Molecular Characterization, Antibiotic Resistance, and Virulence Factors of Methicillin-Resistant <i>Staphylococcus aureus</i> Strains Isolated from Imported and Domestic Meat in Korea
Bibliographic record
Abstract
During a nationwide surveillance in Korea, 13 methicillin-resistant Staphylococcus aureus (MRSA) strains were isolated from imported and domestic meat between 2009 and 2011. The predominant MRSA genotype was SCCmec type V, and only two agr types (I and II) were found. Unexpectedly, sequence type ST72 comprised more than 50% of the isolates; this is the first instance of type ST72 in food from Canada. Two Spanish pork isolates were ST398, which caused human disease in Europe, and they carried leukotoxin genes, lukS, lukF, and lukE-lukD. Furthermore, P71 and P6 harbored all of the known leukocidin genes, lukS-lukF-lukE-lukD-lukM. Our collected MRSA strains were multidrug resistant with various antimicrobial and heavy-metal resistance genes. Toxin genes that are commonly found in clinical MRSA also were detected in our meat strains. One MRSA strain exhibited an uncommon type of enterotoxin, sec-see-seg-sei-sel-sem-sen-seo-sep. Plasmids (1.5-15.0 kb) were found in 12 of the 13 MRSA isolates. Repetitive sequence-based polymerase chain reaction of the genomic DNA showed 3 clusters with 95% similarity. The presence of multidrug-resistant and toxigenic MRSA in meat products suggests that comprehensive surveillance should be continued for imported meats in Korea.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".