Identification of a β-Cell-Specific HLA Class I Restricted Epitope in Type 1 Diabetes
Bibliographic record
Abstract
Type 1 diabetes is an autoimmune disease in which pancreatic beta-cells are destroyed by cytotoxic T-cells that recognize peptide epitopes presented by HLA class I molecules. The identification of human beta-cell epitopes may significantly improve the prospects for immunodiagnosis and immunotherapy in type 1 diabetes. Using algorithms to predict nonameric beta-cell peptides that would bind to the common HLA allele, HLA-A*0201, we identified a potential epitope from the leader sequence of islet amyloid polypeptide (human islet amyloid polypeptide [IAPP] precursor protein [preproIAPP] 5-13: KLQVFLIVL). Peripheral blood mononuclear cells (PBMCs) were isolated from 18 HLA-A*0201 patients with type 1 diabetes (9 with recent-onset [<180 days; range, 1-120 days] and 9 with long-standing diabetes [>180 days; range, 183-3,273 days]) and 9 healthy, nondiabetic control subjects. PBMCs were screened for peptide recognition using interferon-gamma enzyme-linked immunospot (ELISpot) assays. Of the nine patients with recent-onset type 1 diabetes, six had ELISpot responses to preproIAPP 5-13 that were >3 SDs above the mean of the nondiabetic control subjects (P = 0.002). In contrast, no patients with type 1 diabetes for >180 days had a response above this threshold. In summary, preproIAPP 5-13 is a novel HLA class I epitope recognized by a significant proportion of cytotoxic T-cells from HLA-A*0201 patients with recent-onset type 1 diabetes and may prove to be a useful tool for the prediction and/or prevention of this disease.
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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) | 0.001 | 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".