The Role of Artificial Intelligence and Machine Learning in Predicting and Combating Antimicrobial Resistance

Computational and Structural Biotechnology Journal, Journal Year: 2025, Volume and Issue: 27, P. 423 - 439

Published: Jan. 1, 2025

Antimicrobial resistance (AMR) is a major threat to global public health. The current review synthesizes address the possible role of Artificial Intelligence and Machine Learning (AI/ML) in mitigating AMR. Supervised learning, unsupervised deep reinforcement natural language processing are some main tools used this domain. AI/ML models can use various data sources, such as clinical information, genomic sequences, microbiome insights, epidemiological for predicting AMR outbreaks. Although relatively new fields, numerous case studies offer substantial evidence their successful application outbreaks with greater accuracy. These provide insights into discovery novel antimicrobials, repurposing existing drugs, combination therapy through analysis molecular structures. In addition, AI-based decision support systems real-time guide healthcare professionals improve prescribing antibiotics. also outlines how AI surveillance, analyze trends, enable early outbreak identification. Challenges, ethical considerations, privacy, model biases exist, however, continuous development methodologies enables play significant combating

Language: Английский

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Healthcare as a driver, reservoir and amplifier of antimicrobial resistance: opportunities for interventions

Nature Reviews Microbiology, Journal Year: 2024, Volume and Issue: 22(10), P. 636 - 649

Published: July 24, 2024

Language: Английский

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The challenge of antimicrobial resistance (AMR): current status and future prospects

Naunyn-Schmiedeberg s Archives of Pharmacology, Journal Year: 2024, Volume and Issue: 397(12), P. 9603 - 9615

Published: July 25, 2024

Language: Английский

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Engineering Useful Microbial Species for Pharmaceutical Applications

Microorganisms, Journal Year: 2025, Volume and Issue: 13(3), P. 599 - 599

Published: March 5, 2025

Microbial engineering has made a significant breakthrough in pharmaceutical biotechnology, greatly expanding the production of biologically active compounds, therapeutic proteins, and novel drug candidates. Recent advancements genetic engineering, synthetic biology, adaptive evolution have contributed to optimization microbial strains for applications, playing crucial role enhancing their productivity stability. The CRISPR-Cas system is widely utilized as precise genome modification tool, enabling enhancement metabolite biosynthesis activation biological pathways. Additionally, biology approaches allow targeted design microorganisms with improved metabolic efficiency potential, thereby accelerating development new products. integration artificial intelligence (AI) machine learning (ML) plays vital further advancing by predicting network interactions, optimizing bioprocesses, discovery process. However, challenges such efficient pathways, ensuring sustainable industrial-scale production, meeting international regulatory requirements remain critical barriers field. Furthermore, mitigate potential risks, it essential develop stringent biocontainment strategies implement appropriate oversight. This review comprehensively examines recent innovations analyzing key technological advancements, challenges, future perspectives.

Language: Английский

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Biocides as Drivers of Antibiotic Resistance: A Critical Review of Environmental Implications and Public Health Risks

Environmental Science and Ecotechnology, Journal Year: 2025, Volume and Issue: unknown, P. 100557 - 100557

Published: March 1, 2025

The widespread and indiscriminate use of biocides poses significant threats to global health, socioeconomic development, environmental sustainability by accelerating antibiotic resistance. Bacterial resistance development is highly complex influenced significantly factors. Increased biocide usage in households, agriculture, livestock farming, industrial settings, hospitals produces persistent chemical residues that pollute soil aquatic environments. Such contaminants contribute the selection proliferation resistant bacteria antimicrobial genes (ARGs), facilitating their dissemination among humans, animals, ecosystems. In this review, we conduct a critical assessment four issues pertaining topic. Specifically, (i) role exerting selective pressure within resistome, thereby promoting microbial populations contributing spread (ARGs); (ii) triggering transient phenotypic adaptations bacteria, including efflux pump overexpression, membrane alterations, reduced porin expression, which often result cross-resistance multiple antibiotics; (iii) capacity disrupt make genetic content accessible, releasing DNA into environment remains intact under certain conditions, horizontal gene transfer determinants; (iv) bacterial cells, enhancing interactions between biofilms environment, strengthening biofilm cohesion, inducing mechanisms, creating reservoirs for microorganisms ARG dissemination. Collectively, review highlights public health implications use, emphasizing an urgent need strategic interventions mitigate proliferation.

Language: Английский

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Unlocking Mysteries: The Cutting-Edge Fusion of Nanotechnology and Forensic Science

BioNanoScience, Journal Year: 2024, Volume and Issue: 14(3), P. 3572 - 3598

Published: July 24, 2024

Language: Английский

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Microbiological methodologies: Comparative evaluation of microbial community and enhanced antibiotic susceptibility testing

Electronic Journal of Biotechnology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

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Frontiers in superbug management: innovating approaches to combat antimicrobial resistance

Archives of Microbiology, Journal Year: 2025, Volume and Issue: 207(3)

Published: Feb. 14, 2025

Language: Английский

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Detecting Respiratory Pathogens for Diagnosing Lower Respiratory Tract Infections at the Point of Care: Challenges and Opportunities

Biosensors, Journal Year: 2025, Volume and Issue: 15(3), P. 129 - 129

Published: Feb. 20, 2025

Lower respiratory tract infections (LRTIs) are a leading cause of mortality worldwide, claiming millions lives each year and imposing significant healthcare costs. Accurate detection pathogens is essential for the effective management LRTIs. However, this process often relies on sputum analysis, which requires extensive pretreatment steps. The viscous nature complex composition present additional challenges, especially in settings where rapid diagnosis at point care essential. In review, we describe main types LRTI, highlighting different patient pathway points care. We review current methods liquefying samples provide an overview commercially available diagnostic tools used hospitals LRTI detection. Furthermore, critically recent advancements literature focused detecting mechanisms antimicrobial resistance sputum, including nucleic acid amplification tests, immunoassays other innovative approaches. Throughout paper, highlight challenges opportunities associated with developing new biosensor technologies tailored lower specimens. By shedding light these pressing issues, aim to inspire scientific community create address urgent burden lung diseases.

Language: Английский

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Decoding Host-Pathogen Interactions in Staphylococcus aureus: Insights into Allelic Variation and Antimicrobial Resistance Prediction Using Artificial Intelligence and Machine Learning based approaches

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 23, 2025

Abstract This novel study leveraged advanced machine learning techniques to elucidate the molecular mechanisms of antimicrobial resistance (AMR) in 300 Staphylococcus aureus isolates across six critical antibiotics. Employing a diverse array deep and ensemble models, we conducted an in-depth analysis genetic markers allelic variations characterize determinants. Our investigation revealed that XGBoost model demonstrated most exceptional performance, achieving remarkable 95% test accuracy, 100% training unprecedented ROC AUC 0.9855. Comparative multiple approaches, including Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Multi-Layer Perceptron (MLP), Decision Tree, Stochastic Gradient Descent (SGD) provided detailed insights into prediction. The SHAP (SHapley Additive exPlanations) unveiled markers, with “cat_allele_Cluster_1015_Allele_8” emerging as influential feature driving predictions. Notably, models exhibited varying performance different antibiotics, consistently high accuracy F1-scores for ciprofloxacin, clindamycin, gentamicin, sulfamethoxazole/trimethoprim. findings not only demonstrate potential predicting but also provide crucial underlying S. drug resistance. By identifying key determinants their relative importance, this offers sophisticated approach understanding patterns, potentially guiding future diagnostic strategies, targeted therapies, stewardship practices clinical settings.

Language: Английский

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