Association Between Human Leukocyte Antigen Polymorphism and Human Papillomavirus Infection in Brazilian Women
Bibliographic record
Abstract
Background Human papillomavirus (HPV) infection is a necessary cause for cervical cancer, but a majority of genital HPV infections clear spontaneously. Human leukocyte antigen (HLA) polymorphism influences immune response and genetic susceptibility, and its association with cervical cancer was extensively investigated, but few reports focused on HPV infection. Methods We performed molecular typing of HLA-A, HLA-B, HLA-DQB1, and HLA-DRB1 genes as well as of HPV in 1226 women enrolled in the Ludwig-McGill cohort study and investigated the influence on cumulative HPV positivity. HPV types were grouped according to Alphapapillomavirus subgenera that exhibit similar tissue tropism and biological behavior concerning cancer risk. The associations between HLA polymorphisms and HPV infections were estimated using unconditional logistic regression analysis adjusted for age and race. Results HLA-B*08 and HLA-DRB1*15:01 were negatively associated with HPV positivity, and similar effects were observed for HPV subgenus 2 only, which includes HPV16. HLA-DRB1*08:07 was associated with overall HPV infection and subgenus 2 positivity. The haplotypes HLA-B*08-DRB1*03:01-DQB1*02:01 and HLA-DRB1*08:07-DQB1*04:02 were negatively and positively associated with cumulative HPV positivity, respectively. Conclusions Our data suggest that HLA class I and II polymorphism can influence HPV natural infection.
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 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".