This study evaluated the beneficial protective effect of cotreatment of curcumin (CUR) and quercetin (QUE) on atrazine (ATZ)‐induced testicular toxicity in rats. ATZ challenge diminished luteinizing hormone, follicular stimulating hormone, testosterone and myeloperoxidase enzyme activity, but these effects were attenuated on co‐treatment with CUR and QUE. Also, co‐treatment of CUR + QUE was better than separate administration of QUE at diminishing malondialdehyde and glutathione and improving tumour necrosis factor‐α concentration, germ cell numbers (spermatogonia, spermatocytes and round spermatids) and epididymal sperm quality. Histologically, smaller sized tubules with degenerated epithelia and few germ cells were seen in the seminiferous tubules of the ATZ group whereas CUR + QUE pretreatment improved the histo‐morphologic features of the tubules compared to the ATZ group and was also better than separate administration of QUE. We conclude that CUR can improve the protective effects of QUE against ATZ‐induced testicular injury by enhancing the levels of reproductive hormones, recovering testicular biochemical parameters and improving the histological features of the testes.

Water is an indispensable requirement for life for health and many other purposes, but not all water is safe for consumption. Thus, various metrics, such as biological, chemical, and physical, could be used to determine the quality of potable water for use. This study presents a machine learning-based model using the adaptive boosting technique with the ability to categorize and evaluate the quality rate of drinking water. The dataset for the study was adopted from Kaggle. Consequently, an experimental analysis of the different machine learning techniques (ensemble) was carried out to create a generic water quality classifier. The results show that the forecast accuracy of the logistic regression model (88.6%), Chi-square Automatic Interaction Detector (93.1%), XGBoost tree (94.3%), as well as multi-layered perceptron (95.3%) improved by the presented ensemble model (96.4%). The study demonstrates that the use of ensemble model presents more precision in predicting water quality compared to other related algorithms. The use of the model presented in this study could go a long way to enhance the regulation of water quality and safety and address the gaps in conventional prediction approach.

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