The Complex Interplay Between Antibiotic Resistance and Pharmaceutical and Personal Care Products in the Environment
Bibliographic record
Abstract
Antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs) are important environmental contaminants. Nonetheless, what drives the evolution, spread, and transmission of antibiotic resistance dissemination is still poorly understood. The abundance of ARB and ARGs is often elevated in human-impacted areas, especially in environments receiving fecal wastes, or in the presence of complex mixtures of chemical contaminants, such as pharmaceuticals and personal care products. Self-replication, mutation, horizontal gene transfer, and adaptation to different environmental conditions contribute to the persistence and proliferation of ARB in habitats under strong anthropogenic influence. Our review discusses the interplay between chemical contaminants and ARB and their respective genes, specifically in reference to co-occurrence, potential biostimulation, and selective pressure effects, and gives an overview of mitigation by existing man-made and natural barriers. Evidence and strategies to improve the assessment of human health risks due to environmental antibiotic resistance are also discussed. Environ Toxicol Chem 2024;43:637-652. © 2022 SETAC.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".