Prevalence and mechanisms of extended-spectrum cephalosporin resistance in clinical and fecal Enterobacteriaceae isolates from dogs in Ontario, Canada
Bibliographic record
Abstract
There is little information on the genetic basis of resistance to the critically important extended-spectrum cephalosporins (ESCs) in Enterobacteriaceae from dogs in Canada. This study assessed the frequency of ESC resistance in Enterobacteriaceae isolated from dogs in Ontario and the distribution of major ESC resistance genes in these bacteria. A total of 542 Enterobacteriaceae were isolated from 506 clinical samples from two diagnostic laboratories in Ontario. Eighty-eight ESC-resistant Enterobacteriaceae and 217 Escherichia coli were isolated from 234 fecal samples from dogs collected at leash-free dog parks. These fecal isolates were tested for ESC resistance along with the clinical isolates. Isolates with reduced ESC susceptibility were screened for bla CMY , bla CTX-M , and bla SHV , and all CTX-M-positive isolates underwent whole-genome sequencing. The prevalence of ESC resistance in clinical Enterobacteriaceae was 10.4%. The average frequency of fecal carriage of ESC-resistant Enterobacteriaceae in healthy dogs was 26.5%. The majority of ESC-resistant isolates were E. coli and the other major Enterobacteriaceae carrying ESC resistance genes were Klebsiella pneumoniae and Proteus mirabilis . The results show that the same ESC resistance genes can be found in clinical and fecal Enterobacteriaceae in dogs. The identified E. coli sequence types (including ST131 and ST648) and CTX-M variants (including CTX-M-14, −15, and −27) support the hypothesis of transfer of resistant bacteria between humans and dogs. CTX-M-1 was frequently found in canine fecal Enterobacteriaceae, while it is still rare in human Enterobacteriaceae in Canada, thus suggesting transfer of resistant bacteria to dogs from food animals or other sources.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".