Lactic acid enhances vaginal epithelial barrier integrity and ameliorates inflammatory effects of dysbiotic short chain fatty acids and HIV-1
Bibliographic record
Abstract
The vaginal microenvironment is key in mediating susceptibility to sexually transmitted infections. A polymicrobial environment with reduced Lactobacilllus spp. is characteristic of vaginal dysbiosis, associated with increased production of several short chain fatty acids (SCFAs), vaginal inflammation and an increased risk of HIV-1 acquisition. In contrast, a eubiotic vaginal microbiome (VMB), dominated by Lactobacillus spp. correlates with increased production of lactic acid (LA), an acidic milieu and protection against HIV-1. Vaginal metabolites, specifically LA and SCFAs including butyric, succinic and acetic acids are associated with modulation of HIV-1 risk. We assessed the impact of combined and individual SCFAs and LA on vaginal epithelial cells (VK2) grown in air-liquid interface cultures. Treatment of VK2 cells with eubiotic SCFA + LA mixture showed increased epithelial barrier integrity, reduced FITC dextran leakage and enhanced expression of cell-cell adhesion proteins. Treatment with dysbiotic SCFA + LA mixture diminished epithelial barrier integrity, increased NFκB activation and inflammatory mediators: TNF-α, IL-6, IL-8 and RANTES. LA was found to be the primary contributor of the beneficial effects. Eubiotic SCFA + LA mixture ameliorated HIV-1 mediated barrier disruption and HIV-1 leakage, whereas dysbiotic SCFA + LA treatment exacerbated HIV-1 effects. These findings indicate a key role for LA in future prophylactic strategies.
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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 | 0.001 |
| 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".