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10.1038/s43856-021-00042-y http://scihub22266oqcxt.onion/10.1038/s43856-021-00042-y 35072167!8767772!35072167     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\35072167.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Commun+Med+(Lond) 2021 ; 1 (1): 42 Nephropedia Template TP {{tp|p=35072167|t=2021. Host methylation predicts SARS-CoV-2 infection and clinical outcome |pdf=|usr=}}{{35072167}} gab.com Text Host methylation predicts SARS-CoV-2 infection and clinical outcome JO: Commun Med (Lond) http://www.kidney.de/pget.php?&tw=1&pmid=35072167 #FOAvip BACKGROUND: Since the onset of the SARS-CoV-2 pandemic, most clinical testing has focused on RT-PCR(1). Host epigenome manipulation post coronavirus infection(2-4) suggests that DNA methylation signatures may differentiate patients with SARS-CoV-2 infection from uninfected individuals, and help predict COVID-19 disease severity, even at initial presentation. METHODS: We customized Illumina's Infinium MethylationEPIC array to enhance immune response detection and profiled peripheral blood samples from 164 COVID-19 patients with longitudinal measurements of disease severity and 296 patient controls. RESULTS: Epigenome-wide association analysis revealed 13,033 genome-wide significant methylation sites for case-vs-control status. Genes and pathways involved in interferon signaling and viral response were significantly enriched among differentially methylated sites. We observe highly significant associations at genes previously reported in genetic association studies (e.g. IRF7, OAS1). Using machine learning techniques, models built using sparse regression yielded highly predictive findings: cross-validated best fit AUC was 93.6% for case-vs-control status, and 79.1%, 80.8%, and 84.4% for hospitalization, ICU admission, and progression to death, respectively. CONCLUSIONS: In summary, the strong COVID-19-specific epigenetic signature in peripheral blood driven by key immune-related pathways related to infection status, disease severity, and clinical deterioration provides insights useful for diagnosis and prognosis of patients with viral infections. Twit Text FOAVip Host methylation predicts SARS-CoV-2 infection and clinical outcome ????Commun Med (Lond) http://www.kidney.de/pget.php?&tw=1&pmid=35072167 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=35072167 #FOAvip English Wikipedia <ref>{{Cite pmid|35072167}}</ref> Host methylation predicts SARS-CoV-2 infection and clinical outcome #MMPMID35072167 Konigsberg IR; Barnes B; Campbell M; Davidson E; Zhen Y; Pallisard O; Boorgula MP; Cox C; Nandy D; Seal S; Crooks K; Sticca E; Harrison GF; Hopkinson A; Vest A; Arnold CG; Kahn MG; Kao DP; Peterson BR; Wicks SJ; Ghosh D; Horvath S; Zhou W; Mathias RA; Norman PJ; Porecha R; Yang IV; Gignoux CR; Monte AA; Taye A; Barnes KC Commun Med (Lond) 2021[]; 1 (1): 42 PMID35072167show ga BACKGROUND: Since the onset of the SARS-CoV-2 pandemic, most clinical testing has focused on RT-PCR(1). Host epigenome manipulation post coronavirus infection(2-4) suggests that DNA methylation signatures may differentiate patients with SARS-CoV-2 infection from uninfected individuals, and help predict COVID-19 disease severity, even at initial presentation. METHODS: We customized Illumina's Infinium MethylationEPIC array to enhance immune response detection and profiled peripheral blood samples from 164 COVID-19 patients with longitudinal measurements of disease severity and 296 patient controls. RESULTS: Epigenome-wide association analysis revealed 13,033 genome-wide significant methylation sites for case-vs-control status. Genes and pathways involved in interferon signaling and viral response were significantly enriched among differentially methylated sites. We observe highly significant associations at genes previously reported in genetic association studies (e.g. IRF7, OAS1). Using machine learning techniques, models built using sparse regression yielded highly predictive findings: cross-validated best fit AUC was 93.6% for case-vs-control status, and 79.1%, 80.8%, and 84.4% for hospitalization, ICU admission, and progression to death, respectively. CONCLUSIONS: In summary, the strong COVID-19-specific epigenetic signature in peripheral blood driven by key immune-related pathways related to infection status, disease severity, and clinical deterioration provides insights useful for diagnosis and prognosis of patients with viral infections. �Host methylation predicts SARS-CoV-2 infection and clinical outcome (epmc).pdf DeepDyve Pubget Overpricing