Lactobacilli require physical contact to reduce staphylococcal TSST-1 secretion and vaginal epithelial inflammatory response
Bibliographic record
Abstract
ITALIC! Staphylococcus aureusbiofilms can be found on vaginal epithelia, secreting toxins and causing inflammation. The co-vaginal species ITALIC! Lactobacilluscan alter staphylococcal-induced epithelial secretion of inflammatory cytokines and quench staphylococcal toxic shock syndrome toxin-1 secretion. It is hypothesized that these effects of lactobacilli require direct physical contact between lactobacilli, staphylococci and the epithelium. Indeed, lactobacilli only reduced ITALIC! S. aureus-induced inflammatory cytokine expression when allowed physical contact with vaginal epithelial cells. Furthermore, a reduction in toxic shock syndrome toxin-1 secretion only occurred when a probiotic ITALIC! Lactobacillusstrain was allowed contact, but not when being physically separated from ITALIC! S. aureus Bacterial-probe atomic force microscopy demonstrated that lactobacilli and staphylococci strongly adhere to epithelial cells, while lactobacilli adhere stronger to staphylococci than staphylococci to each other, giving lactobacilli opportunity to penetrate and reside in staphylococcal biofilms, as visualized using confocal laser scanning microscopy with fluorescence ITALIC! in situhybridization probes. These results identify that physical contact and biochemical signaling by lactobacilli are intrinsically linked mechanisms that reduce virulence of ITALIC! S. aureusbiofilm.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".