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Machine learning models based on fluid immunoproteins that predict non-AIDS adverse events in people with HIV

Summary: Despite the success of antiretroviral therapy (ART), individuals with HIV remain at risk for experiencing non-AIDS adverse events (NAEs), including cardiovascular complications and malignancy. Several surrogate immune biomarkers in blood have shown predictive value in predicting NAEs; howev...

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Principais autores: Thomas A. Premeaux, Scott Bowler, Courtney M. Friday, Carlee B. Moser, Martin Hoenigl, Michael M. Lederman, Alan L. Landay, Sara Gianella, Lishomwa C. Ndhlovu
Formato: Artigo
Idioma:Inglês
Publicado em: Elsevier 2024-06-01
coleção:iScience
Assuntos:
Health sciences
Immunology
Machine learning
Public health
Virology
Acesso em linha:http://www.sciencedirect.com/science/article/pii/S2589004224011672
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