Bibliographic record
Abstract
Embryonic stem (ES) cells are a pluripotent and renewable cellular resource with tremendous potential for broad applications in regenerative medicine. Arguably the most important consideration for stem cell-based therapies is the ability to precisely direct the differentiation of stem cells along a preferred cellular lineage. During development, lineage commitment is a multistep process requiring the activation and repression of sets of genes at various stages, from an ES cell identity to a tissue-specific stem cell identity and beyond. Thus, the challenge is to ensure that the pattern of genomic regulation is recapitulated during the in vitro differentiation of ES cells into stem/progenitor cells of the appropriate tissue in a robust, predictable and stable manner. To address this issue, we must understand the ontogeny of tissue-specific stem cells during normal embryogenesis and compare the ontogeny of tissue-specific stem cells in ES cell models. Here, we discuss the issue of directed differentiation of pluripotent ES cells into neural stem cells, which is fundamentally linked to two early events in the development of the mammalian nervous system: the 'decision' of the ectoderm to acquire a neural identity (neural determination) and the origin of neural stem cells within this neural-committed population of cells. A clearer understanding of the molecular and cellular mechanisms that govern mammalian neural cell fate determination will lead to improved ES technology applications in neural regeneration.
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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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".