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10.3390/cells10123504 http://scihub22266oqcxt.onion/10.3390/cells10123504 34944012!8700362!34944012     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Cells 2021 ; 10 (12): ä Nephropedia Template TP {{tp|p=34944012|t=2021. Identifying Putative Causal Links between MicroRNAs and Severe COVID-19 Using Mendelian Randomization |pdf=|usr=}}{{34944012}} gab.com Text Identifying Putative Causal Links between MicroRNAs and Severe COVID-19 Using Mendelian Randomization JO: Cells http://www.kidney.de/pget.php?&tw=1&pmid=34944012 #FOAvip The SARS-CoV-2 (COVID-19) pandemic has caused millions of deaths worldwide. Early risk assessment of COVID-19 cases can help direct early treatment measures that have been shown to improve the prognosis of severe cases. Currently, circulating miRNAs have not been evaluated as canonical COVID-19 biomarkers, and identifying biomarkers that have a causal relationship with COVID-19 is imperative. To bridge these gaps, we aim to examine the causal effects of miRNAs on COVID-19 severity in this study using two-sample Mendelian randomization approaches. Multiple studies with available GWAS summary statistics data were retrieved. Using circulating miRNA expression data as exposure, and severe COVID-19 cases as outcomes, we identified ten unique miRNAs that showed causality across three phenotype groups of COVID-19. Using expression data from an independent study, we validated and identified two high-confidence miRNAs, namely, hsa-miR-30a-3p and hsa-miR-139-5p, which have putative causal effects on developing cases of severe COVID-19. Using existing literature and publicly available databases, the potential causative roles of these miRNAs were investigated. This study provides a novel way of utilizing miRNA eQTL data to help us identify potential miRNA biomarkers to make better and early diagnoses and risk assessments of severe COVID-19 cases. Twit Text FOAVip Identifying Putative Causal Links between MicroRNAs and Severe COVID-19 Using Mendelian Randomization ????Cells http://www.kidney.de/pget.php?&tw=1&pmid=34944012 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34944012 #FOAvip English Wikipedia <ref>{{Cite pmid|34944012}}</ref> Identifying Putative Causal Links between MicroRNAs and Severe COVID-19 Using Mendelian Randomization #MMPMID34944012 Li C; Wu A; Song K; Gao J; Huang E; Bai Y; Liu X Cells 2021[Dec]; 10 (12): ä PMID34944012show ga The SARS-CoV-2 (COVID-19) pandemic has caused millions of deaths worldwide. Early risk assessment of COVID-19 cases can help direct early treatment measures that have been shown to improve the prognosis of severe cases. Currently, circulating miRNAs have not been evaluated as canonical COVID-19 biomarkers, and identifying biomarkers that have a causal relationship with COVID-19 is imperative. To bridge these gaps, we aim to examine the causal effects of miRNAs on COVID-19 severity in this study using two-sample Mendelian randomization approaches. Multiple studies with available GWAS summary statistics data were retrieved. Using circulating miRNA expression data as exposure, and severe COVID-19 cases as outcomes, we identified ten unique miRNAs that showed causality across three phenotype groups of COVID-19. Using expression data from an independent study, we validated and identified two high-confidence miRNAs, namely, hsa-miR-30a-3p and hsa-miR-139-5p, which have putative causal effects on developing cases of severe COVID-19. Using existing literature and publicly available databases, the potential causative roles of these miRNAs were investigated. This study provides a novel way of utilizing miRNA eQTL data to help us identify potential miRNA biomarkers to make better and early diagnoses and risk assessments of severe COVID-19 cases. |*Patient Acuity[MESH] |Biomarkers/blood[MESH] |COVID-19/blood/*genetics[MESH] |Circulating MicroRNA/blood/*genetics[MESH] |Genome-Wide Association Study[MESH] |Humans[MESH] |Mendelian Randomization Analysis[MESH] |MicroRNAs/blood/*genetics[MESH]Identifying Putative Causal Links between MicroRNAs and Severe COVID-1(epmc).pdf DeepDyve Pubget Overpricing