Regulation of type 1 diabetes by a self-MHC class II peptide: role of transforming growth factor beta (TGF-beta).
Bibliographic record
Abstract
The present study was undertaken to analyze the regulatory T cells generated in response to class I derived self-I-A beta(g7) (54-76) peptide. It was observed T cells from young unprimed type 1 diabetes (T1D) prone NOD mice did not respond to self-I-A beta(g7) (54-76) peptide although T cells from primed young NOD mice showed a strong response. T cells from young unprimed BALB/c mice responded to self-I-A beta(d) (62-78) peptide. However, a breakdown of tolerance to these peptides was observed with age in both the strains. Culture supernatant from I-A beta(g7) (54-76) peptide-primed cells secreted large amounts of TGF-beta and inhibited T cell responses in allogeneic-MLR. Further, I-A beta(g7) (54-76) peptide specific T cell lines from young (I-A.Y) and diabetic (I-A.D) NOD mice were established. I-A.Y secreted IL-4, TGF-beta and IL-10 while I-A.D T cell line secreted IL-10 and IFN-gamma. We found that I-A.D T cell line induced diabetes when transferred in NOD/SCID mice but I-A.Y T cell line did not induce disease. These results show that immunization of NOD mice with I-A beta(g7) (54-76) peptide at a younger age induces a regulatory T cell response suggesting that correcting the defects in immunoregulatory mechanisms using self-MHC peptides may be one of the approaches to prevent autoimmune diseases like T1D.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".