </span></h6>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">While representational decisions concerning what variables to include as
end points in the ICL model (e.g., ‘Should learning loss be an end point in the
model?’) have a particularly obvious ethical importance, decisions about what <em>other</em>
variables to represent are value-laden too. One example is the decision to
represent the ages of inhabitants of the United States and the United Kingdom,
and their status as workers or students, but not their race, income, postal
code, or occupation. As a result of this decision, the ICL model is not
adequate for exploring research questions like: (1) whether a mitigation or
suppression strategy will lead to a racially unjust distribution of infections,
hospitalizations, and deaths; or (2) whether such a strategy will
differentially affect people of different professions. For example, will the
closing of non-essential industries result in the burden of the disease falling
primarily on those who provide essential services? As we noted in </span><a data-sectionid="#A-sec-3" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#A-sec-3"><span lang="EN-US" style="mso-ansi-language: EN-US;">Section
1</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">, at the heart of
computational models like climate models and models of disease spread are the
bits of math that jointly create the behaviour of the model, given its starting
state, and the choice of counterfactual scenarios (from among all the infinite
possible counterfactual scenarios) that it is designed to investigate. Usually,
this requires picking other variables besides the ones being studied as end
points, the mathematical relationships between them, and the values of
parameters that feature in the equations that specify those relationships. The
ICL model, for example, does not include among its variables the time of year
and its possible impact on viral transmission. This makes it impossible for the
model to explore the possibility that the virus could come in waves due to an
underlying seasonality of the virus.</span></p>
<h6 style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">Parameters
</span></h6>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">Overall, the ICL model employed equations with almost 900 different
parameters. Given its purpose, the model needed inputs for the expected
Covid-19 death rate, hospitalization rate, and ICU admittance rate for every
100,000 people infected in each of several different age brackets, given in a
table in </span><a data-sectionid="#FIGm-fig-11" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#FIGm-fig-11"><span lang="EN-US" style="mso-ansi-language: EN-US;">Figure
11</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">.</span></p>
<p class="MsoNormal" style="text-align: justify;"></p><section><br /><p class="p" style="text-align: justify;"><span class="label"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-fareast-font-family: "Times New Roman"; mso-fareast-theme-font: major-fareast;"></span></span></p></section><article class="book elements BITS"><section>Figure
11<span lang="EN-US" style="mso-ansi-language: EN-US;"> Estimates of
virus morbidity and fatality from </span><a data-sectionid="#REFe-r-112" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-112"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Verity, Okell and Dorigatti</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Verity et al. (2020</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">) used in Report 9.</span><p></p>
<p class="p" style="text-align: justify;"><strong><span lang="EN-US" style="mso-ansi-language: EN-US; mso-fareast-font-family: "Times New Roman"; mso-fareast-theme-font: major-fareast;">Note:</span></strong><span lang="EN-US" style="mso-ansi-language: EN-US;"> The table is somewhat confusing because
fatalities are given as a percentage of infections, but hospitalizations are
given as a percentage of ‘symptomatic infections’, and ICU beds are given as a
percentage of hospitalizations. If we dig into the code, we can determine that
two-thirds of infections are expected to be symptomatic, and so the percentage
of overall infections that will require hospitalization is 0.66 times the
numbers in this column.</span></p>
<p class="MsoNormal" style="text-align: justify;"><strong><span lang="EN-US" style="font-family: "Calibri","sans-serif"; mso-ansi-language: EN-US; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: "Times New Roman"; mso-bidi-theme-font: minor-bidi; mso-hansi-theme-font: minor-latin;">Source:</span></strong><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span><a data-sectionid="#REFe-r-038" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-038"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Ferguson, Laydon and Nedjati Gilani</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Ferguson et al. (2020</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">, table 1, 5).</span></p>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;"></span></p></section>At the time that Report 9 came out, in the middle of March
2020, none of the correct values of these inputs were well known. (Indeed, our
estimates of them at the time of writing now remain imperfect.) One moral
judgement that could have been made at the time was that our confidence
intervals around these input values were too large to make modelling the impact
of different possible policy choices a worthwhile project. If, for example, one
thought that the possible values for the infection fatality rate of SARS-CoV-2
at the time could be anywhere between 0.1% and 3%, and one thought, as the ICL
team clearly did, that you had to pick a single value (at least relative to
each age bracket) then you might think that a model of this kind would be
useless for assessing the costs and benefits of policy choices. That’s because
you might think that it is fairly obvious that, if the infection fatality rate
(IFR) is 0.1%, the most stringent strategies are almost certainly going to look
too costly, and if it’s 3%, they are almost certainly going to look like they
are morally required. If you thought that, you might conclude that getting a
better estimate of the numbers in Table 2 was a much higher priority than
building a model like the ICL model – indeed, it might even be a condition on
the moral permissibility of building it.<p></p>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">Since the ICL team obviously decided to proceed with the modelling
project, how did they choose those values? In early March 2020, researchers had
at least two sources of data available to them for the parameters in </span><a data-sectionid="#REFe-r-038" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-038"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Ferguson, Laydon and Nedjati Gilani</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Ferguson et al. (2020</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">, table 1) (</span><a data-sectionid="#REFe-r-112" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-112"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Verity, Okell and Dorigatti</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Verity et al, 2020</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">). The first was the Diamond
Princess (DP) cruise ship. This was the first ‘natural experiment’ of a
Covid-19 outbreak, where every single passenger had been tested for infection
and the health outcomes of each passenger were well known. The second was the
data available from the first ‘epicentre’ of the pandemic: Hubei province in
China. The advantages and disadvantages of each data source were clear. The
advantage of the DP data was that they were comprehensive. The exact number of
infected people was known as was the exact number of each health outcome. The
disadvantage was that the number of people was relatively small, and the age
structure of the population was unusual. Most of the passengers were rather old
and most of the crew were very young. There were very few intermediate-aged
people in the data set. The advantage of the Hubei dataset was the mirror image
of the disadvantages of the DP set. The dataset was large and every age
demographic was included. The main disadvantage was that while the numerators
for all of these outcomes were relatively well known,</span><a data-sectionid="#FN-fn-11" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#FN-fn-11"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Footnote </span></span><sup><span lang="EN-US" style="mso-ansi-language: EN-US;">11</span></sup></a><span lang="EN-US" style="mso-ansi-language: EN-US;"> the denominators were not known at all. That
is, how many people had died of Covid-19 in Hubei and how many had been
hospitalized was known, but it was not known how many infections this
represented. Knowing the <em>ratios</em> of these numbers, however, was crucial
to estimating the parameters needed for the ICL model.</span></p>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">The ICL group responded to this lacuna by looking at repatriation
flights from Hubei into the United States and Europe. That is, at the time of
the outbreak, citizens of the United States and some European countries were
evacuated from Hubei province and returned to their home countries. Each person
on these flights was carefully screened for infection with SARS-CoV-2. The ICL
group used the proportion of expatriates who were infected to estimate the
incidence of infection in Hubei at large. The disadvantages of these methods
were obvious. For one thing, this ended up being a very small sample. In total,
only six people were found to be infected on the flights.</span><a data-sectionid="#FN-fn-12" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#FN-fn-12"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Footnote </span></span><sup><span lang="EN-US" style="mso-ansi-language: EN-US;">12</span></sup></a><span lang="EN-US" style="mso-ansi-language: EN-US;"> Had, by chance, a seventh person tested
positive, then all the values in Table 2 would have been six-sevenths the size
they are. Another disadvantage was that the method assumed that very wealthy
and culturally outlying expatriates had a degree of infection that was
representative of the underlying population.</span></p>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">So the makers of the ICL model had at least two choices with regard to
this ‘how to represent’ question: they could have generated something like </span><a data-sectionid="#REFe-r-038" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-038"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Ferguson, Laydon and Nedjati Gilani</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Ferguson et al.’s (2020</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">) Table 1 from the Hubei data or
from the DP data. In what way did values influence this choice? The
model-builders could clearly see that the IFR and hospitalization rates that
came from the Hubei data were considerably higher than from the data from the
DP.</span><a data-sectionid="#FN-fn-13" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#FN-fn-13"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Footnote </span></span><sup><span lang="EN-US" style="mso-ansi-language: EN-US;">13</span></sup></a><span lang="EN-US" style="mso-ansi-language: EN-US;"> Thus, choosing the Hubei data made it more
likely the IFR would be overestimated than opting for the DP data, and less
likely that it would be underestimated. Thus, the more serious you consider the
harm of a Covid-19 death and/or hospitalization, and the less serious you
regard the various harms of the mitigation and suppression strategies being
considered, the more inclined you will be to choose the Hubei data, and perhaps
the more inclined you will be to use the repatriation flight method for
estimating the denominator than some other method that produced a lower IFR.</span></p>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">To return to a point we made in </span><a data-sectionid="#B-sec-18" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#B-sec-18"><span lang="EN-US" style="mso-ansi-language: EN-US;">Section
4.4</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;"> that we called
the <em>inarticulability thesis</em>, it is probably not reasonable for us to
think that Ferguson and his colleagues had in mind exact probabilities that the
Hubei data were closer to reality than the DP data. Recall that what rationally
guides representational decisions like the choice between the Hubei data and
the DP data is utility maximization, and that is a function of the values we
assign to the various states of affairs that might follow from our
representational decisions being good or bad, along with the probabilities we
assign to the various good and bad outcomes that could follow from particular
representational decisions.</span></p>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">Suppose that the members of the ICL group had one, and only one, binary
choice to make: use the DP data or the Hubei data. And suppose that they had in
mind exact probability distributions that they assigned to each dataset
overestimating and underestimating the effectiveness of each intervention to
various degrees, say P(DP) and P(H). And suppose they had a complicated
function of utilities over the space of each of those degrees of overestimation
and underestimation. Even articulating just this amount of information would be
overwhelming. But the situation is far worse than this: they have over 900
representational choices to make (just regarding the items they chose to
represent, excluding those they chose to ignore!), and most of those choices
are not in fact binary. They could, in principle, have had whole probability
distributions over possible values of all the numbers in </span><a data-sectionid="#REFe-r-038" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-038"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Ferguson, Laydon and Nedjati Gilani</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Ferguson et al.’s (2020</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">) table 1. This suggests that, in
fact, in a complex modelling situation like the one faced by the ICL team, a
whole host of representational choices have to be made in conjunction with each
other, with only a very coarse-grained and holistic assessment of the expected
utility of a small subset of a nearly infinite set of possible choices they
could have made. This is why the inarticulability thesis cautions that the
decision could only have been made based on a rough expected utility
estimation, without the modellers having precise values of probabilities and
utilities in mind. Their choice of data set is ultimately the result of an
inchoate mix of epistemic assessments and value commitments that cannot be
fully articulated.</span></p>
<h3 style="text-align: justify;"><span class="label" style="color: #660000;"><span lang="EN-US" style="mso-ansi-language: EN-US;">4.5.4</span></span><span lang="EN-US" style="color: #660000;"> Uncertainty </span></h3>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">Another ‘how to represent’ question concerns how to represent, in a
model, our uncertainty regarding the best value of the relevant parameters. For
example, the ICL model assumes that when people socially distance, their
probability of getting infected at home increases by 25%. But why 25%? Why not
35%? In fact, there were no data or research to support any particular choice
in the model, since we had few well-established rates for any past virus, let
alone rates for the novel SARS-CoV-2 virus. It is very common in modelling to
attempt to deal with uncertainty about the correct value of parameters by
running a sensitivity analysis. The idea is to run the model on a wide sample
of parameter values, in order to try to figure out how sensitive the model is
to small differences in those values, and to try to figure out which values do
the best job of capturing known data. In climate modelling, this is referred to
as doing a ‘perturbed physics ensemble’. But the ICL model was used to
influence major policy decisions in the absence of any study of parameter
sensitivity.</span></p>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">Fortunately, </span><a data-sectionid="#REFe-r-032" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-032"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Wouter, Arabnejad and Sinclair</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Edeling et al. (2021</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">) finally undertook such a study in
November 2020. They wrote: </span></p>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">Here we report on parametric sensitivity analysis and uncertainty
quantification of the code. From the 940 parameters used as input into
CovidSim, we find a subset of 19 to which the code output is most sensitive –
imperfect knowledge of these inputs is magnified in the outputs by up to 300%.
The model displays substantial bias with respect to observed data, failing to
describe validation data well. Quantifying parametric input uncertainty is
therefore not sufficient: the effect of model structure and scenario
uncertainty must also be properly understood.</span></p>
<p class="MsoNormal" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">(</span><a data-sectionid="#REFe-r-032" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-032"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Wouter, Arabnejad and Sinclair</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Edeling et al. 2021</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">, 128).</span></p>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">They found, in particular, that almost two-thirds of the differences in
the model’s results could be attributed to changes in just three especially
important variables: the length of the latent period during which an infected
person has no symptoms and can’t pass the virus on; the effectiveness of social
distancing; and how long after getting infected a person goes into isolation.
More importantly, </span><a data-sectionid="#REFe-r-032" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-032"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Wouter, Arabnejad and Sinclair</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Edeling et al. (2021</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">) found that for most values of
these parameters, 5–6 times as many people die during ‘suppression’ than the
model predicted using the values that the ICL group used. Thus, </span><a data-sectionid="#REFe-r-032" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-032"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Wouter, Arabnejad and Sinclair</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Edeling et al. (2021</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">) show that, had Ferguson’s group
done a sensitivity analysis over the range of parameter values that were
consistent with what was known about the virus, they would have been unable to
show that the suppression strategy they appeared to be recommending would have
had much benefit with respect to Covid-19 outcomes.</span></p>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">It is not hard to see that had Report 9 (</span><a data-sectionid="#REFe-r-038" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-038"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Ferguson, Laydon and Nedjati Gilani</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Ferguson et al. 2020</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">) included a sensitivity analysis of
the kind found in </span><a data-sectionid="#REFe-r-032" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-032"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Wouter, Arabnejad and Sinclair</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Edeling et al. (2021</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">), its influence on policy-makers
and the public might have been less dramatic. After all, </span><a data-sectionid="#REFe-r-032" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-032"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Wouter, Arabnejad and Sinclair</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Edeling et al. (2021</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">) seemed to show that it was
consistent with what we knew in March 2020 that the mitigation and suppression
strategies considered in Report 9 were all going to be equally ineffective. So
we can see two ways in which the choice of how to represent these parameter
values was value-laden. First, the ICL team chose values of parameters that
maximized the projected benefits of the strategy they appeared to be
recommending. Thus, they appear to be judging it to be less serious a mistake
to overestimate the effectiveness of those strategies than to underestimate
them.</span></p>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">Second, the choice not to include a sensitivity analysis in the
characterization of the model output we find in the report was itself highly
value-laden. One possibility is that it reflected the value judgement that
urgent action was required before there was time for the sensitivity analysis
to be carried out. Another possibility is that it reflected the value judgement
that an estimation of the degree of uncertainty regarding the effectiveness of
the measures would be less valuable than the precise, fine-grained projection
they in fact made. This is always a balance of values that model-builders and
interpreters face: how to balance the benefit of the informativeness of a
precise projection against the value of the confidence one can have in a wider,
imprecise estimate of that same benefit (</span><a data-sectionid="#REFe-r-119" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-119"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Winsberg</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Winsberg 2018</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">). Another possible explanation of the ICL
group’s failure to do a sensitivity analysis is that they deemed that doing so
would be too likely to cause governments to wrongly choose to abstain from
maximum suppression (i.e., by emphasizing the degree of existing uncertainty).
This obviously would reflect a value judgement about how bad such an outcome
would be.</span></p>
<h5 style="text-align: justify;"><span class="label"><span lang="EN-US" style="mso-ansi-language: EN-US;">4.5.5</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;"> Choice of Counterfactuals for Projection </span></h5>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">Let’s compare the way that the ICL model and climate models make
projections. The ICL model projections are conditional on policy choices, while
climate models are conditional on representative carbon pathways (RCPs). The
latter are not policy choices: they are outcomes that are conditional on policy
choices and numerous other factors acting in complex interaction with one
another; there are no uncontroversial connections between policy choices and
carbon pathways. In comparison, the ICL model takes policy choices as <em>counterfactuals
for projection</em>. Model developers are thus put in the position of
estimating how, for example, university and school closures will affect social
contact rates – but there is enormous uncertainty around such relationships,
not least because they stand to vary from setting to setting. Imagine putting
climate modellers in a similar position: for example, asking climate modellers to
assume that regulations imposed on nuclear power plant builders are reduced and
subsidies are provided to electric utilities that build out solar power
infrastructure. Our confidence in the model would have to be relatively low:
the results would no longer reflect a causal pathway of which scientists have a
good understanding (</span><a data-sectionid="#REFe-r-052" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-052"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Harvard and Winsberg</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Harvard and Winsberg 2021</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">). So the decision to represent
possible policies in the model, rather than simply putting in contact rates
that would be the <em>outcome</em> of policy choices, forced the modellers to
choose how to represent those choices. This opens up a huge number of choices
to make. And there was enormous uncertainty concerning nearly every one of
these choices, many of which take the form of parameters built into the model’s
coding. Most modelling choices were relatively unconstrained by data or
background knowledge; and when there was data, it was of poor quality.</span></p>
<h4 style="text-align: justify;"><span class="label" style="color: #783f04;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-fareast-font-family: "Times New Roman"; mso-fareast-theme-font: major-fareast;">4.6</span></span><span lang="EN-US" style="color: #783f04;"> Moral Responsibilities in Modelling </span></h4>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">In this section, we have explored the consequences of the claim,
developed in </span><a data-sectionid="#A-sec-5" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#A-sec-5"><span lang="EN-US" style="mso-ansi-language: EN-US;">Section
3</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">, that
model-building involves a kind of epistemic risk that is fundamentally
different from the kind of epistemic risk involved in endorsing a truth-apt
claim as a fact. In particular, we used the example of the ICL computational
model of Covid-19, called ‘CovidSim’, to highlight the ways in which making
representational decisions – of what to represent and how to represent it – is
highly value-laden.</span></p>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">One thing this discussion highlights is how the value-ladenness of
modelling laces the practice of model-building and model-using with significant
moral responsibilities. Model-builders and model users, especially model users
who are policy-makers, face significant moral responsibilities because the
choice to model at all, and the choices of how to model, can have serious moral
consequences. Thus, model-builders and users are morally responsible for
building the <em>right</em> models for the <em>right</em> purposes and for
making representational choices that embody the <em>right</em> balance of risks
(i.e., that their models will fail to be adequate for purpose in one way rather
than another).</span></p>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">But, of course, every use of the word ‘right’ in the previous sentence will
be highly value-dependent. And when model-builders and model users are working,
as they so often do, on behalf of the public, the question of what constitutes
the <em>right</em> set of values for informing the modelling process can become
overwhelmingly vexed. How can model-builders and model users possibly navigate
these incredibly turbulent waters? How can they ensure they are not imposing
their idiosyncratic values on the public?</span></p>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">One proposal that we find in the general ‘values in science’ literature
is that scientists should strive to make their own reasonable methodological
decisions and then be transparent about what values guided those decisions (</span><a data-sectionid="#REFe-r-030" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-030"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Douglas</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Douglas 2009</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">; </span><a data-sectionid="#REFe-r-033" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-033"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Elliott</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Elliott 2017</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">; </span><a data-sectionid="#REFe-r-034" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-034"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Elliott and McKaughan</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Elliott and McKaughan 2014</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">; </span><a data-sectionid="#REFe-r-098" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-098"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Schroeder</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Schroeder 2017</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">). There are two considerations here that
suggest this is unlikely to do the work it needs to do – to avoid imposing
idiosyncratic values on the public. For example, the ICL group (see </span><a data-sectionid="#REFe-r-112" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-112"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Verity, Okell and Dorigatti</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Verity et al. 2020</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">) are relatively ‘transparent’ about
the fact that they chose the Hubei data set over the DP data set. (As we saw, </span><a data-sectionid="#REFe-r-112" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-112"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Verity, Okell and Dorigatti</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Verity et al. (2020)</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;"> showed that the estimate of IFR
from Hubei was at least twice as large as the one you would get from the DP,
and they chose to use the former exclusively.) But for ‘transparency’ to
mitigate the problems discussed here, it should enable members of the public to
figure out whether the choice the ICL group made is or isn’t the one <em>they
would have made</em>, given their values. If the public can see that they would
have made the same choice, then no idiosyncratic values risk being involved. If
they can’t tell that, then the strong possibility exists that the ICL group is
being value-laden in a way that the public would fundamentally object to, and
that this fact remains hidden. Therefore, it is a criterion of success for the
transparency proposal, that transparency leads to members of the public being
able to tell if modellers are making choices that fail to accord with their
values. We can call this the ‘congruence’ criterion.</span></p>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">In the kinds of modelling projects we are looking at here, that is,
complex models that incorporate diverse sources of evidence and aim to directly
inform policy, it seems unclear whether the congruency criterion could be met.
