Antibiotics

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Dear Colleagues,

Antimicrobial resistance (AMR) represents a critical and escalating global threat, undermining the effective treatment of infectious diseases and endangering human health, animal welfare, food safety, and environmental sustainability. The widespread and often inappropriate use of antimicrobials in human medicine, veterinary practice, agriculture, and aquaculture has accelerated the emergence and spread of antimicrobial resistance. Growing evidence indicates that environmental compartments serve as important reservoirs and transmission pathways for antimicrobial-resistant bacteria and resistance genes.

The One Health approach provides a holistic framework for addressing AMR by explicitly recognizing the interconnections between human, animal, and environmental health. Rather than treating AMR as an isolated clinical or veterinary concern, this approach emphasizes integrated surveillance, interdisciplinary research, and coordinated mitigation strategies across sectors. Advances in molecular microbiology, omics-based technologies, environmental monitoring, and data-driven modeling have significantly improved our understanding of AMR dynamics; however, substantial gaps remain in elucidating resistance dissemination at human–animal–environment interfaces and in translating knowledge into effective control measures.

The aim of this Special Issue, Combating Antimicrobial Resistance Through the One Health Approach, is to bring together high-quality interdisciplinary research that advances understanding of the emergence, spread, surveillance, and mitigation of AMR within a unified One Health perspective. The Guest Editors of this Special Issue are members of The TRACE project, supported by the Science Fund of the Republic of Serbia (Grant No. 7042), which focuses on strengthening One Health-based research on antimicrobial resistance across interconnected systems. Original research and review articles addressing environmental reservoirs of AMR, antibiotic occurrence and fate, integrated surveillance frameworks, and innovative analytical, biotechnological, and policy-oriented solutions are particularly encouraged.

Dr. Ana Tomić
Dr. Olja Šovljanski
Dr. Ina Gajić
Dr. Milos Jovicevic
Guest Editors

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23 pages, 1874 KB

Open AccessArticle

Culture-Based Assessment of Presumptive Resistant Bacterial Taxa in the Urban Danube River near Novi Sad: Environmental Associations Revealed by Machine Learning

by Miloš Jovićević, Dušan Kekić, Ana Tomić, Olja Šovljanski, Lato Pezo, Nemanja Mirković, Radmila Novaković, Ivan Vicic, Nikola Bajcetic, Milica Mirkovic, Nedjeljko Karabasil, Nataša Opavski and Ina Gajić

Antibiotics 2026, 15(6), 553; https://doi.org/10.3390/antibiotics15060553 - 30 May 2026

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Abstract

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. Full article

(This article belongs to the Special Issue Combating Antimicrobial Resistance Through the One Health Approach)

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