Environmental Consequences of Anthropogenic Pollution: Non-antibiotic-Drug-Driven Antibiotic Resistance Selection in a Model Aquatic Ecosystem
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Non-antibiotic drugs (NADs) used in human therapy may induce antibiotic resistance selection and dissemination in vitro. However, the potential risks of antibiotic resistance emergence associated with environmental NAD pollution have not been addressed. Here, we conducted a multidisciplinary study on river water microcosms using growth kinetics, qPCR, metagenomics, 16S rRNA sequencing, and liquid chromatography–tandem mass spectrometry (LC-MS/MS) to determine whether NADs alter river bacterial ecology and select for antibiotic resistance genes (ARGs). Four NADs with different mechanisms of action were included at a high (mg/L) and low (μg/L) dose to establish dose–response relationships: chlorpromazine (antipsychotic), diclofenac (anti-inflammatory), diphenhydramine (antihistamine), and fluoxetine (antidepressant). Although the community response to NAD pollution was compound-specific and dose-dependent, all NADs and doses were stable in the environment, altered the composition and activity of bacterial communities, and selected for several ARGs, mostly β-lactamases and aminoglycoside resistance genes, some of which were associated with horizontal gene transfer genes. Pseudomonas (including some ARG-harboring subpopulations) was identified as a key player in the response to NAD pollution. Here, we demonstrate NAD-driven antibiotic resistance selection in complex aquatic communities, raising concerns about the collateral effects on human and environmental health due to the extensive anthropocentric use of NADs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".