Islet Transplantation in Type 1 Diabetes: Ongoing Challenges, Refined Procedures, and Long-Term Outcome
Bibliographic record
Abstract
Remarkable progress has been made in islet transplantation over a span of 40 years. Once just an experimental curiosity in mice, this therapy has moved forward, and can now provide robust therapy for highly selected patients with type 1 diabetes (T1D), refractory to stabilization by other means. This progress could not have occurred without extensive dynamic international collaboration. Currently, 1,085 patients have undergone islet transplantation at 40 international sites since the Edmonton Protocol was reported in 2000 (752 allografts, 333 autografts), according to the Collaborative Islet Transplant Registry. The long-term results of islet transplantation in selected centers now match registry data of pancreas-alone transplantation, with 6 sites reporting five-year insulin independence rates ≥50%. Islet transplantation has been criticized for the use of multiple donor pancreas organs, but progress has also occurred in single-donor success, with 10 sites reporting increased single-donor engraftment. The next wave of innovative clinical trial interventions will address instant blood-mediated inflammatory reaction (IBMIR), apoptosis, and inflammation, and will translate into further marked improvements in single-donor success. Effective control of auto- and alloimmunity is the key to long-term islet function, and high-resolution cellular and antibody-based assays will add considerable precision to this process. Advances in immunosuppression, with new antibody-based targeting of costimulatory blockade and other T-B cellular signaling, will have further profound impact on the safety record of immunotherapy. Clinical trials will move forward shortly to test out new human stem cell derived islets, and in parallel trials will move forward, testing pig islets for compatibility in patients. Induction of immunological tolerance to self-islet antigens and to allografts is a difficult challenge, but potentially within our grasp.
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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 | 0.015 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".