Differentiation of human adult skin‐derived neuronal precursors into mature neurons
Bibliographic record
Abstract
The isolation of autologous neuronal precursors from skin-derived precursor cells extracted from adult human skin would be a very efficient source of neurons for the treatment of various neurodegenerative diseases. The purpose of this study was to demonstrate that these neuronal precursors were able to differentiate into mature neurons. We isolated neuronal precursors from breast skin and expanded them in vitro for over ten passages. We showed that 48% of these cells were proliferating after the first passage, while this growth rate decreased after the second passage. We demonstrated that 70% of these cells were nestin-positive after the third passage, while only 17% were neurofilament M-positive after 7 days of differentiation. These neuronal precursors expressed betaIII tubulin, the dendritic marker MAP2 and the presynaptic marker synaptophysin after 7 days of in vitro maturation. They also expressed the postsynaptic marker PSD95 and the late neuronal markers NeuN and neurofilament H after 21 days of differentiation, demonstrating they became terminally differentiated neurons. These markers were still expressed after 50 days of culture. The generation of autologous neurons from an accessible adult human source opens many potential therapeutic applications and has a great potential for the development of experimental studies on normal human neurons.
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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".