Beta-cell replacement strategies for diabetes
Bibliographic record
Abstract
Diabetes is characterized by elevated levels of blood glucose as a result of insufficient production of insulin from loss or dysfunction of pancreatic islet β‐cells. Here, we review several approaches to replacing β‐cells that were recently discussed at a symposium held in Kyoto, Japan. Transplant of donor human islets can effectively treat diabetes and eliminate the need for insulin injections, supporting research aimed at identifying abundant supplies of cells. Studies showing the feasibility of producing mouse islets in rats support the concept of generating pigs with human pancreas that can serve as donors of human islets, although scientific and ethical challenges remain. Alternatively, in vitro differentiation of both human embryonic stem cells and induced pluripotent stem cells is being actively pursued as an islet cell source, and embryonic stem cell‐derived pancreatic progenitor cells are now in clinical trials in North America in patients with diabetes. Macro‐encapsulation devices are being used to contain and protect the cells from immune attack, and alternate strategies of immune‐isolation are being pursued, such as islets contained within long microfibers. Recent advancements in genetic engineering tools offer exciting opportunities to broaden therapeutic strategies and to probe the genetic involvement in β‐cell failure that contributes to diabetes. Personalized medicine might eventually become a possibility with genetically edited patient‐induced pluripotent stem cells, and the development of simplified robust differentiation protocols that ideally become standardized and automated. Additional efforts to develop a safe and effective β‐cell replacement strategy to treat diabetes are warranted.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".