Fertility factors affect the vaginal microbiome in women of reproductive age
Bibliographic record
Abstract
PROBLEM: For women of reproductive age, achieving a successful pregnancy requires both the normal functioning of reproductive endocrine and the health of the reproductive tract environment. We aimed to study how these fertility factors, such as female age, baseline sexual hormone levels, tubal patency, and vaginal pH, affect the composition of vaginal microbiome. METHOD OF STUDY: The 16S rRNA sequencing was carried on vaginal microbiome samples from 85 women of reproductive age without vaginal infections or reproductive endocrine diseases. The detailed correlations between fertility factors and vaginal microbiome were quantified by Spearman's rank tests. A linear discriminant analysis was carried out to explore the effects of fertility factors on the relative abundances of vaginal bacterial species. RESULTS: The vaginal pH, levels of basal E2, LH, and FSH all had significant effects on the distribution of vaginal microbiome. The relative abundances of vaginal bacterial species, including Escherichia coli, Streptococcus agalactiae, and Prevotella intermedia, were significantly different due to the host's state of reproductive endocrine and tubal patency. It was worth noting that women with tubal obstruction, or prolonged menstrual cycle, or antral follicle count >15, or vaginal pH > 4.5 all had a higher abundance of Escherichia coli in vagina. CONCLUSION: The fertility factors associated with the reproductive endocrine and the genital tract environment affected vaginal microbiome in women of reproductive age. The species Escherichia coli, Streptococcus agalactiae, Prevotella intermedia, etc could be used as biomarkers to reflect the pathological state of reproductive endocrine and genital tract.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".