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10.1038/s42003-020-01439-6 http://scihub22266oqcxt.onion/10.1038/s42003-020-01439-6 33244050!7692497!33244050     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Commun+Biol 2020 ; 3 (1): 711 Nephropedia Template TP {{tp|p=33244050|t=2020. Predicting clinical resistance prevalence using sewage metagenomic data |pdf=|usr=}}{{33244050}} gab.com Text Predicting clinical resistance prevalence using sewage metagenomic data JO: Commun Biol http://www.kidney.de/pget.php?&tw=1&pmid=33244050 #FOAvip Antibiotic resistance surveillance through regional and up-to-date testing of clinical isolates is a foundation for implementing effective empirical treatment. Surveillance data also provides an overview of geographical and temporal changes that are invaluable for guiding interventions. Still, due to limited infrastructure and resources, clinical surveillance data is lacking in many parts of the world. Given that sewage is largely made up of human fecal bacteria from many people, sewage epidemiology could provide a cost-efficient strategy to partly fill the current gap in clinical surveillance of antibiotic resistance. Here we explored the potential of sewage metagenomic data to assess clinical antibiotic resistance prevalence using environmental and clinical surveillance data from across the world. The sewage resistome correlated to clinical surveillance data of invasive Escherichia coli isolates, but none of several tested approaches provided a sufficient resolution for clear discrimination between resistance towards different classes of antibiotics. However, in combination with socioeconomic data, the overall clinical resistance situation could be predicted with good precision. We conclude that analyses of bacterial genes in sewage could contribute to informing management of antibiotic resistance. Twit Text FOAVip Predicting clinical resistance prevalence using sewage metagenomic data ????Commun Biol http://www.kidney.de/pget.php?&tw=1&pmid=33244050 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33244050 #FOAvip English Wikipedia <ref>{{Cite pmid|33244050}}</ref> Predicting clinical resistance prevalence using sewage metagenomic data #MMPMID33244050 Karkman A; Berglund F; Flach CF; Kristiansson E; Larsson DGJ Commun Biol 2020[Nov]; 3 (1): 711 PMID33244050show ga Antibiotic resistance surveillance through regional and up-to-date testing of clinical isolates is a foundation for implementing effective empirical treatment. Surveillance data also provides an overview of geographical and temporal changes that are invaluable for guiding interventions. Still, due to limited infrastructure and resources, clinical surveillance data is lacking in many parts of the world. Given that sewage is largely made up of human fecal bacteria from many people, sewage epidemiology could provide a cost-efficient strategy to partly fill the current gap in clinical surveillance of antibiotic resistance. Here we explored the potential of sewage metagenomic data to assess clinical antibiotic resistance prevalence using environmental and clinical surveillance data from across the world. The sewage resistome correlated to clinical surveillance data of invasive Escherichia coli isolates, but none of several tested approaches provided a sufficient resolution for clear discrimination between resistance towards different classes of antibiotics. However, in combination with socioeconomic data, the overall clinical resistance situation could be predicted with good precision. We conclude that analyses of bacterial genes in sewage could contribute to informing management of antibiotic resistance. |*Bacteria/drug effects/genetics[MESH] |Anti-Bacterial Agents/*pharmacology[MESH] |Drug Resistance, Bacterial/*genetics[MESH] |Feces/microbiology[MESH] |Humans[MESH] |Metagenomics/*methods[MESH] |Models, Statistical[MESH] |Prevalence[MESH] |Public Health Surveillance[MESH]Predicting clinical resistance prevalence using sewage metagenomic dat(epmc).pdf DeepDyve Pubget Overpricing