ABSTRACT
Background: The annual death toll of over 1.2 million worldwide is attributed to infections caused by resistant bacteria, driven by the significant impact of antibiotic misuse and overuse in spreading these bacteria and their associated antibiotic resistance genes (ARGs). While limited data suggest the presence of ARGs in beach environments, efficient prediction tools are needed for monitoring and detecting ARGs to ensure public health safety. This study aims to develop interpretable machine learning methods for predicting ARGs in beach waters, addressing the challenge of black-box models and enhancing our understanding of their internal mechanisms. Methods: In this study, we systematically collected beach water samples and subsequently isolated bacteria from these samples using various differential and selective media supplemented with different antibiotics. Resistance profiles of bacteria were determined by using Kirby-Bauer disk diffusion method. Further, ARGs were enumerated by using the quantitative polymerase chain reaction (qPCR) to detect and quantify ARGs. The obtained qPCR data and hydro-meteorological were used to create an ML model with high prediction performance and we further used two explainable artificial intelligence (xAI) model-agnostic interpretation methods to describe the internal behavior of ML model. Results: Using qPCR, we detected blaCTX-M, blaNDM, blaCMY, blaOXA, blatetX, blasul1, and blaaac(6'-Ib-cr) in the beach waters. Further, we developed ML prediction models for blaaac(6'-Ib-cr), blasul1, and blatetX using the hydro-metrological and qPCR-derived data and the models demonstrated strong performance, with R2 values of 0.957, 0.997, and 0.976, respectively. Conclusions: Our findings show that environmental factors, such as water temperature, precipitation, and tide, are among the important predictors of the abundance of resistance genes at beaches.
ABSTRACT
Citrus sinensis is an important member of the genus Citrus which contains phenolic compounds and bioflavonoids which have antihyperlipidemic and antiatherogenic effects. It also has the potential to reduce oxidative stress. To investigate the antihyperlipidemic effect of orange peel powder was encapsulated and analyzed in hyperlipidemic patients. Results showed that it contains moisture (12.2%), ash content (7.9%), crude fat (0.78%), crude protein (12.37%) and crude fiber (13.2%). Total phenolic content and total flavonoid content were observed as 163.17 mg and 17.23mg in quercetin equivalent per gram a dry weight basis. Furthermore, the Orange peel powder was given in the form of medicinal capsules to hyperlipidemia male subjects. The experimental groups (G1 and G2) were given orange peel powder in capsules 400mg/d to the G1 group and 800mg/d to the G2 group for the time of 45 days. The serum lipid profile of patients was measured before and after the experimental trial. The result showed that G1 and G2 showed a decrease in plasma lipid parameters and increased high-density lipoprotein content in blood substantially as compared to G0. Thus, it was concluded from the results that orange peel powder depicts a significant impact on treating hyperlipidemia.