Pet and Stray Dogs as Reservoirs of Antimicrobial-Resistant Escherichia coli
Bibliographic record
Abstract
The close contact between dogs and humans creates the best bridge for interspecies transmission of antimicrobial-resistant bacteria. The surveillance of its resistance including the detection of extended-spectrum beta-lactamases (ESBLs) in Escherichia coli as indicator bacteria is an important tool to control the use of antimicrobials. The aim of this research was to evaluate the E. coli resistance in strains by phenotypic methods, isolated from pet and stray dogs of La Plata city, Argentina. Faecal samples were collected using rectal swabs from 50 dogs with owners (home dogs = HD) and 50 homeless dogs (stray dogs = SD). They were cultured in 3 MacConkey agar plates, with and without antibiotics (ciprofloxacin and cefotaxime). 197 strains were isolated, of which only 95 strains were biochemically identified as E. coli, 46 strains were from HD, and 49 were from SD. Antimicrobial susceptibility was evaluated by the Kirby–Bauer disk diffusion method. The most prevalent resistance was for tetracycline, streptomycin, and ampicillin. In both groups, the level of resistance to 3rd generation cephalosporins was high, and there were multiresistant strains. There was a higher level of antimicrobial resistance in strains from SD compared to HD. There were 8% of strains suspected of being ESBLs among samples of HD and 36% of SD. One (2%) of the strains isolated from HD and 11 (22%) from SD were phenotypically confirmed as ESBL. Pets and stray dogs are a potential source of E. coli antibiotic resistance in Argentina; therefore, its surveillance must be guaranteed.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".