Role of Egr1 on Pancreatic Endoderm Differentiation
Bibliographic record
Abstract
The low efficiency of in vitro differentiation of human embryonic stem cells (hESCs) or human-induced pluripotent stem cells (iPSCs) into insulin-producing cells is a crucial hurdle for the clinical implementation of human pluripotent stem cells (PSCs). Our previous investigation into the key factors for the differentiation of PSCs into insulin-producing cells suggested that the expression of GATA binding protein 6 (GATA6) and Gremlin 1 (GREM1) and inhibition of early growth response protein 1 (Egr1) may be important factors. In this study, we investigated the role of Egr1 in pancreas development. The transfection of small interfering RNA (siRNA) of Egr1 in the early phase induced the differentiation of iPSCs derived from fibroblasts (FiPSCs) into pancreatic endoderm and insulin-producing cells. In contrast, the downregulation of Egr1 in the late phase suppressed the differentiation of FiPSCs into pancreatic endoderm and insulin-producing cells. In addition, the overexpression of Egr1 suppressed the differentiation of iPSCs derived from pancreatic cells into pancreatic endoderm and insulin-producing cells. These data suggest that the downregulation of Egr1 in the early phase can efficiently induce the differentiation of iPSCs into insulin-producing cells.
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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 | 0.000 |
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".