HLA-G alleles impact the perinatal father-child HPV transmission
Bibliographic record
Abstract
Abstract BackgroundThe host factors that influence the father-to-child human papillomavirus (HPV) transmission remain unknown. This study evaluated whether human leukocyte antigen (HLA)-G alleles are important in father-to-child HPV transmission during the perinatal period.MethodsAltogether, 134 father-newborn dyads from the Finnish Family HPV Study were included in the analyses. Oral, semen and urethral samples from the fathers were collected before the delivery, and oral samples from their offspring at delivery and postpartum at birth, day-3, at 1-, 2- and 6-month follow-up visits. HLA-G alleles were tested by direct sequencing. Unconditional logistic regression was used to determine the associations between the father-child HLA-G allele and genotype concordance and the father-child HPV prevalence and concordance at birth and during follow-up.ResultsHLA-G allele G*01:01:03 concordance was associated with father’s urethral and child’s oral high-risk (HR)-HPV concordance at birth (OR 17.00, 95%CI:1.24-232.22). Controversially at postpartum period G*01:01:03 discordance was associated with father’s urethral and child’s oral HR-HPV concordance (OR 6.67, 95%CI:1.08-40.97). HLA-G allele G*01:04:01 concordance increased the father’s oral and child’s postpartum oral any- and HR-HPV concordance, with OR 7.50 (95%CI:1.47-38.16) and OR 7.78 (95%CI:1.38-43.85), respectively. There was no association between different HLA-G genotypes and HPV concordance among the father-child dyads at birth or postpartum.ConclusionThe HLA-G allele concordance appears to impact the HPV transmission between the father and his offspring. This suggests that the father might have an important regulatory role in the natural history of his child’s oral HPV infection, which should be further explored.
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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 | 0.002 |
| 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) | 0.004 | 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".