IgG and IgM Seroreactivity Against Natural HPV16 Infection and <i>HLA‐DRB1</i> and <i>‐DQB1</i> Polymorphism
Bibliographic record
Abstract
Little is known about the factors associated with the natural humoral immune response against human papillomavirus (HPV) infection. The association between HLA-DRB1 and -DQB1 polymorphism and (1) HPV16 DNA detection, (2) naturally developed humoral immune response (IgG and IgM) against HPV16 L1 and L2 capsid proteins, and (3) type of HPV16 infection (transient or persistent) was investigated. Data from 943 women participating in the Ludwig-McGill cohort study was analyzed. HLA-DRB1 and HLA-DQB1 genotyping was done by PCR-based methods. HPV DNA was assessed every 4 months during the first year of follow-up, and HPV16 IgG and IgM antibody measurements were performed by ELISA in serum samples from the first and/or second visits. Associations were estimated by adjusted logistic regression models. HLA-DRB1*03:02 was associated with HPV16 DNA positivity (OR = 2.49, 95% CI: 1.11-5.60). HLA-DRB1*13:01 and HLA-DQB1*04:02 were associated with low levels of HPV16 IgG antibodies (OR = 0.44, 95% CI:0.23-0.87; OR = 0.10, 95% CI: 0.02-0.55, respectively). Neither IgM HPV16 antibodies nor the type of HPV16 infection (transient or persistent) were associated with HLA-DRB1 or -DQB1 variants. HLA-DRB1 and -DQB1 polymorphism might be associated with different levels of IgG against HPV16 infection, but IgM production and the type of infection seem to occur independently of these variants.
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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 | 0.000 |
| 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) | 0.003 | 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".