The vaginal microbiome in bacterial vaginosis: Pathogenesis, reproductive impacts, and emerging therapies
Bibliographic record
Abstract
Bacterial vaginosis (BV), the leading gynecological condition affecting women of reproductive age globally, is marked by a reduction in the dominant protective bacterial species Lactobacillus within the vaginal microbiome (VMB). This condition is triggered by an overgrowth of anaerobic bacteria and leads to many gynecological and reproductive repercussions, such as increased susceptibility to sexually transmitted infections and infertility. Moreover, BV's effects extend to pregnancy, contributing to adverse obstetric outcomes such as miscarriages, preterm delivery, and postpartum complications. While antibiotics remain the standard treatment for BV, their efficacy is compromised by high recurrence rates due to their inability to restore Lactobacillus and concerns about their negative impact on neonatal health during pregnancy. Recent research suggests probiotics as promising complementary or alternative therapies with their capacity to restore Lactobacillus in the VMB. In this review, we provide an in-depth examination of the impact of BV on gynecological health, pregnancy and fetal development and explore the latest advancements in BV treatments, including probiotics, vaginal microbiome transplantation (VMT), and biofilm disrupters, as preventative and therapeutic measures in addressing the multigenerational effects of this condition.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".