Efficient Generation of NKX6-1+ Pancreatic Progenitors from Multiple Human Pluripotent Stem Cell Lines
Bibliographic record
Abstract
Human pluripotent stem cells (hPSCs) represent a renewable source of pancreatic beta cells for both basic research and therapeutic applications. Given this outstanding potential, significant efforts have been made to identify the signaling pathways that regulate pancreatic development in hPSC differentiation cultures. In this study, we demonstrate that the combination of epidermal growth factor (EGF) and nicotinamide signaling induces the generation of NKX6-1(+) progenitors from all hPSC lines tested. Furthermore, we show that the size of the NKX6-1(+) population is regulated by the duration of treatment with retinoic acid, fibroblast growth factor 10 (FGF10), and inhibitors of bone morphogenetic protein (BMP) and hedgehog signaling pathways. When transplanted into NOD scid gamma (NSG) recipients, these progenitors differentiate to give rise to exocrine and endocrine cells, including monohormonal insulin(+) cells. Together, these findings provide an efficient and reproducible strategy for generating highly enriched populations of hPSC-derived beta cell progenitors for studies aimed at further characterizing their developmental potential in vivo and deciphering the pathways that regulate their maturation in vitro.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".