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Updated: May 24, 2026
06:54
Enhanced Extraction of Low-Molecular Weight DNA from Wastewater for Comprehensive Assessment of Antimicrobial Resistance
Published on: July 19, 2024
Environmental Antibiotic Contamination and AMR: Integrating Pathways, Impacts, and AI-Driven Mitigation
Rachana Singh1, Pallavi Gupta1, Roshni Singh1
1Amity Institute of Biotechnology, Amity University Uttar Pradesh Lucknow Campus, Uttar Pradesh, India.
Environmental Toxicology and Chemistry
|May 22, 2026
Summary
Environmental antibiotic contamination drives antimicrobial resistance (AMR). New methods like AI-enhanced genomics offer better surveillance of antibiotic resistance genes than traditional techniques.
Area of Science:
- Environmental Science
- Microbiology
- Public Health
Background:
- Antibiotic residues in the environment are a major driver of the global antimicrobial resistance (AMR) crisis.
- Sources include wastewater, agriculture, and pharmaceutical waste, creating hotspots for antibiotic resistance genes (ARGs).
- Emergence of multidrug-resistant pathogens is facilitated by horizontal gene transfer in these environmental reservoirs.
Purpose of the Study:
- To examine pathways of environmental antibiotic contamination and their contribution to the AMR crisis.
- To assess the ecological and health impacts of AMR.
- To evaluate limitations of conventional detection methods and identify research gaps.
Main Methods:
- Review of existing literature on environmental antibiotic contamination and AMR.
- Analysis of conventional detection methods (culture-based, chromatography, molecular diagnostics).
- Exploration of advanced techniques: whole-genome sequencing, metagenomics, biosensors, and artificial intelligence (AI).
Main Results:
- Conventional methods have limitations in throughput, sensitivity, and real-time monitoring.
- Advanced methods like WGS, metagenomics, and biosensors offer comprehensive resistome surveillance.
- AI enhances data interpretation and pattern recognition for improved AMR surveillance.
Conclusions:
- Environmental antibiotic contamination significantly fuels the AMR crisis.
- Advanced detection methods, augmented by AI, are crucial for effective AMR surveillance.
- Further research is needed to address limitations in current surveillance techniques and understand AMR pathways.
Keywords:
Antimicrobial Resistance (AMR) detectionAntimicrobial Resistance (AMR) surveillanceantimicrobial resistanceartificial intelligenceenvironmental contaminationMore Related Videos
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