<i><scp>HLA</scp>‐A*33‐<scp>DR</scp>3</i> and <i>A*33‐<scp>DR</scp>9</i> haplotypes enhance the risk of type 1 diabetes in Han Chinese
Bibliographic record
Abstract
AIMS/INTRODUCTION: To investigate the typing for human leukocyte antigen (HLA) class I in Chinese patients with type 1 diabetes as a complement screening for HLA class II. MATERIALS AND METHODS: A total of 212 type 1 diabetic patients and 200 healthy controls were enrolled. The genetic polymorphisms of HLA class I and II were examined with a high-resolution polymerase chain reaction sequence-based typing method. RESULTS: The haplotype, A*33:03-B*58:01-C*03:02(A33), was associated with type 1 diabetes (P = 1.0 × 10(-4) , odds ratio 3.2 [1.738-5.843]). The A33-DR3 and A33-DR9 haplotypes significantly enhanced the risk of type 1 diabetes (A33-DR3, odds ratio 5.1 [2.40-10.78], P = 4.0 × 10(-6) ; A33-DR9, odds ratio 13.0 [1.69-100.32], P = 0.004). In type 1 diabetic patients, compared with A33-DR3-negative carriers, A33-DR3-positive carriers had significantly lower percentages of CD3(+) CD4(+) T cells (42.5 ± 7.72 vs 37.0 ± 8.35%, P = 0.023), higher percentages of CD3(+) CD8(+) T cells (27.4 ± 7.09 vs 32.8 ± 5.98%, P = 0.005) and T-cell receptor α/β T cells (70.0 ± 7.00 vs 73.6 ± 6.25%, P = 0.031), and lower CD4/CD8 ratios (1.71 ± 0.75 vs 1.16 ± 0.35, P = 0.003). CONCLUSIONS: It is the first time that the haplotypes A33-DR3 and A33-DR9 were found with an enhanced predisposition to type 1 diabetes in Han Chinese. A33-DR3 was associated with a reduction in the helper-to-cytotoxic cell ratio and preferential increase of T-cell receptor α/β T cell. The typing for HLA class I and its immunogenetic effects are important for more accurate HLA class II haplotype risk prediction and etiology research in type 1 diabetic 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 | 0.000 | 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) | 0.002 | 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".