bHLH transcription factors in neural development, disease, and reprogramming
Bibliographic record
Abstract
The formation of functional neural circuits in the vertebrate central nervous system (CNS) requires that appropriate numbers of the correct types of neuronal and glial cells are generated in their proper places and times during development. In the embryonic CNS, multipotent progenitor cells first acquire regional identities, and then undergo precisely choreographed temporal identity transitions (i.e. time-dependent changes in their identity) that determine how many neuronal and glial cells of each type they will generate. Transcription factors of the basic-helix-loop-helix (bHLH) family have emerged as key determinants of neural cell fate specification and differentiation, ensuring that appropriate numbers of specific neuronal and glial cell types are produced. Recent studies have further revealed that the functions of these bHLH factors are strictly regulated. Given their essential developmental roles, it is not surprising that bHLH mutations and de-regulated expression are associated with various neurological diseases and cancers. Moreover, the powerful ability of bHLH factors to direct neuronal and glial cell fate specification and differentiation has been exploited in the relatively new field of cellular reprogramming, in which pluripotent stem cells or somatic stem cells are converted to neural lineages, often with a transcription factor-based lineage conversion strategy that includes one or more of the bHLH genes. These concepts are reviewed herein.
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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) | 0.001 | 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) | 0.004 | 0.003 |
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".