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Antibiotic resistance is a serious threat to humanity and its environment. Aberrant usage of antibiotics in the human, animal, and environmental sectors, as well as the dissemination of resistant bacteria and resistance genes among these sectors and globally, are all contributing factors. In humans, antibiotics are generally used to treat infections and prevent illnesses. Antibiotic usage in food-producing animals has lately emerged as a major public health concern. These medicines are currently being utilized to prevent and treat infectious diseases and also for its growth-promoting qualities. These methods have resulted in the induction and spread of antibiotic resistant infections from animals to humans. Antibiotics can be introduced into the environment from a variety of sources, including human wastes, veterinary wastes, and livestock husbandry waste. The soil has been recognized as a reservoir of ABR genes, not only because of the presence of a wide and varied range of bacteria capable of producing natural antibiotics but also for the usage of natural manure on crop fields, which may contain ABR genes or antibiotics. Fears about the human health hazards of ABR related to environmental antibiotic residues include the possible threat of modifying the human microbiota and promoting the rise and selection of resistant bacteria, and the possible danger of generating a selection pressure on the environmental microflora resulting in environmental antibiotic resistance. Because of the connectivity of these sectors, antibiotic use, antibiotic residue persistence, and the existence of antibiotic-resistant bacteria in human-animal-environment habitats are all linked to the One Health triangle. The pillars of support including rigorous ABR surveillance among different sectors individually and in combination, and at national and international level, overcoming laboratory resource challenges, and core plan and action execution should be strictly implemented to combat and contain ABR under one health approach. Implementing One Health could help to avoid the emergence and dissemination of antibiotic resistance while also promoting a healthier One World. This review aims to emphasize antibiotic resistance and its regulatory approaches from the perspective of One Health by highlighting the interconnectedness and multi-sectoral nature of the human, animal, and environmental health or ill-health facets.

Fungicides among agrochemicals are consistently used in high throughput agricultural practices to protect plants from damaging impact of phytopathogens and hence to optimize crop production. However, the negative impact of fungicides on composition and functions of soil microbiota, plants and via food chain, on human health is a matter of grave concern. Considering such agrochemical threats, the present study was undertaken to know that how fungicide-tolerant symbiotic bacterium, Mesorhizobium ciceri affects the Cicer arietinum crop while growing in kitazin (KITZ) stressed soils under greenhouse conditions. Both in vitro and soil systems, KITZ imparted deleterious impacts on C. arietinum as a function of dose. The three-time more of normal rate of KITZ dose detrimentally but maximally reduced the germination efficiency, vigor index, dry matter production, symbiotic features, leaf pigments and seed attributes of C. arietinum . KITZ-induced morphological alterations in root tips, oxidative damage and cell death in root cells of C. arietinum were visible under scanning electron microscope (SEM). M. ciceri tolerated up to 2400 µg mL −1 of KITZ, synthesized considerable amounts of bioactive molecules including indole-3-acetic-acid (IAA), 1-aminocyclopropane 1-carboxylate (ACC) deaminase, siderophores, exopolysaccharides (EPS), hydrogen cyanide, ammonia, and solubilised inorganic phosphate even in fungicide-stressed media. Following application to soil, M. ciceri improved performance of C. arietinum and enhanced dry biomass production, yield, symbiosis and leaf pigments even in a fungicide-polluted environment. At 96 µg KITZ kg −1 soil, M. ciceri maximally and significantly ( p  ≤ 0.05) augmented the length of plants by 41%, total dry matter by 18%, carotenoid content by 9%, LHb content by 21%, root N by 9%, shoot P by 11% and pod yield by 15% over control plants. Additionally, the nodule bacterium M. ciceri efficiently colonized the plant rhizosphere/rhizoplane and considerably decreased the levels of stressor molecules (proline and malondialdehyde) and antioxidant defence enzymes viz. ascorbate peroxidise (APX), guaiacol peroxidise (GPX), catalase (CAT) and peroxidises (POD) of C. arietinum plants when inoculated in soil. The symbiotic strain effectively colonized the plant rhizosphere/rhizoplane. Conclusively, the ability to endure higher fungicide concentrations, capacity to secrete plant growth modulators even under fungicide pressure, and inherent features to lower the level of proline and plant defence enzymes makes this M. ciceri as a superb choice for augmenting the safe production of C. arietinum even under fungicide-contaminated soils.

