Navigating challenges in human pluripotent stem cell-derived islet therapy for type 1 diabetes
Bibliographic record
Abstract
In the past two decades, several tissues have been generated from the differentiation of human pluripotent stem cells (hPSCs) to model development or disease, and for use in drug testing and cell replacement therapies. A frontliner of hPSC-derived tissues used in cell replacement therapies are the pancreatic cells, which have entered multiple clinical trials since 2014 for the treatment of type 1 diabetes (T1D). Despite challenges in early trials, the detection of endogenous C-peptide in recipients was encouraging. The results and challenges of these trials inspired new areas of research, leading to incremental advances in cell differentiation and delivery technologies, and a deeper understanding of the transplantation microenvironment to enhance therapeutic efficacy and longevity. Reports from the most recent trials demonstrated success in reducing or eliminating exogenous insulin administration for people with T1D, increasing hope for a cure for T1D via regenerative medicine. Recent efforts can be broadly categorized into: (1) improving the cell product as surrogates of native beta cells, (2) promoting engraftment post-transplant to support cell survival, integration into the host, and endocrine function, and (3) developing immunomodulation strategies to reduce or circumvent immunosuppression regimen. In this review, we discuss recent and emerging advances in these three areas and the potential, risk, and scalability of experimental models to the clinic.
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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 | 0.000 |
| 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 | 0.002 |
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".