Clinical islet transplant: current and future directions towards tolerance
Bibliographic record
Abstract
The ultimate goal of islet transplantation is to completely correct the diabetic state from an unlimited donor source, without the need for chronic immunosuppressive drug therapy. Although islet transplantation provides an opportunity to develop innovative strategies for tolerance in the clinic, both alloimmune and autoimmune barriers must be controlled, if stable graft function is to be maintained long-term. After islet extraction from the pancreas, the cellular graft may be stored in tissue culture or cryopreserved for banking, providing an opportunity not only to optimally condition the recipient but also to allow in vitro immunologic manipulation of the graft before transplantation, unlike solid organ grafts. As such, islets may be considered a "special case." Remarkable progress has occurred in the last three years, with dramatic improvements in outcomes after clinical islet transplantation. The introduction of a steroid-free, sirolimus-based, anti-rejection protocol and islets prepared from two (or rarely three) donors led to high rates of insulin independence. The "Edmonton Protocol" has been successfully replicated by other centers in an international multicenter trial. A number of key refinements in pancreas transportation, processing, purification on non-ficoll-based media, storage of islets in culture for two days and newer immunological conditioning and induction therapies have led to continued advancement through extensive collaboration between key centers. This review outlines the historical development of islet transplantation over the past 30 years, provides an update on current clinical outcomes, and summarizes a series of unique opportunities for development and early testing of tolerance protocols in patients.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".