Neural stem cell therapies for spinal cord injury repair: an update on recent preclinical and clinical advances
Bibliographic record
Abstract
Traumatic spinal cord injury (SCI) is a leading cause of lifelong disabilities. Permanent sensory, motor and autonomic impairments after SCI are substantially attributed to degeneration of spinal cord neurons and axons, and disintegration of neural network. To date, minimal regenerative treatments are available for SCI with an unmet need for new therapies to reconstruct the damaged spinal cord neuron-glia network and restore connectivity with the supraspinal pathways. Multipotent neural precursor cells (NPCs) have a unique capacity to generate neurons, oligodendrocytes and astrocytes. Due to this capacity, NPCs have been an attractive cell source for cellular therapies for SCI. Transplantation of NPCs has been extensively tested in preclinical models of SCI in the past two decades. These studies have identified opportunities and challenges associated with NPC therapies. While NPCs have the potential to promote neuroregeneration through various mechanisms, their low long-term survival and integration within the host injured spinal cord limit the functional benefits of NPC-based therapies for SCI. To address this challenge, combinatorial strategies have been developed to optimize the outcomes of NPC therapies by enriching SCI microenvironment through biomaterials, genetic and pharmacological therapies. In this review, we will provide an in-depth discussion on recent advances in preclinical NPC-based therapies for SCI. We will discuss modes of actions and mechanism by which engrafted NPCs contribute to the repair process and functional recovery. We will also provide an update on current clinical trials and new technologies that have facilitated preparation of medical-grade human NPCs suitable for transplantation in clinical studies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".