Tregs and Mixed Chimerism as Approaches for Tolerance Induction in Islet Transplantation
Bibliographic record
Abstract
Pancreatic islet transplantation is a promising method for the treatment of type 1 and type 3 diabetes whereby replacement of islets may be curative. However, long-term treatment with immunosuppressive drugs (ISDs) remains essential for islet graft survival. Current ISD regimens carry significant side-effects for transplant recipients, and are also toxic to the transplanted islets. Pre-clinical efforts to induce immune tolerance to islet allografts identify ways in which the recipient immune system may be reeducated to induce a sustained transplant tolerance and even overcome autoimmune islet destruction. The goal of these efforts is to induce tolerance to transplanted islets with minimal to no long-term immunosuppression. Two most promising cell-based therapeutic strategies for inducing immune tolerance include T regulatory cells (T regs ) and donor and recipient hematopoietic mixed chimerism. Here, we review preclinical studies which utilize T regs for tolerance induction in islet transplantation. We also review myeloablative and non-myeloablative hematopoietic stem cell transplantation (HSCT) strategies in preclinical and clinical studies to induce sustained mixed chimerism and allograft tolerance, in particular in islet transplantation. Since T regs play a critical role in the establishment of mixed chimerism, it follows that the combination of T reg and HSCT may be synergistic. Since the success of the Edmonton protocol, the feasibility of clinical islet transplantation has been established and nascent clinical trials testing immune tolerance strategies using T regs and/or hematopoietic mixed chimerism are underway or being formulated.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".