From Edmonton to Lantidra and beyond: immunoengineering islet transplantation to cure type 1 diabetes
Bibliographic record
Abstract
Type 1 diabetes (T1D) is characterized by the autoimmune destruction of insulin-producing β cells within pancreatic islets, the specialized endocrine cell clusters of the pancreas. Islet transplantation has emerged as a β cell replacement therapy, involving the infusion of cadaveric islets into a patient's liver through the portal vein. This procedure offers individuals with T1D the potential to restore glucose control, reducing or even eliminating the need for exogenous insulin therapy. However, it does not address the underlying autoimmune condition responsible for T1D. The need for systemic immunosuppression remains the primary barrier to making islet transplantation a more widespread therapy for patients with T1D. Here, we review recent progress in addressing the key limitations of islet transplantation as a viable treatment for T1D. Concerns over systemic immunosuppression arise from its potential to cause severe side effects, including opportunistic infections, malignancies, and toxicity to transplanted islets. Recognizing the risks, the Edmonton protocol (2000) marked a shift away from glucocorticoids to prevent β cell damage specifically. This transition led to the development of combination immunosuppressive therapies and the emergence of less toxic immunosuppressive and anti-inflammatory drugs. More recent advances in islet transplantation derive from islet encapsulation devices, biomaterial platforms releasing immunomodulatory compounds or surface-modified with immune regulating ligands, islet engineering and co-transplantation with accessory cells. While most of the highlighted studies in this review remain at the preclinical stage using mouse and non-human primate models, they hold significant potential for clinical translation if a transdisciplinary research approach is prioritized.
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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 | 0.001 | 0.000 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".