Association Pattern of Interleukin-1 Receptor-Associated Kinase-4 Gene Polymorphisms with Allergic Rhinitis in a Han Chinese Population
Bibliographic record
Abstract
OBJECTIVE: Interleukin-1 receptor-associated kinase-4 (IRAK-4) encodes a kinase that is essential for NF-kB activation in Toll-like receptor and T-cell receptor signaling pathways, indicating a possible crosstalk between innate and acquired immunities. We attempted to determine whether the polymorphisms in the Interleukin-1 receptor-associated kinase-4 (IRAK-4) gene are associated with allergic rhinitis (AR) in the Han Chinese population. METHODS: A population of 379 patients with AR and 333 healthy controls was studied. Blood was drawn for DNA extraction and total serum immunoglobulin E (IgE). A total of 11 single nucleotide polymorphisms (SNPs) in IRAK-4 were selected and individually genotyped. RESULTS: Significant allelic differences between cases and controls were obtained for the SNP of rs3794262 in the IRAK-4 gene. In the stratified analysis for gender, two SNPs (rs4251431 and rs6582484) in males appeared as significant associations. Subgroup analysis for the presence of different allergen sensitivities displayed associations only in the house dust mite-allergic cohorts (rs3794262, rs4251481). None of the selected SNPs in IRAK-4 was associated with total IgE level. The haplotype analysis indicated GCCTGCGA was significantly associated with AR. The SNP-SNP interaction information analysis indicated that the selected sets of polymorphisms had no synergistic effect. CONCLUSIONS: Our findings did not support the potential contribution of the IRAK-4 gene to serum IgE levels. However, the results demonstrated a gender- and allergen-dependant association pattern between polymorphisms in IRAK-4 and AR in Chinese population.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".