Environmental drivers of antibiotic resistance: Synergistic effects of climate change, co-pollutants, and microplastics
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is an urgent global health concern, increasingly driven by environmental factors such as climate change, chemical co-pollutants, and microplastics (MPs). MPs, synthetic particles smaller than 5 mm, facilitate the spread of antibiotic resistance genes (ARGs) by providing surfaces for biofilm development and concentrating pollutants like antibiotics and heavy metals. The interplay among these environmental stressors intensifies under the influence of climate change, which exacerbates ARG proliferation through elevated temperatures, extreme weather events, and enhanced horizontal gene transfer (HGT). The seasonal and pollutant-induced mechanisms of ARG proliferation underscore the intricate interaction of environmental factors, particularly in hotspots such as wastewater treatment plants. Key drivers of ARG enrichment includes antibiotics, heavy metals, organic pollutants (e.g., pesticides, non-antibiotic pharmaceuticals, etc.), and MPs. They contribute to resistance proliferation through synergistic mechanisms such as co-resistance, cross-resistance, and enhanced HGT. Aging MPs, enriched by biofilm formation, amplify their pollutant adsorption capacities and modulate ARG dynamics in polluted environments. This review examines the complex synergies among environmental drivers of antibiotic resistance, highlighting their collective and individual contributions to ARG proliferation. It integrates knowledge of ARG dynamics in ecosystems and assesses associated public health risks, such as pathogen dissemination, biofilm-mediated resistance transfer, and ecological disturbances. Addressing these challenges requires integrating advanced wastewater treatment technologies with innovative therapeutics, such as next-generation antibiotics and bacteriophage therapy while targeting mobile genetic elements. Prioritizing cost-effective, scalable, and site-specific solutions is essential to mitigate the global AMR crisis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".