Targeting the vaginal microbiota with probiotics as a means to counteract infections
Bibliographic record
Abstract
PURPOSE OF REVIEW: The microbial composition of the vagina of healthy and infected women is becoming more fully elucidated with molecular techniques. The purpose of this review is to examine our current understanding of the vaginal microbiota and assess how probiotic bacteria might reduce infectivity. RECENT FINDINGS: It appears that there are some remarkable similarities in the bacterial species that inhabit the vagina of women from diverse ethnic backgrounds. Yet, distinct outliers exist in which a small portion of apparently healthy women have extremely complex microbiota, whereas most have a relatively simple microbiota. Bacterial vaginosis is the most common aberrant condition in women, yet its pathogenesis is poorly understood and it is often asymptomatic. Vulvovaginal candidiasis is better known, yet many women self-treat with antifungals when in fact they have bacterial vaginosis. Urinary tract infection (UTI) remains extremely common, with no real breakthrough treatment or prevention strategy developed in the past 30 or more years. The ability of lactobacilli probiotic interventions to prevent, treat and improve the cure of these infections has long been considered and is now supported by some clinical evidence. SUMMARY: The mechanisms whereby certain probiotic lactobacilli improve urogenital health include immune modulation, pathogen displacement and creation of a niche less conducive to proliferation of pathogens and their virulence factors. Probiotics offer a potential new means to prevent urogenital infections and help maintain a healthy vaginal ecosystem.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".