Schwann Cells Generated from Neonatal Skin-Derived Precursors or Neonatal Peripheral Nerve Improve Functional Recovery after Acute Transplantation into the Partially Injured Cervical Spinal Cord of the Rat
Bibliographic record
Abstract
The transplantation of Schwann cells (SCs) holds considerable promise as a therapy for spinal cord injury, but the optimal source of these cells and the best timing for intervention remains debatable. Previously, we demonstrated that delayed transplantation of SCs generated from neonatal mouse skin-derived precursors (SKP-SCs) promoted repair and functional recovery in rats with thoracic contusions. Here, we conducted two experiments using neonatal rat cells and an incomplete cervical injury model to examine the efficacy of acute SKP-SC transplantation versus media control (Experiment 1) and versus nerve-derived SC or dermal fibroblast (Fibro) transplantation (Experiment 2). Despite limited graft survival, by 10 weeks after injury, rats that received SCs from either source showed improved functional recovery compared with media- or fibroblast-treated animals. Compared with media treatment, SKP-SC-transplanted rats showed enhanced rubrospinal tract (RST) sparing/plasticity in the gray matter (GM) rostral to injury, particularly in the absence of immunosuppression. The functional benefits of SC transplantations over fibroblast treatment correlated with the enhanced preservation of host tissue, reduced RST atrophy, and/or increased RST sparing/plasticity in the GM. In summary, our results indicate that: (1) early transplantation of neonatal SCs generated from skin or nerve promotes repair and functional recovery after incomplete cervical crush injury; (2) either of these cell types is preferable to Fibros for these purposes; and (3) age-matched SCs from these two sources do not differ in terms of their reparative effects or functional efficacy after transplantation into the injured cervical 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".