Coinhibitory T-Cell Signaling in Islet Allograft Rejection and Tolerance
Bibliographic record
Abstract
Autoaggressive T cells directed against insulin secreting pancreatic beta-cells mediate the development of type 1 diabetes. Islet transplantation offers superior glycemic control over exogenous insulin, but chronic immunosuppression limits its broad application. Pathogenic T cells are also important in allograft rejection. Inducing and maintaining antigen-specific peripheral T-cell tolerance toward beta-cells is an attractive strategy to prevent autoimmune disease, and to facilitate treatment of diabetes with islet allografts without long-term immunosuppression. Recent efforts have focused on blocking costimulatory T-cell signals for tolerance induction. Although costimulatory blockade can prolong graft survival, true immunological tolerance remains elusive. Costimulatory signals may even be required for the maintenance of peripheral tolerance. The discovery of novel coinhibitory T-cell pathways, including CTLA-4, PD-1, and BTLA, offers an alternative approach. Stimulating negative T cell cosignals alone or in combination may help induce tolerance. The focus of this review is to summarize the strategies directed at turning off the immune response by exploiting these negative cosignaling pathways in tolerance induction in islet transplantation. Activating several coinhibitory pathways together may be synergistic in preventing pathogenic T-cell responses. Tolerance induction will likely rely on understanding the balance of positive and negative signals affecting the state of T-cell activation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".