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10.1016/j.isci.2022.104612 http://scihub22266oqcxt.onion/10.1016/j.isci.2022.104612 35756895!9212983!35756895     free     free     free Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       iScience 2022 ; 25 (7): 104612 Nephropedia Template TP {{tp|p=35756895|t=2022. Integrative metabolomic and proteomic signatures define clinical outcomes in severe COVID-19 |pdf=|usr=}}{{35756895}} gab.com Text Integrative metabolomic and proteomic signatures define clinical outcomes in severe COVID-19 JO: iScience http://www.kidney.de/pget.php?&tw=1&pmid=35756895 #FOAvip The coronavirus disease-19 (COVID-19) pandemic has ravaged global healthcare with previously unseen levels of morbidity and mortality. In this study, we performed large-scale integrative multi-omics analyses of serum obtained from COVID-19 patients with the goal of uncovering novel pathogenic complexities of this disease and identifying molecular signatures that predict clinical outcomes. We assembled a network of protein-metabolite interactions through targeted metabolomic and proteomic profiling in 330 COVID-19 patients compared to 97 non-COVID, hospitalized controls. Our network identified distinct protein-metabolite cross talk related to immune modulation, energy and nucleotide metabolism, vascular homeostasis, and collagen catabolism. Additionally, our data linked multiple proteins and metabolites to clinical indices associated with long-term mortality and morbidity. Finally, we developed a novel composite outcome measure for COVID-19 disease severity based on metabolomics data. The model predicts severe disease with a concordance index of around 0.69, and shows high predictive power of 0.83-0.93 in two independent datasets. Twit Text FOAVip Integrative metabolomic and proteomic signatures define clinical outcomes in severe COVID-19 ????iScience http://www.kidney.de/pget.php?&tw=1&pmid=35756895 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=35756895 #FOAvip English Wikipedia <ref>{{Cite pmid|35756895}}</ref> Integrative metabolomic and proteomic signatures define clinical outcomes in severe COVID-19 #MMPMID35756895 Buyukozkan M; Alvarez-Mulett S; Racanelli AC; Schmidt F; Batra R; Hoffman KL; Sarwath H; Engelke R; Gomez-Escobar L; Simmons W; Benedetti E; Chetnik K; Zhang G; Schenck E; Suhre K; Choi JJ; Zhao Z; Racine-Brzostek S; Yang HS; Choi ME; Choi AMK; Cho SJ; Krumsiek J iScience 2022[Jul]; 25 (7): 104612 PMID35756895show ga The coronavirus disease-19 (COVID-19) pandemic has ravaged global healthcare with previously unseen levels of morbidity and mortality. In this study, we performed large-scale integrative multi-omics analyses of serum obtained from COVID-19 patients with the goal of uncovering novel pathogenic complexities of this disease and identifying molecular signatures that predict clinical outcomes. We assembled a network of protein-metabolite interactions through targeted metabolomic and proteomic profiling in 330 COVID-19 patients compared to 97 non-COVID, hospitalized controls. Our network identified distinct protein-metabolite cross talk related to immune modulation, energy and nucleotide metabolism, vascular homeostasis, and collagen catabolism. Additionally, our data linked multiple proteins and metabolites to clinical indices associated with long-term mortality and morbidity. Finally, we developed a novel composite outcome measure for COVID-19 disease severity based on metabolomics data. The model predicts severe disease with a concordance index of around 0.69, and shows high predictive power of 0.83-0.93 in two independent datasets. äIntegrative metabolomic and proteomic signatures define clinical outco(epmc).pdf DeepDyve Pubget Overpricing