Indeed, the inarticulability thesis seems strongly to suggest otherwise. The
inarticulability thesis, recall, says that it is unrealistic to ask modellers
to articulate all of the values and probabilities that drive their
representational choices. In principle, how could modellers say more than ‘We
chose the Hubei data over the DP data’, in a way that would satisfy the
congruence criterion? What the literature seems to suggest is that modellers
should issue transparent statements in a form such as, ‘We chose the data set
that erred on the side of overestimating the risk of death from Covid-19’
(e.g., </span><a data-sectionid="#REFe-r-030" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-030"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Douglas</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Douglas 2009</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">). In principle, this sort of statement could
be adequate for satisfying the congruence criterion. However, notice that it is
only adequate if what modellers mean by it is that ‘no matter how high a risk
estimate a data set would yield, and regardless of our assessment of the
quality or accuracy of a dataset, we would always pick the data set that erred
on the side of overestimating the risk of death from Covid-19’ and if members
of the public share this extreme view. Otherwise, a statement of this type does
not allow members of the public to determine whether they would have acted as
the modellers did, because it doesn’t tell them the ICL group’s <em>relative</em>
weighting of different harms. It doesn’t, crucially, tell a member of the
public how much more the ICL group values avoiding a Covid-19 death than they
do a job loss, or a child losing a year of education. And unless members of the
public know this, they can’t be sure whether they would have chosen the data
set that produced a higher death rate, irrespective of how high that death rate
is and how likely that data set was to be the better one. Unless the ICL group
can tell members of the public what <em>all</em> their value commitments are,
in a fine-grained form like ‘We think avoiding one Covid-19 death is worth
losing 100 child learning years’ for every single relevant policy consequence,
then members of the public won’t be able to tell whether the ICL group made the
same representational decisions they would have. And the inarticulability
thesis suggests it is not feasible for transparency to take this form.</span></p>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">Instead of merely asking for transparency, we could ask that scientists
simply make the choices that reflect the ‘right’ values (</span><a data-sectionid="#REFe-r-098" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-098"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Schroeder</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Schroeder 2017</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">). But what would this mean? Roughly speaking,
there are two things we could mean by the ‘right’ purposes and the ‘right’
balances of risks. ‘Right’ here could mean the ethically correct ones, or it
could mean the ones that are actually held by the public (</span><a data-sectionid="#REFe-r-098" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-098"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Schroeder</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Schroeder 2017</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">). Arguably, not being the ethically <em>wrong</em>
purposes and balance of risks is a minimal condition on being the satisfactory
ones. A model that assumes men’s health outcomes are more important than
women’s is not an ethically defensible model. But, also arguably, not being
ethically wrong is not a narrow enough constraint on representational choices
in a model. There might have been, for example, a value commitment about the
relative value of preventing Covid-19 deaths versus preventing job losses, and
all their attendant harms, on which choosing the Hubei data set was the right
choice, and another value commitment in relation to the same consequences on
which the DP data set would have been the right choice. And it might very well
have been the case that reasonable people would disagree about which was the
right set of values to have. If that’s right, then having scientists limit
themselves to ethically permissible representational choices will
underdetermine those choices and leave them open to making choices that do not
reflect the values of the majority of the people on whose behalf decision-makers
will be acting when they make use of the model.</span></p>
<h4 style="text-align: justify;"><span class="label" style="color: #783f04;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-fareast-font-family: "Times New Roman"; mso-fareast-theme-font: major-fareast;">4.7</span></span><span lang="EN-US" style="color: #783f04;"> Public Participation in Modelling </span></h4>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">In modelling projects that aim to directly inform public policy, it
seems to us that scientists have an obligation to make the ‘right’ choices in
the sense of ‘right’ that means ‘in accord with publicly held values’.
Something like this line of argument is defended by </span><a data-sectionid="#REFe-r-003" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-003"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Alexandrova and Fabian</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Alexandrova and Fabian (2021</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">) with respect to decisions
regarding which ‘thick concepts’ to employ in science. Their idea is that if
scientists are going to theorize about something like ‘well-being’, they need
to use a concept of well-being that accords with the public’s. Here, we are in
broad agreement with them: in fact, we think their basic idea needs to be
extended far more widely to include representational decisions in modelling
generally. To ensure that thick concepts in science reflect public values, </span><a data-sectionid="#REFe-r-003" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-003"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Alexandrova and Fabian</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Alexandrova and Fabian (2021)</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;"> propose a process of
‘co-production’, whereby scientists and members of the public work together to
determine how to construct relevant measures such as ‘well-being’. A similar
process of co-production to build policy-relevant models seems like a fruitful
one to explore, and in fact various research groups across disciplines have
endeavoured to involve members of the public in <em>participatory modelling
projects</em> (</span><a data-sectionid="#REFe-r-022" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-022"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Bunka, Ghanbarian and Riches</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Bunka et al. 2022</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">; </span><a data-sectionid="#REFe-r-048" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-048"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Gray, Paolisso, Jordan and Gray</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Gray et al. 2016</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">; </span><a data-sectionid="#REFe-r-101" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-101"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Staniszewska, Hill and Grant</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Staniszewska et al. 2021</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">; </span><a data-sectionid="#REFe-r-113" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-113"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Voinov and Bousquet</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Voinov and Bousquet 2010</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">; </span><a data-sectionid="#REFe-r-122" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-122"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Xie, Malik, Linthicum and Bright</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Xie et al. 2021</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">). Currently, it is unclear whether
participatory modelling projects are meeting the goal of ensuring that models
reflect public values, and various challenges with this type of co-production
will have to be addressed in future research. Among various challenges are the
idea that co-production could become a ‘box-checking’ exercise (</span><a data-sectionid="#REFe-r-003" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-003"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Alexandrova and Fabian</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Alexandrova and Fabian 2021</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">), that members of the public won’t
be able to understand what’s going on in the modelling process, or that
modellers won’t be able to articulate the relevant considerations that would
link their values to representational choices.</span></p>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">Prima facie, it seems like asking the public to co-produce a model is a
bigger ask than co-producing a thick concept like well-being. For example,
asking members of the public to weigh in on what aspects of well-being are most
important to them (e.g., having a feeling of purpose in life) requires much
less technical understanding than does expecting them to recognize that
shortening the latency period of a virus will make NPIs look more effective
than lengthening it and therefore implicitly weights as more serious the risk
of allowing too many Covid-19 deaths than the risk of unduly damaging the
economy, and by how much. Or figuring out that one version of a sensitivity
analysis privileges precision over confidence, and by how much. What seems to
be required are normative guidelines for public modelling projects that
articulate how representational decisions should be made collaboratively
between modellers and stakeholders (</span><a data-sectionid="#REFe-r-054" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-054"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Harvard and Winsberg</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Harvard and Winsberg 2023</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">; </span><a data-sectionid="#REFe-r-062" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-062"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Husereau, Drummond and Augustovski</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Husereau et al. 2022</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">). In addition to this, one might
think that normative guidelines are required not only with respect to how
representational decisions should be made, but with respect to how those
decisions should be <em>implemented in code</em> when the representations in
question are complex computational models. Indeed, this is the focus of </span><a data-sectionid="#REFe-r-060" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-060"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Horner and Symons</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Horner and Symons (2020)</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">, who point to the concrete
challenges involved in software engineering and the various potential errors
that can result from this aspect of modelling practice (cf. </span><a data-sectionid="#REFe-r-094" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-094"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Primiero</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Primiero 2014</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;">). </span><a data-sectionid="#REFe-r-060" href="https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA#REFe-r-060"><span class="show-for-sr"><span lang="EN-US" style="color: windowtext; mso-ansi-language: EN-US; text-decoration: none; text-underline: none;">Reference Horner and Symons</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Horner and Symons (2020)</span></a><span lang="EN-US" style="mso-ansi-language: EN-US;"> argue that software engineering
standards, too, are negotiable matters that should involve public deliberation
concerning trade-offs (e.g., regarding safety, uncertainty, urgency, resources,
risk, etc.). As representational decisions intersect with numerous other
socially significant decisions throughout the modelling process (What software
should modellers use? Should all models for public decision-making be ‘open
source’? How should such models be validated?), an important initial question
concerns the appropriate <em>scope</em> for public participation in modelling
and corresponding normative guidelines. More philosophical and empirical work
is required to conceptualize and address these questions.</span></p>
<h4 style="text-align: justify;"><span class="label"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-fareast-font-family: "Times New Roman"; mso-fareast-theme-font: major-fareast;">4.8</span></span><span lang="EN-US" style="mso-ansi-language: EN-US;"> Conclusion </span></h4>
<p class="p" style="text-align: justify;"><span lang="EN-US" style="mso-ansi-language: EN-US;">In this section we used ‘CovidSim’ as an example to illustrate the ways
in which representational choices in modelling, and the stage of model
interpretation at which facts are endorsed, both involve values, and we argued
that this places moral responsibilities on model-builders, model interpreters,
and the policy-makers who engage with them. Regarding model-builders, we
canvassed three different ways in which they can discharge their
responsibilities: by being transparent about their values, by using ethically
correct values, or by appealing to publicly held values. We highlighted the
respects in which the third way seems to be the only fully satisfactory one of
the three, but also by far the most difficult to achieve.</span></p>
<p class="p" style="margin-left: 36.0pt; mso-list: l2 level1 lfo6; tab-stops: list 36.0pt; text-align: justify; text-indent: -18.0pt;"><span style="font-family: Symbol; font-size: 10.0pt; mso-bidi-font-family: Symbol; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";">
</span></span></span>Jacob Stegenga</p>
<p class="p" style="margin-left: 36.0pt; mso-list: l2 level1 lfo6; tab-stops: list 36.0pt; text-align: justify; text-indent: -18.0pt;"><span style="font-family: Symbol; font-size: 10.0pt; mso-bidi-font-family: Symbol; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";">
</span></span></span><em>University of Cambridge</em></p>
<p class="p" style="margin-left: 36.0pt; mso-list: l2 level1 lfo6; tab-stops: list 36.0pt; text-align: justify; text-indent: -18.0pt;"><span lang="EN-US" style="font-family: Symbol; font-size: 10.0pt; mso-ansi-language: EN-US; mso-bidi-font-family: Symbol; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";">
</span></span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">Jacob
Stegenga is a Reader in the Department of History and Philosophy of Science at
the University of Cambridge. He has published widely on fundamental topics in
reasoning and rationality and philosophical problems in medicine and biology.
Prior to joining Cambridge he taught in the United States and Canada, and he
received his PhD from the University of California–San Diego.</span></p>
<h2 style="text-align: justify;"><section><header><hgroup>About the Series </hgroup></header></section></h2>
<p class="p" style="margin-left: 36.0pt; mso-list: l1 level1 lfo7; tab-stops: list 36.0pt; text-align: justify; text-indent: -18.0pt;"><span lang="EN-US" style="font-family: Symbol; font-size: 10.0pt; mso-ansi-language: EN-US; mso-bidi-font-family: Symbol; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";">
</span></span></span><span lang="EN-US" style="mso-ansi-language: EN-US;">This
series of Elements in Philosophy of Science provides an extensive overview of
the themes, topics and debates which constitute the philosophy of science.
Distinguished specialists provide an up-to-date summary of the results of
current research on their topics, as well as offering their own take on those
topics and drawing original conclusions.</span></p>
<div align="center" class="MsoNormal" style="text-align: center;">
</div></article> size=3 width="100%" align=center data-v-7036083a=""
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<h2 style="text-align: justify;">References</h2>
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<p class="p"> </p><p class="p"> https://www.cambridge.org/core/elements/scientific-models-and-decision-making/B7AC2159C941E7D0A08D9981FC8822F1?utm_source=hootsuite&utm_medium=facebook&utm_campaign=Elements_Philosophy_January_IOC&fbclid=IwAR0Ud2-ZZldn6RKaeSHYehx4l64HWKuUICG-MocZfjdy2XuSn56-n9Qs3fA</p></div></div><p class="p"> </p></div><p class="p"> </p>Ramon.Moratahttp://www.blogger.com/profile/16398587758151257300noreply@blogger.com0tag:blogger.com,1999:blog-1131336482330646116.post-60775088201890218412024-01-15T16:45:00.001-08:002024-01-15T16:45:16.214-08:00El Fenómeno del Chivo Expiatorio, según Girard<p> <span style="background-color: white; color: #222222; font-family: verdana; font-size: large; text-align: justify;">Todo esto que Girard trató antes de la existencia de Internet tiene una importancia enorme en el mundo de las redes sociales. Se habla mucho de la adicción a los móviles, a las pantalla y a la tecnología </span><b style="background-color: white; color: #222222; font-family: verdana; font-size: large; text-align: justify;">pero no se habla de la que es la amenaza metafísica más grave: nuestra adicción a los deseos de los demás</b><span style="background-color: white; color: #222222; font-family: verdana; font-size: large; text-align: justify;">. El deseo mimético es el motor real de las redes sociales.</span></p><h3 class="post-title entry-title" itemprop="name" style="background-color: white; color: #222222; font-family: Arial, Tahoma, Helvetica, FreeSans, sans-serif; font-feature-settings: normal; font-kerning: auto; font-optical-sizing: auto; font-size: 22px; font-stretch: normal; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-numeric: normal; font-variant-position: normal; font-variation-settings: normal; font-weight: normal; line-height: normal; margin: 0.75em 0px 0px; position: relative;"><a href="https://evolucionyneurociencias.blogspot.com/2022/03/el-fenomeno-del-chivo-expiatorio-segun.html" style="color: #2288bb; text-decoration-line: none;">El Fenómeno del Chivo Expiatorio, según Girard</a></h3><div class="post-header" style="background-color: white; color: #222222; font-family: Arial, Tahoma, Helvetica, FreeSans, sans-serif; font-size: 10.8px; line-height: 1.6; margin: 0px 0px 1.5em;"><div class="post-header-line-1"></div></div><div class="post-body entry-content" id="post-body-899988787139831665" itemprop="description articleBody" style="background-color: white; color: #222222; font-family: Arial, Tahoma, Helvetica, FreeSans, sans-serif; font-size: 13.2px; line-height: 1.4; position: relative; width: 660px;"><p style="text-align: justify;"><br /></p><p style="font-stretch: normal; line-height: normal; margin: 0px; min-height: 12px; text-align: justify;"><span style="font-family: verdana; font-size: large;"><br /></span></p><p style="font-stretch: normal; line-height: normal; margin: 0px; text-align: justify;"><span style="font-family: verdana; font-size: medium;">“El ser humano es la criatura que no sabe qué desear y se vuelve hacia los demás para decidirlo.</span></p><p style="font-stretch: normal; line-height: normal; margin: 0px; text-align: justify;"><span style="font-family: verdana; font-size: medium;">-René Girard</span></p><p style="font-stretch: normal; line-height: normal; margin: 0px; min-height: 12px; text-align: justify;"><span style="font-family: verdana; font-size: medium;"><br /></span></p><p style="font-stretch: normal; line-height: normal; margin: 0px; text-align: justify;"><span style="font-family: verdana; font-size: medium;">"Si los individuos están naturalmente inclinados a desear lo que sus vecinos poseen, o a desear lo que sus vecinos simplemente desean, esto significa que la rivalidad existe en el corazón mismo de las relaciones sociales humanas. Esta rivalidad, si no se frustra, pondría permanentemente en peligro la armonía e incluso la supervivencia de las comunidades humanas".</span></p><p style="font-stretch: normal; line-height: normal; margin: 0px; text-align: justify;"><span style="font-family: verdana; font-size: medium;">-René Girard</span></p><p style="font-stretch: normal; line-height: normal; margin: 0px; text-align: justify;"><span style="font-family: verdana; font-size: medium;"><br /></span></p><p style="font-stretch: normal; line-height: normal; margin: 0px; text-align: justify;"><a href="https://evolucionyneurociencias.blogspot.com/search?updated-max=2022-04-03T08:15:00-07:00&max-results=7&start=4&by-date=false">Evolución y Neurociencias (evolucionyneurociencias.blogspot.com)</a></p></div>Ramon.Moratahttp://www.blogger.com/profile/16398587758151257300noreply@blogger.com0tag:blogger.com,1999:blog-1131336482330646116.post-80329204805087190132024-01-15T16:45:00.000-08:002024-01-15T16:45:01.773-08:00Elon Musk ha revelado para qué servirá Starship V3: para "llevar todas las criaturas" a Marte como un arca de Noé<p> <span style="background-color: white; color: #111111; font-family: "IBM Plex Serif", -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Oxygen, Ubuntu, Cantarell, "Open Sans", "Helvetica Neue", sans-serif; font-size: 21px;">Musk quiere conseguir que la humanidad sea una especie multiplanetaria antes de que la vida en la Tierra sea insufrible. "No queremos ser como esas lamentables civilizaciones uniplanetarias", bromeó.</span></p><p><a href="https://www.xataka.com/espacio/elon-musk-ha-revelado-servira-starship-v3-para-llevar-todas-criaturas-a-marte-como-arca-noe">Elon Musk ha revelado para qué servirá Starship V3: para "llevar todas las criaturas" a Marte como un arca de Noé (xataka.com)</a></p><p>Un poco de genio loco tiene, pero quizas el tiempo le de la razón</p><p><span style="background-color: white; color: #111111; font-family: "IBM Plex Serif", -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Oxygen, Ubuntu, Cantarell, "Open Sans", "Helvetica Neue", sans-serif; font-size: 21px;"><br /></span></p>Ramon.Moratahttp://www.blogger.com/profile/16398587758151257300noreply@blogger.com0tag:blogger.com,1999:blog-1131336482330646116.post-5544886123251150032024-01-15T15:56:00.000-08:002024-01-15T15:56:03.625-08:00Las dos visones del mundo<p> <span style="background-color: white; color: #222222; font-family: verdana;">Esta entrada es un comentario del libro de Thomas Sowell A Conflict of Visions. Ideological Origins of Political Struggles. Se trata de un libro publicado en 1987 pero que nos habla en realidad de los tiempos que estamos viviendo actualmente. En conjunto, el libro me parece una aportación importante -una historia de las ideas- para comprender nuestras sociedades y el mundo en que vivimos y por eso considero que merece la pena que sea más conocido y con esa intención inicio este recorrido por sus páginas. Es un artículo un poco largo aunque creo que no es pesado o arduo para los que os interese el tema. Voy a citar mucho al propio Sowell porque realmente es imposible explicar las cosas mejor de lo que él lo hace, con una prosa rica y clara.</span></p><p><a href="https://evolucionyneurociencias.blogspot.com/2023/09/las-dos-visiones-del-mundo.html">Evolución y Neurociencias: LAS DOS VISIONES DEL MUNDO (evolucionyneurociencias.blogspot.com)</a></p>Ramon.Moratahttp://www.blogger.com/profile/16398587758151257300noreply@blogger.com0tag:blogger.com,1999:blog-1131336482330646116.post-44380948833114428332024-01-15T08:50:00.000-08:002024-01-15T08:50:09.591-08:00Las sinapsis excitatorias directas entre neuronas y células tumorales impulsan la siembra metastásica cerebral del cáncer de mama y el melanoma<p> </p><h1 class="highwire-cite-title" id="page-title">Direct excitatory synapses between neurons and tumor cells drive brain metastatic seeding of breast cancer and melanoma</h1>
