Predisposition of HLA-DRB1*04:01/*15 heterozygous genotypes to Japanese mixed connective tissue disease
Bibliographic record
Abstract
Abstract Mixed connective tissue disease (MCTD) is a rare systemic autoimmune disease characterized by the production of anti-U1 ribonucleoprotein antibodies and systemic symptoms similar to those of some other autoimmune diseases. HLA-DRB1 polymorphisms are important genetic risk factors for MCTD, but precise associations of DRB1 genotypes with MCTD have not been reported in Japanese people. Genotyping of HLA-DRB1 and -DQB1 was performed in Japanese MCTD patients (n = 116) and controls (n = 413). Associations of specific allele carriers and genotype frequencies with MCTD were analyzed.The following alleles were found to be associated with predisposition to MCTD: HLA-DRB1*04:01 (P = 8.66 × 10–6, Pc = 0.0003, odds ratio [OR] 7.96, 95% confidence interval [CI] 3.13‒20.24) and DRB1*09:01 (P = 0.0189, Pc = 0.5468, OR 1.73, 95% CI 1.12‒2.67). In contrast, the carrier frequency of the DRB1*13:02 allele (P = 0.0032, Pc = 0.0929, OR 0.28, 95% CI 0.11‒0.72) was lower in MCTD patients than in controls. The frequencies of heterozygosity for HLA-DRB1*04:01/*15 (P = 1.88 × 10–7, OR 81.54, 95% CI 4.74‒1402.63) and DRB1*09:01/*15 (P = 0.0061, OR 2.94, 95% CI 1.38‒6.25) were also higher in MCTD patients. Haplotype and logistic regression analyses suggested a predisposing role for HLA-DRB1*04:01, DQB1*03:03, and a protective role for DRB1*13:02. Increased frequencies of HLA-DRB1*04:01/*15 and DRB1*09:01/*15 heterozygous genotypes were found in Japanese MCTD patients.
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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 | 0.000 | 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) | 0.005 | 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".