Association of IL1A, IL1B, and TNF Gene Polymorphisms With Chronic Rhinosinusitis With and Without Nasal Polyposis
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To replicate and extend recent findings in a Turkish population of associations between chronic rhinosinusitis (CRS) with nasal polyposis and single-nucleotide polymorphisms (SNPs) in the IL1A (rs17561 and Ser114Ala), IL1B (rs16944), and TNF (rs361525 and rs1800629) genes. DESIGN: In a case-control replication study, DNA samples were obtained from 206 patients with severe CRS (cases) and from 196 postal code-matched controls. For IL1A and TNF, the 3 reported SNPs were complemented with tagging SNPs using an International HapMap genotyping data set to ensure complete genetic coverage. For IL1B, only the single reported SNP was assessed. A total of 24 SNPs (7 in IL1A, 1 in IL1B, and 16 in TNF) were individually genotyped. The PLINK software package was used to perform genetic association tests. SETTING: Academic research. PATIENTS: Canadian population of individuals with severe CRS. MAIN OUTCOME MEASURES: Allelic differences between cases and controls. RESULTS: Significant allelic differences between cases and controls were obtained for IL1A rs17561 (odds ratio [OR], 1.48; P = .02). The following 3 additional SNPs in this gene were associated with CRS: rs2856838 (OR, 0.63; P = .003), rs2048874 (OR, 0.57; P = .01), and rs1800587 (OR, 1.49; P = .02). These 3 SNPs remained significant after correction for multiple testing. No association was found with IL1B or TNF. CONCLUSIONS: We replicated the previously reported association between the IL1A polymorphism and severe CRS and identified 3 potential new associations in the same gene. This further supports the potential contribution of IL1A to the development of CRS. We were unable to replicate previous reports of associations with IL1B or TNF.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it