A stepwise approach to deriving functional β-cells from human embryonic or induced pluripotent stem cells
Bibliographic record
Abstract
Our understanding of β-cell differentiation from pluripotent stem cells (PSCs) is rapidly evolving. Although progress has been made, challenges remain, particularly in achieving glucose-stimulated insulin secretion (GSIS). Human embryonic stem cells (hESCs) are valuable due to their pluripotent ability. A fixed protocol targeting master regulatory genes initiates stem cells into pancreatic lineage commitment. Due to the observations that a single stem cell can differentiate into multiple cell types depending on various factors and conditions, non-linear differentiation pathways exist. Co-expression of key factors remains essential for successful β-cell differentiation. The mature β-cell marker MAFA plays a critical role in maintaining the differentiation state and preventing dedifferentiation. Recapitulating pancreatic islet clustering enhances physiological responses, offering potential avenues for diabetes treatment. On the other hand, several enhanced differentiation protocols from induced pluripotent stem cells (iPSCs) have improved the functional insulin producing β-cells generated. These findings, with their potential to revolutionize diabetes treatment, highlight the complexity of β-cell differentiation and guide further advancements in regenerative medicine.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".