Mesenchymal Cells in the Treatment of Spinal Cord Injury: Current & Future Perspectives
Bibliographic record
Abstract
Nearly 1.5 million people in the US live with a spinal cord injury (SCI). The cost to the healthcare system is estimated at over $10 billion annually likely because over 65% of SCIs occur at the cervical level. Despite medical advances, many SCI patients still experience substantial neurological disability and high dependency with severe loss of motor, sensory and autonomic function. The consensus from a combination of in vivo studies and in vivo models is that mesenchymal or stromal cells, and possibly even neural progenitors, regardless of derivation act through the provision of trophic support and inflammatory modulation. Indeed, they have been found to secrete a wide spectrum of diffusible factors with known roles in both. As such, mesenchymal cells, obtainable from multiple tissues, are ideally suited to addressing many pathophysiological consequences of SCI. Advances in understanding the latter, structural and functional magnetic resonance imaging, image-guided microneurosurgical techniques and transplantable cell biology have enabled the clinical use of cell-based therapies. Of the twenty most recent cell therapy clinical trials for SCI, seven involve adult bone marrow mesenchymal cells and six others umbilical cord cells. This reflects the growing recognition of the clinical potential of perinatal cells. However, a limited understanding of how best to exploit the capabilities of these cells impedes a full-scale clinical deployment. This mini-review focuses on recent developments that are likely to facilitate the targeted application of these cells to treat specific secondary pathophysiological processes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".