Inhibition of enterococci by lactobacillus species in vitro
Bibliographic record
Abstract
Infections of the urogenital tract in women represent a major burden on the quality of life of women and on the health care system of Canada and other countries. Complications arising from bacterial vaginosis (BV) include increased risk of sexually transmitted diseases including human immunodeficiency virus and elevated risk of preterm birth (PTB). Pharmaceutical interventions, such as antibiotics, have been suboptimally effective and have failed to reduce the incidence of PTB. The absence of lactobacilli in the vagina, a specific feature of BV, raises the question as to whether restoration of lactobacilli, by probiotic therapy, can restore the normal flora and improve the chances of having a healthy term pregnancy. The rationale for probiotic use in pregnant women is quite strong. Certain lactobacilli strains can safely colonize the vagina after oral and vaginal administration, displace and kill pathogens including Gardnerella vaginalis and Escherichia coli, and modulate the immune response to interfere with the inflammatory cascade that leads to PTB. Additional attributes of probiotics include their potential to degrade lipids and enhance cytokine levels, which promote embryo development. In a society that focuses on disease rather than health and drug therapy rather than natural preventive measures, it will take some effort to get remedies such as probiotics into mainstream care. Perhaps the escalating health care budgets and emergence of "superbugs" will provide the incentives to put in place clinical trials designed to evaluate how best to use the commensal organisms that, after all, make up more of our body than human cells, and without which none of us would survive.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".