Chloramphenicol and kanamycin resistance among porcine Escherichia coli in Ontario
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to compare the distribution of chloramphenicol and kanamycin resistance genes across three populations of porcine Escherichia coli. METHODS: PCR was used to assess the distribution of the major chloramphenicol and kanamycin resistance genes catA1, cmlA and floR, and aphA1, aphA2 and aadB in enterotoxigenic E. coli (ETEC), non-ETEC isolates from cases of diarrhoea and commensal E. coli from healthy pigs. Associations between these genes and resistance genes for other antimicrobials or virulence genes were assessed. RESULTS: The chloramphenicol and kanamycin resistance genes were distributed differently among the three E. coli populations. While aphA1, aphA2 and aadB were evenly distributed among resistant ETEC, non-ETEC and commensals, the catA1 gene was significantly more frequent in ETEC than in non-ETEC and commensals. Transformation experiments confirmed statistical associations by demonstrating that elt, estB, astA, aadA and sul1 were located with catA1 on a large ETEC plasmid. Plasmids carrying cmlA also carried sul3 and aadA. Other plasmids carrying floR and aadB also carried tet(A), sul2, strA/strB, bla(CMY-2) and occasionally aac(3)IV. CONCLUSIONS: The clustering of genes observed is a likely cause for chloramphenicol resistance persistence. Similar to tetracycline, chloramphenicol resistance genes are physically linked to virulence genes. This is not the case for kanamycin resistance determinants, which were linked to other resistance genes only.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".