Zhongqing Yang, Bei Zhu, Juqing Ke, Lulu Yu, Huandi Zhao
The effect of nomophobic behaviors among nurses on their clinical decision-making perceptions
Nurse education in practice
microbial composition stress molecules main drivers influencing development spread antibiotic resistance bacteria arbs genes args environment reliable rapid method identifying associations microbiome composition resistome remains challenging present study secondary metagenome data sewage hospital wastewaters assessed differential taxonomic arg profiling subsequently random forest rfbased ml models used predict arg profiles based taxonomic composition model validation hospital wastewaters total arg abundance significantly higher hospital wastewaters 15 ppm sewage 5 ppm resistance towards methicillin carbapenem fluoroquinolone predominant although pseudomonas constituted major fraction streptomyces enterobacter klebsiella characteristic hospital wastewaters prediction modeling showed relative abundance pathogenic genera escherichia vibrio pseudomonas contributed towards variations total arg count moreover model able identify hostspecific patterns contributing taxa related args 90 accuracy predicting arg subtype abundance 80 accuracy obtained hospital wastewaters demonstrating model validly extrapolated different types wastewater systems findings study showed ml approach could identify arg profile based bacterial composition including 16s rdna amplicon data serve viable alternative metagenomic binning identification potential hosts args overall study demonstrates promising application ml techniques predicting spread args provides guidance early warning arbs emergence