Chronic kidney disease (CKD) has become a major global health crisis, causing millions of yearly deaths. Predicting the possibility of a person being affected by the disease will allow timely diagnosis and precautionary measures leading to preventive strategies for health. Machine learning techniques have been popularly applied in various disease diagnoses and predictions. Ensemble learning approaches have become useful for predicting many complex diseases. In this paper, we utilise the boosting method, one of the popular ensemble learnings, to achieve a higher prediction accuracy for CKD. Five boosting algorithms are employed: XGBoost, CatBoost, LightGBM, AdaBoost, and gradient boosting. We experimented with the CKD data set from the UCI machine learning repository. Various preprocessing steps are employed to achieve better prediction performance, along with suitable hyperparameter tuning and feature selection. We assessed the degree of importance of each feature in the dataset leading to CKD. The performance of each model was evaluated with accuracy, precision, recall, F1-score, Area under the curve-receiving operator characteristic (AUC-ROC), and runtime. AdaBoost was found to have the overall best performance among the five algorithms, scoring the highest in almost all the performance measures. It attained 100% and 98.47% accuracy for training and testing sets. This model also exhibited better precision, recall, and AUC-ROC curve performance.

Wild plants supply food and shelter to several organisms; they also act as important sources of many nutrients and pharmaceutical agents for mankind. These plants are widely used in traditional medicinal systems and folk medicines. The present study analyzed the nutritional and proximate composition of various compounds in selected wild plants available in the UAE, viz., Chenopodium murale L., Dipterygium glaucum Decne., Heliotropium digynum Asch. ex C.Chr., Heliotropium kotschyi Gürke., Salsola imbricata Forssk., Tribulus pentandrus Forssk., Zygophyllum qatarense Hadidi. The predominant amino acids detected in the plants were glycine, threonine, histidine, cysteine, proline, serine, and tyrosine; the highest quantities were observed in H. digynum and T. pentandrus. The major fatty acids present were long-chain saturated fatty acids; however, lauric acid was only present in S. imbricata. The presence of essential fatty acids such as oleic acid, α-Linoleic acid, and linolenic acid was observed in H. digynum, S. imbricata, and H. kotschyi. These plants also exhibited higher content of nutrients such as carbohydrates, proteins, fats, ash, and fiber. The predominant vitamins in the plants were vitamin B complex and vitamin C. C. murale had higher vitamin A, whereas vitamin B complex was seen in T. pentandrus and D. glaucum. The phosphorus and zinc content were high in T. pentandrus; the nitrogen, calcium, and potassium contents were high in H. digynum, and D. glaucum. Overall, these plants, especially H. digynum and T. pentandrus contain high amounts of nutritionally active compounds and important antioxidants including trace elements and vitamins. The results from the experiment provide an understanding of the nutritional composition of these desert plant species and can be better utilized as important agents for pharmacological drug discovery, food, and sustainable livestock production in the desert ecosystem.

Heart disease is one of the leading causes of death worldwide. Predicting and detecting heart disease early is crucial, as it allows medical professionals to take appropriate and necessary actions at earlier stages. Healthcare professionals can diagnose cardiac conditions more accurately by applying machine learning technology. This study aimed to enhance heart disease prediction using stacking and voting ensemble methods. Fifteen base models were trained on two different heart disease datasets. After evaluating various combinations, six base models were pipelined to develop ensemble models employing a meta-model (stacking) and a majority vote (voting). The performance of the stacking and voting models was compared to that of the individual base models. To ensure the robustness of the performance evaluation, we conducted a statistical analysis using the Friedman aligned ranks test and Holm post-hoc pairwise comparisons. The results indicated that the developed ensemble models, particularly stacking, consistently outperformed the other models, achieving higher accuracy and improved predictive outcomes. This rigorous statistical validation emphasised the reliability of the proposed methods. Furthermore, we incorporated explainable AI (XAI) through SHAP analysis to interpret the model predictions, providing transparency and insight into how individual features influence heart disease prediction. These findings suggest that combining the predictions of multiple models through stacking or voting may enhance the performance of heart disease prediction and serve as a valuable tool in clinical decision-making.

The swift urbanization of cities has given rise to an unparalleled surge in vehicular traffic, leading to substantial congestion, heightened pollution, and a diminished quality of life. This investigation explores the capacity of artificial intelligence (AI) to transform urban mobility by optimizing vehicle routing and alleviating traffic congestion. The objective is to create AI-powered solutions that augment transportation efficiency, diminish travel times, and mitigate environmental repercussions. This paper thoroughly scrutinizes existing AI algorithms, vehicle routing, and traffic management techniques. The study integrates real-time traffic data, road network characteristics, and individual travel patterns to formulate intelligent routing strategies. The proposed AI system adjusts to dynamic traffic conditions through machine learning and optimization algorithms, pinpointing optimal routes and redistributing traffic flows to minimize congestion hotspots. To assess the effectiveness of the AI-driven approach, extensive simulations and case studies are conducted in representative urban areas. Performance metrics, including travel time reduction, fuel consumption, and emissions reduction, are employed to quantify the impact of the proposed system on traffic congestion and environmental sustainability. Furthermore, the study evaluates the scalability, feasibility, and economic viability of implementing AI-based traffic management solutions on a larger scale. The outcomes of this research provide valuable insights into the potential advantages of AI in reshaping urban mobility. By optimizing vehicle routing and diminishing traffic congestion, the proposed AI-driven system has the potential to elevate overall transportation efficiency, reduce energy consumption, and contribute to a healthier urban environment. The findings carry substantial implications for policymakers, urban planners, and transportation authorities seeking innovative solutions to tackle the challenges of contemporary urbanization while promoting sustainable development.

