Considerations and challenges of islet transplantation and future therapies on the horizon
Bibliographic record
Abstract
Islet transplantation is a treatment for selected adults with type 1 diabetes and severe hypoglycemia. Islets from two or more donor pancreases, a scarce resource, are usually required to impact glycemic control, but the treatment falls short of a cure. Islets are avascular when transplanted into the hypoxic liver environment and subjected to inflammatory insults, immune attack, and toxicity from systemic immunosuppression. The Collaborative Islet Transplant Registry, with outcome data on over 1,000 islet transplant recipients, has demonstrated that larger islet numbers transplanted and older age of recipients are associated with better outcomes. Induction with T-cell depleting agents and the TNF-α inhibitor etanercept and maintenance systemic immunosuppression with mTOR inhibitors in combination with calcineurin inhibitors also appear advantageous, but concerns remain over immunosuppressive toxicity. We discuss strategies and therapeutics that address specific challenges of islet transplantation, many of which are at the preclinical stage of development. On the horizon are adjuvant cell therapies with mesenchymal stromal cells and regulatory T cells that have been used in preclinical models and in humans in other contexts; such a strategy may enable reductions in immunosuppression in the early peri-transplant period when the islets are vulnerable to apoptosis. Human embryonic stem cell-derived islets are in early-phase clinical trials and hold the promise of an inexhaustible supply of insulin-producing cells; effective encapsulation of such cells or, silencing of the human leukocyte antigen (HLA) complex would eliminate the need for immunosuppression, enabling this therapy to be used in all those with type 1 diabetes.
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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 | 0.010 | 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 | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 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".