Global and cell type-specific immunological hallmarks of severe dengue progression identified via a systems immunology approach

Author:

Ghita Luca,Yao Zhiyuan,Xie Yike,Duran Veronica,Cagirici Halise BusraORCID,Samir Jerome,Osman Ilham,Rebellón-Sánchez David EstebanORCID,Agudelo-Rojas Olga Lucia,Sanz Ana Maria,Sahoo Malaya KumarORCID,Robinson Makeda L.,Gelvez-Ramirez Rosa Margarita,Bueno Nathalia,Luciani FabioORCID,Pinsky Benjamin A.ORCID,Montoya Jose G.,Estupiñan-Cardenas Maria Isabel,Villar-Centeno Luis Angel,Rojas-Garrido Elsa Marina,Rosso Fernando,Quake Stephen R.,Zanini FabioORCID,Einav ShiritORCID

Funder

United States Department of Defense | United States Army | Army Medical Command | Congressionally Directed Medical Research Programs

U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases

Dr. Ralph and Marian Falk Medical Research Trust

United States Department of Defense | Defense Threat Reduction Agency

Chan Zuckerberg biohub San Francisco

European Molecular Biology Organization

Thrasher Research Fund

Stanford Maternal and Child Research Institute

Chan Zuckerberg Biohub Collaborative Postdoctoral Fellowship

Sue Merigan Student Scholar Fund in Infectious Diseases and Geographic Medicine

A.P. Giannini Foundation

Harold Amos Medical Faculty Development Program

Chan Zuckerberg Biohub

Publisher

Springer Science and Business Media LLC

Subject

Immunology,Immunology and Allergy

Reference56 articles.

1. Bhatt, S. et al. The global distribution and burden of dengue. Nature 496, 504–507 (2013).

2. Khursheed, M. et al. A comparison of WHO guidelines issued in 1997 and 2009 for dengue fever—single centre experience. J. Pak. Med. Assoc. 63, 670–674 (2013).

3. World Health Organization. Dengue: guidelines for diagnosis, treatment, prevention and control: new edition. (WHO Press, 2009).

4. Barniol, J. et al. Usefulness and applicability of the revised dengue case classification by disease: multi-centre study in 18 countries. BMC Infect. Dis. 11, 106 (2011).

5. Liu, Y. E. et al. An 8-gene machine learning model improves clinical prediction of severe dengue progression. Genome Med. 14, 33 (2022).

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