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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".