Hazard Characterization of Antibiotic-resistant Aeromonas spp. Isolated from Mussel and Oyster Shellstock Available for Retail Purchase in Canada
Bibliographic record
Abstract
Surveillance and monitoring of foods for the presence of antimicrobial-resistant (AMR) bacteria are required to assess the risks these bacteria pose to human health. Frequently consumed raw or lightly cooked, live bivalve shellfish such as mussels and oysters can be a source of exposure to AMR bacteria. This study sought to determine the prevalence of third-generation cephalosporin (3GC) and carbapenem-resistant bacteria in live mussel and oyster shellstock available for retail purchase through the course of one calendar year. Just over half of the 180 samples (52%) tested positive for the presence of 3GC-resistant bacteria belonging to thirty distinct bacterial species. Speciation of the isolates was carried out using the Bruker MALDI Biotyper. Serratia spp., Aeromonas spp., and Rahnella spp. were the most frequently isolated groups of bacteria. Antibiotic resistance testing confirmed reduced susceptibility for 3GCs and/or carbapenems in 15 of the 29 Aeromonas isolates. Based on AMR patterns, and species identity, a subset of ten Aeromonas strains was chosen for further characterization by whole genome sequence analysis. Genomic analysis revealed the presence of multiple antibiotic resistance and virulence genes. A number of mobile genetic elements were also identified indicating the potential for horizontal gene transfer. Differences in gene detection by the bioinformatic tools and databases used (ResFinder. CARD RGI, PlasmidFinder, and MobSuite) are discussed. This study highlights the strengths and limitations of using genomics tools to perform hazard characterization of diverse foodborne bacterial species.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".