Directed Differentiation of Pluripotent Stem Cells: From Developmental Biology to Therapeutic Applications
Bibliographic record
Abstract
The discovery of human pluripotent stem cells has laid the foundation for an emerging new field of biomedical research that holds promise to develop models of human development and disease, establish new strategies for discovering and testing drugs, and provide systems for the generation of cells and tissues for transplantation for the treatment of disease. The remarkable potential of pluripotent stem cells has sparked interest and excitement in academia, the biotechnology and pharmaceutical industries, as well as the lay public. Although the potential of human pluripotent stem cells is truly outstanding, fulfilling this potential is solely dependent on our ability to efficiently generate functional cell types from them. Some of the most successful approaches in this area to date are those that have applied the principles of developmental biology to stem cell differentiation. In this chapter, we review these concepts and highlight specific examples demonstrating that pluripotent stem cell differentiation in culture recapitulates the key aspects of early embryonic development. By continuing to translate insights from embryology to stem cell biology, progress in our ability to generate specific cell types from pluripotent stem cells will advance, yielding enriched populations of human cell types, including cardiomyocytes, hematopoietic cells, hepatocytes, pancreatic beta cells, and neural cells, for drug discovery, functional evaluation in preclinical models of human disease, and ultimately clinical applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".