Progress in Translational Regulatory T Cell Therapies for Type 1 Diabetes and Islet Transplantation
Bibliographic record
Abstract
Regulatory T cells (Tregs) have become highly relevant in the pathophysiology and treatment of autoimmune diseases, such as type 1 diabetes (T1D). As these cells are known to be defective in T1D, recent efforts have explored ex vivo and in vivo Treg expansion and enhancement as a means for restoring self-tolerance in this disease. Given their capacity to also modulate alloimmune responses, studies using Treg-based therapies have recently been undertaken in transplantation. Islet transplantation provides a unique opportunity to study the critical immunological crossroads between auto- and alloimmunity. This procedure has advanced greatly in recent years, and reports of complete abrogation of severe hypoglycemia and long-term insulin independence have become increasingly reported. It is clear that cellular transplantation has the potential to be a true cure in T1D, provided the remaining barriers of cell supply and abrogated need for immune suppression can be overcome. However, the role that Tregs play in islet transplantation remains to be defined. Herein, we synthesize the progress and current state of Treg-based therapies in T1D and islet transplantation. We provide an extensive, but concise, background to understand the physiology and function of these cells and discuss the clinical evidence supporting potency and potential Treg-based therapies in the context of T1D and islet transplantation. Finally, we discuss some areas of opportunity and potential research avenues to guide effective future clinical application. This review provides a basic framework of knowledge for clinicians and researchers involved in the care of patients with T1D and islet transplantation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".