Carbapenemase Producing Bacteria in the Food Supply Escaping Detection
Bibliographic record
Abstract
Carbapenem antimicrobials are critically important to human health and they are often the only remaining effective antibiotics for treating serious infections. Resistance to these drugs mediated by acquired carbapenemase enzymes is increasingly encountered in gram-negative bacteria and is considered a public health emergency. Animal origin food products are recognized as a potential source of resistant organisms, although carbapenem resistance has only recently been reported. In western countries there are active resistance surveillance programs targeting food animals and retail meat products. These programs primarily target beef, pork and poultry and focus exclusively on E. coli, Salmonella, Campylobacter spp. and Enterococcus spp. This global surveillance strategy does not capture the diversity of foods available nor does it address the presence of resistance gene-bearing mobile genetic elements in non-pathogenic bacterial taxa. To address this gap, a total of 121 seafood products originating in Asia purchased from retail groceries in Canada were tested. Samples were processed using a taxa-independent method for the selective isolation of carbapenem resistant organisms. Isolates were characterized by phenotypic antimicrobial susceptibility testing, PCR and DNA sequencing. Carbapenemase producing bacteria, all blaOXA-48, were isolated from 4 (3.3%) of the samples tested. Positive samples originated from China (n=2) and Korea (n=2) and included squid, sea squirt, clams and seafood medley. Carbapenemase producing organisms found include Pseudomonas, Stenotrophomonas and Myroides species. These findings suggest that non-pathogenic bacteria, excluded from resistance surveillance programs, in niche market meats may serve as a reservoir of carbapenemase genes in the food supply.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".