Polymorphisms in interleukin‐1 receptor‐associated kinase 4 are associated with total serum IgE
Bibliographic record
Abstract
BACKGROUND: Serum immunoglobulin E (IgE) level is recognized to be under strong genetic control, but the causal and susceptibility genes remain to be identified. We sought to investigate the association between single nucleotide polymorphisms (SNPs) in the Toll-like receptor (TLR) signaling pathway and total serum IgE level. METHODS: A population of 206 patients with severe chronic rhinosinusitis (CRS) was used. Precise phenotyping of patients was accomplished by means of a questionnaire and clinical examination. Blood was drawn for measurement of total serum IgE, as well as DNA extraction. A maximally informative set of SNPs in the TLR1, 2, 3, 4, 6, 9, 10, CD14, MD2, MyD88, IRAK4, and TRAF6 genes were selected and genotyped. Significant findings were replicated in a second independent population of 956 subjects from 227 families with asthma. RESULTS: A total of 97 out of 104 SNPs were successfully genotyped. Three SNPs in IRAK4--rs1461567, rs4251513, and rs4251559--were associated with total serum IgE levels (P < 0.004). In the replication sample, the same SNPs as well as the same orientation of the risk allele were associated with IgE levels (P < 0.031). CONCLUSIONS: These results demonstrate a clear association between polymorphisms in the IRAK4 gene and serum IgE levels in patients with CRS and asthma. IRAK4 may be important in the regulation of IgE levels in patients with inflammatory diseases of the airways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".