Characterization of the vaginal microbiota of healthy Canadian women through the menstrual cycle
Bibliographic record
Abstract
BACKGROUND: The vaginal microbial community plays a vital role in maintaining women's health. Understanding the precise bacterial composition is challenging because of the diverse and difficult-to-culture nature of many bacterial constituents, necessitating culture-independent methodology. During a natural menstrual cycle, physiological changes could have an impact on bacterial growth, colonization, and community structure. The objective of this study was to assess the stability of the vaginal microbiome of healthy Canadian women throughout a menstrual cycle by using cpn60-based microbiota analysis. Vaginal swabs from 27 naturally cycling reproductive-age women were collected weekly through a single menstrual cycle. Polymerase chain reaction (PCR) was performed to amplify the universal target region of the cpn60 gene and generate amplicons representative of the microbial community. Amplicons were pyrosequenced, assembled into operational taxonomic units, and analyzed. Samples were also assayed for total 16S rRNA gene content and Gardnerella vaginalis by quantitative PCR and screened for the presence of Mollicutes by using family and genus-specific PCR. RESULTS: Overall, the vaginal microbiome of most women remained relatively stable throughout the menstrual cycle, with little variation in diversity and only modest fluctuations in species richness. Microbiomes between women were more different than were those collected consecutively from individual women. Clustering of microbial profiles revealed the expected groupings dominated by Lactobacillus crispatus, Lactobacillus iners, and Lactobacillus jensenii. Interestingly, two additional clusters were dominated by either Bifidobacterium breve or a heterogeneous mixture of nonlactobacilli. Direct G. vaginalis quantification correlated strongly with its pyrosequencing-read abundance, and Mollicutes, including Mycoplasma hominis, Ureaplasma parvum, and Ureaplasma urealyticum, were detected in most samples. CONCLUSIONS: Our cpn60-based investigation of the vaginal microbiome demonstrated that in healthy women most vaginal microbiomes remained stable through their menstrual cycle. Of interest in these findings was the presence of Bifidobacteriales beyond just Gardnerella species. Bifidobacteriales are frequently underrepresented in 16S rRNA gene-based studies, and their detection by cpn60-based investigation suggests that their significance in the vaginal community may be underappreciated.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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".