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10.1371/journal.pone.0265670

http://scihub22266oqcxt.onion/10.1371/journal.pone.0265670
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35381016!8982876!35381016
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suck abstract from ncbi


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pmid35381016      PLoS+One 2022 ; 17 (4): e0265670
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  • Detection of SARS-CoV-2 infection by microRNA profiling of the upper respiratory tract #MMPMID35381016
  • Farr RJ; Rootes CL; Stenos J; Foo CH; Cowled C; Stewart CR
  • PLoS One 2022[]; 17 (4): e0265670 PMID35381016show ga
  • Host biomarkers are increasingly being considered as tools for improved COVID-19 detection and prognosis. We recently profiled circulating host-encoded microRNA (miRNAs) during SARS-CoV-2 infection, revealing a signature that classified COVID-19 cases with 99.9% accuracy. Here we sought to develop a signature suited for clinical application by analyzing specimens collected using minimally invasive procedures. Eight miRNAs displayed altered expression in anterior nasal tissues from COVID-19 patients, with miR-142-3p, a negative regulator of interleukin-6 (IL-6) production, the most strongly upregulated. Supervised machine learning analysis revealed that a three-miRNA signature (miR-30c-2-3p, miR-628-3p and miR-93-5p) independently classifies COVID-19 cases with 100% accuracy. This study further defines the host miRNA response to SARS-CoV-2 infection and identifies candidate biomarkers for improved COVID-19 detection.
  • |*COVID-19/diagnosis[MESH]
  • |*MicroRNAs/genetics/metabolism[MESH]
  • |Biomarkers[MESH]
  • |Gene Expression Profiling[MESH]
  • |Humans[MESH]
  • |Respiratory System/metabolism[MESH]


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  • suck abstract from ncbi

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