Cervical Microbiome and Cytokine Profile at Various Stages of Cervical Cancer: A Pilot Study
Bibliographic record
Abstract
Cervical cancer (CC) is caused by high-risk human papillomavirus persistence due to the immunosuppressive tumor microenvironment mediated by cytokines. Vaginal microbiota determines the presence of certain cytokines locally. We assessed the association between cervical microbiota diversity and the histopathological diagnosis of each stage of CC, and we evaluated mRNA cervical expression levels of IL-4, IL-6, IL-10, TGF-β1, TNF-α and IFN-γ across the histopathological diagnosis and specific bacterial clusters. We determined the cervical microbiota by high throughput sequencing of 16S rDNA amplicons and classified it in community state types (CST). Mean difference analyses between alpha-diversity and histopathological diagnosis were carried out, as well as a β-diversity analysis within the histological diagnosis. Cervical cytokine mRNA expression was analyzed across the CSTs and the histopathological diagnoses. We found a significant difference in microbiota's diversity in NCL-HPV negative women vs those with squamous intraepithelial lesions (SIL) and CC(p = 0.006, p = 0.036).When β-diversity was evaluated, the CC samples showed the highest variation within groups (p<0.0006) and the largest distance compared to NCL-HPV negative ones (p<0.00001). The predominant bacteria in women with normal cytology were L. crispatus and L. iners, whereas for SIL, it was Sneathia spp. and for CC, Fusobacterium spp. We found higher median cervical levels of IL-4 and TGF-β1 mRNA in the CST dominated by Fusobacterium spp. These results suggest that the cervical microbiota may be implicated in cervical cancer pathology. Further cohort studies are needed to validate these findings.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".