Skin-Derived Precursors Generate Myelinating Schwann Cells That Promote Remyelination and Functional Recovery after Contusion Spinal Cord Injury
Bibliographic record
Abstract
Transplantation of exogenous cells is one approach to spinal cord repair that could potentially enhance the growth and myelination of endogenous axons. Here, we asked whether skin-derived precursors (SKPs), a neural crest-like precursor that can be isolated and expanded from mammalian skin, could be used to repair the injured rat spinal cord. To ask this question, we isolated and expanded genetically tagged murine SKPs and either transplanted them directly into the contused rat spinal cord or differentiated them into Schwann cells (SCs), and performed similar transplantations with the isolated, expanded SKP-derived SCs. Neuroanatomical analysis of these transplants 12 weeks after transplantation revealed that both cell types survived well within the injured spinal cord, reduced the size of the contusion cavity, myelinated endogenous host axons, and recruited endogenous SCs into the injured cord. However, SKP-derived SCs also provided a bridge across the lesion site, increased the size of the spared tissue rim, myelinated spared axons within the tissue rim, reduced reactive gliosis, and provided an environment that was highly conducive to axonal growth. Importantly, SKP-derived SCs provided enhanced locomotor recovery relative to both SKPs and forebrain subventricular zone neurospheres, and had no impact on mechanical or heat sensitivity thresholds. Thus, SKP-derived SCs provide an accessible, potentially autologous source of cells for transplantation into and treatment of the injured spinal cord.
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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 | 0.001 |
| 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".