Pet treats, Salmonella, and antimicrobial resistance; a One Health problem
Bibliographic record
Abstract
Zoonotic pathogens, including Salmonella and antimicrobial resistant bacteria, may contaminate the food or treats consumed by our pets. These may directly impact the health of the pets or may be transferred to humans who are in close contact. To better understand the potential risk, we purchased 505 pet treats from pet and farm supply stores, grocery stores, and online retailers in the U.S. over a period of 16 months to identify and characterize Salmonella and Enterobacterales resistant to Highest Priority Critically Important Antimicrobials. We used selective media to detect Salmonella and bacteria resistant to colistin, carbapenems, fluoroquinolones, and 3 rd and 4 th generation cephalosporins. Four pig ear treats from Brazil were positive for Salmonella, with serotypes , Muenchen , Derby , Agona and Regent. We found that S. Muenchen and S. Derby were closely related to clinical and environmental isolates from the U.S., Canada, Venezuela, and Colombia. We detected three colistin resistant isolates, Klebsiella pneumoniae , Escherichia coli , and Enterobacter hormaechei , all from pig ear treats from Brazil, harboring the mcr 1.18 resistance gene on identical IncX4 plasmids. In addition, we recovered one carbapenem resistant E. coli harboring both bla KPC-2 and bla NDM-5 from a “bully stick”. We found that treats originating from North America and treats purchased in grocery stores had a lower risk of contamination with bacteria resistant to the antimicrobials tested. Outreach and extension activities are needed to increase awareness of the risks of contaminated pet treats and to highlight the importance of hand hygiene when feeding and interacting with pets.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".