Development and evaluation of a sensitive approach for detection and recovery of third-generation cephalosporin- and carbapenem-resistant Enterobacterales from ready-to-eat frozen stone fruit
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is a global public health threat, but the role of foods in its dissemination is poorly understood. We examined the incidence of foodborne bacteria carrying AMR genes considered high-priority research targets by the World Health Organization. Frozen, ready-to-eat, avocado, coconut, mango, and peach ( n = 161) were tested for bacteria encoding extended-spectrum β-lactamases (ESBLs) and carbapenemases. Over 600 presumptive-positive isolates were recovered and analyzed with a pooled sequencing (Pool-seq) strategy. Coconut samples exhibited the highest bacterial loads and prevalence/diversity of AMR genes. Isolates harbouring the β-lactamase genes blactx-m, blatem, and blashv, identified in 14 coconut and 2 mango samples, were further characterized by whole-genome sequencing and antimicrobial susceptibility testing. The most common gene was blactx-m-15, detected in 20 unique strains. Two carbapenemase-producing strains were isolated from coconut: Enterobacter roggenkampii encoding blandm-1 and Escherichia coli encoding blandm-5. Subsequent quantitative PCR (qPCR) analysis of enrichments for blactx-m/ blandm indicated a potentially higher prevalence of these genes than observed by colony screening. This study presents a practical method for recovering ESBL- and carbapenemase-producing bacteria from foods. Mapping their distribution in food products is crucial to assessing the role of foods in the global spread of AMR and developing effective public health interventions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".