Interleukin-10 Gene (<i>IL10</i>) Polymorphisms and Human Papillomavirus Clearance among Immunosuppressed Adolescents
Bibliographic record
Abstract
Persistent infection with high-risk human papillomavirus (HPV) is a major risk factor for cervical cancer, and HPV clearance seems to be under host genetic influence. This study evaluated associations between three single nucleotide polymorphisms in the IL10 promoter and clearance of low- or high-risk HPV infection in a cohort of 226 largely HIV-1-infected African-American adolescent females. Among immunosuppressed individuals (HIV-1 seropositive and CD4(+) </= 500), the GCC haplotype in the IL10 promoter was associated with reduced clearance of high-risk HPV16-like [relative hazard (RH), 0.46; 95% confidence interval (95% CI), 0.25-0.85; P = 0.01], HPV18-like (RH, 0.33; 95% CI, 0.16-0.67; P = 0.002), and any high-risk type (RH, 0.37; 95% CI, 0.20-0.68; P = 0.002) but not with low-risk HPV type (RH, 0.60; 95% CI, 0.29-1.25; P = 0.17). No associations were observed among immunocompetent individuals. The IL10 GCC haplotype has been associated with production of relatively high levels of interleukin (IL)-10, which could (a) inhibit cytokines such as IL-2, TNF-alpha, IL-4, IL-6, and IL-12 that are involved in the T(H)1-T(H)2 immunoregulation; (b) down-regulate expression of MHC class I and class II molecules; or (c) induce the transcription of early promoter of HPV, all potentially contributing to duration of HPV infection among immunosuppressed individuals. These results support the hypothesis that IL10 polymorphisms influence the clearance of infection with high-risk HPV types and warrant further studies of host genetic control of HPV pathogenesis and cervical cancer in the context of immunosuppression.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".