Occurrence of β-Lactamase Genes Among Non-Typhi <i>Salmonella enterica</i> Isolated from Humans, Food Animals, and Retail Meats in the United States and Canada
Bibliographic record
Abstract
Non-Typhi Salmonella cause over 1.7 million cases of gastroenteritis in North America each year, and food-animal products are commonly implicated in human infections. For invasive infections, antimicrobial therapy is indicated. In North America, the antimicrobial susceptibility of Salmonella is monitored by the U.S. National Antimicrobial Resistance Monitoring System (NARMS) and The Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS). In this study, we determined the susceptibility to cephalosporins by broth microdilution among 5,041 non-Typhi Salmonella enterica isolated from food animals, retail meats, and humans. In the United States, 109 (4.6%) of isolates collected from humans, 77 (15.7%) from retail meat, and 140 (10.6%) from food animals displayed decreased susceptibility to cephalosporins (DSC). Among the Canadian retail meat and food animal isolates, 52 (13.0%) and 42 (9.4%) displayed DSC. All isolates displaying DSC were screened for β-lactamase genes (bla(TEM), bla(SHV), bla(CMY), bla(CTX-M), and bla(OXA-1)) by polymerase chain reaction. At least one β-lactamase gene was detected in 74/109 (67.9%) isolates collected from humans, and the bla(CMY) genes were most prevalent (69/109; 63.3%). Similarly, the bla(CMY) genes predominated among the β-lactamase-producing isolates collected from retail meats and food animals. Three isolates from humans harbored a bla(CTX-M-15) gene. No animal or retail meat isolates harbored a bla(CTX-M) or bla(OXA-1) gene. A bla(TEM) gene was found in 5 human, 9 retail meat, and 17 animal isolates. Although serotype distributions varied among human, retail meat, and animal sources, overlap in bla(CMY)-positive serotypes across sample sources supports meat and food-animal sources as reservoirs for human infection.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".