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10.1016/j.csbj.2021.01.042 http://scihub22266oqcxt.onion/10.1016/j.csbj.2021.01.042 33584997!7870437!33584997     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 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\33584997.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Comput+Struct+Biotechnol+J 2021 ; 19 (ä): 1163-1175 Nephropedia Template TP {{tp|p=33584997|t=2021. 3044 Cases reveal important prognosis signatures of COVID-19 patients |pdf=|usr=}}{{33584997}} gab.com Text 3044 Cases reveal important prognosis signatures of COVID-19 patients JO: Comput Struct Biotechnol J http://www.kidney.de/pget.php?&tw=1&pmid=33584997 #FOAvip Critical patients and intensive care unit (ICU) patients are the main population of COVID-19 deaths. Therefore, establishing a reliable method is necessary for COVID-19 patients to distinguish patients who may have critical symptoms from other patients. In this retrospective study, we firstly evaluated the effects of 54 laboratory indicators on critical illness and death in 3044 COVID-19 patients from the Huoshenshan hospital in Wuhan, China. Secondly, we identify the eight most important prognostic indicators (neutrophil percentage, procalcitonin, neutrophil absolute value, C-reactive protein, albumin, interleukin-6, lymphocyte absolute value and myoglobin) by using the random forest algorithm, and find that dynamic changes of the eight prognostic indicators present significantly distinct within differently clinical severities. Thirdly, our study reveals that a model containing age and these eight prognostic indicators can accurately predict which patients may develop serious illness or death. Fourthly, our results demonstrate that different genders have different critical illness rates compared with different ages, in particular the mortality is more likely to be attributed to some key genes (e.g. ACE2, TMPRSS2 and FURIN) by combining the analysis of public lung single cells and bulk transcriptome data. Taken together, we urge that the prognostic model and first-hand clinical trial data generated in this study have important clinical practical significance for predicting and exploring the disease progression of COVID-19 patients. Twit Text FOAVip 3044 Cases reveal important prognosis signatures of COVID-19 patients ????Comput Struct Biotechnol J http://www.kidney.de/pget.php?&tw=1&pmid=33584997 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33584997 #FOAvip English Wikipedia <ref>{{Cite pmid|33584997}}</ref> 3044 Cases reveal important prognosis signatures of COVID-19 patients #MMPMID33584997 Qin S; Li W; Shi X; Wu Y; Wang C; Shen J; Pang R; He B; Zhao J; Qiao Q; Luo T; Guo Y; Yang Y; Han Y; Wu Q; Wu J; Dai W; Zhang L; Chen L; Xue C; Jin P; Gan Z; Ma F; Xia X Comput Struct Biotechnol J 2021[]; 19 (ä): 1163-1175 PMID33584997show ga Critical patients and intensive care unit (ICU) patients are the main population of COVID-19 deaths. Therefore, establishing a reliable method is necessary for COVID-19 patients to distinguish patients who may have critical symptoms from other patients. In this retrospective study, we firstly evaluated the effects of 54 laboratory indicators on critical illness and death in 3044 COVID-19 patients from the Huoshenshan hospital in Wuhan, China. Secondly, we identify the eight most important prognostic indicators (neutrophil percentage, procalcitonin, neutrophil absolute value, C-reactive protein, albumin, interleukin-6, lymphocyte absolute value and myoglobin) by using the random forest algorithm, and find that dynamic changes of the eight prognostic indicators present significantly distinct within differently clinical severities. Thirdly, our study reveals that a model containing age and these eight prognostic indicators can accurately predict which patients may develop serious illness or death. Fourthly, our results demonstrate that different genders have different critical illness rates compared with different ages, in particular the mortality is more likely to be attributed to some key genes (e.g. ACE2, TMPRSS2 and FURIN) by combining the analysis of public lung single cells and bulk transcriptome data. Taken together, we urge that the prognostic model and first-hand clinical trial data generated in this study have important clinical practical significance for predicting and exploring the disease progression of COVID-19 patients. ä3044 Cases reveal important prognosis signatures of COVID-19 patients (epmc).pdf DeepDyve Pubget Overpricing