El cos humà com un ecosistema cel·lular (2)

 A ‘Wikipedia for cells’: researchers get an updated look at the Human Cell Atlas, and it’s remarkable

Cellular atlases are unlocking the mysteries of the human body

The Human Cell Atlas: towards a first draft atlas

Figure 1 | Human cellular atlases. a, The Human Cell Atlas project aims to create cellular maps of human organs and tissues throughout life, and in health and disease. Cells are isolated from tissues during different stages of development and from cell-based organ models (organoids). Cell types and states can be captured using single-cell profiling techniques, mainly transcriptomics (using RNA transcripts to examine gene expression) but also other methods such as epigenomics (examining regulation of gene expression by assessing modifications to DNA and histone proteins). Sophisticated computational analyses are used to classify cell types and integrate information from different data types. b, Cellular atlases can be used to make inferences about human biology: spatial transcriptomics enables cells of defined type to be mapped to the tissue from which they originated, so that tissue architecture can be examined; cell differentiation and maturation during developmental processes can be traced; interactions between cells of different types can be inferred; and comparisons can be made between healthy and diseased tissue.

The opportunity costs of excessive medical practice variations

 Atlas de utilización de procedimientos de dudoso valor. Actualización datos 2017

From the new report on practice variations:

La literatura científica abunda en estimaciones de la proporción de asistencia sanitaria cuyo valor para el paciente es cuando menos escaso. Este cuerpo de evidencia no ha hecho sino crecer en la última década, dando origen a varias iniciativas tanto académicas como gubernamentales para identificar y abordar lo que se considera uno de los principales problemas de los sistemas sanitarios modernos. Hay consenso: se trata de un fenómeno altamente prevalente que pone en cuestión el buen uso de los recursos sanitarios.
La actividad sanitaria de dudoso valor incluye tanto la utilización de procedimientos escasamente efectivos o para los que existen alternativas superiores, como el uso de intervenciones efectivas en indicaciones en las que los beneficios para el paciente son prácticamente nulos y en ocasiones incluso generan efectos negativos. Obviamente, para el sistema sanitario y la sociedad que destina los recursos necesarios, el coste oportunidad derivado de este tipo de actividad es sustancial.

So many years talking about it and nothing happens...

Great report, something should be done.
 Angulo-Pueyo E, Seral-Rodríguez M, Ridao-Lopez M, Estupiñán-Romero F, Martínez-Lizaga N, Comendeiro-Maaloe M, Ibañez-Beroiz B, Librero-López J, Millán-Ortuondo E, Peiró-Moreno S, Bernal-Delgado E, por el grupo Atlas VPM. Atlas de variaciones en la práctica médica en utilización de procedimientos de dudoso valor en el Sistema Nacional de Salud, 2017. Marzo 2020; Disponible en: www.atlasvpm.org/atlas/desinversion-2017

PS. Some books I'm waiting for.

Medical practice variation in oncology

 Atlas de variaciones en cirugía oncológica.

Why there is still so much variation in medical practice?

Some details inside this atlas.

Aliza Nisenbaum

El llenguatge de la vida

 

L'Eric Topol ens ofereix un resum d'alta qualitat per entendre el moment que viu la ciència mitjançant la intel·ligència artificial. 

Impressionant. Aquesta és la llista de grans models de llenguatge en ciències de la vida (LLLMs):

1. Evo. This model was trained with 2.7 million evolutionary diverse organisms (prokaryotes—without a nucleus, and bacteriophages) representing 300 billion nucleotides to serve as a foundation model (with 7 billion parameters) for DNA language, predicting function of DNA, essentiality of a gene, impact of variants, and DNA sequence or function, and CRISPR-Cas prediction. It’s multimodal, cutting across protein-RNA and protein-DNA design.

   Figure below from accompanying perspective by Christina Theodoris.

2. Human Cell Atlas A collection of publications from this herculean effort involving 3,000 scientists, 1,700 institutions, and 100 countries, mapping 62 million cells (on the way to 1 billion), with 20 new papers that can be found here. We have about 37 trillion cells in our body and until fairly recently it was thought there were about 200 cell types. That was way off—-now we know there are over 5,000.

   One of the foundation models built is Single-Cell (SC) SCimilarity, which acts as a nearest neighbor analysis for identifying a cell type, and includes perturbation markers for cells (Figure below). Other foundation models used in this initiative are scGPT, GeneFormeR, SC Foundation, and universal cell embedding. Collectively, this effort has been called th “Periodic Table of Cells” or a Wikipedia for cells and is fully open-source. Among so many new publications, a couple of notable outputs from the blitz of new reports include the finding of cancer-like (aneuploid) changes in 3% of normal breast tissue, representing clones of rare cells and metaplasia of gut tissue in people with inflammatory bowel disease.

