Exploring the diversity of vaginal microbiota between healthy women and cervical cancer patients in India
Bibliographic record
Abstract
Introduction. Cervicovaginal diversity has been reported as a predictive biomarker for cervical cancer risk. We recently reported the bio-therapeutic potential of vaginal probiotics from healthy Indian women against vaginal pathogens, isolated from the invasive cervical cancer (ICC) patients. Gap Statement. The cervicovaginal microflora from cervical cancer patients has not yet been reported from Indian population. Aim. The present study aimed at comparing the cervicovaginal microbiome between healthy controls (HC) and ICC patients from the Indian population. Methodology. In total, 30 vaginal swabs (15 from HC and 15 from ICC) were subjected to 16S rRNA gene sequencing. Alpha diversity was evaluated by Shannon and Chao1 index; and beta diversity by principle coordinate analysis (PCoA) of weighted and unweighted UniFrac distances. The relative abundance of the microbial taxa was done according to linear discriminant analysis effect size (LEfSe). Results. Predominance of Staphylococcus spp. in ICC and Lactobacillus gasseri in HC groups was observed. Alpha-diversity was found to be higher in ICC as compared to HC but was statistically non-significant. LEfSe analysis revealed Bacteroides fragilis and Escherichia coli as the marker genera in ICC with a marked decrease in Lactobacillus sp. Contrarily, in HC, L. gasseri, L. iners and L. fermentum were found to be abundant. Conclusion. Differences in the vaginal microbiome between healthy and ICC women could help in the early prediction of cervical cancer risk and thus in designing prevention strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".