<div class="highwire-cite-authors"><span class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0"><span class="nlm-given-names">V.</span> <span class="nlm-surname">Venkataramani</span></span>, <span class="highwire-citation-author has-tooltip hasTooltip" data-delta="1" data-hasqtip="4"><span class="nlm-given-names">M.A.</span> <span class="nlm-surname">Karreman</span></span>, <span aria-describedby="qtip-5" class="highwire-citation-author has-tooltip hasTooltip" data-delta="2" data-hasqtip="5"><span class="nlm-given-names">L.C.</span> <span class="nlm-surname">Nguyen</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">C.</span> <span class="nlm-surname">Tehranian</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">N.</span> <span class="nlm-surname">Hebach</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">C.D.</span> <span class="nlm-surname">Mayer</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">L.</span> <span class="nlm-surname">Meyer</span></span>, <span aria-describedby="qtip-6" class="highwire-citation-author has-tooltip hasTooltip" data-delta="7" data-hasqtip="6"><span class="nlm-given-names">S.S.</span> <span class="nlm-surname">Mughal</span></span>, <span class="highwire-citation-author has-tooltip hasTooltip" data-delta="8" data-hasqtip="0"><span class="nlm-given-names">G.</span> <span class="nlm-surname">Reifenberger</span></span>, <span class="highwire-citation-author" data-delta="9"><span class="nlm-given-names">J.</span> <span class="nlm-surname">Felsberg</span></span>, <span class="highwire-citation-author" data-delta="10"><span class="nlm-given-names">K.</span> <span class="nlm-surname">Köhrer</span></span>, <span class="highwire-citation-author has-tooltip hasTooltip" data-delta="11" data-hasqtip="3"><span class="nlm-given-names">M.C.</span> <span class="nlm-surname">Schubert</span></span>, <span class="highwire-citation-author has-tooltip hasTooltip" data-delta="12" data-hasqtip="7"><span class="nlm-given-names">D.</span> <span class="nlm-surname">Westphal</span></span>, <span class="highwire-citation-author" data-delta="13"><span class="nlm-given-names">M.O.</span> <span class="nlm-surname">Breckwoldt</span></span>, <span class="highwire-citation-author has-tooltip hasTooltip" data-delta="14" data-hasqtip="2"><span class="nlm-given-names">B.</span> <span class="nlm-surname">Brors</span></span>, <span class="highwire-citation-author" data-delta="15"><span class="nlm-given-names">W.</span> <span class="nlm-surname">Wick</span></span>, <span class="highwire-citation-author hw-author-orcid-logo-wrapper" data-delta="16"><a class="hw-author-orcid-logo link-icon-only link-icon" href="http://orcid.org/0000-0003-1896-9031" target="_blank"><span class="hw-icon-orcid hw-icon-color-orcid"></span> <span class="title element-invisible">View ORCID Profile</span></a><span class="nlm-given-names">T.</span> <span class="nlm-surname">Kuner</span></span>, <span aria-describedby="qtip-1" class="highwire-citation-author has-tooltip hasTooltip author-popup-hover" data-delta="17" data-hasqtip="1"><span class="nlm-given-names">F.</span> <span class="nlm-surname">Winkler</span></span></span></div><p> <span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">Las neuronas y las células cancerosas tienen comunicación sináptica directa en tumores no neuronales, lo que favorece la metástasis y acelera la progresión del cáncer. </span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">El sistema nervioso puede contribuir al desarrollo y progresión de tumores intra y extracraneales a través de varios mecanismos.</span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> </span></p><div class="css-175oi2r r-xoduu5"><span class="r-18u37iz"><a class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3 r-1loqt21" dir="ltr" href="https://twitter.com/MA_Karreman" role="link" style="color: #1d9bf0; text-overflow: unset;">@MA_Karreman</a></span></div><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> </span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">Uno de los hallazgos más inesperados fue la formación de sinapsis excitadoras entre neuronas presinápticas y células cancerosas postsinápticas, que se ha informado que estimulan el crecimiento tumoral y la invasión en ciertos tipos de cáncer de origen neural</span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">Este estudio muestra como se forman sinapsis excitadoras entre las neuronas y las células de melanoma metastásico cerebral y de cáncer de mama.</span></p><div class="css-175oi2r"><div class="css-1rynq56 r-8akbws r-krxsd3 r-dnmrzs r-1udh08x r-bcqeeo r-qvutc0 r-1qd0xha r-a023e6 r-rjixqe r-16dba41 r-bnwqim" data-testid="tweetText" dir="auto" id="id__zk5hh7lr93" lang="es" style="-webkit-line-clamp: 10; color: #0f1419; text-overflow: unset;"><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">Esto comienza en una etapa microscópica temprana después de la extravasación al parénquima cerebral, durante la residencia de las células cancerosas en el nicho perivascular, un paso crítico para su supervivencia.</span></div><div class="css-1rynq56 r-8akbws r-krxsd3 r-dnmrzs r-1udh08x r-bcqeeo r-qvutc0 r-1qd0xha r-a023e6 r-rjixqe r-16dba41 r-bnwqim" data-testid="tweetText" dir="auto" id="id__zk5hh7lr93" lang="es" style="-webkit-line-clamp: 10; color: #0f1419; text-overflow: unset;"><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> </span></div></div><div class="css-175oi2r"><div class="css-175oi2r"><div aria-label="1 reply, 3 likes, 35 views" class="css-175oi2r r-1kbdv8c r-18u37iz r-1wtj0ep r-1ye8kvj r-1s2bzr4" id="id__kuc99sittwq" role="group"><div class="css-175oi2r r-18u37iz r-1h0z5md r-13awgt0"></div></div></div></div><div class="css-175oi2r r-18u37iz r-1h0z5md r-13awgt0"></div><div class="css-175oi2r r-18u37iz r-1h0z5md r-13awgt0"></div><div class="css-175oi2r r-18u37iz r-1h0z5md r-13awgt0"><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">Actividad de Ca 2+ en células metastásicas cerebrales de melanoma y cáncer de mama en etapa temprana in vivo</span></div><div class="css-175oi2r r-18u37iz r-1h0z5md r-13awgt0"><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjDMUL0lerPJt-5y7PQzCtPmDA3pKGVNm2igqNtSsMrDtFAn8xFOiwMJDPp7bYYNpMCRyAXwMQ6iiOAhixU0nAn_X-3dY_1GXWq1vVdrn1_-hp3W9cnXsro-Q3hKXSxB-OgWpwhWz10mo8J1awR7hdsQPEr9rup40YrhhuY56Cv_BUOGe7uOEIB-NP5EEk/s440/c1.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="421" data-original-width="440" height="492" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjDMUL0lerPJt-5y7PQzCtPmDA3pKGVNm2igqNtSsMrDtFAn8xFOiwMJDPp7bYYNpMCRyAXwMQ6iiOAhixU0nAn_X-3dY_1GXWq1vVdrn1_-hp3W9cnXsro-Q3hKXSxB-OgWpwhWz10mo8J1awR7hdsQPEr9rup40YrhhuY56Cv_BUOGe7uOEIB-NP5EEk/w514-h492/c1.jpg" width="514" /></a></div><br /> </span></div><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> </span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> </span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">Observaron transitorios de Ca2+ en las primeras etapas de las metástasis cerebrales que podrían reducirse con anestesia, lo que indica que dependen de la actividad neuronal</span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">Profundizando más, utilizaron la electrofisiología para mostrar que estas sinapsis neurona-cáncer mostraron una ultraestructura sináptica genuina y generaron corrientes postsinápticas excitadoras mediadas por receptores de glutamato del subtipo AMPA en las células cancerosas</span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"></span></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgyUHN8LahHbubniP6HdXmagOch1gsqGZ-lHfwcKLywleHCnRNvzJAetDHsLd-CRulE9mGxaiwCmaesx_BI9N57KjC5Qul3_qDQiYt1gA6EZz_esVwaBLL8Yb2RROjBb5M9hgkuajd0jtC3aI9b_zoXLIy3LTJ0rdG_hTYUYZHsefEhyfKjpTBM0NgP1kE/s440/fi.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="349" data-original-width="440" height="400" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgyUHN8LahHbubniP6HdXmagOch1gsqGZ-lHfwcKLywleHCnRNvzJAetDHsLd-CRulE9mGxaiwCmaesx_BI9N57KjC5Qul3_qDQiYt1gA6EZz_esVwaBLL8Yb2RROjBb5M9hgkuajd0jtC3aI9b_zoXLIy3LTJ0rdG_hTYUYZHsefEhyfKjpTBM0NgP1kE/w503-h400/fi.jpg" width="503" /></a></div><br /> <span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> https://twitter.com/guty2370/status/1746549593397961141</span><p></p><p id="p-4">Interactions
between neurons and cancer cells are found in many malignancies, but
their relevance for metastatic organ colonization remain largely
unknown. It is also unclear whether any direct synaptic communication
between neurons and cancer cells of non-neural tumor types exists, and
if so, whether this can support metastasis and thus cancer progression.
Here we show that excitatory synapses are formed between neurons and
brain-metastatic melanoma and breast cancer cells. This starts at an
early microscopic stage after extravasation into the brain parenchyma,
during residence of cancer cells in the perivascular niche, a critical
step for their survival. These neuron-cancer synapses showed a <em>bona fide</em>
synaptic ultrastructure, and generated excitatory postsynaptic currents
mediated by glutamate receptors of the AMPA subtype in cancer cells. In
accordance, AMPA receptor signatures were consistently detected in
preclinical and patient samples of melanoma and breast cancer brain
metastases. Genetic perturbation and pharmacological inhibition of AMPA
receptors with the approved antiepileptic drug perampanel in models of
breast and melanoma cancer reduced the number of brain metastases and
overall brain metastatic burden. These findings demonstrate for the
first time that neurons can form biologically relevant direct synapses
with non-neural cancer cells. In brain metastasis, a particularly
challenging complication of many common malignancies, this non-canonical
stimulatory synaptic interaction offers novel therapeutic
opportunities.</p><h3>Competing Interest Statement</h3><p id="p-5">F.W.
and W.W. report the patent (WO2017020982A1) “Agents for use in the
treatment of glioma.” F.W. is co-founder of DC Europa Ltd (a company
trading under the name Divide & Conquer) that is developing new
medicines for the treatment of glioma. Divide & Conquer also
provides research funding to F.W.’s lab under a research collaboration
agreement. </p><p id="p-5">https://www.biorxiv.org/content/10.1101/2024.01.08.574608v1 <br /></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> </span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"> </span></p>Ramon.Moratahttp://www.blogger.com/profile/16398587758151257300noreply@blogger.com0tag:blogger.com,1999:blog-1131336482330646116.post-49049447558425019502024-01-14T07:59:00.000-08:002024-01-14T07:59:20.583-08:00La Ciencia en 2023. La terapia contra la obesidad, el cambio climático y la IA marcaron el ritmo de la ciencia en 2023<p> </p><header style="background-color: white; border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; color: rgba(0, 0, 0, 0.9); display: var(--artdeco-reset-base-display-block); font-family: -apple-system, system-ui, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", "Fira Sans", Ubuntu, Oxygen, "Oxygen Sans", Cantarell, "Droid Sans", "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Lucida Grande", Helvetica, Arial, sans-serif; font-size: 16px; margin: var(--artdeco-reset-base-margin-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline);"><h1 class="text-display-large-bold pt6" dir="ltr" style="--artdeco-reset-typography_getfontsize: 4rem; --artdeco-reset-typography_getlineheight: 1.2; background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; color: var(--color-text); font-size: var(--font-size-display-large); font-weight: var(--font-weight-bold); line-height: var(--line-height-default); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding-top: 3.2rem !important; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);"><span data-scaffold-immersive-reader-title="" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline);">La Ciencia en 2023. La terapia contra la obesidad, el cambio climático y la IA marcaron el ritmo de la ciencia en 2023</span></h1></header><div class="relative reader__grid mt6" style="background-color: white; border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; color: rgba(0, 0, 0, 0.9); float: none; font-family: -apple-system, system-ui, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", "Fira Sans", Ubuntu, Oxygen, "Oxygen Sans", Cantarell, "Droid Sans", "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Lucida Grande", Helvetica, Arial, sans-serif; font-size: 16px; margin-top: 3.2rem !important; overflow-wrap: break-word; padding: var(--artdeco-reset-base-padding-zero); position: relative !important; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);"><div class="reader-author-info__container" style="background-color: var(--color-background-container); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); 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--artdeco-reset-typography_getlineheight: 1.5; background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; color: var(--color-text) !important; display: flex; font-family: var(--artdeco-typography-sans); font-size: var(--artdeco-reset-typography_getFontSize); font-weight: var(--artdeco-reset-typography-font-weight-bold); line-height: var(--artdeco-reset-typography_getLineHeight); margin: var(--artdeco-reset-base-margin-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline);"><a class="ember-view reader-author-info__meta-name align-items-center" href="https://www.linkedin.com/in/orlando-alfonso-molano-gaona-marketing-digital-business2business/" id="ember1919" style="align-items: center !important; background-color: var(--artdeco-reset-link-background-color-transparent); border: var(--artdeco-reset-link-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); font-weight: var(--font-weight-bold); margin: var(--artdeco-reset-base-margin-zero); padding: var(--artdeco-reset-base-padding-zero); text-decoration: var(--artdeco-reset-link-text-decoration-none); touch-action: manipulation; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);"><h2 class="reader-author-info__text reader-author-info__name t-16 t-bold reader-author-info__meta-author-detail--has-hover mr2" style="--artdeco-reset-typography_getfontsize: 1.6rem; --artdeco-reset-typography_getlineheight: 1.5; background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; color: var(--color-text); font-size: var(--artdeco-reset-typography_getFontSize); font-weight: var(--artdeco-reset-typography-font-weight-bold); line-height: var(--artdeco-reset-typography_getLineHeight); margin-right: 0.8rem !important; max-width: 100%; outline: var(--artdeco-reset-base-outline-zero); overflow: hidden; padding: var(--artdeco-reset-base-padding-zero); text-overflow: ellipsis; text-wrap: nowrap; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);">ORLANDO ALFONSO MOLANO GAONA</h2></a></div><div class="artdeco-entity-lockup__subtitle ember-view" id="ember1920" style="--artdeco-reset-typography_getfontsize: 1.4rem; --artdeco-reset-typography_getlineheight: 1.42857; background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; color: var(--color-text) !important; font-family: var(--artdeco-typography-sans); font-size: var(--artdeco-reset-typography_getFontSize); font-weight: var(--artdeco-reset-typography-font-weight-normal); line-height: var(--artdeco-reset-typography_getLineHeight); margin: var(--artdeco-reset-base-margin-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline);"><div class="ember-view lt-line-clamp lt-line-clamp--multi-line t-black--light break-words" id="ember1922" style="-webkit-box-orient: vertical; -webkit-line-clamp: 2; background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; color: var(--color-text-low-emphasis); display: -webkit-box; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); overflow-wrap: break-word !important; overflow: hidden; padding: var(--artdeco-reset-base-padding-zero); position: relative; text-overflow: ellipsis; vertical-align: var(--artdeco-reset-base-vertical-align-baseline); word-break: break-word !important;">Quimico Farmaceutico con experiencia en Marketing Farmaceutico , Digital, estratega y especialista en Google, Inteligencia Artificial (IA) y ChaptGpt</div></div></div></div><div class="flex-shrink-zero ml2" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; flex-shrink: 0 !important; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin-left: 0.8rem !important; padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline);"><a class="ember-view reader-author-info__total-articles link-without-visited-state" href="https://www.linkedin.com/in/orlando-alfonso-molano-gaona-marketing-digital-business2business/recent-activity/posts/" id="ember1948" style="background-color: var(--artdeco-reset-link-background-color-transparent); border: var(--artdeco-reset-link-border-zero); box-sizing: inherit; font-size: 14px; font-weight: var(--font-weight-bold); margin: var(--artdeco-reset-base-margin-zero); padding: var(--artdeco-reset-base-padding-zero); text-decoration: var(--artdeco-reset-link-text-decoration-none); touch-action: manipulation; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);">73 artículos </a><button aria-label="Seguir" aria-live="polite" class="follow reader-author-info__follow-button artdeco-button artdeco-button--secondary ml2" style="align-items: center; background-attachment: initial; background-clip: initial; background-image: none; background-origin: initial; background-position: initial; background-repeat: initial; background-size: initial; border-color: initial; border-radius: var(--corner-radius-large) !important; border-style: none; border-width: initial; box-shadow: inset 0 0 0 1px var(--color-action); cursor: pointer; display: inline-flex; font-family: inherit; font-size: 1.6rem; font-weight: 600; justify-content: center; line-height: 2rem; margin-bottom: 0px; margin-left: 0.8rem !important; max-width: 480px; min-height: 3.2rem; min-width: 0px; overflow: hidden; padding-bottom: 0.6rem; padding-left: 1.6rem !important; padding-right: 1.6rem !important; padding-top: 0.6rem; touch-action: manipulation; transition-duration: 167ms; transition-property: background-color, box-shadow, color; transition-timing-function: cubic-bezier(0.4, 0, 0.2, 1); vertical-align: middle;" type="button"><svg aria-hidden="true" class="artdeco-button__icon" data-supported-dps="16x16" data-test-icon="add-small" height="16" role="none" viewbox="0 0 16 16" width="16" xmlns="http://www.w3.org/2000/svg"><use height="16" href="#add-small" width="16"></use></svg><span aria-hidden="true" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline);"><span style="color: var(--color-text); font-size: var(--font-size-display-small); font-weight: var(--font-weight-bold); text-align: left;">Una hormona intestinal, la ralentización de la bomba de carbono y la predicción inteligente del tiempo, estrellas del año que termina</span></span></button></div></div></div><div data-scaffold-immersive-reader-content="" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline);"><div style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline);"><div class="reader-article-content reader-article-content--content-blocks" dir="ltr" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline);"><p class="ember-view reader-content-blocks__paragraph" id="ember1924" style="--artdeco-reset-typography_getfontsize: 1.6rem; --artdeco-reset-typography_getlineheight: 1.5; background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; color: var(--color-text); font-size: var(--font-size-large); font-weight: var(--artdeco-reset-typography-font-weight-normal); line-height: 1.75; margin: 1.6rem 0px; padding: var(--artdeco-reset-base-padding-zero); pointer-events: all; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);">Los<span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span><span style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); font-weight: var(--artdeco-reset-typography-font-weight-bold); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline);">avances científicos de 2023</span><span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span>basculan en torno a los nuevos medicamentos para la reducción de la obesidad, la escalada de la<span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span><a class="app-aware-link " data-test-app-aware-link="" href="https://www.epe.es/es/temas/crisis-climatica-1758909" style="background-color: var(--artdeco-reset-link-background-color-transparent); border: var(--artdeco-reset-link-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); font-weight: var(--font-weight-bold); margin: var(--artdeco-reset-base-margin-zero); overflow-wrap: break-word; padding: var(--artdeco-reset-base-padding-zero); text-decoration: var(--artdeco-reset-link-text-decoration-none); touch-action: manipulation; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);" target="_self">crisis climática</a><span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span>y disminución de la resiliencia de los océanos, según dos revistas emblemáticas. La IA figura también en el podio, sobre todo porque va a revolucionar la predicción del tiempo.</p><p class="ember-view reader-content-blocks__paragraph" id="ember1925" style="--artdeco-reset-typography_getfontsize: 1.6rem; --artdeco-reset-typography_getlineheight: 1.5; background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; color: var(--color-text); font-size: var(--font-size-large); font-weight: var(--artdeco-reset-typography-font-weight-normal); line-height: 1.75; margin: 1.6rem 0px; padding: var(--artdeco-reset-base-padding-zero); pointer-events: all; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);">Se trata de una revista dirigida a investigadores. La otra, una revista popular. Pero ambas coinciden: entre sus “<a class="app-aware-link " data-test-app-aware-link="" href="https://www.science.org/content/article/breakthrough-of-the-year-2023" style="background-color: var(--artdeco-reset-link-background-color-transparent); border: var(--artdeco-reset-link-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); font-weight: var(--font-weight-bold); margin: var(--artdeco-reset-base-margin-zero); overflow-wrap: break-word; padding: var(--artdeco-reset-base-padding-zero); text-decoration: var(--artdeco-reset-link-text-decoration-none); touch-action: manipulation; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);" target="_self">avances del año</a>” o sus “<a class="app-aware-link " data-test-app-aware-link="" href="https://www.newscientist.com/article/2408764-the-biggest-science-news-stories-of-2023-as-chosen-by-new-scientist/" style="background-color: var(--artdeco-reset-link-background-color-transparent); border: var(--artdeco-reset-link-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); font-weight: var(--font-weight-bold); margin: var(--artdeco-reset-base-margin-zero); overflow-wrap: break-word; padding: var(--artdeco-reset-base-padding-zero); text-decoration: var(--artdeco-reset-link-text-decoration-none); touch-action: manipulation; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);" target="_self">grandes historias científicas</a>” de 2023, incluyen tanto la lucha contra la obesidad como las consecuencias del cambio climático. Además de la imprescindible inteligencia artificial.