Analysis of T-Cell Assays to Measure Autoimmune Responses in Subjects With Type 1 Diabetes
Bibliographic record
Abstract
Type 1 diabetes is a chronic autoimmune disease mediated by autoreactive T-cells. Several experimental therapies targeting T-cells are in clinical trials. To understand how these therapies affect T-cell responses in vivo, assays that directly measure human T-cell function are needed. In a blinded, multicenter, case-controlled study conducted by the Immune Tolerance Network, we tested responses in an immunoblot and T-cell proliferative assay to distinguish type 1 diabetic patients from healthy control subjects. Peripheral blood cells from 39 healthy control subjects selected for DR4 and 23 subjects with recently diagnosed type 1 diabetes were studied. Autoantibody responses were measured in serum samples. Positive responses in both assays were more common in peripheral blood mononuclear cells from new-onset type 1 diabetic patients compared with control subjects. The proliferative, immunoblot, and autoantibody assays had sensitivities of 58, 91, and 78% with specificities of 94, 83, and 85%, respectively. When cellular assays were combined with autoantibody measurements, the sensitivity of the measurements was 75% with 100% specificity. We conclude that cellular assays performed on peripheral blood have a high degree of accuracy in discriminating responses in subjects with type 1 diabetes from healthy control subjects. They may be useful for assessment of cellular autoimmune responses involved in type 1 diabetes.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".