Skin-Derived Precursors Generate Myelinating Schwann Cells for the Injured and Dysmyelinated Nervous System
Bibliographic record
Abstract
Although neural stem cells hold considerable promise for treatment of the injured or degenerating nervous system, their current human sources are embryonic stem cells and fetally derived neural tissue. Here, we asked whether rodent and human skin-derived precursors (SKPs), neural crest-related precursors found in neonatal dermis, represent a source of functional, myelinating Schwann cells. Specifically, cultured SKPs responded to neural crest cues such as neuregulins to generate Schwann cells, and these Schwann cells proliferated and induced myelin proteins when in contact with sensory neuron axons in culture. Similar results were obtained in vivo; 6 weeks after transplantation of naive SKPs or SKP-derived Schwann cells into the injured peripheral nerve of wild-type or shiverer mutant mice (which are genetically deficient in myelin basic protein), the majority of SKP-derived cells had associated with and myelinated axons. Naive rodent or human SKPs also generated Schwann cells that myelinated CNS axons when transplanted into the dysmyelinated brain of neonatal shiverer mice. Thus, neonatal SKPs generate functional neural progeny in response to appropriate neural crest cues and, in so doing, provide a highly accessible source of myelinating cells for treatment of nervous system injury, congenital leukodystrophies, and dysmyelinating disorders.
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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".