Environmental drivers of antibiotic resistance: Synergistic effects of climate change, co-pollutants, and microplastics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it