Transplanted Neurally Modified Bone Marrow–Derived Mesenchymal Stem Cells Promote Tissue Protection and Locomotor Recovery in Spinal Cord Injured Rats
Bibliographic record
Abstract
BACKGROUND: Stem cell-based therapy for repair and replacement of lost neural cells is a promising treatment for central nervous system (CNS) diseases. Bone marrow (BM)-derived mesenchymal stem cells (MSCs) can differentiate into neural phenotypes and be isolated and expanded for autotransplantation with no risk of rejection. OBJECTIVE: The authors examined whether transplanted neurally induced human MSCs (NI hMSCs), developed by a new procedure, can survive, differentiate, and promote tissue protection and functional recovery in injured spinal cord (ISC) rats. METHODS: Neural induction was achieved by exposing cells simultaneously to inhibitors of DNA methylation, histone deacetylation, and pharmacological agents that increased cAMP levels. Three groups of adult female Sprague-Dawley rats were injected immediately rostral and caudal to the midline lesion with phosphate-buffered saline, MSCs, or NI hMSCs, 1 week after a spinal cord impact injury at T-8. Functional outcome was measured using the Basso Beattie Bresnahan (BBB) locomotor rating scale and thermal sensitivity test on a weekly basis up to 12 weeks postinjury. Graft integration and anatomy of spinal cord was assessed by stereological, histochemical, and immunohistochemical techniques. RESULTS: The transplanted NI hMSCs survived, differentiated, and significantly improved locomotor recovery of ISC rats. Transplantation also reduced the volume of lesion cavity and white matter loss. CONCLUSION: This method of hMSC modification may provide an alternative source of autologous adult stem cells for CNS repair.
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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 | 0.000 |
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".