Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1098/rspb.2021.1651 http://scihub22266oqcxt.onion/10.1098/rspb.2021.1651 34784766!8596006!34784766     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Proc+Biol+Sci 2021 ; 288 (1963): 20211651 Nephropedia Template TP {{tp|p=34784766|t=2021. Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2 |pdf=|usr=}}{{34784766}} gab.com Text Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2 JO: Proc Biol Sci http://www.kidney.de/pget.php?&tw=1&pmid=34784766 #FOAvip Back and forth transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between humans and animals will establish wild reservoirs of virus that endanger long-term efforts to control COVID-19 in people and to protect vulnerable animal populations. Better targeting surveillance and laboratory experiments to validate zoonotic potential requires predicting high-risk host species. A major bottleneck to this effort is the few species with available sequences for angiotensin-converting enzyme 2 receptor, a key receptor required for viral cell entry. We overcome this bottleneck by combining species' ecological and biological traits with three-dimensional modelling of host-virus protein-protein interactions using machine learning. This approach enables predictions about the zoonotic capacity of SARS-CoV-2 for greater than 5000 mammals-an order of magnitude more species than previously possible. Our predictions are strongly corroborated by in vivo studies. The predicted zoonotic capacity and proximity to humans suggest enhanced transmission risk from several common mammals, and priority areas of geographic overlap between these species and global COVID-19 hotspots. With molecular data available for only a small fraction of potential animal hosts, linking data across biological scales offers a conceptual advance that may expand our predictive modelling capacity for zoonotic viruses with similarly unknown host ranges. Twit Text FOAVip Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2 ????Proc Biol Sci http://www.kidney.de/pget.php?&tw=1&pmid=34784766 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34784766 #FOAvip English Wikipedia <ref>{{Cite pmid|34784766}}</ref> Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2 #MMPMID34784766 Fischhoff IR; Castellanos AA; Rodrigues JPGLM; Varsani A; Han BA Proc Biol Sci 2021[Nov]; 288 (1963): 20211651 PMID34784766show ga Back and forth transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between humans and animals will establish wild reservoirs of virus that endanger long-term efforts to control COVID-19 in people and to protect vulnerable animal populations. Better targeting surveillance and laboratory experiments to validate zoonotic potential requires predicting high-risk host species. A major bottleneck to this effort is the few species with available sequences for angiotensin-converting enzyme 2 receptor, a key receptor required for viral cell entry. We overcome this bottleneck by combining species' ecological and biological traits with three-dimensional modelling of host-virus protein-protein interactions using machine learning. This approach enables predictions about the zoonotic capacity of SARS-CoV-2 for greater than 5000 mammals-an order of magnitude more species than previously possible. Our predictions are strongly corroborated by in vivo studies. The predicted zoonotic capacity and proximity to humans suggest enhanced transmission risk from several common mammals, and priority areas of geographic overlap between these species and global COVID-19 hotspots. With molecular data available for only a small fraction of potential animal hosts, linking data across biological scales offers a conceptual advance that may expand our predictive modelling capacity for zoonotic viruses with similarly unknown host ranges. |*COVID-19[MESH] |*SARS-CoV-2[MESH] |Animals[MESH] |Host Specificity[MESH] |Humans[MESH] |Mammals[MESH]Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2 (epmc).pdf DeepDyve Pubget Overpricing