AT7867 promotes pancreatic progenitor differentiation of human iPSCs
Bibliographic record
Abstract
Generation of pure pancreatic progenitor (PP) cells is critical for clinical translation of stem cell-derived islets. Herein, we performed PP differentiation with and without AKT/P70 inhibitor AT7867 and characterized the resulting cells at protein and transcript level in vitro and in vivo upon transplantation into diabetic mice. AT7867 treatment increased the percentage of PDX1 + NKX6.1 + (−AT7867: 50.9% [IQR 48.9%–53.8%]; +AT7867: 90.8% [IQR 88.9%–93.7%]; p = 0.0021) and PDX1 + GP2 + PP cells (−AT7867: 39.22% [IQR 36.7%–44.1%]; +AT7867: 90.0% [IQR 88.2%–93.6%]; p = 0.0021). Transcriptionally, AT7867 treatment significantly upregulated PDX1 (p = 0.0001), NKX6 . 1 (p = 0.0005), and GP2 (p = 0.002) expression compared with controls, while off-target markers PODXL (p < 0.0001) and TBX2 (p < 0.0001) were significantly downregulated. Transplantation of AT7867-treated PPs resulted in faster hyperglycemia reversal in diabetic mice compared with controls (time and group: p < 0.0001). Overall, our data show that AT7867 enhances PP cell differentiation leading to accelerated diabetes reversal.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".