Virulence and toxigenicity of coagulase-negative staphylococci in Nigerian traditional fermented foods
Bibliographic record
Abstract
The incidence of coagulase-negative staphylococci (CoNS) may render food unsafe, as the clinical isolates have been reported to exude virulent traits. A total of 255 CoNS isolates from 6 traditional fermented foods (nono, kunu, wara, iru, ogi, and kindirmo) from North Central Nigeria, identified as Staphylococcus epidermidis, Staphylococcus simulans, Staphylococcus xylosus, Staphylococcus kloosii, and Staphylococcus caprae, were investigated for virulence traits. The strains were examined for biofilm formation and production of hyaluronidase, DNase, TNase, haemolysins, and superantigenic toxins (SEA, SEB, SEC, SED, and TSST-1) using standard and genotypic methods. The analysis of virulence factors revealed the production of slime in 200 isolates (78.4%); α-haemolysin in 136 (53.3%); β-haemolysin in 43 (16.9%); DNase in 199 (78.0%); TNase in 29 (11.4%); hyaluronidase in 125 (49.0%); TSST-1 in 119 (46.7%); and enterotoxin-producing isolates SEA, SEB, SEC, and SED in 61 (23.9%), 19 (7.5%), 9 (3.5%), and 8 (3.1%), respectively. PCR analysis detected tsst-1, sea, seb, and sec genes. The ability of these microorganisms to exhibit virulence evokes the potential to cause disease especially under determinate conditions or in immune-compromised patients. The occurrence of CoNS in food should not be ignored nor their pathogenic potential considered as insignificant, rather safety measures should be taken to reduce or totally eliminate their occurrence in foods.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".