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Viral waterborne diseases are widespread in cities due largely to the occurrence of enteric viruses in urban rivers, which pose a significant concern to human health. Yet, the application of rapid detection technology for enteric viruses in environmental water remains undeveloped globally. Here, multiple linear regression (MLR) modeling and artificial neural network (ANN) modeling, which used frequently measured physicochemical parameters in river water, were constructed to predict the concentration of enteric viruses including human enteroviruses (EnVs), rotaviruses (HRVs), astroviruses (AstVs), noroviruses GⅡ (HuNoVs GⅡ), and adenoviruses (HAdVs) in rivers. After training, testing, and validating, ANN models showed better performance than any MLR model for predicting the viral concentration in Jinhe River. All determined R-values for ANN models exceeded 0.89, suggesting a strong correlation between the predicted and measured outputs for target enteric viruses. Furthermore, ANN mo ....
Antibiotic resistant bacteria-associated infections are responsible for more than 1.2 million annual deaths worldwide. In low- and middle-income countries (LMICs), the consumption of antibiotics for human and veterinary uses is not regulated effectively. Overused and misused antibiotics can end up in aquatic environments, which may act as a conduit for antibiotic resistance dissemination. However, data on the prevalence of antibiotic resistance determinants in aquatic environments are still limited for LMICs. In this study, we evaluated the prevalence and concentration of antibiotic resistance genes in different drinking and environmental water sources collected from the Kathmandu Valley, Nepal using droplet digital PCR to understand the current situation of ARG contamination. River water and shallow dug well water were the most contaminated with ARGs. Almost all samples contained sul1 (95%), and intI1 and tet(A) were detected in 83% and 60% of the samples, respectively. Maximum ARG co ....