Influence of the Vaginal Microbiota on Toxic Shock Syndrome Toxin 1 Production by Staphylococcus aureus
Bibliographic record
Abstract
Menstrual toxic shock syndrome (TSS) is a serious illness that afflicts women of premenopausal age worldwide and arises from vaginal infection by Staphylococcus aureus and concurrent production of toxic shock syndrome toxin-1 (TSST-1). Studies have illustrated the capacity of lactobacilli to reduce S. aureus virulence, including the capacity to suppress TSST-1. We hypothesized that an aberrant microbiota characteristic of pathogenic bacteria would induce the increased production of TSST-1 and that this might represent a risk factor for the development of TSS. A S. aureus TSST-1 reporter strain was grown in the presence of vaginal swab contents collected from women with a clinically healthy vaginal status, women with an intermediate status, and those diagnosed with bacterial vaginosis (BV). Bacterial supernatant challenge assays were also performed to test the effects of aerobic vaginitis (AV)-associated pathogens toward TSST-1 production. While clinical samples from healthy and BV women suppressed toxin production, in vitro studies demonstrated that Streptococcus agalactiae and Enterococcus spp. significantly induced TSST-1 production, while some Lactobacillus spp. suppressed it. The findings suggest that women colonized by S. aureus and with AV, but not BV, may be more susceptible to menstrual TSS and would most benefit from prophylactic treatment.
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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 | 0.000 | 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) | 0.001 | 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".