Exercise and Peripheral Nerve Grafts as a Strategy To Promote Regeneration after Acute or Chronic Spinal Cord Injury
Bibliographic record
Abstract
Therapeutic interventions after spinal cord injury (SCI) routinely are designed to address multiple aspects of the primary and/or secondary damage that occurs. Exercise has a demonstrated efficacy for post-SCI complications such as cardiovascular dysfunction, neuropathic pain, and chronic inflammation, yet there is little understanding of the mechanisms by which improvements might result from this non-invasive approach. Here we review several of our observations of molecular and cellular changes within the injured spinal cord following acute or delayed exercise regimens that illustrate the potential for positive effects on neuroprotection and rehabilitation. Further, we provide new information about the role of exercise in promoting the regeneration of spinal axons into peripheral nerve grafts (PNGs) placed immediately or 6 weeks after injury. Acute and chronically injured propriospinal neurons within the lumbar spinal cord displayed the greatest propensity for enhanced regeneration after exercise, which correlates with the direct sensory input to this region from exercised hindlimb muscles. Future studies will extend these observations by testing whether exercise will boost the regenerative effort of axons to extend beyond the graft, interact with intraspinal targets, and establish functional connections across a lesion.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".