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• W3201609912 abstract "COVID-19 is a disease with a spectrum of clinical responses ranging from moderate to critical. To study and control its effects, a large number of researchers are focused on two substantial aims. On the one hand, the discovery of diverse biomarkers to classify and potentially anticipate the disease severity of patients. These biomarkers could serve as a medical criterion to prioritize attention to those patients with higher prone to severe responses. On the other hand, understanding how the immune system orchestrates its responses in this spectrum of disease severities is a fundamental issue required to design new and optimized therapeutic strategies. In this work, using single-cell RNAseq of bronchoalveolar lavage fluid of nine patients with COVID-19 and three healthy controls, we contribute to both aspects. First, we presented computational supervised machine-learning models with high accuracy in classifying the disease severity (moderate and severe) in patients with COVID-19 starting from single-cell data from bronchoalveolar lavage fluid. Second, we identified regulatory mechanisms from the heterogeneous cell populations in the lungs microenvironment that correlated with different clinical responses. Given the results, patients with moderate COVID-19 symptoms showed an activation/inactivation profile for their analyzed cells leading to a sequential and innocuous immune response. In comparison, severe patients might be promoting cytotoxic and pro-inflammatory responses in a systemic fashion involving epithelial and immune cells without the possibility to develop viral clearance and immune memory. Consequently, we present an in-depth landscape analysis of how transcriptional factors and pathways from these heterogeneous populations can regulate their expression to promote or restrain an effective immune response directly linked to the patients prognosis." @default.
• W3201609912 created "2021-09-27" @default.
• W3201609912 creator A5017937746 @default.
• W3201609912 creator A5021782839 @default.
• W3201609912 creator A5025799655 @default.
• W3201609912 creator A5052962106 @default.
• W3201609912 creator A5066518521 @default.
• W3201609912 creator A5079221995 @default.
• W3201609912 creator A5080302176 @default.
• W3201609912 date "2021-09-16" @default.
• W3201609912 modified "2023-10-06" @default.
• W3201609912 title "On Deep Landscape Exploration of COVID-19 Patients Cells and Severity Markers" @default.
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