Detection of multidrug‐resistant Gram‐negative bacteria from imported reptile and amphibian meats
Bibliographic record
Abstract
The food supply is a potential source of antimicrobial resistance. Current surveillance programmes targeting food are limited to beef, pork and poultry and do not capture niche products. In this study, imported reptile and amphibian products were screened for antimicrobial‐resistant bacteria. In all, 53 items including soft shell turtles, frog legs, geckos, snakes and a turtle carapace were purchased from specialty markets in Vancouver and Saskatoon, Canada. Samples were selectively cultured for Salmonella sp., Escherichia coli, extended‐spectrum β‐lactamase (ESBL)‐producing Enterobacteriaceae and meropenem‐resistant organisms. Salmonella, all pan‐susceptible, were grown from six dried geckos. Escherichia coli were isolated from 19 samples, including ESBL producers from six items. One multidrug‐resistant E. coli possessed both the blaCTX‐M‐55 and mcr‐1 genes. An NDM‐1‐producing Acinetobacter sp. was also isolated from a dried turtle carapace. Our results suggest that imported reptile and amphibian meats are an underappreciated source of resistant bacteria. The international trade of food may play a role in the dissemination of resistant bacteria. The presence of these bacteria in niche market foods represents a risk of unknown magnitude to public health and a gap in current national resistance surveillance programmes.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".