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10.7554/eLife.64827 http://scihub22266oqcxt.onion/10.7554/eLife.64827 33704068!8064756!33704068     free     free     free Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Elife 2021 ; 10 (ä): ä Nephropedia Template TP {{tp|p=33704068|t=2021. Longitudinal proteomic profiling of dialysis patients with COVID-19 reveals markers of severity and predictors of death |pdf=|usr=}}{{33704068}} gab.com Text Longitudinal proteomic profiling of dialysis patients with COVID-19 reveals markers of severity and predictors of death JO: Elife http://www.kidney.de/pget.php?&tw=1&pmid=33704068 #FOAvip End-stage kidney disease (ESKD) patients are at high risk of severe COVID-19. We measured 436 circulating proteins in serial blood samples from hospitalised and non-hospitalised ESKD patients with COVID-19 (n = 256 samples from 55 patients). Comparison to 51 non-infected patients revealed 221 differentially expressed proteins, with consistent results in a separate subcohort of 46 COVID-19 patients. Two hundred and three proteins were associated with clinical severity, including IL6, markers of monocyte recruitment (e.g. CCL2, CCL7), neutrophil activation (e.g. proteinase-3), and epithelial injury (e.g. KRT19). Machine-learning identified predictors of severity including IL18BP, CTSD, GDF15, and KRT19. Survival analysis with joint models revealed 69 predictors of death. Longitudinal modelling with linear mixed models uncovered 32 proteins displaying different temporal profiles in severe versus non-severe disease, including integrins and adhesion molecules. These data implicate epithelial damage, innate immune activation, and leucocyte-endothelial interactions in the pathology of severe COVID-19 and provide a resource for identifying drug targets. Twit Text FOAVip Longitudinal proteomic profiling of dialysis patients with COVID-19 reveals markers of severity and predictors of death ????Elife http://www.kidney.de/pget.php?&tw=1&pmid=33704068 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33704068 #FOAvip English Wikipedia <ref>{{Cite pmid|33704068}}</ref> Longitudinal proteomic profiling of dialysis patients with COVID-19 reveals markers of severity and predictors of death #MMPMID33704068 Gisby J; Clarke CL; Medjeral-Thomas N; Malik TH; Papadaki A; Mortimer PM; Buang NB; Lewis S; Pereira M; Toulza F; Fagnano E; Mawhin MA; Dutton EE; Tapeng L; Richard AC; Kirk PD; Behmoaras J; Sandhu E; McAdoo SP; Prendecki MF; Pickering MC; Botto M; Willicombe M; Thomas DC; Peters JE Elife 2021[Mar]; 10 (ä): ä PMID33704068show ga End-stage kidney disease (ESKD) patients are at high risk of severe COVID-19. We measured 436 circulating proteins in serial blood samples from hospitalised and non-hospitalised ESKD patients with COVID-19 (n = 256 samples from 55 patients). Comparison to 51 non-infected patients revealed 221 differentially expressed proteins, with consistent results in a separate subcohort of 46 COVID-19 patients. Two hundred and three proteins were associated with clinical severity, including IL6, markers of monocyte recruitment (e.g. CCL2, CCL7), neutrophil activation (e.g. proteinase-3), and epithelial injury (e.g. KRT19). Machine-learning identified predictors of severity including IL18BP, CTSD, GDF15, and KRT19. Survival analysis with joint models revealed 69 predictors of death. Longitudinal modelling with linear mixed models uncovered 32 proteins displaying different temporal profiles in severe versus non-severe disease, including integrins and adhesion molecules. These data implicate epithelial damage, innate immune activation, and leucocyte-endothelial interactions in the pathology of severe COVID-19 and provide a resource for identifying drug targets. |Aged[MESH] |Biomarkers/blood[MESH] |COVID-19/*blood/mortality/virology[MESH] |Female[MESH] |Forecasting[MESH] |Hospitalization[MESH] |Humans[MESH] |Kidney Failure, Chronic/*blood/mortality/therapy/*virology[MESH] |Longitudinal Studies[MESH] |Male[MESH] |Middle Aged[MESH] |Prognosis[MESH] |Proteomics/methods[MESH] |Renal Dialysis/*methods/mortality[MESH] |SARS-CoV-2/isolation & purification[MESH]Longitudinal proteomic profiling of dialysis patients with COVID-19 re(epmc).pdf DeepDyve Pubget Overpricing