Neuroprotection and Regeneration Strategies for Spinal Cord Repair
Bibliographic record
Abstract
The journey toward a cure for spinal cord injury (SCI) has taken many paths. In this article, we review these paths, and highlight the clinical applications of these experimental repair strategies. Initial strategies involved attempts at neuroprotection with steroids and other anti-inflammatory drugs. Other anti-ischemia treatments, agents to eliminate the damage from excitotoxicity, and anti-apoptotic agents were also tried. Another avenue involved enhancing the function of the remaining uninjured axons by measures to produce remyelination and medications to improve axonal conduction. In the last two decades there has been a major effort to enhance spinal cord axonal regeneration through a variety of techniques including neutralization of neurite inhibition, administration of neurotrophic factors, implantation of synthetic channels, and transplantation of a variety of cell types. Indeed, several of these strategies have been so promising in animals that clinicians have been stimulated to explore their potential human application. We also examine the different experimental models of SCI used to assess repair, and discuss how the injury model impacts on the assessment of axonal regeneration and functional recovery after SCI. The mechanisms of recovery that may be involved after SCI will be analyzed, and their relevance toward finding a cure for human SCI. Unfortunately, the goal of producing significant functional regeneration of the human spinal cord has not yet been achieved despite the many strategies that have been developed. It is our hope that improved understanding of the mechanisms underlying functional recovery will lead to successful therapeutic strategies in humans.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".