Inducible Pluripotent Stem Cells as a Potential Cure for Diabetes
Bibliographic record
Abstract
Over the last century, diabetes has been treated with subcutaneous insulin, a discovery that enabled patients to forego death from hyperglycemia. Despite novel insulin formulations, patients with diabetes continue to suffer morbidity and mortality with unsustainable costs to the health care system. Continuous glucose monitoring, wearable insulin pumps, and closed-loop artificial pancreas systems represent an advance, but still fail to recreate physiologic euglycemia and are not universally available. Islet cell transplantation has evolved into a successful modality for treating a subset of patients with 'brittle' diabetes but is limited by organ donor supply and immunosuppression requirements. A novel approach involves generating autologous or immune-protected islet cells for transplant from inducible pluripotent stem cells to eliminate detrimental immune responses and organ supply limitations. In this review, we briefly discuss novel mechanisms for subcutaneous insulin delivery and define their shortfalls. We describe embryological development and physiology of islets to better understand their role in glycemic control and, finally, discuss cell-based therapies for diabetes and barriers to widespread use. In response to these barriers, we present the promise of stem cell therapy, and review the current gaps requiring solutions to enable widespread use of stem cells as a potential cure for 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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".