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10.1021/acs.jproteome.0c00326 http://scihub22266oqcxt.onion/10.1021/acs.jproteome.0c00326 32657586!7384384!32657586     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Proteome+Res 2020 ; 19 (11): 4219-4232 Nephropedia Template TP {{tp|p=32657586|t=2020. Proteomics and Informatics for Understanding Phases and Identifying Biomarkers in COVID-19 Disease |pdf=|usr=}}{{32657586}} gab.com Text Proteomics and Informatics for Understanding Phases and Identifying Biomarkers in COVID-19 Disease JO: J Proteome Res http://www.kidney.de/pget.php?&tw=1&pmid=32657586 #FOAvip The emergence of novel coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 coronavirus, has necessitated the urgent development of new diagnostic and therapeutic strategies. Rapid research and development, on an international scale, has already generated assays for detecting SARS-CoV-2 RNA and host immunoglobulins. However, the complexities of COVID-19 are such that fuller definitions of patient status, trajectory, sequelae, and responses to therapy are now required. There is accumulating evidence-from studies of both COVID-19 and the related disease SARS-that protein biomarkers could help to provide this definition. Proteins associated with blood coagulation (D-dimer), cell damage (lactate dehydrogenase), and the inflammatory response (e.g., C-reactive protein) have already been identified as possible predictors of COVID-19 severity or mortality. Proteomics technologies, with their ability to detect many proteins per analysis, have begun to extend these early findings. To be effective, proteomics strategies must include not only methods for comprehensive data acquisition (e.g., using mass spectrometry) but also informatics approaches via which to derive actionable information from large data sets. Here we review applications of proteomics to COVID-19 and SARS and outline how pipelines involving technologies such as artificial intelligence could be of value for research on these diseases. Twit Text FOAVip Proteomics and Informatics for Understanding Phases and Identifying Biomarkers in COVID-19 Disease ????J Proteome Res http://www.kidney.de/pget.php?&tw=1&pmid=32657586 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32657586 #FOAvip English Wikipedia <ref>{{Cite pmid|32657586}}</ref> Proteomics and Informatics for Understanding Phases and Identifying Biomarkers in COVID-19 Disease #MMPMID32657586 Whetton AD; Preston GW; Abubeker S; Geifman N J Proteome Res 2020[Nov]; 19 (11): 4219-4232 PMID32657586show ga The emergence of novel coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 coronavirus, has necessitated the urgent development of new diagnostic and therapeutic strategies. Rapid research and development, on an international scale, has already generated assays for detecting SARS-CoV-2 RNA and host immunoglobulins. However, the complexities of COVID-19 are such that fuller definitions of patient status, trajectory, sequelae, and responses to therapy are now required. There is accumulating evidence-from studies of both COVID-19 and the related disease SARS-that protein biomarkers could help to provide this definition. Proteins associated with blood coagulation (D-dimer), cell damage (lactate dehydrogenase), and the inflammatory response (e.g., C-reactive protein) have already been identified as possible predictors of COVID-19 severity or mortality. Proteomics technologies, with their ability to detect many proteins per analysis, have begun to extend these early findings. To be effective, proteomics strategies must include not only methods for comprehensive data acquisition (e.g., using mass spectrometry) but also informatics approaches via which to derive actionable information from large data sets. Here we review applications of proteomics to COVID-19 and SARS and outline how pipelines involving technologies such as artificial intelligence could be of value for research on these diseases. |*Betacoronavirus[MESH] |*Coronavirus Infections/blood/diagnosis/metabolism/physiopathology[MESH] |*Pandemics[MESH] |*Pneumonia, Viral/blood/diagnosis/metabolism/physiopathology[MESH] |*Proteomics[MESH] |Artificial Intelligence[MESH] |Biomarkers/analysis[MESH] |COVID-19[MESH] |Diagnosis, Computer-Assisted[MESH] |Humans[MESH] |Prognosis[MESH]Proteomics and Informatics for Understanding Phases and Identifying Bi(epmc).pdf DeepDyve Pubget Overpricing