Prevention and treatment of urogenital infections and complications: lactobacilli's multi-pronged effects
Bibliographic record
Abstract
The association between depletion of vaginal lactobacilli and increased risk of various urogenital infections and complications such as preterm labor, has been known for some time. Restoration of depleted lactobacilli by administration of probiotic strains has been demonstrated in humans using Lactobacillus rhamnosus GR-1 and L. reuteri RC-14. These organisms also augment antibiotic efficacy, lessen the side effects of these drugs, and alleviate diarrhea in AIDS patients. The mechanisms appear to be multi-factorial, and include production of: (i) anti-microbial factors such as lactic acid, bacteriocins, hydrogen peroxide, (ii) biosurfactants or other components that affect pathogen colonization and biofilm formation, (iii) signalling compounds that influence pathogen virulence expression, and (iv) signalling compounds that modulate immunity. Recombinant strains have been produced that inhibit or kill HIV, offering hope for microbicide applications that can be self-used by women. As more information becomes available about the ‘normal’ versus ‘disease prone’ vaginal microbiota and strains that confer the most benefits, current and new probiotic remedies will potentially provide improved restorative and therapeutic options to lower the one billion urogenital infections currently afflicting women around the world.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".