Generation of defined neural populations from pluripotent stem cells
Bibliographic record
Abstract
Effective and efficient generation of human neural stem cells and subsequently functional neural populations from pluripotent stem cells has facilitated advancements in the study of human development and disease modelling. This review will discuss the established protocols for the generation of defined neural populations including regionalized neurons and astrocytes, oligodendrocytes and microglia. Early protocols were established in embryonic stem cells (ESC) but the discovery of induced pluripotent stem cells (iPSC) in 2006 provided a new platform for modelling human disorders of the central nervous system (CNS). The ability to produce patient- and disease-specific iPSC lines has created a new age of disease modelling. Human iPSC may be derived from adult somatic cells and subsequently patterned into numerous distinct cell types. The ability to derive defined and regionalized neural populations from iPSC provides a powerful in vitro model of CNS disorders. This article is part of the theme issue ‘Designer human tissue: coming to a lab near you’.
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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 | 0.001 |
| 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".