</p><p class="ember-view reader-content-blocks__paragraph" id="ember1926" style="--artdeco-reset-typography_getfontsize: 1.6rem; --artdeco-reset-typography_getlineheight: 1.5; background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; color: var(--color-text); font-size: var(--font-size-large); font-weight: var(--artdeco-reset-typography-font-weight-normal); line-height: 1.75; margin: 1.6rem 0px; padding: var(--artdeco-reset-base-padding-zero); pointer-events: all; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);">La demanda de<span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span><span style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); font-weight: var(--artdeco-reset-typography-font-weight-bold); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline);">Wegovy</span>, un medicamento inyectable para bajar de peso, ha alcanzado nuevas cotas este año, recuerda la popular revista<span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span><span style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); font-style: var(--artdeco-reset-typography-font-style-italic); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline);">The New Scientist</span><span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span>y es la culminación de un viaje de varias décadas, recuerda la revista<span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span><span style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); font-style: var(--artdeco-reset-typography-font-style-italic); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline);">Science</span><span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span>: en los años 1980, los investigadores<span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span><a class="app-aware-link " data-test-app-aware-link="" href="https://www.science.org/content/article/her-work-paved-way-blockbuster-obesity-drugs-now-she-s-fighting-recognition" style="background-color: var(--artdeco-reset-link-background-color-transparent); border: var(--artdeco-reset-link-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); font-weight: var(--font-weight-bold); margin: var(--artdeco-reset-base-margin-zero); overflow-wrap: break-word; padding: var(--artdeco-reset-base-padding-zero); text-decoration: var(--artdeco-reset-link-text-decoration-none); touch-action: manipulation; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);" target="_self">descubrieron</a><span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span>que una hormona intestinal llamada GLP-1 (<span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span><span style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); font-style: var(--artdeco-reset-typography-font-style-italic); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline);">péptido similar al glucagón-1</span><span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span>) parecía ser capaz de contrarrestar el desarrollo de la diabetes.<span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span></p><p class="ember-view reader-content-blocks__paragraph" id="ember1927" style="--artdeco-reset-typography_getfontsize: 1.6rem; --artdeco-reset-typography_getlineheight: 1.5; background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; color: var(--color-text); font-size: var(--font-size-large); font-weight: var(--artdeco-reset-typography-font-weight-normal); line-height: 1.75; margin: 1.6rem 0px; padding: var(--artdeco-reset-base-padding-zero); pointer-events: all; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);">Hubo que esperar hasta 2005 para poder producir un primer medicamento (Byetta) contra la diabetes tipo 2, luego hasta 2010 para un segundo medicamento que posteriormente, en 2014, sería aprobado por las autoridades para la lucha contra la obesidad.<span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span></p><p class="ember-view reader-content-blocks__paragraph" id="ember1928" style="--artdeco-reset-typography_getfontsize: 1.6rem; --artdeco-reset-typography_getlineheight: 1.5; background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; color: var(--color-text); font-size: var(--font-size-large); font-weight: var(--artdeco-reset-typography-font-weight-normal); line-height: 1.75; margin: 1.6rem 0px; padding: var(--artdeco-reset-base-padding-zero); pointer-events: all; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);">Una nueva versión surgiría de los laboratorios de la farmacéutica Novo Nordisk en 2016, con el nombre de semaglutida: es la que se vende con el nombre de Ozempic contra la diabetes y Wegovy contra la obesidad.<span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span></p><p class="ember-view reader-content-blocks__paragraph" id="ember1929" style="--artdeco-reset-typography_getfontsize: 1.6rem; --artdeco-reset-typography_getlineheight: 1.5; background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; color: var(--color-text); font-size: var(--font-size-large); font-weight: var(--artdeco-reset-typography-font-weight-normal); line-height: 1.75; margin: 1.6rem 0px; padding: var(--artdeco-reset-base-padding-zero); pointer-events: all; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);"><a class="app-aware-link " data-test-app-aware-link="" href="https://www.nejm.org/doi/full/10.1056/NEJMoa2032183" style="background-color: var(--artdeco-reset-link-background-color-transparent); border: var(--artdeco-reset-link-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); font-weight: var(--font-weight-bold); margin: var(--artdeco-reset-base-margin-zero); overflow-wrap: break-word; padding: var(--artdeco-reset-base-padding-zero); text-decoration: var(--artdeco-reset-link-text-decoration-none); touch-action: manipulation; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);" target="_self">En 2021</a>, un ensayo clínico atribuyó una caída del 15% en la masa corporal en quienes lo tomaron durante 16 meses. Y desde entonces,<a class="app-aware-link " data-test-app-aware-link="" href="https://www.newscientist.com/article/mg26034690-600-wegovy-and-ozempic-made-2023-a-revolutionary-year-for-weight-loss/" style="background-color: var(--artdeco-reset-link-background-color-transparent); border: var(--artdeco-reset-link-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); font-weight: var(--font-weight-bold); margin: var(--artdeco-reset-base-margin-zero); overflow-wrap: break-word; padding: var(--artdeco-reset-base-padding-zero); text-decoration: var(--artdeco-reset-link-text-decoration-none); touch-action: manipulation; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);" target="_self"><span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span>las ventas se han disparado</a><span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span>en Norteamérica y Europa.<span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span></p><p class="ember-view reader-content-blocks__paragraph" id="ember1930" style="--artdeco-reset-typography_getfontsize: 1.6rem; --artdeco-reset-typography_getlineheight: 1.5; background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; color: var(--color-text); font-size: var(--font-size-large); font-weight: var(--artdeco-reset-typography-font-weight-normal); line-height: 1.75; margin: 1.6rem 0px; padding: var(--artdeco-reset-base-padding-zero); pointer-events: all; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);">Teniendo en cuenta que la obesidad se ha convertido en un importante problema de salud pública en los países ricos, ese avance,<span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span><a class="app-aware-link " data-test-app-aware-link="" href="https://www.nejm.org/doi/full/10.1056/NEJMoa2306963?logout=true" style="background-color: var(--artdeco-reset-link-background-color-transparent); border: var(--artdeco-reset-link-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); font-weight: var(--font-weight-bold); margin: var(--artdeco-reset-base-margin-zero); overflow-wrap: break-word; padding: var(--artdeco-reset-base-padding-zero); text-decoration: var(--artdeco-reset-link-text-decoration-none); touch-action: manipulation; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);" target="_self">aparentemente acompañado</a><span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span>de una<span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span><a class="app-aware-link " data-test-app-aware-link="" href="https://www.nejm.org/doi/full/10.1056/NEJMoa2307563" style="background-color: var(--artdeco-reset-link-background-color-transparent); border: var(--artdeco-reset-link-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); font-weight: var(--font-weight-bold); margin: var(--artdeco-reset-base-margin-zero); overflow-wrap: break-word; padding: var(--artdeco-reset-base-padding-zero); text-decoration: var(--artdeco-reset-link-text-decoration-none); touch-action: manipulation; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);" target="_self">reducción</a><span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span>de los problemas cardíacos, era necesario desde hacía mucho tiempo. Incluso si los médicos<span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span><a class="app-aware-link " data-test-app-aware-link="" href="https://www.newscientist.com/article/0-weight-loss-injections-need-to-be-for-life-not-just-two-years/" style="background-color: var(--artdeco-reset-link-background-color-transparent); border: var(--artdeco-reset-link-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); font-weight: var(--font-weight-bold); margin: var(--artdeco-reset-base-margin-zero); overflow-wrap: break-word; padding: var(--artdeco-reset-base-padding-zero); text-decoration: var(--artdeco-reset-link-text-decoration-none); touch-action: manipulation; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);" target="_self">hubieran preferido</a><span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span>que sus pacientes adoptaran mejores hábitos de vida...<span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span></p><h3 class="ember-view" id="ember1931" style="--artdeco-reset-typography_getfontsize: 2.4rem; --artdeco-reset-typography_getlineheight: 1.33333; background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; color: var(--color-text); font-size: var(--font-size-display-small); font-weight: var(--font-weight-bold); line-height: var(--artdeco-reset-typography_getLineHeight); margin: 3.2rem 0px; outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline);">Cambio climático e inteligencia artificial</h3><p class="ember-view reader-content-blocks__paragraph" id="ember1932" style="--artdeco-reset-typography_getfontsize: 1.6rem; --artdeco-reset-typography_getlineheight: 1.5; background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; color: var(--color-text); font-size: var(--font-size-large); font-weight: var(--artdeco-reset-typography-font-weight-normal); line-height: 1.75; margin: 1.6rem 0px; padding: var(--artdeco-reset-base-padding-zero); pointer-events: all; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);">New<span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span><span style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); font-style: var(--artdeco-reset-typography-font-style-italic); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline);">Scientist</span><span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span>ha seleccionado los récords de temperatura como lo más destacado de 2023 y señala<span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span><a class="app-aware-link " data-test-app-aware-link="" href="https://www.newscientist.com/article/2378692-why-2023-is-shaping-up-to-be-the-hottest-year-on-record/" style="background-color: var(--artdeco-reset-link-background-color-transparent); border: var(--artdeco-reset-link-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); font-weight: var(--font-weight-bold); margin: var(--artdeco-reset-base-margin-zero); overflow-wrap: break-word; padding: var(--artdeco-reset-base-padding-zero); text-decoration: var(--artdeco-reset-link-text-decoration-none); touch-action: manipulation; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);" target="_self">que ya en junio</a><span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span>podríamos predecir que el año 2023 batiría un récord. Mientras, la revista<span style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); font-style: var(--artdeco-reset-typography-font-style-italic); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline);"><span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span>Science<span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span></span><a class="app-aware-link " data-test-app-aware-link="" href="https://www.science.org/content/article/breakthrough-of-the-year-2023#section_breakthrough" style="background-color: var(--artdeco-reset-link-background-color-transparent); border: var(--artdeco-reset-link-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); font-weight: var(--font-weight-bold); margin: var(--artdeco-reset-base-margin-zero); overflow-wrap: break-word; padding: var(--artdeco-reset-base-padding-zero); text-decoration: var(--artdeco-reset-link-text-decoration-none); touch-action: manipulation; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);" target="_self">prefiere</a><span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span>destacar la “ralentización de la bomba de carbono” de nuestro planeta: el flujo masivo que conecta todos los océanos del mundo, ayudándolos a capturar un tercio de las emisiones anuales de carbono de la humanidad.</p><p class="ember-view reader-content-blocks__paragraph" id="ember1933" style="--artdeco-reset-typography_getfontsize: 1.6rem; --artdeco-reset-typography_getlineheight: 1.5; background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; color: var(--color-text); font-size: var(--font-size-large); font-weight: var(--artdeco-reset-typography-font-weight-normal); line-height: 1.75; margin: 1.6rem 0px; padding: var(--artdeco-reset-base-padding-zero); pointer-events: all; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);">Se refiere a dos estudios publicados este año que confirmaron una desaceleración de las corrientes oceánicas profundas en el hemisferio sur, consecuencia del derretimiento del hielo antártico. Se trata del flujo masivo que conecta todos los océanos del mundo, ayudándolos a capturar un tercio de las emisiones anuales de carbono de la humanidad.</p><p class="ember-view reader-content-blocks__paragraph" id="ember1934" style="--artdeco-reset-typography_getfontsize: 1.6rem; --artdeco-reset-typography_getlineheight: 1.5; background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; color: var(--color-text); font-size: var(--font-size-large); font-weight: var(--artdeco-reset-typography-font-weight-normal); line-height: 1.75; margin: 1.6rem 0px; padding: var(--artdeco-reset-base-padding-zero); pointer-events: all; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);">Esta desaceleración fue predicha por los modelos climáticos, pero no tan temprano: mientras que hasta ahora se estimaba una desaceleración del 40% para 2050, uno de los estudios,<span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span><a class="app-aware-link " data-test-app-aware-link="" href="https://www.nature.com/articles/s41558-023-01667-8" style="background-color: var(--artdeco-reset-link-background-color-transparent); border: var(--artdeco-reset-link-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); font-weight: var(--font-weight-bold); margin: var(--artdeco-reset-base-margin-zero); overflow-wrap: break-word; padding: var(--artdeco-reset-base-padding-zero); text-decoration: var(--artdeco-reset-link-text-decoration-none); touch-action: manipulation; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);" target="_self">publicado en mayo</a>, estima que las corrientes ya se han desacelerado un 30% entre 1992 y 2017.</p><p class="ember-view reader-content-blocks__paragraph" id="ember1935" style="--artdeco-reset-typography_getfontsize: 1.6rem; --artdeco-reset-typography_getlineheight: 1.5; background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; color: var(--color-text); font-size: var(--font-size-large); font-weight: var(--artdeco-reset-typography-font-weight-normal); line-height: 1.75; margin: 1.6rem 0px; padding: var(--artdeco-reset-base-padding-zero); pointer-events: all; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);">Se trata de un impacto del cambio climático menos visible que los récords de temperatura, pero que podría tener un efecto perturbador en todas las corrientes oceánicas, a través de las cuales viajan nutrientes, oxígeno y carbono.<span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span></p><h3 class="ember-view" id="ember1936" style="--artdeco-reset-typography_getfontsize: 2.4rem; --artdeco-reset-typography_getlineheight: 1.33333; background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; color: var(--color-text); font-size: var(--font-size-display-small); font-weight: var(--font-weight-bold); line-height: var(--artdeco-reset-typography_getLineHeight); margin: 3.2rem 0px; outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline);">Frenético desarrollo de la IA</h3><p class="ember-view reader-content-blocks__paragraph" id="ember1937" style="--artdeco-reset-typography_getfontsize: 1.6rem; --artdeco-reset-typography_getlineheight: 1.5; background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; color: var(--color-text); font-size: var(--font-size-large); font-weight: var(--artdeco-reset-typography-font-weight-normal); line-height: 1.75; margin: 1.6rem 0px; padding: var(--artdeco-reset-base-padding-zero); pointer-events: all; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);">En cuanto al frenético desarrollo de la inteligencia artificial, seguramente se ganaría un lugar en las clasificaciones científicas de fin de año. Pero<span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span><span style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); font-style: var(--artdeco-reset-typography-font-style-italic); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline);">Science<span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span></span>ha optado por tender un puente con el clima, destacando la llegada este año de las “predicciones meteorológicas por IA”.</p><p class="ember-view reader-content-blocks__paragraph" id="ember1938" style="--artdeco-reset-typography_getfontsize: 1.6rem; --artdeco-reset-typography_getlineheight: 1.5; background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; color: var(--color-text); font-size: var(--font-size-large); font-weight: var(--artdeco-reset-typography-font-weight-normal); line-height: 1.75; margin: 1.6rem 0px; padding: var(--artdeco-reset-base-padding-zero); pointer-events: all; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);">En noviembre,<span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span><a class="app-aware-link " data-test-app-aware-link="" href="https://www.levante-emv.com/tendencias21/20231121/inteligencia-artificial-revoluciona-pronostico-tiempo-94722535.html?_sid=1703794852" style="background-color: var(--artdeco-reset-link-background-color-transparent); border: var(--artdeco-reset-link-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); font-weight: var(--font-weight-bold); margin: var(--artdeco-reset-base-margin-zero); overflow-wrap: break-word; padding: var(--artdeco-reset-base-padding-zero); text-decoration: var(--artdeco-reset-link-text-decoration-none); touch-action: manipulation; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);" target="_self">supimos</a><span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span>que una IA creada por investigadores de Google solo tardó un minuto en producir pronósticos de 10 días para cualquier región del mundo. Otras empresas también están compitiendo por vencer a sus competidores.<span class="white-space-pre" style="background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; font-size: var(--artdeco-reset-base-font-size-hundred-percent); margin: var(--artdeco-reset-base-margin-zero); outline: var(--artdeco-reset-base-outline-zero); padding: var(--artdeco-reset-base-padding-zero); vertical-align: var(--artdeco-reset-base-vertical-align-baseline); white-space: pre !important;"> </span></p><p class="ember-view reader-content-blocks__paragraph" id="ember1939" style="--artdeco-reset-typography_getfontsize: 1.6rem; --artdeco-reset-typography_getlineheight: 1.5; background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; color: var(--color-text); font-size: var(--font-size-large); font-weight: var(--artdeco-reset-typography-font-weight-normal); line-height: 1.75; margin: 1.6rem 0px; padding: var(--artdeco-reset-base-padding-zero); pointer-events: all; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);">Estos nuevos modelos no son perfectos: les cuesta especialmente predecir eventos importantes como los huracanes. Pero cuanto más haya, debido al cambio climático, más demanda tendrán estas IA en las regiones que deseen equiparse con sistemas de alerta avanzados</p><p class="ember-view reader-content-blocks__paragraph" id="ember1939" style="--artdeco-reset-typography_getfontsize: 1.6rem; --artdeco-reset-typography_getlineheight: 1.5; background: var(--artdeco-reset-base-background-transparent); border: var(--artdeco-reset-base-border-zero); box-sizing: inherit; color: var(--color-text); font-size: var(--font-size-large); font-weight: var(--artdeco-reset-typography-font-weight-normal); line-height: 1.75; margin: 1.6rem 0px; padding: var(--artdeco-reset-base-padding-zero); pointer-events: all; vertical-align: var(--artdeco-reset-base-vertical-align-baseline);"><a href="https://www.epe.es/es/tendencias-21/20231225/terapia-obesidad-cambio-climatico-ia-96239305">CIENCIA | La terapia contra la obesidad, el cambio climático y la IA marcaron el ritmo de la ciencia en 2023 (epe.es)</a></p></div></div></div></div>Ramon.Moratahttp://www.blogger.com/profile/16398587758151257300noreply@blogger.com0tag:blogger.com,1999:blog-1131336482330646116.post-40769124273797969132024-01-13T04:29:00.000-08:002024-01-18T08:56:10.303-08:00 El ictus es una de las manifestaciones neurológicas del COVID-19, que conlleva un riesgo importante de morbilidad y mortalidad.<p>" <span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">El ictus es una de las manifestaciones neurológicas del COVID-19, que conlleva un riesgo importante de morbilidad y mortalidad."</span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"></span></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgy019w7r9kCyylEdUZ8t2H271eNhnbLeJricRHDlXLgmL9Xz9mVQcqTcSJglVoJLGlfc48cojvFlH86SaJyMPel6d6fE9l-4UN6c1LZ4reTFMKZ1qEWvzDzG_tXzIch1jR4GtCM9d478Xllx8zsayd4ZRWi-0lz7harktMJ5zopLsUzWx6O-2ByNR8rlA/s680/ic.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="386" data-original-width="680" height="289" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgy019w7r9kCyylEdUZ8t2H271eNhnbLeJricRHDlXLgmL9Xz9mVQcqTcSJglVoJLGlfc48cojvFlH86SaJyMPel6d6fE9l-4UN6c1LZ4reTFMKZ1qEWvzDzG_tXzIch1jR4GtCM9d478Xllx8zsayd4ZRWi-0lz7harktMJ5zopLsUzWx6O-2ByNR8rlA/w507-h289/ic.jpg" width="507" /></a></div><br /> <span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">Estudio: “Se observó grado severo de COVID-19 en el 52,8% de los pacientes, con dificultad respiratoria y tasas de mortalidad del 56,9% y 58,3%”</span><p></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"><span class="r-b88u0q">Degree of COVID-19 severity and mortality in stroke: correlation of clinical and laboratory parameters</span><span> </span></span>-<span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"><span>Nearly half of their stroke patients weren’t severe C19 patients with still a high mortality rate </span></span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"><span> </span></span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"><span> </span></span>-<span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"><span>"Stroke is one of the neurological manifestations of COVID-19, leading to a significant risk of morbidity and mortality."
