Antimicrobial resistance (AMR) is a multi-sectoral, systemic, and global issue worldwide. Antimicrobial use (AMU) is a key factor in the selection of resistant bacteria within different ecological niches, from agriculture to food-producing animals to humans. There is a question regarding the extent to which the use of antibiotics in livestock production and the primary food production sector influences the selection and transmission of resistant bacteria and/or resistant genes throughout the food chain and thus contributes to the complexity in the development of AMR in humans. Although the trends in the prevalence of foodborne pathogens have changed over time, the burden of ecological niches with resistance genes, primarily in commensal microorganisms, is of concern. The implementation of the harmonized surveillance of AMU and AMR would provide comprehensive insights into the actual status of resistance and further interventions leading to its reduction. Tracking AMR in different ecological niches by applying advanced genome-based techniques and developing shared AMR data repositories would strengthen the One Health concept.

Infectious diseases remain a major global health concern, with a growing burden of antimicrobial resistance and consequent higher mortality in the human population. Accurate bacterial identification is fundamental across clinical, veterinary, agricultural, and research settings, supporting effective diagnosis, antimicrobial stewardship, infection control, food safety, and environmental monitoring; however, conventional approaches are limited by time constraints, reduced sensitivity, and challenges in detecting fastidious or uncultivable organisms. This review provides a comprehensive overview of classical and advanced methods, including microscopy, culture, biochemical testing, immunological and serological assays, proteomic and spectroscopy-based techniques, and molecular approaches, such as polymerase chain reaction (PCR), digital PCR, DNA hybridization, 16S rRNA gene sequencing, whole-genome sequencing, and metagenomics. The integration of artificial intelligence has further enhanced analytical performance. Nevertheless, harmonization of bioinformatics frameworks remains essential, as variability in algorithm-defined cut-off values limits standardized implementation of whole-genome sequencing in routine laboratories. Emerging technologies, including CRISPR-based diagnostics and phage- and nanomaterial-based detection systems, offer promising alternatives. Overall, the integration of these approaches is expected to improve the accuracy, speed, and applicability of bacterial identification across diverse settings; however, these advances should be implemented cautiously, with standardization remaining a key priority alongside technological modernization.

The aim of this study was to determine the effects of loading density and gender on blood welfare indicators, carcass bruises and horsemeat quality. Data were collected from twelve transports of 89 slaughter horses originating from the same collection centre. The transportation of slaughter horses at high loading densities (>200 kg/m²), especially stallions, resulted in increased levels of lactate (p = 0.021), glucose (p < 0.0001), ceruloplasmin (p < 0.0001) and AOPP (p < 0.0001), but lower GSH levels (p < 0.0001). Compared to stallions and mares, geldings subjected to high loading density (>200 kg/m²) during transport had lower levels of the aforementioned blood metabolites. In addition, stallions exposed to a high load density (>200 kg/m²) had the highest frequency of severe (p = 0.0002), large (p < 0.0001) and circular (p = 0.0001) carcass bruises, which were predominantly located on the abdominal (p = 0.0056) and thoracic (p = 0.0004) wall. In contrast, a higher percentage (p < 0.0001) of undamaged carcasses was found in slaughter horses exposed to a low loading density (≤200 kg/m²) during transport. Stallions exposed to high loading densities (>200 kg/m²) during transport had the highest initial pH (p < 0.0001) and ultimate pH (p = 0.005) in terms of m. longissimus lumborum and redness (p = 0.017), but the lowest drip loss (p = 0.050) and lightness (p < 0.0001), which, consequently, led to the highest (p = 0.0045) proportion of DFD-like defects in the meat quality. In conclusion, the results of this study showed that high loading densities (>200 kg/m²), regardless of gender, negatively affect horse welfare during transport. In addition, stallions were more sensitive to poor pre-slaughter conditions and produced the lowest meat quality, while geldings were the most resistant. To determine the optimal transport density, further research is needed to determine the effects of different loading densities on the behaviour, physiology, carcass and meat quality of slaughter horses.

Background/Objectives: Environmental dissemination of antimicrobial resistance (AMR) is increasingly driven by wastewater-impacted aquatic systems, yet the key factors controlling multidrug-resistant (MDR) bacterial distribution remain unclear. This study evaluated environmental factors associated with MDR bacteria in the urban Danube River (Novi Sad, Serbia) using a machine learning framework. Methods: Surface-water and wastewater samples were collected during summer and autumn 2024. Bacterial isolates were obtained through membrane filtration onto chromogenic media and identified using MALDI-TOF MS. Physicochemical parameters (including COD, BOD₅, turbidity, pH, and temperature) were used as predictors in seven machine learning models (ANN, RF, SVM, XGB, MARS, TREE, NB). Model performance was assessed using AUC, accuracy, and error metrics. Results: Wastewater samples showed higher bacterial abundance and taxonomic richness than river surface-water samples, with frequent recovery of Klebsiella pneumoniae, Escherichia coli, Aeromonas veronii, and Pseudomonas spp. Tree-based models (RF, XGB) performed best. Organic pollution indicators, turbidity, pH, and water temperature were the most prominent factors. Conclusions: Wastewater-related pollution gradients, reflected by organic load parameters, turbidity, pH, and water temperature, were associated with the occurrence of selected bacterial taxa recovered on selective media. These associations were more pronounced in wastewater samples, while river surface-water samples showed lower abundance and taxonomic richness but still contained selected environmental and opportunistic taxa. Because antimicrobial susceptibility testing and molecular confirmation of resistance determinants were not performed, the findings should be interpreted as culture-based and exploratory. Machine learning approaches may support environmental screening and hypothesis generation for AMR-oriented surveillance, but future studies should include standardized phenotypic and molecular confirmation.

Background/Objectives: Antibiotic residues in aquatic environments reflect interacting physicochemical, climatic, and microbiological processes. This study characterized selected antibiotics in wastewater and surface water associated with the Danube River near Novi Sad, Serbia, and evaluated explainable stacked machine-learning models for concentration prediction. Methods: Thirty-six samples collected during summer and autumn 2024 were analyzed using SPE-HPLC-MS/MS. Artificial neural network, random forest, support vector machine, XGBoost, stacked linear, and stacked random forest (STACK-RF) models were developed using environmental/physicochemical variables or presumptive resistant bacterial taxa. Models were evaluated by fivefold cross-validation, complementary error metrics, Holm-adjusted Diebold–Mariano tests, XGBoost Gain, and SHAP analysis. Results: All target antibiotics were detected at least once. Azithromycin was most prevalent (75.0%), followed by sulfamethoxazole (58.3%), trimethoprim, and ciprofloxacin (52.8% each), while wastewater generally exhibited broader antibiotic profiles and higher concentrations than surface water. Standalone algorithms showed weak-to-moderate performance, whereas STACK-RF achieved the highest numerical accuracy for all environmental/physicochemical models (R² = 0.745–0.913) and the available microbial-taxa models (R² = 0.819–0.945), with consistently lower prediction errors. However, most pairwise differences were not significant after Holm correction. Influential environmental predictors were compound-specific and included COD, BOD₅, pH, turbidity, electrical conductivity, water temperature, and relative humidity. Leading bacterial predictors included Klebsiella pneumoniae, Escherichia coli, Citrobacter freundii, and Aeromonas veronii. Conclusions: The results provide a proof-of-concept for machine-learning-assisted antibiotic prediction. Explainable STACK-RF modeling captured nonlinear, antibiotic-specific associations among residues, water-quality conditions, and microbial indicators. It may complement targeted chemical monitoring and support hypothesis generation, although larger, externally validated datasets are required before broader application.