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10.1126/sciadv.abe5735

http://scihub22266oqcxt.onion/10.1126/sciadv.abe5735
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33536217!ä!33536217

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suck abstract from ncbi


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pmid33536217      Sci+Adv 2021 ; 7 (6): ä
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  • Leveraging systems biology for predicting modulators of inflammation in patients with COVID-19 #MMPMID33536217
  • Jung S; Potapov I; Chillara S; Del Sol A
  • Sci Adv 2021[Feb]; 7 (6): ä PMID33536217show ga
  • Dysregulations in the inflammatory response of the body to pathogens could progress toward a hyperinflammatory condition amplified by positive feedback loops and associated with increased severity and mortality. Hence, there is a need for identifying therapeutic targets to modulate this pathological immune response. Here, we propose a single cell-based computational methodology for predicting proteins to modulate the dysregulated inflammatory response based on the reconstruction and analysis of functional cell-cell communication networks of physiological and pathological conditions. We validated the proposed method in 12 human disease datasets and performed an in-depth study of patients with mild and severe symptomatology of the coronavirus disease 2019 for predicting novel therapeutic targets. As a result, we identified the extracellular matrix protein versican and Toll-like receptor 2 as potential targets for modulating the inflammatory response. In summary, the proposed method can be of great utility in systematically identifying therapeutic targets for modulating pathological immune responses.
  • |COVID-19/immunology/*pathology/virology[MESH]
  • |Cell Communication[MESH]
  • |Cytokines/genetics/metabolism[MESH]
  • |Humans[MESH]
  • |Immunity, Innate[MESH]
  • |Immunologic Factors/*metabolism[MESH]
  • |Inflammation/*pathology[MESH]
  • |Lymphocytes/cytology/immunology/metabolism[MESH]
  • |Receptors, Chemokine/genetics/metabolism[MESH]
  • |SARS-CoV-2/isolation & purification[MESH]
  • |Severity of Illness Index[MESH]
  • |Systems Biology/*methods[MESH]
  • |Toll-Like Receptor 2/antagonists & inhibitors/metabolism[MESH]


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