</span></span><img alt="➡️" class="r-4qtqp9 r-dflpy8 r-zw8f10 r-sjv1od r-10akycc r-h9hxbl" draggable="false" src="https://abs-0.twimg.com/emoji/v2/svg/27a1.svg" title="Rightwards arrow" /><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"><span>Study: </span><span class="r-b88u0q">"Severe degree of COVID-19 was observed in 52.8% of patients, with respiratory distress and death rates of 56.9% and 58.3%"</span><span> </span></span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"><span> -</span></span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"><span>Mortality in COVID-19 patients with stroke was significantly associated with loss of consciousness, motor deficit, respiratory distress, and raised monocyte count. </span></span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"><span>- </span></span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"><span>The risk of mortality is heightened when multiple factors coexist. </span></span><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"></span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w</span></p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"></span><br /></p><h1 class="c-article-title" data-article-title="" data-test="article-title"><span style="color: #274e13;">Degree of COVID-19 severity and mortality in stroke: correlation of clinical and laboratory parameters</span></h1>
<ul class="c-article-author-list c-article-author-list--short js-no-scroll" data-component-authors-activator="authors-list" data-test="authors-list"><li class="c-article-author-list__item"><a data-author-popup="auth-Abdul-Gofir-Aff1" data-corresp-id="c1" data-test="author-name" data-track-action="open author" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#auth-Abdul-Gofir-Aff1">Abdul Gofir<svg aria-hidden="true" class="u-icon" height="16" width="16"><use></use></svg></a>, </li><li class="c-article-author-list__item"><a data-author-popup="auth-Irawan-Satriotomo-Aff2" data-test="author-name" data-track-action="open author" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#auth-Irawan-Satriotomo-Aff2">Irawan Satriotomo</a>, </li><li class="c-article-author-list__item c-article-author-list__item--hide-small-screen"><a data-author-popup="auth-Yossy_Catarina_Budi_Nur-Syamsah-Aff1" data-test="author-name" data-track-action="open author" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#auth-Yossy_Catarina_Budi_Nur-Syamsah-Aff1">Yossy Catarina Budi Nur Syamsah</a>, </li><li class="c-article-author-list__item c-article-author-list__item--hide-small-screen"><a data-author-popup="auth-Mawaddah_Ar-Rochmah-Aff1" data-test="author-name" data-track-action="open author" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#auth-Mawaddah_Ar-Rochmah-Aff1">Mawaddah Ar Rochmah</a>, </li><li class="c-article-author-list__item c-article-author-list__item--hide-small-screen"><a data-author-popup="auth-Tommy_Rachmat-Setyawan-Aff1" data-test="author-name" data-track-action="open author" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#auth-Tommy_Rachmat-Setyawan-Aff1">Tommy Rachmat Setyawan</a>, </li><li class="c-article-author-list__item c-article-author-list__item--hide-small-screen"><a data-author-popup="auth-Adika-Mianoki-Aff3" data-test="author-name" data-track-action="open author" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#auth-Adika-Mianoki-Aff3">Adika Mianoki</a>, </li><li class="c-article-author-list__item c-article-author-list__item--hide-small-screen"><a data-author-popup="auth-Raymond_Aris_Nimrod_Alvonsius-Silalahi-Aff1" data-test="author-name" data-track-action="open author" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#auth-Raymond_Aris_Nimrod_Alvonsius-Silalahi-Aff1">Raymond Aris Nimrod Alvonsius Silalahi</a> & </li><li class="c-article-author-list__item"><a data-author-popup="auth-Dhite_Bayu-Nugroho-Aff4" data-test="author-name" data-track-action="open author" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#auth-Dhite_Bayu-Nugroho-Aff4">Dhite Bayu Nugroho</a> </li></ul>
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<section aria-labelledby="Abs1" data-title="Abstract" lang="en"><div class="c-article-section" id="Abs1-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Abs1">Abstract</h2><div class="c-article-section__content" id="Abs1-content"><h3 class="c-article__sub-heading" data-test="abstract-sub-heading">Background</h3><p>Stroke
is one of the neurological manifestations of COVID-19, leading to a
significant risk of morbidity and mortality. Clinical manifestations and
laboratory parameters were investigated to determine mortality
predictors in this case.</p><h3 class="c-article__sub-heading" data-test="abstract-sub-heading">Method</h3><p>The
case control study was conducted at Dr. Sardjito General
Hospital,Yogyakarta, Indonesia, with data collected between July 2020
and August 2021. All recorded clinical and laboratory data from acute
stroke patients with confirmed COVID-19 were collected. Baseline
characteristics, bivariate, and multivariate analyses were assessed to
determine significant predictors for mortality.</p><h3 class="c-article__sub-heading" data-test="abstract-sub-heading">Result</h3><p>This
study involved 72 subjects with COVID-19 and stroke. The majority
experienced ischemic stroke, with hypertension as the most prevalent
comorbidity. Notably, 45.8% of subjects (p < 0.05) loss of
consciousness and 72.2% of exhibited motor deficits (p < 0.05).
Severe degree of COVID-19 was observed in 52.8% of patients, with
respiratory distress and death rates of 56.9% and 58.3%. Comparison of
surviving and deceased groups highlighted significant differences in
various clinical and laboratory characteristics differences. Hazard
ratio (HR) analysis identified loss of consciousness (HR = 2.68;
p = 0.01), motor deficit (HR = 2.34; p = 0.03), respiratory distress
(HR = 81.51; p < 0.001), and monocyte count (HR:1.002; p = 0.04) as
significant predictors of mortality.</p><h3 class="c-article__sub-heading" data-test="abstract-sub-heading">Conclusion</h3><p>Mortality
in COVID-19 patients with stroke was significantly associated with loss
of consciousness, motor deficit, respiratory distress, and raised
monocyte count. The risk of mortality is heightened when multiple
factors coexist.</p></div></div></section>
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<section data-title="Background"><div class="c-article-section" id="Sec1-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Sec1">Background</h2><div class="c-article-section__content" id="Sec1-content"><p>Severe
Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infection, also
known as Coronavirus Disease 2019 (COVID-19), has become a major health
problem since the end of 2019 with over 750 million confirmed cumulative
cases and about 1% total deaths recorded globally [<a aria-label="Reference 1" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR1" id="ref-link-section-d434215214e849" title="World Health Organization. Coronavirus disease (COVID-19) Dashboard. WHO,
https://covid19.who.int/
. Accessed 15 Oct 2023">1</a>].
Since the World Health Organization (WHO) declared that COVID-19 is no
longer a public health emergency of international concern in May 2023,
advancements in disease treatment and global transmission prevention,
including vaccination programs, have made it possible to reach the end
of COVID-19 pandemic era [<a aria-label="Reference 2" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR2" id="ref-link-section-d434215214e852" title="World Health Organization. Statement on the fifteenth meeting of the IHR (2005) Emergency Committee on the COVID-19 pandemic. May 2023.
https://www.who.int/news/item/05-05-2023-statement-on-the-fifteenth-meeting-of-the-international-health-regulations-(2005)-emergency-committee-regarding-the-coronavirus-disease-(covid-19)-pandemic
. Accessed 15 Oct 2023">2</a>].
The WHO emphasized the decrease in COVID-19 fatalities,
hospitalizations, and intensive care admissions, as well as the increase
in population immunity against SARS-CoV-2 on this occasion. However,
COVID-19 awareness should still be considered imperative because several
surges of cases have been reported and new variants have been
identified in various locations, such as the increase in COVID-19 cases
in Western Pacific areas at the end of year 2022 despite the low number
of cases in other parts of the world. [<a aria-label="Reference 1" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR1" id="ref-link-section-d434215214e855" title="World Health Organization. Coronavirus disease (COVID-19) Dashboard. WHO,
https://covid19.who.int/
. Accessed 15 Oct 2023">1</a>]</p><p>Neurological
manifestations, such as encephalitis, encephalopathy, and stroke were
found in 36.4% of COVID-19 patients with involvement of central nervous
system symptoms [<a aria-label="Reference 3" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR3" id="ref-link-section-d434215214e861" title="Mao L, Wang M, Chen S, et al. Neurological manifestations of hospitalized patients with COVID-19 in Wuhan, China: a retrospective case series study. MedRxiv. 2020.
https://doi.org/10.1001/jamaneurol.2020.1127
.">3</a>].
The incidence of stroke among COVID-19 patients was reported up to 3.4%
in the Philippines with ischemic stroke being the most prevalent type [<a aria-label="Reference 4" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR4" id="ref-link-section-d434215214e864" title="Jamora RD, Prado MB Jr, Anlacan VM, et al. Incidence and risk factors for stroke in patients with COVID-19 in the Philippines: an analysis of 10,881 cases. J Stroke Cerebrovasc Dis. 2022;31(11):106776.">4</a>]. COVID-19 increases the risk of ischemic stroke 7.8 times in the first three days after respiratory distress [<a aria-label="Reference 5" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR5" id="ref-link-section-d434215214e867" title="Qureshi AI, Baskett WI, Huang W, et al. Acute ischemic stroke and COVID-19: an analysis of 27 676 patients. Stroke. 2021;52(3):905–12.">5</a>].
The pathophysiology of stroke in COVID-19 is related to viral affinity
towards the Angiotensin Converting Enzyme (ACE-2) receptors, which
directly causes brain neuron damage [<a aria-label="Reference 6" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR6" id="ref-link-section-d434215214e870" title="Valderrama EV, Humbert K, Lord A, et al. Severe acute respiratory syndrome coronavirus 2 infection and ischemic stroke. Stroke. 2020;51(7):e124–7.">6</a>], activation on the immune system in the form of cytokine storms, which increases the incidence of inflammation [<a aria-label="Reference 7" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR7" id="ref-link-section-d434215214e873" title="Fotuhi M, Mian A, Meysami S, et al. Neurobiology of COVID-19. J Alzheimers Dis. 2020;76(1):3–19.">7</a>], a combination of hypercoagulability and endothelial dysfunction [<a aria-label="Reference 8" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR8" id="ref-link-section-d434215214e877" title="Snell J. SARS-CoV-2 infection and its association with thrombosis and ischemic stroke: a review. Am J Emerg Med. 2021;40:188–92.">8</a>], and hypoxemia due to respiratory distress. [<a aria-label="Reference 9" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR9" id="ref-link-section-d434215214e880" title="Spence JD, De Freitas GR, Pettigrew LC, et al. Mechanisms of stroke in COVID-19. Cerebrovasc Dis. 2020;49(4):451–8.">9</a>]</p><p style="text-align: justify;">Death is more common in acute stroke patients with COVID-19 [<a aria-label="Reference 4" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR4" id="ref-link-section-d434215214e886" title="Jamora RD, Prado MB Jr, Anlacan VM, et al. Incidence and risk factors for stroke in patients with COVID-19 in the Philippines: an analysis of 10,881 cases. J Stroke Cerebrovasc Dis. 2022;31(11):106776.">4</a>, <a aria-label="Reference 5" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR5" id="ref-link-section-d434215214e889" title="Qureshi AI, Baskett WI, Huang W, et al. Acute ischemic stroke and COVID-19: an analysis of 27 676 patients. Stroke. 2021;52(3):905–12.">5</a>]. In individuals with COVID-19, the risk of mortality doubles with the occurrence of acute ischemic stroke [<a aria-label="Reference 5" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR5" id="ref-link-section-d434215214e892" title="Qureshi AI, Baskett WI, Huang W, et al. Acute ischemic stroke and COVID-19: an analysis of 27 676 patients. Stroke. 2021;52(3):905–12.">5</a>].
COVID-19 patients with stroke had poor outcomes due to limited
admission to intensive care facilities and use of ventilators [<a aria-label="Reference 4" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR4" id="ref-link-section-d434215214e895" title="Jamora RD, Prado MB Jr, Anlacan VM, et al. Incidence and risk factors for stroke in patients with COVID-19 in the Philippines: an analysis of 10,881 cases. J Stroke Cerebrovasc Dis. 2022;31(11):106776.">4</a>, <a aria-label="Reference 10" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR10" id="ref-link-section-d434215214e898" title="Siow I, Lee KS, Zhang JJ, et al. Stroke as a neurological complication of COVID-19: a systematic review and meta-analysis of incidence, outcomes and predictors. J Stroke Cerebrovasc Dis. 2021;30(3): 105549.">10</a>].
Although it is uncommon, the mortality rate of hemorrhagic stroke
patients with COVID-19 infection was also double compared to those
without hemorrhagic stroke [<a aria-label="Reference 11" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR11" id="ref-link-section-d434215214e902" title="Leasure AC, Khan YM, Iyer R, et al. Intracerebral hemorrhage in patients with COVID-19: an analysis from the COVID-19 cardiovascular disease registry. Stroke. 2021;52(7):e321–3.">11</a>].
Mortality in stroke patients with COVID-19 is also linked to older age,
dyspnea, smoking, kidney disease, hypertension, malignancy, diabetes
mellitus (DM), and lung disease [<a aria-label="Reference 4" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR4" id="ref-link-section-d434215214e905" title="Jamora RD, Prado MB Jr, Anlacan VM, et al. Incidence and risk factors for stroke in patients with COVID-19 in the Philippines: an analysis of 10,881 cases. J Stroke Cerebrovasc Dis. 2022;31(11):106776.">4</a>, <a aria-label="Reference 10" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR10" id="ref-link-section-d434215214e908" title="Siow I, Lee KS, Zhang JJ, et al. Stroke as a neurological complication of COVID-19: a systematic review and meta-analysis of incidence, outcomes and predictors. J Stroke Cerebrovasc Dis. 2021;30(3): 105549.">10</a>].
Several laboratory markers from previous studies, such as increased
D-dimer, decreased platelet count, decreased hemoglobin concentration,
increased creatinine, increased Interleukin-6 (IL-6), and elevated
cardiac troponin-I have become predictors of COVID-19 mortality [<a aria-label="Reference 12" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR12" id="ref-link-section-d434215214e911" title="Mesas AE, Cavero-Redondo I, Álvarez-Bueno C, et al. Predictors of in-hospital COVID-19 mortality: a comprehensive systematic review and meta-analysis exploring differences by age, sex and health conditions. PLoS ONE. 2020;15(11): e0241742.">12</a>].
Other predictors of COVID-19 mortality include albumin, total
bilirubin, Serum Glutamic Oxaloacetic Transaminase (SGOT), Serum
Glutamic Pyruvic Transaminase (SGPT), nitrogen urea (p-ES), C-reactive
protein (CRP), lactate dehydrogenase (LDH) and ferritin levels. [<a aria-label="Reference 12" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR12" id="ref-link-section-d434215214e914" title="Mesas AE, Cavero-Redondo I, Álvarez-Bueno C, et al. Predictors of in-hospital COVID-19 mortality: a comprehensive systematic review and meta-analysis exploring differences by age, sex and health conditions. PLoS ONE. 2020;15(11): e0241742.">12</a>]</p><p style="text-align: justify;">Despite
various studies that have extensively investigated predictors of
mortality in COVID-19 and acute stroke, separately, there is a notable
gap in understanding which factors significantly contribute to the
mortality risks in COVID-19 patients with stroke. Numerous studies have
highlighted the role of coagulopathy, leading to a hypercoagulable
state, and its adverse impact on outcomes in this specific population.
For instance, elevated d-dimer levels (> 5.15 μg/ml FEU) were found
to be associated with nearly three times increased mortality in
hospitalized COVID-19 patients with acute ischemic stroke [<a aria-label="Reference 13" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR13" id="ref-link-section-d434215214e920" title="Kim Y, Khose S, Abdelkhaleq R, Salazar-Marioni S, Zhang GQ, Sheth SA. Predicting in-hospital mortality using D-dimer in COVID-19 patients with acute ischemic stroke. Front Neurol. 2021;16(12): 702927.">13</a>].
However, there remains a lack of exploration into other clinically and
laboratory-relevant parameters that may be collinearly associated with
mortality in COVID-19 patients with stroke. This study addresses this
gap by examining a comprehensive set of clinical and laboratory
parameters to identify mortality predictors in patients with acute
stroke and COVID-19.</p></div></div></section><section data-title="Methods"><div class="c-article-section" id="Sec2-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Sec2">Methods</h2><div class="c-article-section__content" id="Sec2-content"><h3 class="c-article__sub-heading" id="Sec3">Research subject</h3><p>This
retrospective case–control study used medical record data at the Dr.
Sardjito General Hospital in Yogyakarta, Indonesia from July 2020 to
August 2021. All acute stroke patients with the evidence from
non-contrast head computerized tomography (CT) scan imaging and
confirmed COVID-19 from polymerase chain reaction (PCR) of SARS-Cov-2
from nasal swab sampling, age > 18 years with complete medical
records covering all clinical and laboratory parameters were included in
this study. Patients with missing or incomplete data were excluded.</p><h3 class="c-article__sub-heading" id="Sec4">Research variables</h3><p>The
demographic information, medical history, clinical features, degree of
COVID-19, laboratory results upon admission, and mortality outcome of
the patients were all obtained. Data on the patient's medical history
included a history of previous stroke, hypertension, diabetes mellitus,
dyslipidemia, and renal insufficiency. Clinical data assessed routinely
at admission included systolic and diastolic blood pressure, level of
consciousness, motor deficits, aphasia, cranial nerves paresis, focal
seizures, generalized seizures, any incidence of respiratory failure,
and the patient's functional status with Activity Daily Living (ADL)
score, Instrumental Activity of Daily Living (IADL), Barthel Index,
modified Rankin Scale (mRS), and degree of neurological deficit
according to the National Institutes of Health Stroke Scale (NIHSS)
score, as well as the patients’ severity of COVID-19. COVID-19 severity
was classified as mild, moderate, or severe based on the categorization
of medical records by internal medicine specialists in accordance with
the 3rd edition of the 2020 COVID-19 Management Guidelines in Indonesia [<a aria-label="Reference 14" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR14" id="ref-link-section-d434215214e943" title="PDPI, PERKI, PAPDI, PERDATIN, IDAI (2020) Guidelines for managing COVID-19 December 3, 2020 Edition. Guidelines for the Management of COVID-19">14</a>].
Laboratory data included routine blood analysis at admission
(hemoglobin, erythrocytes, leukocytes, neutrophils, lymphocytes,
neutrophil-to-lymphocyte ratio (NLR), monocytes, eosinophils, basophils,
and platelets), random blood glucose (RBG) level, coagulation factors:
plasma prothrombin time (PPT), activated partial thromboplastin time
(APTT), internationalized normalized ratio (INR), and D-dimer), markers
of tissue damage (CRP, LDH); renal function biomarkers: blood urea
nitrogen (BUN) and creatinine; and electrolytes (sodium, potassium, and
chloride). Stroke type was determined based on the description
non-contrast head computerized tomography (CT) scan.</p><h3 class="c-article__sub-heading" id="Sec5">Statistical analysis</h3><p>Statistical
analysis was performed using SPSS version 23 (IBM Corp., Armonk, NY).
The univariate descriptive analysis assessed the characteristics and
demographics of the research subjects. The bivariate analysis compared
clinical and laboratory characteristics between patients who died
(deceased group) with those who survived (surviving group) at hospital
release. Shapiro–Wilk analysis was used to determine the distribution of
numerical data. Independent T-tests were used for data with normal
distribution, whereas Mann–Whitney tests were used for non-normal
distributions. Data were considered significant when <i>p</i> < 0.05.
A regression analysis was performed to determine the most influential
variables in the mortality of stroke patients with COVID-19. The hazard
ratio was calculated by the Cox regression method with the backward
system with the dependent variables being mortality and length of stay,.
