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10.21203/rs.3.rs-25643/v1

http://scihub22266oqcxt.onion/10.21203/rs.3.rs-25643/v1
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32702077!7336712!32702077
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suck abstract from ncbi


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pmid32702077      Res+Sq 2020 ; ä (ä): ä
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  • Identification of new drug treatments to combat COVID19: A signature-based approach using iLINCS #MMPMID32702077
  • O'Donovan SM; Eby H; Henkel ND; Creeden J; Imami A; Asah S; Zhang X; Wu X; Alnafisah R; Taylor RT; Reigle J; Thorman A; Shamsaei B; Meller J; McCullumsmith RE
  • Res Sq 2020[Apr]; ä (ä): ä PMID32702077show ga
  • The COVID-19 pandemic caused by the novel SARS-CoV-2 is more contagious than other coronaviruses and has higher rates of mortality than influenza. As no vaccine or drugs are currently approved to specifically treat COVID-19, identification of effective therapeutics is crucial to treat the afflicted and limit disease spread. We deployed a bioinformatics workflow to identify candidate drugs for the treatment of COVID-19. Using an "omics" repository, the Library of Integrated Network-Based Cellular Signatures (LINCS), we simultaneously probed transcriptomic signatures of putative COVID-19 drugs and signatures of coronavirus-infected cell lines to identify therapeutics with concordant signatures and discordant signatures, respectively. Our findings include three FDA approved drugs that have established antiviral activity, including protein kinase inhibitors, providing a promising new category of candidates for COVID-19 interventions.
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