Interleukin 1 and nuclear factor-kappaB polymorphisms in ankylosing spondylitis in Canada and Korea.
Bibliographic record
Abstract
OBJECTIVE: The interleukin 1alpha and 1beta (IL-1alpha, IL-1beta) are potent mediators of inflammation and immunity. IL-1 receptor antagonist (IL-1Ra) is a protein that binds to IL-1 receptors and competitively inhibits the binding of IL-1alpha and IL-1beta. There are reports of IL-1 complex gene polymorphisms in ankylosing spondylitis (AS), but the results have been inconsistent. NFKB1 encodes the genes for the p50 and p101 nuclear factor-kappaB (NF-kappaB) isoforms, which are recognized as critical to inflammatory disease. To date there have been no reports examining an association between NFKB1 and AS. We investigated polymorphisms of IL-1 complex and NF-kappaB1 with 2 genetically and geographically different populations. METHODS: Subjects with AS satisfied modified New York criteria for AS. Healthy controls were recruited at each respective site. Subjects with AS were genotyped for the following: IL-1alpha-889 single nucleotide polymorphism (SNP); IL-1beta +3953 SNP; IL-1Ra (86 base pair variable number tandem repeat within intron 2); and NFKB1 (-94 insertion/deletion polymorphism). RESULTS: In total, 205 subjects with AS and 200 controls from Seoul, Korea, and 68 subjects with AS and 164 controls from Toronto, Canada, were genotyped for the IL-1alpha and IL-1beta polymorphisms and 115 controls for the IL-1Ra and NF-kappaB polymorphisms. There were no differences of IL-1alpha, IL-1beta, IL-1Ra, and NF-kappaB polymorphisms between AS patients and controls in these populations. CONCLUSION: Our analysis of these SNP in the IL-1 complex and NF-kappaB genes does not support a major role for either in AS susceptibility in the Seoul and Toronto populations.
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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 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".