Occurrence of methicillin-resistant staphylococci in the pig-production chain in Ibadan, Nigeria
Bibliographic record
Abstract
Staphylococcus species colonises humans and animals and is a major food contaminant with public health significance. Here, we assessed the occurrence of methicillin-resistant staphylococci (MRS) in the pig-production chain in Ibadan, Nigeria. Nares of 120 pigs and 10 farmers were sampled with sterile swabs whilst 54 pork samples were collected from a retail slaughterhouse. Staphylococcus species were isolated using enrichment, cefoxitin-aztreonam selective broth and Mannitol salt agar. Isolates were tested for susceptibility to cefoxitin (30 μg), oxacillin (1 μg) and vancomycin (30 μg). Methicillin-resistant staphylococci isolates were characterised using conventional biochemical tests. From 184 samples, 364 staphylococcal isolates were obtained. Amongst the 54 pork samples, 44.0% were contaminated with Staphylococcus species. Overall, 9 (2.5%) MRS were obtained and presumptively identified as Staphylococcus xylosus (n = 3), Staphylococcus sciuri (n = 3), Staphylococcus warneri (n = 2) and Staphylococcus cohnii (n = 1). There was no relationship between the prevalence of MRS between pigs and pig handlers in the farms, but Farm 2 had the highest frequency of 66.7% (p 0.05). Piglets had the highest prevalence of 66.7% (p 0.05) whilst MRS was absent in workers and pork samples. This study raises concerns about the cross-contamination of staphylococci in the food chain. Constant surveillance is imperative to ensure food safety.
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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 | 0.000 | 0.001 |
| 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".