Groundwater quality deterioration due to anthropogenic activities has become a subject of prime concern. The objective of the study was to assess the spatial and temporal variations in groundwater quality and to identify the sources in the western half of the Bengaluru city using multivariate statistical techniques. Water quality index rating was calculated for pre and post monsoon seasons to quantify overall water quality for human consumption. The post-monsoon samples show signs of poor quality in drinking purpose compared to pre-monsoon. Cluster analysis (CA), principal component analysis (PCA) and discriminant analysis (DA) were applied to the groundwater quality data measured on 14 parameters from 67 sites distributed across the city. Hierarchical cluster analysis (CA) grouped the 67 sampling stations into two groups, cluster 1 having high pollution and cluster 2 having lesser pollution. Discriminant analysis (DA) was applied to delineate the most meaningful parameters accounting for temporal and spatial variations in groundwater quality of the study area. Temporal DA identified pH as the most important parameter, which discriminates between water quality in the pre-monsoon and post-monsoon seasons and accounts for 72% seasonal assignation of cases. Spatial DA identified Mg, Cl and NO3 as the three most important parameters discriminating between two clusters and accounting for 89% spatial assignation of cases. Principal component analysis was applied to the dataset obtained from the two clusters, which evolved three factors in each cluster, explaining 85.4 and 84% of the total variance, respectively. Varifactors obtained from principal component analysis showed that groundwater quality variation is mainly explained by dissolution of minerals from rock water interactions in the aquifer, effect of anthropogenic activities and ion exchange processes in water.

Obesity is a major public health concern. Predicting obesity risk from lifestyle data can guide targeted interventions, but current models remain limited. This study first evaluates ensemble learning methods and then combines approaches to improve prediction accuracy and generalizability. Four ensemble techniques—boosting, bagging, stacking, and voting—were tested. Five boosting and five bagging models were constructed alongside voting and stacking models. Hyperparameter tuning optimized performance, and feature importance analysis guided potential feature elemination. In phase two, hybrid stacking and voting models integrated the best-performing boosting and bagging models to enhance predictive capability. Model robustness was ensured through k-fold cross-validation and statistical validation. SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) improved interpretability by analyzing feature contributions. Hybrid stacking and voting models outperformed other ensemble methods, with stacking achieving the best performance (accuracy: 96.88%, precision: 97.01%, and recall: 96.88%). Feature importance analysis identified key predictors, including sex, weight, food habits, and alcohol consumption. The results demonstrated that hybrid ensembles significantly improved obesity risk prediction while preserving interpretability. Integrating multiple ensemble and explainability techniques provides a reliable framework for obesity prediction, supporting clinical decisions and personalized healthcare strategies to mitigate obesity risk.

Breast cancer is a serious health concern and one of the leading causes of cancer-related deaths among women worldwide. The early diagnosis of breast cancer is crucial for successful treatment and improved patient outcomes. Advanced computational techniques, particularly deep transfer learning have gained considerable attention for their effectiveness in medical image computing. Initially, six transfer learning models with different specifications are trained, validated and tested on the breast ultrasound image dataset. Additionally, a novel hybrid model, DRM-Net, is developed by stacking the top three TL models based on concatenation and flattening of deep dense layers. Various techniques, including image preprocessing, image masking, data augmentation, and hyperparameter tuning, are incorporated to enhance the overall performance of the considered models. The models are thoroughly evaluated using various standard metrics and statistical methods. Furthermore, to ensure transparent decision-making, the proposed model is interpreted using XAI-based class activation mapping (CAM) method with different versions. The proposed DRM-Net model demonstrated superior predictive performance compared to the other six transfer learning models. Among all the models, DRM-Net achieved the highest overall performance, with an accuracy of 96.71%, precision of 96%, recall of 97%, F1-score of 97%, and an AUC value of 99%, respectively. It also outperformed state-of-the-art similar studies in the literature. The experimental results demonstrate the potential utility of the hybrid DRM-Net model in improving the accuracy of breast cancer diagnosis, which could facilitate informed clinical decision-making. The proposed model can also be applied to other diseases where ultrasound imaging is available