Update on islet cell transplantation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Chronic diabetes-related complications continue to exert a rapidly growing and unsustainable pressure on healthcare systems worldwide. In type 1 diabetes, glycemic control is particularly challenging, as intensive management substantially increase the risk of severe hypoglycemic episodes. Alternative approaches to address this issue are required. Islet cell transplantation offers the best approach to reduce hypoglycemic risks and glycemic lability, while providing optimal glycemic control. Although ongoing efforts have improved clinical outcomes, the constraints in tissue sources and the need for chronic immunosuppression limit the application of islet cell transplantation as a curative therapy for diabetes. This review provides an update on islet cell transplantation, focusing on recent clinical experience, ongoing research, and future challenges. RECENT FINDINGS: Current evidence demonstrates advances in terms of long-term glycemic control, improved insulin independence rates, and novel approaches to eliminate chronic immunosuppression requirements after islet cell transplantation. Advances in stem cell-based therapies provide a promising path towards truly personalized regenerative therapies, solving both tissue supply shortage and the need for lifelong immunosuppression, enabling widespread use of this potentially curative treatment. However, as these therapies enter the clinical realm, regional access variability and ethical questions regarding commercialization are becoming increasingly important and require a collaborative solution. SUMMARY: In this state-of-the-art review, we discuss current clinical evidence and discuss key aspects on the present and future of islet cell transplantation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".