Nucleic Acid-Based Diagnosis of Bacterial Vaginosis and Improved Management Using Probiotic Lactobacilli
Bibliographic record
Abstract
Bacterial vaginosis (BV) is a common condition in women that represents an imbalance of the vaginal microflora, lactobacilli depletion, and excess growth of mainly anaerobic Gram-negative pathogens. Diagnosis is made using a series of tests or a Gram stain of a vaginal smear. Treatment with antibiotics is quite effective, but recurrences are common. A study of 55 vaginal samples from 11 postmenopausal women showed the presence of BV by the Gram stain-based Nugent scoring system, and polymerase chain reaction-denaturing gradient gel electrophoresis showed that Bacteroides or Prevotella species were the most common isolates recovered (24 of 25), with Escherichia coli, Staphylococcus aureus, and Streptococcus agalactiae also found in some samples. In one case, only Gardnerella vaginalis was found. These findings illustrate that BV remains common even among otherwise healthy women, but it is not caused solely by either Gardnerella or Mobiluncus. Use of a FemExam system (Cooper Surgical, Shelton, CT), based upon elevated pH and trimethylamine levels, to screen vaginal smears from 59 healthy women showed poor correlation with the Gram stain method. A randomized, placebo-controlled trial of these subjects showed that the lactobacilli-dominant microbiota was restored in subjects with BV but not in controls, following 2 months of daily oral intake of Lactobacillus rhamnosus GR-1 and Lactobacillus fermentum RC-14. These studies show that nucleic acid-based methods are effective at identifying bacteria responsible for BV. If such methods could be used to develop a commercially available, self-use kit, women would be much better placed to take control of their own health, for example, using medicinal food or dietary supplement products such as the clinically proven probiotic strains L. rhamnosus GR-1 and L. fermentum RC-14.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".