To demonstrate survival analysis, the significant variables were
evaluated in a Kaplan–Meier model. The interval variables were then
transformed into categorical variables by measuring the cut-off point
with a receiver operating characteristic (ROC) curve and J Youden Index.</p></div></div></section><section data-title="Results"><div class="c-article-section" id="Sec6-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Sec6">Results</h2><div class="c-article-section__content" id="Sec6-content"><p>The demographic and clinical characteristics of all the study subjects are listed in Table <a data-track-action="table anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#Tab1">1</a>.
A total of 72 subjects were included in the study. The respondents’
mean age was 62.33 ± 12.28 years, with more males than females. The vast
majority of stroke occurred for first-time occurrences. Almost every
patients (90.3%) suffred an ischemic stroke. Hypertension was the most
prevalent comorbidity from the history of prior diseases (63.9%).
Diabetes mellitus (DM), heart disease, dyslipidemia, and renal
insufficiency were all reported by < 50% of the subjects. The average
blood pressure indicated systolic hypertension, whis is defined by
systolic above 140 mmHg and diastolic values below 90 mmHg. The median
of quantitative GCS value was 15, with a 5-point interquartile range,
ranging from <i>compos mentis</i> to stuporous state. There were 33
subjects (45.8%) who experienced a loss of consciousness (GCS < 15).
The majority of subjects had motor deficits (72.2%) and cranial nerve
paralysis (51.4%), while only a few suffered aphasia, focal seizures, or
generalized seizures. The classification of neurological deficits based
on the NIHSS score revealed a moderate average of neurological
deficits. More than half of the patients (52.8%) have severe COVID-19.
The percentages of patients’ who experienced severe respiratory failure
and death were 56.9% and 58.3%, respectively. Tables <a data-track-action="table anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#Tab2">2</a> and <a data-track-action="table anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#Tab3">3</a>
show the comparison of the clinical and laboratory characteristics
between the surviving and deceased groups of COVID-19 stroke patients.
Essential laboratory characteristics showed increased total leukocytes,
neutrophil lymphocyte ratio (NLR), coagulation factors, decreased kidney
function marked by increased blood urea nitrogen (BUN) and creatinine,
and liver damage indicated by increased SGOT and SGPT.</p><div class="c-article-table" data-container-section="table" data-test="inline-table" id="table-1"><figure><figcaption class="c-article-table__figcaption"><b data-test="table-caption" id="Tab1">Table 1 Demographic, Clinical, and Laboratory Characteristic of Subjects</b></figcaption><div class="u-text-right u-hide-print"><a aria-label="Full size table 1" class="c-article__pill-button" data-test="table-link" data-track-action="view table" data-track-label="button" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w/tables/1" rel="nofollow"><span>Full size table</span><svg aria-hidden="true" class="u-icon" height="16" width="16"><use></use></svg></a></div></figure></div><div class="c-article-table" data-container-section="table" data-test="inline-table" id="table-2"><figure><figcaption class="c-article-table__figcaption"><b data-test="table-caption" id="Tab2">Table 2 Clinical Characteristics of the Survive vs Dead Subjects</b></figcaption><div class="u-text-right u-hide-print"><a aria-label="Full size table 2" class="c-article__pill-button" data-test="table-link" data-track-action="view table" data-track-label="button" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w/tables/2" rel="nofollow"><span>Full size table</span><svg aria-hidden="true" class="u-icon" height="16" width="16"><use></use></svg></a></div></figure></div><div class="c-article-table" data-container-section="table" data-test="inline-table" id="table-3"><figure><figcaption class="c-article-table__figcaption"><b data-test="table-caption" id="Tab3">Table 3 Laboratory Characteristics of the Survive vs Dead Subjects</b></figcaption><div class="u-text-right u-hide-print"><a aria-label="Full size table 3" class="c-article__pill-button" data-test="table-link" data-track-action="view table" data-track-label="button" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w/tables/3" rel="nofollow"><span>Full size table</span><svg aria-hidden="true" class="u-icon" height="16" width="16"><use></use></svg></a></div></figure></div><p>Table <a data-track-action="table anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#Tab2">2</a>
showed no significant differences in age, sex, or number of stroke
episodes between COVID-19 stroke survivors and those who died (<i>p</i> > 0.05).
Diastolic blood pressure, quantitative GCS, loss of consciousness,
generalized seizures, NIHSS score, history of dyslipidemia, COVID-19
severity, respiratory distress, and length of stay were significantly
different between the survive and deceased groups of patients (<i>p</i> < 0.05).</p><p>Table <a data-track-action="table anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#Tab3">3</a>
demonstrated that deceased group had significantly lower monocyte
count, higher serum CRP level, higher serum BUN level, and a higher
random blood glucose level than the survivors (<i>p</i> < 0.05). All of these laboratory values were collected when patients were admitted to the hospital.</p><p>Based on the important clinical and laboratory criteria from the bivariate analysis depicted in Tables <a data-track-action="table anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#Tab2">2</a> and <a data-track-action="table anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#Tab3">3</a>, the clinically valuable and significant variables (<i>p</i> < 0.25)
were assessed using the hazard ratio (HR) analysis by the Cox
regression method with the backward system. After cox regression
analysis with backward LR system within 22 steps, there were retained
significant variables which associated with the mortality risk of
COVID-19 patients with stroke, as presented in Table <a data-track-action="table anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#Tab4">4</a>.</p><div class="c-article-table" data-container-section="table" data-test="inline-table" id="table-4"><figure><figcaption class="c-article-table__figcaption"><b data-test="table-caption" id="Tab4">Table 4 Hazard Ratio Analysis</b></figcaption><div class="u-text-right u-hide-print"><a aria-label="Full size table 4" class="c-article__pill-button" data-test="table-link" data-track-action="view table" data-track-label="button" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w/tables/4" rel="nofollow"><span>Full size table</span><svg aria-hidden="true" class="u-icon" height="16" width="16"><use></use></svg></a></div></figure></div><p>Except for APTT, regression analysis (Table <a data-track-action="table anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#Tab4">4</a>)
revealed that almost all of the retained significant variables were
significant,. We further confirmed the important of its size effect with
Area Under Curve (AUC) > 0.5, and only loss of consciousness and
respiratory distress were accounted for mortality prediction.
Kaplan–Meier curve (Fig. <a data-track-action="figure anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#Fig1">1</a>)
was used to show the hazard rate for significant predictors in the HR
model. In this study, we conducted the univariate analysis to predict
the solitary risk of predictors toward mortality based on significant
predictors identified in cox regression model,. We found that loss of
consciousness and respiratory distress significantly increase mortality
rate. Subjects who lost consciousness had a 2.68-fold greater mortality
risk than those who did not. Subjects with respiratory distress had and
81.51-fold greater mortality risk than those without respiratory
distress.</p><div class="c-article-section__figure js-c-reading-companion-figures-item" data-container-section="figure" data-test="figure" data-title="Fig. 1" id="figure-1"><figure><figcaption><b class="c-article-section__figure-caption" data-test="figure-caption-text" id="Fig1">Fig. 1</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><a class="c-article-section__figure-link" data-test="img-link" data-track-action="view figure" data-track-label="image" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w/figures/1" rel="nofollow"><picture><source type="image/webp"></source><img alt="figure 1" aria-describedby="Fig1" height="351" src="https://media.springernature.com/lw685/springer-static/image/art%3A10.1186%2Fs12868-023-00837-w/MediaObjects/12868_2023_837_Fig1_HTML.png" width="685" /></picture></a></div><div class="c-article-section__figure-description" data-test="bottom-caption" id="figure-1-desc"><p>Hazard Analysis on Predictors of Mortality in Stroke Patients with COVID-19; <b>A</b> Kaplan–Meier Curve predicted by Loss of Consciousness, <b>B</b> predicted by Respiratory Distress</p></div></div><div class="u-text-right u-hide-print"><a aria-label="Full size image figure 1" class="c-article__pill-button" data-test="article-link" data-track-action="view figure" data-track-dest="link:Figure1 Full size image" data-track-label="button" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w/figures/1" rel="nofollow"><span>Full size image</span><svg aria-hidden="true" class="u-icon" height="16" width="16"><use></use></svg></a></div></figure></div></div></div></section><section data-title="Discussion"><div class="c-article-section" id="Sec7-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Sec7">Discussion</h2><div class="c-article-section__content" id="Sec7-content"><p>The mortality rate of patients with confirmed stroke and COVID-19 was 58.3%, according to our data (Table <a data-track-action="table anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#Tab1">1</a>).
This finding is consistent with previous studies on COVID-19 stroke
patients who had a higher fatality rate than those who did not have
stroke, both hemorrhagic and ischemic [<a aria-label="Reference 4" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR4" id="ref-link-section-d434215214e3589" title="Jamora RD, Prado MB Jr, Anlacan VM, et al. Incidence and risk factors for stroke in patients with COVID-19 in the Philippines: an analysis of 10,881 cases. J Stroke Cerebrovasc Dis. 2022;31(11):106776.">4</a>, <a aria-label="Reference 5" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR5" id="ref-link-section-d434215214e3592" title="Qureshi AI, Baskett WI, Huang W, et al. Acute ischemic stroke and COVID-19: an analysis of 27 676 patients. Stroke. 2021;52(3):905–12.">5</a>, <a aria-label="Reference 12" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR12" id="ref-link-section-d434215214e3595" title="Mesas AE, Cavero-Redondo I, Álvarez-Bueno C, et al. Predictors of in-hospital COVID-19 mortality: a comprehensive systematic review and meta-analysis exploring differences by age, sex and health conditions. PLoS ONE. 2020;15(11): e0241742.">12</a>].
Dyspnea, smoking, cardiovascular problems, kidney and liver diseases,
hypertension, diabetes, and malignancy were all associated with
mortality rates in COVID-19. [<a aria-label="Reference 15" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR15" id="ref-link-section-d434215214e3598" title="Zaki N, Alashwal H, Ibrahim S. Association of hypertension, diabetes, stroke, cancer, kidney disease, and high-cholesterol with COVID-19 disease severity and fatality: a systematic review. Diabetes Metab Syndr. 2020;14(5):1133–42.">15</a>]</p><p>According
to our demographic data, the population’s average age was
62.33 ± 12.28 years old. Based on data from the Centers for Disease
Control and Prevention, the risk of mortality from COVID-19 rose 90
times at the age of 65–74 years and 220 times at the age of 75–84 years
when compared to the age of 18–29 years. [<a aria-label="Reference 16" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR16" id="ref-link-section-d434215214e3604" title="National Center for Immunization and Respiratory Diseases (U.S.). Division of Viral Diseases (2021) Risk for COVID-19 infection, hospitalization, and death by age group. CDC: stacks.cdc.gov">16</a>]
Age > 65 years was also significantly associated with an increased
infection fatality rate of SARS-CoV-2 infected patients in 45 countries [<a aria-label="Reference 16" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR16" id="ref-link-section-d434215214e3607" title="National Center for Immunization and Respiratory Diseases (U.S.). Division of Viral Diseases (2021) Risk for COVID-19 infection, hospitalization, and death by age group. CDC: stacks.cdc.gov">16</a>]. Longer duration of comorbidities and inadequate compensation for organ function resulted in greater patient mortality [<a aria-label="Reference 17" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR17" id="ref-link-section-d434215214e3610" title="O’Driscoll M, Ribeiro Dos Santos G, Wang L, et al. Age-specific mortality and immunity patterns of SARS-CoV-2. Nature. 2021;590(7844):140–5.">17</a>].
Furthermore, our male subjects were more in numbers compared to female
patients. According to one study, sex hormones, particularly
anti-androgen and anti-estrogen, play a roles in ACE-2 protein
expression in the lungs, which influence the risk of COVID-19 infection
indirectly [<a aria-label="Reference 18" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR18" id="ref-link-section-d434215214e3613" title="Majdic G. Could sex/gender differences in ACE2 expression in the lungs contribute to the large gender disparity in the morbidity and mortality of patients infected with the SARS-CoV-2 virus? Front Cell Infect Microbiol. 2020;10:327.">18</a>].
Sex disparities can also be attributed to male smoking prevalence.
Nicotine has direct effects on the ACE-2 receptors, which bind to the
COVID virus and cause lung tissue and brain damage. [<a aria-label="Reference 18" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR18" id="ref-link-section-d434215214e3616" title="Majdic G. Could sex/gender differences in ACE2 expression in the lungs contribute to the large gender disparity in the morbidity and mortality of patients infected with the SARS-CoV-2 virus? Front Cell Infect Microbiol. 2020;10:327.">18</a>]</p><p>Our
data revealed no link between a history of hypertension, smoking,
diabetes mellitus, cardiac disease, renal insufficiency, and higher
systolic blood pressure with mortality in COVID-19 patients with acute
stroke. According to one recent research, uncontrolled blood pressure
was not significantly associated with 30-day mortality or
hospitalization incidence in COVID-19 patients with hypertension [<a aria-label="Reference 19" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR19" id="ref-link-section-d434215214e3622" title="An J, Zhou H, Luong TQ, et al. Risk of hospitalization and mortality associated with uncontrolled blood pressure in patients with hypertension and COVID-19. Int J Cardiol Cardiovasc Risk Prevention. 2021;11: 200117.">19</a>].
On the contrary, a history of hypertension with mean elevated blood
pressure showed that systolic hypertension was associated with a
significantly higher risk of mortality and an increased incidence of
intensive care unit (ICU) admission in COVID-19 patients. [<a aria-label="Reference 4" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR4" id="ref-link-section-d434215214e3625" title="Jamora RD, Prado MB Jr, Anlacan VM, et al. Incidence and risk factors for stroke in patients with COVID-19 in the Philippines: an analysis of 10,881 cases. J Stroke Cerebrovasc Dis. 2022;31(11):106776.">4</a>, <a aria-label="Reference 20" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR20" id="ref-link-section-d434215214e3628" title="Pan W, Zhang J, Wang M, et al. Clinical features of COVID-19 in patients with essential hypertension and the impacts of renin-angiotensin-aldosterone system inhibitors on the prognosis of COVID-19 patients. Hypertension. 2020;76(3):732–41.">20</a>]</p><p>Loss
of consciousness, lower GCS score, and generalized seizure were found
to increase the risk of mortality in COVID-19 patients with stroke.
Previous study has identified the GCS score as a risk factor for
predicting mortality and allowing rapid risk stratification for
SARS-CoV-2 infected stroke patients in tertiary care hospitals in
low-middle-income countries where laboratory findings may be unavailable
during crisis [<a aria-label="Reference 21" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR21" id="ref-link-section-d434215214e3634" title="Zhang L, Sun W, Wang Y, et al. Clinical course and mortality of stroke patients with coronavirus disease 2019 in Wuhan. China Stroke. 2020;51(9):2674–82.">21</a>].
Furthermore, any neurological co-morbidities were found to have a
direct significant association with stroke in patients with COVID-19,
which increased mortality and ICU hospitalizations. [<a aria-label="Reference 4" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR4" id="ref-link-section-d434215214e3637" title="Jamora RD, Prado MB Jr, Anlacan VM, et al. Incidence and risk factors for stroke in patients with COVID-19 in the Philippines: an analysis of 10,881 cases. J Stroke Cerebrovasc Dis. 2022;31(11):106776.">4</a>]</p><p>In
our study, the severity of COVID-19 and the presence of respiratory
distress during hospitalization were found to have a significant
relatioinship with mortality (Table <a data-track-action="table anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#Tab2">2</a>).
COVID-19 with higher severity level of may trigger cytokine cascades,
exaggerated inflammatory responses, coagulation dysfunction, and organ
damage, worsening the prognosis of COVID-19 stroke patients [<a aria-label="Reference 22" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR22" id="ref-link-section-d434215214e3647" title="Gupta A, Madhavan MV, Sehgal K, Nair N, Mahajan S, Sehrawat TS, Bikdeli B, Ahluwalia N, Ausiello JC, Wan EY, Freedberg DE. Extrapulmonary manifestations of COVID-19. Nat Med. 2020;26(7):1017–32.">22</a>, <a aria-label="Reference 23" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR23" id="ref-link-section-d434215214e3650" title="Bhaskar S, Sinha A, Banach M, Mittoo S, Weissert R, Kass JS, Rajagopal S, Pai AR, Kutty S. Cytokine storm in COVID-19—immunopathological mechanisms, clinical considerations, and therapeutic approaches: the REPROGRAM consortium position paper. Front Immunol. 2020;10(11):1648.">23</a>].
Most patients with severe COVID-19 are dyspneic or hypoxemic one week
after onset, with serious lung damage preventing gas exchange. [<a aria-label="Reference 9" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR9" id="ref-link-section-d434215214e3653" title="Spence JD, De Freitas GR, Pettigrew LC, et al. Mechanisms of stroke in COVID-19. Cerebrovasc Dis. 2020;49(4):451–8.">9</a>]</p><p>As shown in Table <a data-track-action="table anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#Tab3">3</a>,
the increase in infection markers, kidney function markers, and blood
glucose levels were significantly different between the surviving and
deceased groups of acute stroke patients with COVID-19. Elevated CRP
levels were related with higher risk of thrombosis, whereas increased
LDH indicated the presence of tissue damage in COVID-19 patients [<a aria-label="Reference 24" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR24" id="ref-link-section-d434215214e3662" title="Dix C, Zeller J, Stevens H, et al. C-reactive protein, immunothrombosis and venous thromboembolism. Front Immunol. 2022;13:1002652.">24</a>, <a aria-label="Reference 25" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR25" id="ref-link-section-d434215214e3665" title="Fialek B, Pruc M, Smereka J, et al. Diagnostic value of lactate dehydrogenase in COVID-19: A systematic review and meta-analysis. Cardiology J. 2022.
https://doi.org/10.5603/CJ.a2022.0056
.">25</a>].
Neutrophils, lymphocytes, and inflammatory mediators are linked to
anti-atherosclerotic vascular damage. Activated neutrophils can secrete a
variety of proteolytic enzymes that stimulate endothelium and basement
membrane breakdown, increasing the atherosclerotic plaque fragility and
causes emboly. [<a aria-label="Reference 26" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR26" id="ref-link-section-d434215214e3668" title="Camare C, Pucelle M, Negre-Salvayre A, Salvayre R. Angiogenesis in the atherosclerotic plaque. Redox Biol. 2017;12:18–34.">26</a>]</p><p>The
laboratory parameters’ predictors with the highest ratio were a lower
monocyte count and a higher BUN level at hospital admission (Table <a data-track-action="table anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#Tab3">3</a>).
Circulating pro-inflammatory stimuli in COVID-19 patients trigger the
activation of blood monocytes by inducing tissue factor expression.
Tissue factors expressed by activated monocytes, monocyte-derived
microvesicles, and endothelial cells activate coagulation pathways,
resulting in fibrin deposition and blood coagulation [<a aria-label="Reference 27" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR27" id="ref-link-section-d434215214e3677" title="Merad M, Martin JC. Pathological inflammation in patients with COVID-19: a key role for monocytes and macrophages. Nat Rev Immunol. 2020;20(6):355–62.">27</a>].
Several etiologies could cause high BUN level in stroke patients with
COVID-19. Involvement of ACE-2 in the kidneys, which is nearly 100 times
higher than in respiratory organs, deposition of viral antigen immune
complexes, specific immunological effector mechanisms, and virus-induced
cytokines or mediators have a direct effect on damage to kidney tissue,
as well as an indirect effect via hypoxia, shock, and rhabdomyolysis. [<a aria-label="Reference 28" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR28" id="ref-link-section-d434215214e3680" title="Yassin A, Ghzawi A, Al-Mistarehi AH, et al. Mortality rate and biomarker expression within COVID-19 patients who develop acute ischemic stroke: a systematic review and meta-analysis. Future Science OA. 2021;7(7):FSO713.">28</a>]</p><p>The
observed coagulation blood markers showed insignificant association in
predicting mortality of stroke patients with COVID-19. The population in
this study consisted of 90.3% ischemic stroke patients, who developed a
hypercoagulability state, as a result of thrombotic events or embolism [<a aria-label="Reference 29" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR29" id="ref-link-section-d434215214e3686" title="Maino A, Rosendaal FR, Algra A, Peyvandi F, Siegerink B. Hypercoagulability is a stronger risk factor for ischaemic stroke than for myocardial infarction: a systematic review. PLoS ONE. 2015;10(8): e0133523.">29</a>].
