KIR3DL1/S1 Allotypes Contribute Differentially to the Development of Behçet Disease
Bibliographic record
Abstract
Abstract Behçet disease is a chronic, relapsing-remitting autoinflammatory syndrome with a strong HLA-B*51 association. In this paper, we describe a human cohort of 267 individuals with Behçet disease and 445 matched controls from a tertiary referral center in the U.K. HLA-B*51 was confirmed as a genetic risk factor in this group (p = 0.0006, Bonferroni–Dunn correction for multiple testing [Pc] = 0.0192, odds ratio [OR] 1.92, 95% confidence interval [CI] 1.33–2.76). KIR3DL1/S1 allele-level analysis indicated that low-expressing KIR3DL1/S1 alleles in combination with KIR3DS1 increased the risk of developing Behçet disease (KIR3DL1LOW/KIR3DS1: p = 0.0004, Pc = 0.0040, OR 2.47, 95% CI 1.43–4.25), whereas high-expressing KIR3DL1/S1 alleles in combination with a null-expressing KIR3DL1 reduced the risk of disease (KIR3DL1HIGH/KIR3DL1NULL: p = 0.0035, Pc = 0.0350, OR 0.53, 95% CI 0.33–0.87). Behçet disease can manifest as a purely mucocutaneous disease or can involve other organ systems such as the eyes. In the U.K. cohort studied in this study, KIR3DL1LOW/KIR3DS1 increased the risk of ophthalmic disease (p = 1.2 × 10−5, OR 3.92, 95% CI 2.06–7.47), whereas KIR3DL1HIGH/KIR3DL1NULL reduced the risk of having purely mucocutaneous disease (p = 0.0048, OR 0.45, 95% CI 0.25–0.81). To our knowledge, this is the first analysis of KIR3DL1/S1 allelic variation in Behçet disease and may provide insight into the pathogenic role of HLA-B*51 and its interaction with KIR3DL1/S1.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".