Unbiased characterization of COVID-19 endotypes leads to prognostication of high-risk individuals using routine blood tests
Unsupervised proteomic analysis identified biologically coherent endotypes that advance understanding of acute lung injury in COVID‑19 and support improved diagnostic and prognostic strategies.
Authors
- Madéleine Durand (ORCID: https://orcid.org/0000-0002-9459-9549)
- Catherine Allard (ORCID: https://orcid.org/0000-0002-8829-4984)
- Antoine Soulé (ORCID: https://orcid.org/0000-0003-4810-4821)
- Simon Rousseau (ORCID: https://orcid.org/0000-0002-8773-575X)
- William Ma (ORCID: https://orcid.org/0000-0002-7110-4865)
- Amin Emad
- Karine Tremblay
Institutions
- Université de Sherbrooke (CA)
- McGill University Health Centre (CA)
- Centre Hospitalier Universitaire de Sherbrooke (CA)
- Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean (CA)
- Centre Hospitalier de l’Université de Montréal (CA)
- Mila - Quebec Artificial Intelligence Institute (CA)
- McGill University (CA)
Publication Details
- Journal
- Communications Medicine
- Published
- 2026-03-14
- DOI
- https://doi.org/10.1038/s43856-026-01520-x
- Primary Topic
- SARS-CoV-2 detection and testing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Public Health Agency of Canada
- Génome Québec
- Ministère de la Santé et des Services sociaux
- Public Health Agency
- Ministère de la Santé
- Natural Sciences and Engineering Research Council of Canada
- Fonds de Recherche du Québec - Santé