Direct Generation of Neurosphere-Like Cells from Human Dermal Fibroblasts
Bibliographic record
Abstract
Neural stem cell (NSC) transplantation replaces damaged brain cells and provides disease-modifying effects in many neurological disorders. However, there has been no efficient way to obtain autologous NSCs in patients. Given that ectopic factors can reprogram somatic cells to be pluripotent, we attempted to generate human NSC-like cells by reprograming human fibroblasts. Fibroblasts were transfected with NSC line-derived cellular extracts and grown in neurosphere culture conditions. The cells were then analyzed for NSC characteristics, including neurosphere formation, gene expression patterns, and ability to differentiate. The obtained induced neurosphere-like cells (iNS), which formed daughter neurospheres after serial passaging, expressed neural stem cell markers, and had demethylated SOX2 regulatory regions, all characteristics of human NSCs. The iNS had gene expression patterns that were a combination of the patterns of NSCs and fibroblasts, but they could be differentiated to express neuroglial markers and neuronal sodium channels. These results show for the first time that iNS can be directly generated from human fibroblasts. Further studies on their application in neurological diseases are warranted.
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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) | 0.002 | 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".