β Cell replacement: improving on the design
Bibliographic record
Abstract
Purpose of review Here we summarize recent advancements in β cell replacement as a therapy for type 1 diabetes. Recent findings β cell replacement therapy has been proposed as a cure for type 1 diabetes with the introduction of the Edmonton protocol for cadaveric islet transplantation. To allow widespread use of this approach, efforts have focused on establishing an abundant source of insulin-producing β cells, protecting transplanted cells from ischemia-mediated death, immune rejection, and re-occurring autoimmunity. Recent developments addressing these issues include generation of insulin-producing cells from human pluripotent stem cells, different encapsulation strategies and prevention of ischemia upon transplant. Summary Despite significant advances in generating functional β cells from human pluripotent stem cells, several key challenges remain in regard to the survival of β cell grafts, protection from (auto-) immune destruction and implementation of additional safety mechanisms before a stem cell-based cell replacement therapy approach can be widely applied. Taking current findings into consideration, we outline a multilayered approach to design immune-privileged β cells from stem cells using state of the art genome editing technologies that if successfully incorporated could result in great benefit for diabetic patients and improve clinical results for cell replacement therapy.
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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 | 0.001 |
| 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) | 0.004 | 0.003 |
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".