Cargando…
Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy
For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before di...
Autores principales: | Barone, Sierra M., Paul, Alberta G.A., Muehling, Lyndsey M., Lannigan, Joanne A., Kwok, William W., Turner, Ronald B., Woodfolk, Judith A., Irish, Jonathan M. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7402038/ https://www.ncbi.nlm.nih.gov/pubmed/32766581 http://dx.doi.org/10.1101/2020.07.31.190454 |
Ejemplares similares
-
Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy
por: Barone, Sierra M, et al.
Publicado: (2021) -
T-bet+ Memory B Cells Link to Local Cross-Reactive IgG upon Human Rhinovirus Infection
por: Eccles, Jacob D., et al.
Publicado: (2020) -
Discrete T-Cell and Inflammatory Profiles in the Blood Define Pulmonary Phenotypes After COVID-19 Illness.
por: Canderan, Glenda, et al.
Publicado: (2023) -
Survivors Of Severe COVID-19 With Long-Haul Respiratory Symptoms Display Enhanced Activation of Circulating T Cells
por: Canderan, Glenda, et al.
Publicado: (2022) -
Cluster analysis of nasal cytokines during rhinovirus infection identifies different immunophenotypes in both children and adults with allergic asthma
por: Muehling, Lyndsey M., et al.
Publicado: (2022)