3. BOLTZ-1 This is a fully open-source model akin to AlphaFold 3, with similar state-of-the-art performance, for democratizing protein-molecular interactions as summarized above (for AlphaFold 3). Unlike AlphaFold 3 which is only available to the research community, this foundation model is open to all. It also has some tweaks incorporated beyond AlphaFold 3, as noted in the preprint.

4. RhoFold For accurate 3D RNA structure prediction, pre-trained on almost 24 million RNA sequences, superior to all existing models (as shown below for one example).

5. EVOLVEPro A large language protein model combined with a regression model for genome editing, antibody binding and many more applications for evolving proteins, all representing a jump forward for the field of A.I. guided protein engineering.

6. PocketGen A model dedicated to defining the atomic structure of protein regions for their ligand interactions, surpassing all previous models for this purpose.

   

7. MassiveFold A version of AlphaFold that does predictions in parallel, enabling a marked reduction of computing time from several months to hours

8. RhoDesign From the same team that produced RhoFold, but this model is for efficient design of RNA aptamers that can be used for diagnostics or as a drug therapy.

9. MethylGPT Built upon scGPT architecture, trained on over 225,000 samples, it captures and can reconstruct almost 50,000 relevant methylation CpG sites which help in predicting diseases and gauging the impact of interventions (see graphic below).

   

10. CpGPT Trained on more than 100,000 samples, it is the optimal model to date fo predicting biological (epigenetic) age, imputing missing data, and understanding biology of methylation patterns.

11. PIONEER A deep learning pipeline dedicated to the protein-protein interactome, identifying almost 600 protein-protein interactions (PPIs) from 11,000 exome sequencing across 33 types of cancer, leading to the capability of prediction which PPIs are associated with survival. (This was published 24 October, the only one not in November on the list!)

Al KBR, Cartier-Bresson

I per aquí aprop, mentrestant anem venent privadament la recerca inicial finançada públicament i perdem l'oremus, mentre alguns se n'aprofiten. Desgavell perfectament dissenyat.

El cos humà com un ecosistema cel·lular

 Impact of the Human Cell Atlas on medicine

El projecte d'Atles de les cèl·lules comença a oferir els seus fruits. Si amb el genoma la seqüenciació del DNA era la tecnologia cabdal, ara per a estudiar les cèl·lules la transcriptòmica ha estat clau (estudi de totes les mol·lècules de RNA d'una cèl·lula, per tant genoma i epigenoma). L'avenç a hores d'ara és notable i així s'explica a Nature.

En moltes ocasions anteriors ja he explicat el paper de l'epigenètica, però ens faltava l'Atles, saber quants tipus de cèl·lules té el cos humà i com el mRNA codifica proteïnes. Ara s'està avançant molt ràpidament. El canvi a la recerca de medicaments i teràpies pot ser crucial.

PD. Avui, Atul Gawande

 

Un experiment docent

Diuen a Forbes que a Hanover, NH. ha començat "la revolució fonamental en la forma com pensarem en l'assistència sanitària". Han creat el  Dartmouth Center for Health Care Delivery Science i fan un master. Però té elements singulars, diuen. Barreja disciplines (economia, gestió assegurança, medicina i politica sanitària) i personalitats (investigadors i gent del sector). Jo no ho sé pas si això és un gran canvi, m'en guardaré bé prou de dir el contrari sense coneixement.
Ara bé, pel que llegeixo, el més interessant és l'esquema de treball 4 sessions de dues setmanes al campus i 15 hores de treball i interacció online a la setmana durant els 18 mesos que dura. I encara més l'aprofitament dels continguts del Darmouth Institute for Health Policy and clinical practice, els que produeixen l'Atlas.
Així doncs cal seguir d'aprop aquesta iniciativa i veure si aporta alguna cosa innovadora a la formació dels metges, qüestió crucial sobre el que una sacsejada com aquesta pot anar bé.
Oblidava un petit detall, l'origen de tot plegat el podeu trobar en un donant anònim de 35 milions de dòlars per a la creació d'aquest centre al Dartmouth College. D'això se'n diu generositat.

PS. Més aprop fem aquest Master de Health Economics and Policy, i ara ja s'han obert les inscripcions pel proper curs.

PS. Comença el boicot a Elsevier, pel tema que comentava ahir.A Marginal Revolution trobareu l'anàlisi.

