Role of Human Leukocyte Antigen Allele Sharing in Human Papillomavirus Infection Transmission Among Heterosexual Couples: Findings From the HITCH Cohort Study
Bibliographic record
Abstract
BACKGROUND: Human leukocyte antigen (HLA) polymorphism influences innate and adaptive immune responses. Among heterosexual couples in the HPV Infection and Transmission Among Couples Through Heterosexual Activity (HITCH) cohort study, we examined whether allele sharing in a couple predicted the partners' infections with the same human papillomavirus (HPV) type. METHODS: We tested genital samples from 271 couples for 36 HPV genotypes by polymerase chain reaction. We used direct DNA sequencing to type HLA-B07, -DRB1, -DQB1 and -G. Generalized estimating equations were used to examine the associations between the extent of allele sharing and HPV type concordance in which at least 1 of the partners was HPV positive. RESULTS: We identified 106 different HLA alleles. The most common HLA alleles among couples were G*01:01:01 (95.6%), G*01:01:02 (60.1%), DQB1*03:01 (57.2%), and DRB1*07:01 (46.9%). Allele sharing was as follows: 19.6% shared none, 43.2% shared 1 only, 25.1% shared 2, and 12.5% shared 3-5. Irrespective of HLA class, grouped or in combination, the extent of allele sharing was not a significant predictor of type-specific HPV concordance in a couple (odds ratio, 1.1 [95% confidence interval, .5-2.1], for 3-5 vs none). CONCLUSIONS: We found no evidence that the extent of HLA allele concordance influences the likelihood of HPV transmission in newly formed heterosexual couples.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".