Occurrence and characterization of plasmid-encoded <i>qnr</i> genes in quinolone-resistant bacteria across diverse aquatic environments in southern Ontario
Bibliographic record
Abstract
Antimicrobial resistance is an ever-increasing threat. The widespread usage of ciprofloxacin has led to the manifestation of resistance due to chromosomal mutations or the acquisition of plasmid-mediated quinolone resistance (PMQR) traits. Some particular PMQR traits, qnr genes, have been identified globally in clinical and environmental isolates. This study aimed to determine the prevalence of ciprofloxacin-resistant bacteria in aquatic environments in southern Ontario and investigate the extent of dissemination of ciprofloxacin resistance traits among the bacterial communities. We surveyed the prevalence of plasmid encoding qnr genes using a multiplex PCR assay of associated PMQR genes, qnrA, qnrB, and qnrS, on 202 isolates. Despite the absence of significant impacts on minimum inhibitory concentration levels, the presence of qnr genes correlates with heightened resistance to quinolones and nalidixic acid in some isolates. Taxonomic analysis highlights distinct differences in the composition and diversity of ciprofloxacin-sensitive (CipS) and ciprofloxacin-resistant (CipR) populations, with Proteobacteria dominating both groups. Importantly, CipR populations exhibit lower genetic diversity but higher prevalence of multiple antibiotic resistances, suggesting co-selection mechanisms. Co-occurrence analysis highlights significant associations between ciprofloxacin resistance and other antibiotic resistances, implicating complex genetic linkages. The results of our study signified the critical role of environmental monitoring in public health.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".