Vaginal bacteriome of Nigerian women in health and disease: A study with 16S rRNA metagenomics
Bibliographic record
Abstract
Introduction: The argument on what bacteria make up healthy vagina and bacterial vaginosis (BV) remain unresolved. Black women most often are placed in grade IV vaginal communities as lacking Lactobacillus-dominated microbes. We sought to determine the vaginal microbiota compositions of healthy and those with BV using 16S rRNA metagenomics methods.Materials and Methods: Twenty-eight women provided vaginal swabs for Nugent scoring. Fifteen had BV (Nugent score 7–10), whereas 13 were normal (Nugent score 0–3). DNA was extracted and 16S rRNA V4 region amplified using custom bar-coded primers prior to sequencing with MiSeq platform. Sequence reads were imported into Illumina BaseSpace Metagenomics pipeline for 16S rRNA recognition. Distribution of taxonomic categories at different levels of resolution was done using Greengenes databases. Manhattan principal component analysis was used for similarity clustering.Results: Non-BV subjects were colonized by 12 taxonomic phyla that represent 182 genera and 357 species. Overall, 23 phyla representing 388 genera and 805 species were identified in BV subjects. Firmicutes represented 95% of the sequence reads in non-BV subjects with Lactobacillus-dominated genera and Lactobacillus crispatus–dominated species, followed by Proteobacteria (3.78%), Actinobacteria (0.74%), and Bacteriodetes (0.05%). In BV subjects, Firmicutes represented 59% of the classified sequence reads, followed by Bacteroidetes (19%), Actinobacteria (15.8%), Fusobacteria (4.08%), Proteobacteria (1.48%), and Tenericutes (1.25%).Conclusion: Non-BV healthy Black African, Nigerian women had Lactobacillus genera as the predominant microbiota, contrary to published reports. The study shows that BV subjects had varying proportions of diverse bacteria similar to studies from other parts of 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".