Inferred bi-directional interactions between vaginal microbiota, metabolome and persistent HPV infection accompanied by high-grade cervical intraepithelial neoplasia
Bibliographic record
Abstract
BACKGROUND: Vaginal microbiota (VM) links to the risk of persistent HPV infection and the progression of cervical intraepithelial neoplasia (CIN). However, a comprehensive understanding of concurrent alterations in VM and metabolome associated with that risk remains elusive in population-based studies, particularly among Chinese women. METHODS: This study conducted an extensive analysis of VM and metabolome profiles in a cohort of 56 Chinese women, classified into HPV_C (natural clearance of HPV, n = 18) and HPV_PH(persistent HPV infection accompanied by high-grade CIN, n = 38), based on the result of 3-6 months follow-up visits. RESULTS: Our analysis revealed a higher prevalence of Lactobacillus-dominated samples in the HPV_C cohort. Notably, the vaginal metabolome exhibited a significant interaction with VM, with Lactobacillus emerging as a pivotal influencer. We identified 386 metabolites that significantly differentiated between HPV_C and HPV_PH groups, of which 364 were associated with VM components such as Lactobacillus, Hoylesella, Fannyhessea and Megasphaera. Further examination showed that 66 of these 364 metabolites positively correlated with Lactobacillus, including citric acid, DL-beta-Leucine, Xanoic acid and Norcholic acid. Conversely, 19 metabolites, including HPV_PH enriched maltotriose and N-Acetyl-L-aspartic acid, negatively correlated with Lactobacillus. Further analysis suggested potential bi-directional modulation between VM and persistent HPV infection accompanied by high-grade, being partially mediated by vaginal metabolites. CONCLUSIONS: This study provides additional insights into the correlations between concurrent alterations in VM and metabolome associated with persistent HPV infection accompanied by high-grade CIN.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".