Tregitope applications to tolerance induction in autoimmune diseases. (116.10)
Bibliographic record
Abstract
Abstract Modulation of T cell responses may contribute to the design of new approaches for the treatment of autoimmune and inflammatory diseases. IVIG is one example of a therapy with this effect, and evidence is accumulating that Tregitopes (De Groot et al. Blood, 2008, 112:3303; natural T regulatory epitopes derived from IgG) provide beneficial immunomodulatory effects that parallel the effects of IVIG. In this presentation, we will provide evidence that Tregitope sequences derived from human IgG can reproduce immunomodulatory effects of IVIG in vitro and in vivo. In vitro, Tregitopes activate CD4+CD25+FoxP3+ natural regulatory T cells (nTreg). In vitro and in vivo, Tregitopes cause Tregs to produce IL-10, and to expand, and iTreg are induced. Induction and functions of nTregs have been examined in model systems such as D011.10 TCR transgenic mice, transplant of BM12 to C57BL/6, AAV-mediated gene transfer and EAE. Together, the data show that effector T cells, Th17, and Th9 cells are modified in the presence of Tregitopes. In OVA-induced allergic airway disease, we observed significant and reproducible expansion of Tregs in conjunction with decreased airway reactivity that was comparable to, if not greater than IVIG. We will provide additional unpublished evidence demonstrating the antigen specificity of tolerance induction using Tregitopes in conjunction with target antigens, and discuss the relevance of Tregitopes to the treatment of human immune-mediated 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) | 0.010 | 0.002 |
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".