Activation of Neural Cell Fate Programs Toward Direct Conversion of Adult Human Fibroblasts into Tri-Potent Neural Progenitors Using <i>OCT-4</i>
Bibliographic record
Abstract
Several transcription factors and methods have been used to convert fibroblasts directly to neural fate and have provided insights into molecular mechanisms as to how each of these required factors orchestrate neural fate conversion. Here, we provide evidence and detailed characterization of the direct conversion process of primary adult human fibroblasts (hFib) to neural progenitor cells (NPC) using OCT4 alone. Factors previously associated with neural cell fate conversion were induced during hFib-NPC(OCT-4) generation, where OCT-4 alone was sufficient to induce neural fate conversion without the use of promiscuous small-molecule manipulation. Human Fib-NPC(OCT-4) proliferate, express neural stem/progenitor markers, and possess developmental potential that gives rise to all three major subtypes of neural cells: astrocytes, oligodendrocytes, and neurons with functional capacity. We propose a de-convoluted reprogramming approach for neural fate conversion in which OCT4 is sufficient for inducing neural conversion from hFib for disease modeling as well as the fundamental study of early neural fate induction.
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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 | 0.000 |
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".