Mapping genetic regulation

The GTEx Consortium atlas of genetic regulatory effects across human tissues

A great step in research:

The GTEx v8 data release represents a deep survey of both intra- and interindividual transcriptome variation across a large number of tissues. With 838 donors and 15,201 samples—approximately twice the size of the v6 release used in the previous set of GTEx Consortium papers—we have created a comprehensive resource of genetic variants that influence gene expression and splicing in cis. This substantially expands and updates the GTEx catalog of sQTLs, doubles the number of eGenes per tissue, and saturates the discovery of eQTLs with greater than twofold effect sizes in ~40 tissues. The fine-mapping data of GTEx cis-eQTLs provide a set of thousands of likely causal functional variants. While trans-QTL discovery and the characterization of sex- and population-specific genetic effects are still limited by sample size, analyses of the v8 data provide important insights into each.

Fig. 1 Sample and data types in the GTEx v8 study.

(A) Illustration of the 54 tissue types examined (including 11 distinct brain regions and two cell lines), with sample numbers from genotyped donors in parentheses and color coding indicated in the adjacent circles. Tissues with 70 or more samples were included in QTL analyses. (B) Illustration of the core data types used throughout the study. Gene expression and splicing were quantified from bulk RNA-seq of heterogeneous tissue samples, and local and distal genetic effects (cis-QTLs and trans-QTLs, respectively) were quantified across individuals for each tissue.

Pasimonious medicine

PRÁCTICAS CLÍNICAS EVITABLES: EL COSTE DEL DESPILFARRO

Tilburg and Cassel wrote in JAMA

Parsimonious medicine is not rationing; it means delivering appropriate health care that fits the needs and circumstances of patients and that actively avoids wasteful care—care that does not benefit patients

And Austin Frakt answered in his blog:

Perhaps the consequences of what they support with good intention will include rationing. Perhaps it’s hard to achieve parsimony with out at least a touch of it. If that’s the case, how much rationing will we tolerate to achieve some additional efficiency? Keep in mind, today we have a high level of rationing by ability to pay and a low level of parsimony. (in USA)

Unfortunately we don't now the level of parsimony in our health system. But if you want to know the size of the waste  in spanish health system, these are some figures for primary care:

El estudio APEAS cifraba en 10,1 por 1000 visitas los eventos adversos en atención primaria de los que un 7,3% graves y un 70,2% evitables (40), mientras que el ENEAS los cifraba en 9,3% por cada 100 hospitalizados, con un 16% graves y 42,8% evitables (41). Mientras que ambos estudios tendían a minimizar el impacto de estas cifras, los 300 millones de visitas no urgentes anuales en atención primaria resultarían en 3 millones de efectos adversos anuales, de los que casi 300.000 graves y al menos 2 millones evitables. En el caso de la hospitalización, los 5,2 millones de hospitalizaciones del año en que se realizó el ENEAS ofrecerían cifras de 450.000 efectos adversos anuales, de los que 90.000 serían graves y unos 200.000 evitables. Estas cifras situarían los eventos adversos derivados de la atención sanitaria como la probable tercera causa de morbi-mortalidad en nuestro país, tras las enfermedades cardiovasculares y el cáncer.

And regarding hospitalizations,

Diversos estudios publicados en la década del 2000 cifran en torno al 10-15% la cuota de este tipo de ingresos hospitalarios sobre el total de hospitalizaciones producidas en España en los años estudiados (42-46). Este porcentaje sería aún mayor para los ingresos por hospitalizaciones evitables en enfermedades crónicas estudiados más recientemente por el grupo Atlas de Variaciones en la Práctica Médica

And the figures for inappropriateness and low value care are more diffiuclt to estimate, though:

En España se han realizado numerosos estudios sobre utilización inapropiada de la hospitalización con cifras que sitúan este problema alrededor del 10% de las admisiones y el 30% del total de estancias hospitalarias

And only one example regarding pharmaceuticals

Añadir lapatinib a capecitabina en el tratamiento en segunda línea del cáncer de mama permite ganar, en promedio, 0,3 meses (10 días) de supervivencia con respecto al tratamiento previo con solo capecitabina, con un coste adicional de 18.298 € (60.996 € por mes de vida adicional) (59). Estas cifras implicarían que socialmente estamos dispuestos a pagar unos 732.000 euros por cada año de vida adicional ganado y, si se tiene en cuenta la baja calidad de vida de estos días ganados en la fase final de los procesos oncológicos, probablemente estaríamos hablando de cifras superiores a los 2 millones de euros por año de vida ajustado por calidad (AVAC o QALY) ganado con la incorporación de este tratamiento a este precio a la cartera de servicios.

If we as a society, we are not able to solve the rationing puzzle, then we could start by a more parsimonious medicine. You'll find more details in the chapter by S. Peiró in this book (p.273).
After reading this chapter, you'll be more concerned than before.

Garry Winogrand