Analysis of genetic signatures of virulence and resistance in foodborne <i>Staphylococcus aureus</i> isolates from Algeria
Bibliographic record
Abstract
Staphylococcus aureus , a frequent commensal of the human microbiota, also frequently associated with various infections. This study aimed to genetically characterize foodborne methicillin resistant S. aureus (MRSA) in Oran, Algeria. From a total of 474 food matrices, 30 MRSA strains were isolated and characterized using whole genome sequencing (WGS) and bioinformatics. The genomes were assessed for antimicrobial resistance and virulence genes, sequence and spa types and single nucleotide polymorphism (SNP) to establish their relationship. The prevalence of S. aureus was 34.38% with MRSA accounting for 18.40%. MRSA strains showed high resistance to penicillinG, ampicillin and tetracycline. However, inconsistencies were noted between phenotypic and genotypic resistance for methicillin, aminoglycosides, and phenicolates, with mecA gene identified in three isolates. The isolates revealed eight different sequence types (predominantly ST97 and ST45) and eleven spa types (mainly t230 and t8503), correlating with SNP clusters except for isolates belonging to ST7200.86.66% of isolates harboured at least one enterotoxin gene underscores their potential public health threat. This investigation highlights the genetic heterogeneity among S. aureus regarding virulence and antimicrobial resistance, shedding light into food safety in Algeria and the ease of using WGS approach to rapidly characterize bacterial pathogens in community environments. • 34.38% of different food samples from various sources in Oran, Algeria were contaminated with S. aureus. • S. aureus isolates showed heterogeneous distributions of antibiotic resistance and virulence genes. • Three out of the thirty phenotypically MRSA isolates were found to carry the mecA gene. • ST97-t8503 and ST45-t230 were predominant typing among S. aureus isolates.
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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) | 0.001 | 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".