Fish as sentinels of antimicrobial resistant bacteria, epidemic carbapenemase genes, and antibiotics in surface water
Bibliographic record
Abstract
Surface waters, especially those receiving wastewater flows, can disseminate antimicrobial resistant bacteria (ARB), antimicrobial resistance genes (ARG), and antibiotics. In the Scioto River of central Ohio, United States, we evaluated fishes as potential sentinels of ARB and antimicrobial contamination and investigated the influence of antimicrobial exposure on the fish intestinal resistome. Seventy-seven fish were collected from river reaches receiving inputs from two wastewater treatment plants that serve the greater Columbus Metropolitan Area. Fish were screened for the presence of cephalosporin-resistant (CeRO) and carbapenem-resistant (CRO) organisms, epidemic carbapenemase genes, and antibiotic drugs and metabolites using culture methods, droplet digital PCR, and ultra-high performance liquid chromatography tandem mass spectroscopy (UHPLC-MS/MS). Nearly 21% of fish harbored a CeRO in their resistome, with 19.4% exhibiting bacteria expressing an AmpC genotype encoded by blaCMY, and 7.7% with bacteria expressing an extended-spectrum β-lactamase phenotype encoded by blaCTX-M. blaKPC and blaNDM were present in 87.7% (57/65) and 80.4% (37/46) of the intestinal samples at an average abundance of 104 copies. Three antibiotics-lincomycin (19.5%), azithromycin (31.2%) and sulfamethoxazole (3.9%)-were found in hepatic samples at average concentrations between 25-31 ng/g. Fish harboring blaCTX-M and those exposed to azithromycin were at greater odds of being downstream of a wastewater treatment plant. Fish that bioconcentrated antibiotics in their liver were not at greater odds of harboring CeRO, CRO, or epidemic carbapenemase gene copies in their resistome. Our findings confirm that fishes can be effective bioindicators of surface waters contaminated with ARB, ARG, and antibiotics. Moreover, our findings highlight the varying importance of different mechanisms that facilitate establishment of ARB in aquatic ecosystems.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".