Description of Antimicrobial-Resistant Escherichia coli and Their Dissemination Mechanisms on Dairy Farms
Bibliographic record
Abstract
Despite its importance in veterinary medicine, there is little information about antimicrobial resistance (AMR) and its transmission in dairy cattle. The aim of this work is to compare AMR phenotypes and genotypes in resistant Escherichia coli and to determine how the resistance genes spread among the E. coli population on dairy farms in Québec, Canada. From an existing culture collection of E. coli isolated from dairy manure, a convenient selection of the most resistant isolates (a high level of multidrug resistance or resistance to broad-spectrum β-lactams or fluoroquinolones) was analyzed (n = 118). An AMR phenotype profile was obtained for each isolate. Whole genome sequencing was used to determine the presence of resistance genes, point mutations, and mobile genetic elements. In addition, a subset of isolates from 86 farms was taken to investigate the phylogenetic relationship and geographic distribution of the isolates. The average agreement between AMR phenotypes and genotypes was 95%. A third-generation cephalosporin resistance gene (blaCTX-M-15), a resistance gene conferring reduced susceptibility to fluoroquinolones (qnrS1), and an insertion sequence (ISKpn19) were detected in the vicinity of each other on the genome. These genes were harbored in one triplet of clonal isolates from three farms located >100 km apart. Our study reveals the dissemination of resistant E. coli clones between dairy farms. Furthermore, these clones are resistant to broad-spectrum β-lactam and fluoroquinolone antimicrobials.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".