COVID-19 infection has been linked to an increased risk for thrombosis
and an increase in D-dimer levels, both of which contribute to worsening
patients outcomes [<a aria-label="Reference 30" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR30" id="ref-link-section-d434215214e3689" title="Abou-Ismail MY, Diamond A, Kapoor S, Arafah Y, Nayak L. The hypercoagulable state in COVID-19: incidence, pathophysiology, and management. Thromb Res. 2020;194:101–15.">30</a>].
Whether the infection cascade causes the hypercoagulability state after
the thrombotic events, or the coagulation event in stroke causes the
similar state, exacerbated by COVID infection, should be further studied
in a larger population in normal, stroke, COVID-19, and stroke-COVID-19
patients.</p><p>In our study, the deceased group had significantly
higher hyperglycemia than the surviving group, although there was no
significant difference between the groups who had previously been
diagnosed with DM (Table <a data-track-action="table anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#Tab2">2</a> and <a data-track-action="table anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#Tab3">3</a>).
Moreover, a significant connection was discovered between the random
blood glucose level and mortality in COVID-19 stroke patients, although
the hazard ratio was not regarded significant (Table <a data-track-action="table anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#Tab4">4</a>).
These findings imply that hyperglycemia-induced stress may alter
immunological and inflammatory responses, resulting in a worse clinical
outcome in stroke patients with and without DM. [<a aria-label="Reference 31" data-test="citation-ref" data-track-action="reference anchor" data-track-label="link" data-track="click" href="https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w#ref-CR31" id="ref-link-section-d434215214e3704" title="Capes SE, Hunt D, Malmberg K, et al. Stress hyperglycemia and prognosis of stroke in nondiabetic and diabetic patients: a systematic overview. Strok. 2001;32(10):2426–32.">31</a>]</p><p style="text-align: justify;">After
considering the discussed factors that could influence the mortality of
hospitalized COVID-19 patients with acute stroke, the multivariate
analysis identified four independent factors: loss of consciousness,
motor deficit, respiratory distress, and monocyte count. Among these,
loss of consciousness and motor deficit are clinical factors associated
with neurological aspects, specifically the presence of acute stroke.
While respiratory distress in COVID-19 can cause global hypoxia and loss
of consciousness, its clinical manifestation is linked to central
nervous system dysfunction. On the other hand, respiratory distress and
monocyte count may be more closely related to the presence of COVID-19.
Despite the fact that the pathogenesis of stroke is similarly link to
the inflammatory response, a considerably reduced monocyte count in
stroke is unusual. In summary, both the clinical and molecular
manifestations of COVID-19 and stroke can result in variables that are
independently associated to the mortality of COVID-19 patients with
acute stroke.</p><p style="text-align: justify;">Differences in the therapy administered to patients
with COVID-19 during our study period resulted from evolving national
treatment guidelines, which is a drawback our study. Additionally, our
investigation was limited to a single health care center, focusing
solely on hospitalized who were patients confirmed positive for
SARS-Cov-2 through PCR at Dr. Sardjito General Hospital, a tertiary
referral hospital in Yogyakarta, Indonesia. As a result, the study
covered a small number of patients, with a larger proportion of severe
COVID-19 cases and complex comorbidities.</p><p style="text-align: justify;">In conclusion, the
mortality in COVID-19 patients with acute stroke can be attributed to
the clinical manifestations and laboratory parameters associated with
both conditions. Several factors, independently linked to mortality in
these patients, include loss of consciousness, motor deficit,
respiratory distress, and lower monocyte count. The correlation to
mortality is heightened when multiple factors coexist, as each might be
caused by COVID-19, acute stroke, or a combination of both. Our study
findings may raise clinicians' awareness of the mortality-related
factors in the management of COVID-19 patients with acute stroke.</p><p>https://bmcneurosci.biomedcentral.com/articles/10.1186/s12868-023-00837-w </p><p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;">"Cada reinfección aumenta el riesgo de desarrollar Long COVID. Si tienes la impresión de que una vez que te has contagiado de COVID no tienes que volver a preocuparte, te equivocas." -</span><span class="r-18u37iz"><a class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3 r-1loqt21" dir="ltr" href="https://twitter.com/BernieSanders" role="link" style="color: #1d9bf0; text-overflow: unset;">@BernieSanders</a></span></p><span class="css-1qaijid r-bcqeeo r-qvutc0 r-poiln3" style="text-overflow: unset;"></span></div></div></section>Ramon.Moratahttp://www.blogger.com/profile/16398587758151257300noreply@blogger.com0tag:blogger.com,1999:blog-1131336482330646116.post-11157570309918002852024-01-12T11:56:00.000-08:002024-01-12T11:56:38.238-08:00 MIT Technology Review ha publicado las 10 Tecnologías Innovadoras para este año 2024.<p> <span class="break-words
"><span><span dir="ltr"><a data-attribute-index="0" data-entity-type="MINI_COMPANY" href="https://www.linkedin.com/company/mit-technology-review/">MIT Technology Review</a> ha publicado las 10 Tecnologías Innovadoras para este año 2024.<br /><br />La
lista de tecnologías innovadoras se publica anualmente y destaca los
avances tecnológicos que, según los editores y reporteros de MIT
Technology Review, tendrán el mayor impacto en el mundo en los próximos
años.<br /><br />Aquí van las 10 tecnologías. Algunas son conocidas (incluso
repetidas hasta la saciedad), otras son realmente sorprendentes, al
menos para mi:<br /><br />✔ IA para todo: Herramientas de IA generativa como
ChatGPT han alcanzado un grado de adopción masiva en tiempo récord y
cambiado el rumbo de todo un sector.<br /><br />✔ Células solares
supereficientes: Las células solares que combinan el silicio tradicional
con perovskitas de última generación podrían elevar la eficiencia de
los paneles solares a nuevas cotas.<br /><br />✔ Apple Vision Pro: La
tecnología micro-OLED lleva desarrollándose más de una década, pero el
Vision Pro será la demostración más destacada de sus capacidades hasta
la fecha.<br /><br />✔ Medicamentos para adelgazar: Los fármacos para
adelgazar son muy populares y eficaces, pero se desconocen sus efectos a
largo plazo sobre la salud.<br /><br />✔ Sistemas geotérmicos mejorados:
Esta avanzada tecnología de perforación podría liberar el potencial de
la energía geotérmica en muchos lugares.<br /><br />✔ Chiplets: Los fabricantes apuestan a que chips más pequeños y especializados pueden prolongar la vida de la Ley de Moore.<br /><br />✔
El primer tratamiento de edición genética: La anemia falciforme es la
primera enfermedad vencida por CRISPR a medida que la tecnología llega
al mercado.<br /><br />✔ Ordenadores a exaescala: Los ordenadores capaces de
realizar quintillones de operaciones por segundo están ampliando los
límites de lo que los científicos pueden simular.<br /><br />✔ Bombas de
calor: Las bombas de calor son una tecnología consolidada. Ahora
empiezan a hacer verdaderos progresos en la descarbonización de los
hogares, edificios e incluso la industria manufacturera.<br /><br />✔ Asesinos de X (antes llamado Twitter). Están apareciendo alternativas como Bluesky o Threads.<br /><br />Tenéis el enlace al informe completo aquí: <a data-attribute-index="2" href="https://lnkd.in/dts2-7vy" target="_self">https://lnkd.in/dts2-7vy</a></span></span></span></p><p><span class="break-words
"><span><span dir="ltr">https://www.technologyreview.com/2024/01/08/1085094/10-breakthrough-technologies-2024/#super-efficient-solar-cells</span></span></span></p><h3 id="tech-4-title" style="opacity: 1; transform: translate(0px, -10px); visibility: inherit;"><span style="color: #783f04;">Weight-loss drugs</span></h3>
<p style="opacity: 1; transform: translate(0px, -10px); visibility: inherit;">The
global rise in obesity has been called an epidemic by the World Health
Organization. Medications like Mounjaro and Wegovy are now among the
most powerful tools that patients and physicians have to treat it.
Evidence suggests they can even protect against heart attacks and
strokes.</p><h3 id="tech-7-title" style="opacity: 1; transform: translate(0px, -10px); visibility: inherit;"><span style="color: #783f04;">The first gene-editing treatment</span></h3>
<p style="opacity: 1; transform: translate(0px, -10px); visibility: inherit;">New
treatments based on CRISPR have been in the works for years. In the
final weeks of 2023, one from Vertex became the first to earn regulatory
approval in both the UK and the US for its ability to cure sickle-cell
disease, a life-threatening condition. It won’t be the last.</p><div><div class="gutenbergContent__content--109b03a769a11e8ae3acbab352a64269 html_0"> <h4 class="wp-block-heading">WHO</h4> <p>CRISPR Therapeutics, Editas Medicine, Precision BioSciences, Vertex Pharmaceuticals</p> <h4 class="wp-block-heading">WHEN</h4> <p>Now</p> </div></div> <p>The first gene-editing cure has arrived. Grateful patients are calling it <a href="https://www.technologyreview.com/2023/12/04/1084209/vertex-exacel-approval-gene-editing-sickle-cell-disease-patient/">“life changing.”</a></p> <p>It
was only 11 years ago that scientists first developed the potent
DNA-snipping technology called CRISPR. Now they’ve brought CRISPR out of
the lab and into real medicine with a treatment that cures the symptoms
of sickle-cell disease.</p><p>Sickle-cell is caused by inheriting two bad copies of one of the
genes that make hemoglobin. Symptoms include bouts of intense pain, and
life expectancy with the disease is just 53 years. It affects 1 in 4,000
people in the US, nearly all of them African-American. </p> <p>So how did this disease become <a href="https://www.technologyreview.com/2023/12/07/1084629/lucky-break-crispr-vertex/">CRISPR’s first success</a>?
A fortuitous fact of biology is part of the answer. Our bodies harbor
another way to make hemoglobin that turns off when we’re born.
Researchers found that a simple DNA edit to cells from the bone marrow
could turn it back on.</p><p style="opacity: 1; transform: translate(0px, -10px); visibility: inherit;"> <br /></p><p>Many CRISPR treatments are in trials, but in 2022, Vertex
Pharmaceuticals, based in Boston, was first to bring one to regulators
for approval. That treatment was for sickle-cell. After their bone
marrow was edited, nearly all the patients who volunteered in the trial
were pain free. </p> <p>Good news. But the expected price tag of the
gene-editing treatment is $2 to $3 million. And Vertex has no immediate
plans to offer it in Africa—where sickle-cell disease is most common,
and where it still kills children.</p><p> The company says this is
because the treatment regimen is so complex. It involves a hospital
stay; doctors remove the bone marrow, edit the cells, and then
transplant them back. In countries that still struggle to cover basic
health needs, the procedure remains too demanding. So simpler, cheaper
ways to deliver CRISPR could come </p><p>https://www.technologyreview.com/2024/01/08/1085101/crispr-gene-editing-sickle-cell-disease-breakthrough-technologies/ <br /></p><p><span class="break-words
"><span><span dir="ltr"> </span></span></span></p><p><span class="break-words
"><span><span dir="ltr"> <br /></span></span></span></p>Ramon.Moratahttp://www.blogger.com/profile/16398587758151257300noreply@blogger.com0tag:blogger.com,1999:blog-1131336482330646116.post-28385702953469301342024-01-11T05:15:00.000-08:002024-01-11T05:15:05.836-08:00Rodrigo Quian Quiroga, el neurocientífico argentino<p> <span class="break-words
"><span><span dir="ltr">"Las neuronas concepto o neuronas de
Jennifer Aniston son las que nos hacen ser humanos y nos distinguen del
resto de los animales."<br />(Fuente: <a data-attribute-index="0" data-entity-type="MINI_COMPANY" href="https://www.linkedin.com/company/el-mundo/">El Mundo</a> - <a data-attribute-index="2" data-entity-type="MINI_PROFILE" href="https://www.linkedin.com/in/ACoAAAND_AgBdVO5CvPFfFvvTmKtv09JBTkEqX4">Daniel Arjona</a>)<br /><a data-attribute-index="4" href="https://lnkd.in/dDAzpGBe" target="_self">https://lnkd.in/dDAzpGBe</a><br /><br />Rodrigo
Quian Quiroga, el neurocientífico argentino, una eminencia en el
conocimiento de cómo funciona la memoria humana, intenta vivir con el
menor número de distracciones posible, de ahí la decisión de prescindir
del celular. «Quiero tener tiempo para pensar y dedicarme a mis
intereses». Después de pasar veinte años en <a data-attribute-index="5" data-entity-type="MINI_COMPANY" href="https://www.linkedin.com/company/university-of-leicester/">University of Leicester</a>, donde ha sido director del Centro de Neurociencias de Sistemas, desde mayo prosigue con sus investigaciones en el <a data-attribute-index="7" data-entity-type="MINI_COMPANY" href="https://www.linkedin.com/company/hmarresearch/">Hospital del Mar Research Institute</a> en Barcelona.</span></span></span></p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhBBjZAB49_25PNeJyBvmLIbtnuLrSSix4sR-Gzq7j41a1-nWpRvNo3GSE2QIRTjiJ6Vw7DyE6xAzxVUERIZy062D92h2AxgUOeJWBA1a0gsvmsREw8Qb3G2yCAlb12mZ-ogVcDm2Vrm1YRckN9r4XnR_rPTiUjHin-Jfea-alKQP6zHSR1_h9e7BX2ZWo/s899/ne.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="644" data-original-width="899" height="321" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhBBjZAB49_25PNeJyBvmLIbtnuLrSSix4sR-Gzq7j41a1-nWpRvNo3GSE2QIRTjiJ6Vw7DyE6xAzxVUERIZy062D92h2AxgUOeJWBA1a0gsvmsREw8Qb3G2yCAlb12mZ-ogVcDm2Vrm1YRckN9r4XnR_rPTiUjHin-Jfea-alKQP6zHSR1_h9e7BX2ZWo/w449-h321/ne.jpg" width="449" /></a></div><span class="break-words
"><span><span dir="ltr"><br /><br />Quian
acaba de publicar "Cosas que nunca creeríais: De la ciencia ficción a
la neurociencia". Un viaje fantástico sobre cómo los avances en la
neurociencia están materializando imaginarios antes reservados para las
películas de ciencia ficción: desde borrar recuerdos hasta implantar
prótesis neuronales o leer la mente. Películas como Origen, Blade
Runner, 2001:Odisea en el espacio o El planeta de los simios, se
convierten en un campo de trabajo, no exento de retos filosóficos, para
los científicos que, como él, trabajan diariamente para aproximar la
fantasía a la realidad."<br /><br />"Las neuronas concepto son la base del
almacenamiento de recuerdos, los pilares de nuestra memoria. Y, por
tanto, la peculiaridad del razonamiento humano consistiría en que
tendemos a pensar en términos de conceptos.<br />No se han encontrado
neuronas concepto en ninguna otra especie animal y si existe una neurona
de Jennifer Aniston, tiene que haber otras miles. En el cerebro de un
profesor de matemáticas hallamos una neurona que respondía al teorema de
Pitágoras pero no encontramos esa neurona en una persona a la que no le
interesen las matemáticas."<br /><br />"La gran pregunta ahora sería qué
cosa concreta debemos implementar en una máquina para volverla
consciente. Y tal vez podamos responderla pronto."<br /><br />"Lo que era ciencia ficción hace unos años, es ciencia hoy. El <a data-attribute-index="9" data-entity-type="MINI_COMPANY" href="https://www.linkedin.com/company/mit/">Massachusetts Institute of Technology</a>,
por ejemplo, ha logrado estimular las neuronas de un ratón para generar
una memoria falsa, para que recuerde algo que nunca ha vivido como en
la película "Desafío total" donde empresas de turismo implantan
recuerdos ficticios de supuestas vacaciones a los clientes."<br /><br />[Otras
de las muchas preguntas que Quian se plantea en su libro son las
siguientes: Si mañana un avance tecnológico permite descargar mi
conciencia en un disco duro, ¿seguiría siendo yo?<br />¿La lucha
científica para vencer a la muerte es una nueva religión o sólo otra
absurda manera de plantar cara a la segunda ley de la termodinámica?
¿Podemos demostrar que nuestros cerebros no están ahora mismo en
realidad dentro de un frasco y vivimos en una simulación estilo Matrix?]</span><br /></span></span><p></p>Ramon.Moratahttp://www.blogger.com/profile/16398587758151257300noreply@blogger.com0tag:blogger.com,1999:blog-1131336482330646116.post-11040923597878560132024-01-11T03:43:00.000-08:002024-01-11T03:43:20.095-08:00How CRISPR could yield the next blockbuster crop<p> </p><h1 class="c-article-magazine-title">How CRISPR could yield the next blockbuster crop</h1>
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Scientists are attempting to rapidly domesticate
wild plant species by editing specific genes, but they face major
technical challenges — and concerns about exploitation of Indigenous
knowledge.
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<li class="c-article-author-list__item"><a data-author-popup="author-0" data-test="author-name" data-track-label="view author info" data-track="click" href="https://www.nature.com/articles/d41586-024-00015-w#author-0">
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<img alt="A scientist next to the wild-type rice plant Oryza Alta, the line used in de novo domestication, at Institute of Genetics and Developmental Biology in Beijing." aria-describedby="Fig2" src="https://media.nature.com/w767/magazine-assets/d41586-024-00015-w/d41586-024-00015-w_26595292.jpg" />
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<figcaption>
<p class="figure__caption u-sans-serif" id="Fig2">Plant
geneticists in China are targeting genes in the wild rice Oryza alta to
make it easier to farm. Credit: Hong Yu and Jiayang Li</p>
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<p>In the space of just a few years, Jiayang Li is
trying to achieve something that once took people centuries. He wants to
turn a wild rice species into a domesticated crop by hacking its
genome. And he is already part of the way there.</p><p>Li, a plant
geneticist at the Institute of Genetics and Developmental Biology in
Beijing, is working on a wild rice species from South America called <i>Oryza alta</i>.
It produces edible, nutritious grains, but they cannot be harvested
because the seeds drop to the ground as soon as they ripen. To tame the
plant, Li and his colleagues need to remove this trait, known as seed
shattering, and alter a few others.</p><p>Li and his co-workers sequenced the <i>O. alta</i>
genome and compared it with that of domestic rice, searching for genes
similar to those that control important traits in the conventional crop,
such as stem diameter, grain size and seed shattering. They then
targeted these genes with <a data-label="https://www.nature.com/articles/d41586-021-00307-5" data-track-category="body text link" data-track="click" href="https://www.nature.com/articles/d41586-021-00307-5">customized gene-editing tools</a>, trying to recapitulate some of the genetic changes that make domesticated rice easy to farm<sup><a data-action="anchor-link" data-track-category="references" data-track-label="go to reference" data-track="click" href="https://www.nature.com/articles/d41586-024-00015-w#ref-CR1">1</a></sup>.
All the traits improved to some degree, says Li, although the plants
still drop their grains too soon. “We are working on that,” he says.</p><p>The
modification of this rice is one of a growing number of efforts to
rapidly domesticate new crops using genome editing. Through this
process, known as <i>de novo</i> domestication, transformations that
took the world’s early farmers millennia could be achieved in just a
handful of years. The work might improve the resilience of the global
food supply: many wild relatives of staple crops have useful traits that
could prove valuable when climate change puts stress on global
agriculture. <i>O. alta</i>, for example, has “very sharp resistance to salt and to drought and to some very severe or very dangerous diseases”, says Li.</p><p>But the technical challenges of <i>de novo</i>
domestication are immense. Most wild plants are understudied, and
without an understanding of their fundamental biology it is impossible
to domesticate them by rewriting their genomes. Targeted gene editing,
using tools such as CRISPR–Cas9, is a powerful approach, but it cannot
fully replicate the thousands of mutations that have fine-tuned modern
domestic crops for growing and harvest.</p><p>
</p><article class="recommended pull pull--left u-sans-serif" data-label="Related">
<a class="u-link-inherit" data-track-label="recommended article" data-track="click" href="https://www.nature.com/articles/d41586-023-03229-6"><img alt="" class="recommended__image" src="https://media.nature.com/w400/magazine-assets/d41586-024-00015-w/d41586-024-00015-w_26605316.png" /><p class="recommended__title u-serif">Apple revival: how science is bringing historic varieties back to life</p></a>
</article><p>“It seems like a very simple idea, but the more you start
unpacking, the more complex it becomes conceptually,” says plant
physiologist Agustin Zsögön at the Federal University of Viçosa in Minas
Gerais, Brazil. As a result, although commercial producers are
interested in the concept, no companies are publicly pursuing it.</p><p>There are also concerns that <i>de novo</i>
domestication could be misused. Many wild plants are well known only to
Indigenous peoples, who have cared for them over many generations.
