A study of primary neural stem cell differentiation in vitro, focusing on the Three Amino acid Loop Extension (TALE) homeobox transcription factors
Bibliographic record
Abstract
Neural stem cells are capable of self-renewal and multilineage differentiation into the main cell types of the central nervous system, which are the neurons, astrocytes, and oligodendrocytes. These properties make neural stem cells an attractive cell source for potential cell-based therapies; however, thoroughly describing their gene expression programs is required to predict their safety and efficacy. In this study, we reveal that mouse embryonic day (E14) forebrain-derived primary neural stem cells have an astrocytic gene expression profile. We show that the NOTCH and BMP signalling pathways exhibit transcription profiles that are specific to the proliferation and differentiation of this neural stem cell source. Finally, we report the expression patterns of the Hox and TALE family homeobox genes in the E14 forebrain, E14 forebrain-derived primary neural stem cells, and their differentiating progeny. Protein expression analysis suggests that PREP2 is involved in neural stem cell proliferation and neuronogenesis, and that MEIS1 is involved in astrocyte differentiation. This is the first report on the expression patterns of TALE genes in forebrain-derived NSC differentiated in vitro, which provides a starting point to investigate the role of TALE genes in forebrain neurogenesis.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".