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10.1038/s41467-021-21056-z http://scihub22266oqcxt.onion/10.1038/s41467-021-21056-z 33589624!7884845!33589624     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 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 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 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\33589624.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Nat+Commun 2021 ; 12 (1): 1024 Nephropedia Template TP {{tp|p=33589624|t=2021. Causal network models of SARS-CoV-2 expression and aging to identify candidates for drug repurposing |pdf=|usr=}}{{33589624}} gab.com Text Causal network models of SARS-CoV-2 expression and aging to identify candidates for drug repurposing JO: Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=33589624 #FOAvip Given the severity of the SARS-CoV-2 pandemic, a major challenge is to rapidly repurpose existing approved drugs for clinical interventions. While a number of data-driven and experimental approaches have been suggested in the context of drug repurposing, a platform that systematically integrates available transcriptomic, proteomic and structural data is missing. More importantly, given that SARS-CoV-2 pathogenicity is highly age-dependent, it is critical to integrate aging signatures into drug discovery platforms. We here take advantage of large-scale transcriptional drug screens combined with RNA-seq data of the lung epithelium with SARS-CoV-2 infection as well as the aging lung. To identify robust druggable protein targets, we propose a principled causal framework that makes use of multiple data modalities. Our analysis highlights the importance of serine/threonine and tyrosine kinases as potential targets that intersect the SARS-CoV-2 and aging pathways. By integrating transcriptomic, proteomic and structural data that is available for many diseases, our drug discovery platform is broadly applicable. Rigorous in vitro experiments as well as clinical trials are needed to validate the identified candidate drugs. Twit Text FOAVip Causal network models of SARS-CoV-2 expression and aging to identify candidates for drug repurposing ????Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=33589624 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33589624 #FOAvip English Wikipedia <ref>{{Cite pmid|33589624}}</ref> Causal network models of SARS-CoV-2 expression and aging to identify candidates for drug repurposing #MMPMID33589624 Belyaeva A; Cammarata L; Radhakrishnan A; Squires C; Yang KD; Shivashankar GV; Uhler C Nat Commun 2021[Feb]; 12 (1): 1024 PMID33589624show ga Given the severity of the SARS-CoV-2 pandemic, a major challenge is to rapidly repurpose existing approved drugs for clinical interventions. While a number of data-driven and experimental approaches have been suggested in the context of drug repurposing, a platform that systematically integrates available transcriptomic, proteomic and structural data is missing. More importantly, given that SARS-CoV-2 pathogenicity is highly age-dependent, it is critical to integrate aging signatures into drug discovery platforms. We here take advantage of large-scale transcriptional drug screens combined with RNA-seq data of the lung epithelium with SARS-CoV-2 infection as well as the aging lung. To identify robust druggable protein targets, we propose a principled causal framework that makes use of multiple data modalities. Our analysis highlights the importance of serine/threonine and tyrosine kinases as potential targets that intersect the SARS-CoV-2 and aging pathways. By integrating transcriptomic, proteomic and structural data that is available for many diseases, our drug discovery platform is broadly applicable. Rigorous in vitro experiments as well as clinical trials are needed to validate the identified candidate drugs. |*COVID-19 Drug Treatment[MESH] |*Drug Repositioning[MESH] |A549 Cells[MESH] |Aging/*physiology[MESH] |Algorithms[MESH] |Angiotensin-Converting Enzyme 2/metabolism[MESH] |Antiviral Agents/therapeutic use[MESH] |COVID-19/*genetics/metabolism[MESH] |Drug Discovery[MESH] |Gene Expression[MESH] |Gene Regulatory Networks[MESH] |Humans[MESH] |Proteomics[MESH] |SARS-CoV-2[MESH]Causal network models of SARS-CoV-2 expression and aging to identify c(epmc).pdf DeepDyve Pubget Overpricing