Throughout history, colonial powers have stolen or exploited the
knowledge of Indigenous peoples — as happened with the tea plant rooibos
(<i>Aspalathus linearis</i>) in South Africa. “I am very conscious of
not repeating the mistakes of the past,” says botanist Madelaine
Bartlett at the University of Massachusetts Amherst.</p><p>There are
proposals for how researchers could work ethically with Indigenous
peoples and their knowledge, but so far these have not been widely
adopted or codified into laws. “In terms of food crops, we probably have
largely ignored Indigenous communities,” says botanist Nokwanda Makunga
at Stellenbosch University in South Africa. “People that are doing <i>de novo </i>domestication need to be more aware.”</p><h2><b>Taming tomatoes</b></h2><p>People have been domesticating plants for around 10,000 years. But <a data-label="https://www.nature.com/articles/d41586-021-01681-w" data-track-category="body text link" data-track="click" href="https://www.nature.com/articles/d41586-021-01681-w">domestication is a fuzzy concept</a>,
says Zsögön. Many plants can be grown to produce food, but they don’t
match the predictability and yields of commonly cultivated crops, such
as maize (corn) or potatoes, and they are not as easy to harvest. A
useful rule of thumb is that domesticated species have developed a
permanent relationship with humans. If they are left to their own
devices they might wither, fail to propagate or simply lose the traits
that humans value over a few generations.</p><p>Although there is no
written record of the first domesticated plant species, it is clear that
they were generated — intentionally or not — through breeding that
selected for desirable traits, such as large fruits or a lack of toxins.
Over many generations, the mutations that control these traits
accumulated, resulting in crops that were very different from the
ancestral line. For instance, the large, soft kernels of modern maize
look almost nothing like the small, hard seeds of its wild ancestor,
teosinte.</p><figure class="figure">
<picture class="embed intensity--high">
<source type="image/webp"></source>
<img alt="Two varieties of tomatoes (wild, left, and domesticate) each cut in half on a black background." class="figure__image" src="https://media.nature.com/lw767/magazine-assets/d41586-024-00015-w/d41586-024-00015-w_26595284.jpg" />
<figcaption>
<p class="figure__caption u-sans-serif"><span class="mr10">Wild (left) and domesticated South American tomatoes, <i>Solanum pimpinellifolium</i>.</span><span>Credit: Agustin Zsögön</span></p>
</figcaption>
</picture>
</figure><p>Selective breeding is still a mainstay of agriculture. But
breeders now target specific traits and often use mutation-causing
radiation or chemicals to speed up the process of creating genetic
variants.</p><p>Despite these advances, many of the methods for
introducing traits to crops or producing entirely new crops rely to some
extent on chance. Breeders have no way to control what mutations arise.
Instead, they must create large numbers of mutants and carefully screen
them, in the hope of finding the few useful mutations among thousands
of harmful ones.</p><p>Gene editing promises to change that, by allowing
researchers to edit the genomes of organisms in a targeted way.
Geneticists have been doing this for decades by using established
methods for adding entire genes to organisms to create ‘transgenic’
crops such as insect-resistant or herbicide-tolerant maize or soya bean
plants. But new gene-editing tools provide much more control, allowing
researchers to precisely edit the existing genome at chosen sites. The
most prominent technique uses CRISPR–Cas9, which was originally part of
the ‘immune system’ of bacteria and can be reprogrammed to edit genomes<sup><a data-action="anchor-link" data-track-category="references" data-track-label="go to reference" data-track="click" href="https://www.nature.com/articles/d41586-024-00015-w#ref-CR2">2</a></sup>.</p><p>The first demonstrations of <i>de novo</i>
domestication through genome editing happened in 2018. In one, Zsögön
and his colleagues domesticated wild South American tomatoes called <i>Solanum pimpinellifolium</i>. They are the closest wild relatives of domesticated tomatoes (<i>Solanum</i> <i>lycopersicum</i>). The fruits of <i>S.</i> <i>pimpinellifolium </i>are
small, even compared with cherry tomato variants, but edible. “They are
sweet and sour with a hint of spiciness,” says Zsögön. His team edited
six key regions of the plant’s genome to produce a version that
resembled a domestic tomato. The new plants produced ten times as many
fruits as the wild plants did, and the fruits were three times the size<sup><a data-action="anchor-link" data-track-category="references" data-track-label="go to reference" data-track="click" href="https://www.nature.com/articles/d41586-024-00015-w#ref-CR3">3</a></sup>.</p><p>In another study<sup><a data-action="anchor-link" data-track-category="references" data-track-label="go to reference" data-track="click" href="https://www.nature.com/articles/d41586-024-00015-w#ref-CR4">4</a></sup>,
a team led by Zachary Lippman at Cold Spring Harbor Laboratory in New
York and Joyce Van Eck at Cornell University in Ithaca, New York, took a
wild groundcherry (<i>Physalis pruinosa</i>) a few steps closer to
domestication. Groundcherry belongs to the same family of plants as
tomatoes, potatoes and peppers. It is grown in parts of Central and
South America for its sweet, golden berries. But harvesting it is
difficult because of the plant’s sprawling growth and because the fruits
are small and drop to the ground quickly once they ripen. The team
modified one gene called <i>Ppr-SP5G</i> to make the plants more compact, and tweaked another, <i>Ppr-CLV1</i>, to make the fruits 24% heavier.</p><p>These
were dramatic breakthroughs, but the new plants are not yet being grown
on a large scale, let alone being sold to consumers. Although that is
the ultimate goal, these first studies were “a proof of concept”, says
Zsögön. “We just showed that it could be done.”</p><p>
</p><article class="recommended pull pull--left u-sans-serif" data-label="Related">
<a class="u-link-inherit" data-track-label="recommended article" data-track="click" href="https://www.nature.com/articles/d41586-022-03837-8"><img alt="" class="recommended__image" src="https://media.nature.com/w400/magazine-assets/d41586-024-00015-w/d41586-024-00015-w_25528702.gif" /><p class="recommended__title u-serif">CRISPR tools found in thousands of viruses could boost gene editing</p></a>
</article><p>He says that <i>de novo</i> domestication should be
particularly useful for creating crops that can resist non-biological
stressors such as drought, because the relevant traits often involve
multiple genes; breeding each one into domestic species would be
enormously time-consuming. With <i>de novo</i> domestication, researchers could, theoretically, take the wild plant and quickly domesticate it by tweaking a handful of genes.</p><p>Some
wild species also use nutrients such as nitrogen more efficiently than
do domesticated varieties, says Li. The domestication of wild plants
should allow farmers to use less fertilizer, reducing costs as well as
harmful run-off into rivers.</p><p>These potential benefits have spurred multiple groups to attempt domestication projects.</p><p>In
2018, molecular geneticist Sophia Gerasimova started trying to
domesticate wild potatoes while at the Siberian Branch of the Russian
Academy of Sciences in Novosibirsk. Her efforts were disrupted by
Russia’s invasion of Ukraine in 2022: she protested against the war and
moved to the Genomics for Climate Change Research Center in Campinas,
Brazil.</p><p>Gerasimova and her colleagues screened wild potato genomes
looking for a good candidate species. To be suitable for domestication,
a plant had to be amenable to CRISPR and have potentially useful
traits. If the plant had ‘bad’ traits, these needed to be controlled by a
small number of genes. The wild potato they eventually settled on, <i>Solanum chacoense</i>,
had many appealing properties: it produced round tubers that looked
like domestic potatoes, was resistant to viruses and pests, and the
plants were easy to work with because they were neat and compact. It was
also resistant to ‘cold sweetening’ — the tendency of some potatoes to
become rich in glucose and fructose when stored in the cold, leading to
an unpleasant taste when cooked. However, the tubers were “small and
bitter”, says Gerasimova. They needed to fix that.</p><p>Gerasimova and
her colleagues identified five target genes for CRISPR editing, which
they think are involved in crucial traits such as the timing of tuber
formation and the accumulation of toxic steroidal glycoalkaloids<sup><a data-action="anchor-link" data-track-category="references" data-track-label="go to reference" data-track="click" href="https://www.nature.com/articles/d41586-024-00015-w#ref-CR5">5</a></sup>.
However, the researchers have struggled to make the necessary edits to
the plants. Gerasimova says that they have succeeded in editing the
genome in plant cells, but have not yet managed to get these mutations
to propagate to an entire plant. She is optimistic that they will
overcome this hurdle.</p><figure class="figure">
<picture class="embed intensity--high">
<source type="image/webp"></source>
<img alt="Close up of the fruit Ground Cherry (Physalis pruinosa)." class="figure__image" src="https://media.nature.com/lw767/magazine-assets/d41586-024-00015-w/d41586-024-00015-w_26595286.jpg" />
<figcaption>
<p class="figure__caption u-sans-serif"><span class="mr10">Researchers are editing the genome of a wild groundcherry to aid harvesting.</span><span>Credit: Getty</span></p>
</figcaption>
</picture>
</figure><p>There are a host of reasons why <i>de novo </i>domesticated
crops are not yet being grown commercially. One is that, as Gerasimova’s
experience illustrates, applying CRISPR to a new species is a challenge
in itself.</p><p>Equally important is the complexity of domestication.
Although it’s true that a handful of genes can cause marked changes,
domesticated crops differ from their wild relatives in many regions of
their genomes, and each difference can have a small but important
effect. “There are many thousands of genes that contribute to making
corn different to teosinte,” says Bartlett. It’s not practical to use
CRISPR to reproduce all these changes.</p><p>So, conventional breeding
techniques will continue to have a large role. Developmental biologist
David Marks at the University of Minnesota in St. Paul is part of a team
working to domesticate field pennycress (<i>Thlaspi arvense</i>) as
part of his institution’s Forever Green initiative. Pennycress has a
single vertical stem, with small cabbage-like leaves and white flowers.
Its seeds contain a useful oil, “extremely similar to canola oil”, Marks
says.</p><p>The entire domestication project has relied on mutagenesis
and selective breeding — conventional methods that Marks notes are still
being improved and are now much faster than in previous decades<sup><a data-action="anchor-link" data-track-category="references" data-track-label="go to reference" data-track="click" href="https://www.nature.com/articles/d41586-024-00015-w#ref-CR6">6</a></sup>. By the time CRISPR took off, the project was already at an advanced stage.</p><p>“Don’t
get me wrong,” says Marks. “The CRISPR technique is elegant, beautiful
and simple. I wish like hell it was available back in my early days.”
However, it is practical only in certain circumstances. “In the case of
pennycress, we’re starting off with a plant that already has desirable
characteristics,” he says. The single-gene changes achievable with
CRISPR were not needed. But many other potentially useful wild plants,
such as <i>O. alta</i>, need these kinds of targeted changes in a small number of genes.</p><h2><b>Fundamental gap</b></h2><p>There is one further obstacle to <i>de novo</i>
domestication by gene editing, and that is botanists’ limited knowledge
of wild-plant biology. Much of what is known about plants comes from a
handful of model species, such as thale cress (<i>Arabidopsis thaliana</i>).
Most wild plants have not even had their genomes sequenced, let alone
been subject to the intensive study required to learn what the DNA
sequences do, which is necessary before <i>de novo</i> domestication can
be attempted. “You have to have basic information and the basic
building blocks in order to be able to do this manipulation,” says
Makunga.</p><p>
</p><article class="recommended pull pull--left u-sans-serif" data-label="Related">
<a class="u-link-inherit" data-track-label="recommended article" data-track="click" href="https://www.nature.com/articles/d41586-022-01443-2"><img alt="" class="recommended__image" src="https://media.nature.com/w400/magazine-assets/d41586-024-00015-w/d41586-024-00015-w_26605314.png" /><p class="recommended__title u-serif">Gene-edited tomatoes could provide new source of vitamin D</p></a>
</article><p>“The technologies have far outpaced our knowledge of the fundamental biology,” says Bartlett.</p><p>Another
complication is finding ways to account for the rights of Indigenous
groups. Bartlett and Makunga argue that these communities need to be
included in any <i>de novo</i> domestication programme from the start<sup><a data-action="anchor-link" data-track-category="references" data-track-label="go to reference" data-track="click" href="https://www.nature.com/articles/d41586-024-00015-w#ref-CR7">7</a></sup>. “We need to be much more ethical in our practice,” says Makunga.</p><p>When
Indigenous people have a claim on a wild plant, “they should be
involved in those projects and benefit from any sorts of innovations
that emerge from them,” says Maui Hudson at the University of Waikato in
Hamilton, New Zealand (see also ref. <sup><a data-action="anchor-link" data-track-category="references" data-track-label="go to reference" data-track="click" href="https://www.nature.com/articles/d41586-024-00015-w#ref-CR8">8</a></sup>).</p><p>South
Africa has taken steps in this direction. Makunga and her colleagues
have met with representatives of the San people to discuss the benefits
of a new project — something that they were required to do under South
Africa’s National Environmental Management: Biodiversity Act 10 of 2004.
The <a data-label="https://www.nature.com/articles/d41586-023-02497-6" data-track-category="body text link" data-track="click" href="https://www.nature.com/articles/d41586-023-02497-6">2010 Nagoya Protocol</a>,
part of the Convention on Biological Diversity, also requires that
benefits from the use of genetic resources are shared with Indigenous
groups. Likewise, Brazil has created a repository for all research that
involves native species, and a mechanism to compensate Indigenous
communities if their knowledge leads to a profit. Zsögön does not expect
his projects to trigger this mechanism because the plants he works with
grow widely. Similarly, the rice Li works on is “widespread in South
America” and “is not tied to any particular Indigenous group and has not
been cultivated by anyone anywhere in our knowledge”.</p><p>However,
arrangements such as those in Brazil remain rare. For example, South
Africa’s commercial rooibos tea industry has existed for more than a
century. The plant is only weakly domesticated, so the industry is only
possible thanks to Traditional Knowledge preserved by the Khoi and San
peoples. Yet it took until 2019 for the industry to sign an agreement
that requires it to pay Khoi and San communities.</p><p>Despite the challenges, both technical and political, researchers are enthused about the potential of <i>de novo</i>
domestication. “I’m excited by a future where we have customizable and
modifiable plant development,” says Bartlett. “I think that that is
actually a prospect that we might see in my lifetime.”</p>
</div>
<p><em>Nature</em> <strong>625</strong>, 230-232 (2024)</p>
<p><em>doi: https://doi.org/10.1038/d41586-024-00015-w</em></p>
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</div>
<div class="excerpt"><p>Contrary to popular belief, ageing is not caused
by just random wear and tear of our bodies over time but is instead
caused by a discrete set of biological mechanisms that we now better
understand and can target with therapies. Life Biosciences are
specifically focused on restoring and prolonging one’s healthspan by
targeting the underlying biology of ageing.</p>
</div>
<div class="articleImage"><br /></div>
<h2><strong>What is “healthspan” and how does it relate to Life Bioscience’s mission? </strong></h2>
<p>It is no mystery that as we age our health starts to deteriorate, and
we become increasingly susceptible to diseases. If we take into
consideration the timeframe of our lives, our “healthspan” is defined as
the number of healthy years we live, which is different than our
lifespan, the number of years of life. Contrary to popular belief,
ageing is not caused by just random wear and tear of our bodies over
time but is instead caused by a discrete set of biological mechanisms
that we now better understand and can target with therapies. At Life
Biosciences, we are specifically focused on restoring and prolonging
one’s healthspan by targeting the underlying biology of ageing. We are
at the forefront of drug development in an area of research called
cellular rejuvenation, which is an approach that has the potential to
address many diseases of ageing by restoring aged and injured cells to a
more youthful and resilient state. With cellular rejuvenation, our goal
is to improve healthspan so as to maximise the number of healthy and
productive years one has in their lifetime, not just their total number
of years.</p>
<h2><strong>What role does the epigenome play in ageing and what does it mean to target “ageing biology” with therapeutics?</strong></h2>
<p>Through years of scientific exploration and research, we now know
there are several biological changes that make our bodies more
susceptible to disease or injury, and we can target those with
therapeutic interventions. One way we are doing this at Life Biosciences
is through partial epigenetic reprogramming. Modifications to the
epigenome, such as DNA hypermethylation, modify the expression of genes
by increasing or decreasing their expression without altering the genes
themselves. The epigenome drifts with age, leading to dysregulated gene
expression. Shinya Yamanaka, who was awarded the Nobel prize in 2012,
discovered that four transcription factors Oct4, Sox2, Klf4 and c-Myc
could reprogram mature, differentiated cells back into stem cells.
Following that work, it was discovered by Dr David Sinclair’s lab at
Harvard that turning on only three of the four factors (OSK; Oct4, Sox2,
Klf4) is the sweet spot for developing therapeutics where you can
partially reprogram cells and restore cells to a more youthful state,
while maintaining cell identity. Our lead program utilises OSK for the
treatment of age-related optic neuropathies.</p>
<h2><strong>What evidence is there that we can reverse aging with drugs?</strong></h2>
<p>Studies in mice have shown that not only can we accelerate ageing by
inducing biological ageing drivers, but we can also reverse ageing in
mice by reversing those drivers. This year, Life Biosciences and our
collaborators were the first to show how cellular rejuvenation can
restore visual function in a non-human primate (NHP) model in the
retinal ganglion cells of the eye, where degeneration is common in older
adults. To summarise, lasers were used to induce damage to the eyes of
NHPs. One day following the laser damage, an intravitreal (in-eye)
injection of an inducible OSK expression system was administered to the
lasered eye. In eyes treated with OSK after laser damage, OSK
significantly restored pattern electroretinogram (pERG) responses, a key
ophthalmic measurement, compared to controls, which is consistent with
restoration of visual function. OSK also significantly reversed
laser-induced reductions in axon density compared to controls.</p><div class="drugt-6d40f233cf34db8abd1d0e3389faba1e drugt-content" id="drugt-6d40f233cf34db8abd1d0e3389faba1e"><div class="drugt-content" id="drugt-491014079"><div class="drugt-target" data-drugt-trackbid="1" data-drugt-trackid="110320" id="drugt-1930690185"><div class="crossPromo">
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<h2><strong>Why did you decide to start with eye diseases?</strong></h2>
<p>Life Biosciences is pursuing indication areas where ageing biology
has a clear link to disease pathogenesis. There are a number of diseases
in the eye that occur with ageing as a major modifiable risk factor,
including the two optic neuropathies on which we are initially focused –
a rare eye disease of aging called non-arteritic anterior ischemic
optic neuropathy (NAION), presenting similar to a stroke but in the eye,
for which no drug is currently available, and primary open angle
glaucoma, a common condition where significant unmet needs remain.</p>
<h2><strong>What’s next for Life Bioscience’s preclinical studies?</strong></h2>
<p>With significant validation of our platform in hand, we are now
rapidly advancing towards the clinic with our lead therapeutic, OSK. We
hope to initiate the first human clinical trials with our lead
indications in 2025, which would represent a monumental step forward for
the field of cellular rejuvenation. Beyond the eye, we are evaluating
the potential for cellular rejuvenation to treat a broad number of
diseases of ageing, with compelling preclinical data in
neurodegenerative diseases like Alzheimer’s disease and frontotemporal
dementia, as well as other ophthalmic disorders including dry
age-related macular degeneration and other forms of retinal
degeneration.</p>
<p><strong>About the author </strong></p>
<p><strong>Jerry McLaughlin</strong></p>Ramon.Moratahttp://www.blogger.com/profile/16398587758151257300noreply@blogger.com0