Peptide‐activated double‐negative T cells can prevent autoimmune type‐1 diabetes development
Bibliographic record
Abstract
Autoimmune diseases may develop because of defective maturation, activation, differentiation and function of regulatory T cells. Previous studies have shown that exposure to donor antigen activates peripheral TCRalphabeta+CD3+CD4-CD8-NK1.1-, double-negative (DN) T cells, which specifically suppress anti-donor T cells and enhance survival of skin and heart grafts from allogeneic and xenogeneic donors. However, the role of DN T cells in preventing T cell-mediated autoimmune disease is unknown. Here, we analyzed the ability of DN T cells to recognize peptides expressed on self MHC and to suppress peptide-reactive CD8+ T cells, using the P14 mouse model that expresses a transgenic TCR specific for gp33 peptide presented on self MHC class I-Db. We found that injection of gp33 peptide resulted in increased DN and decreased CD8+ T cell numbers in the lymph nodes when compared to untreated mice. Injection of gp33, but not TCR-non-specific AV peptide, increased expression of T cell activation markers on DN T cells. Moreover, gp33-activated DN T cells suppressed proliferation of syngeneic CD8+ T cells via killing activated CD8+ T cells in an antigen-specific fashion in vitro. Furthermore, transferring gp33-activated DN T cells inhibited the development of autoimmune diabetes, suggesting that DN T cells may provide a novel therapy for T cell-mediated autoimmune diseases.
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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 | 0.000 |
| 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 | 0.001 |
| 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".