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- W4225835056 abstract "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." @default.
- W4225835056 created "2022-05-05" @default.
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- W4225835056 date "2022-04-05" @default.
- W4225835056 modified "2023-09-26" @default.
- W4225835056 title "Detection of SARS-CoV-2 infection by microRNA profiling of the upper respiratory tract" @default.
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- W4225835056 doi "https://doi.org/10.1371/journal.pone.0265670" @default.
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