Tankyrase inhibition promotes endocrine commitment of hPSC-derived pancreatic progenitors
Bibliographic record
Abstract
Human pluripotent stem cells (hPSCs) have the potential to differentiate into various cell types, including pancreatic insulin-producing β cells, which are crucial for developing therapies for diabetes. However, current methods for directing hPSC differentiation towards pancreatic β-like cells are often inefficient and produce cells that do not fully resemble the native counterparts. Here, we report that highly selective tankyrase inhibitors, such as WIKI4, significantly enhances pancreatic differentiation from hPSCs. Our results show that WIKI4 promotes the formation of pancreatic progenitors that give rise to islet-like cells with improved β-like cell frequencies and glucose responsiveness compared to our standard cultures. These findings not only advance our understanding of pancreatic development, but also provide a promising new tool for generating pancreatic cells for research and potential therapeutic applications. “Here the authors demonstrate that ß cell differentiation from human pluripotent stem cells can be improved by replacing nicotinamide with tankyrase inhibitors. This results in pancreatic progenitors that form islet-like cells with increased ß cell frequencies and glucose responsiveness.”
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".