Passive Leg Cycling Comprehensively Restores Cerebrovascular Health After Experimental High‐thoracic Spinal Cord Injury
Bibliographic record
Abstract
Individuals living with chronic spinal cord injury exhibit a 3–4 fold increased risk of stroke. Reduced cerebral blood flow following is associated with cognitive decline, increased risk of stroke, and development of white matter irregularities. Previously, we have reported impairment in cerebrovascular health following experimental high‐thoracic spinal cord injury, including cerebrovascular reactivity declines, as well as inward remodelling and profibrosis of the middle cerebral artery. Passive hind‐limb exercise after spinal cord injury has been shown to restore systemic cardiovascular function, however it has never been explored for improving cerebrovascular health. Objective To preclinically investigate the effect of passive hind‐limb exercise in preventing cerebrovascular decline following high‐thoracic spinal cord injury. Design/Method Male Wistar rats (n=45) were uninjured, T3‐level complete transected or T3‐transected with passive exercise. Six days after surgery, the hind‐limb passive cycling intervention was initiated and lasted for five weeks. Six weeks after surgery, Magnetic resonance imaging was performed to evaluate baseline cerebral blood flow. Middle cerebral arteries were then harvested for functional and structural assessments, including in vitro pressure myography and immunohistochemistry. Conclusion There was a 44% reduction in global cerebral blood flow ( p =0.001) after spinal cord injury, which was restored with passive exercise (). Passive exercise also prevented the endothelial dysfunction seen after spinal cord injury ( p =0.046). Profibrotic remodelling of middle cerebral arteries demonstrated by increased collagen (95%, p =0.004), was also reversed by passive exercise. As such, passive hind‐limb cycling is effective in restoring healthy brain blood flow, endothelial function, and preventing profibrosis in the cerebral vasculature. Clearly, passive exercise is a promising pre‐clinical therapy to restore cardiovascular health after spinal cord injury, and the present data extend these benefits even to the brain. Support or Funding Information Support: AAP: Killam Trust, Craig H. Neilsen Foundation Postdoctoral Fellowship 337427, Heart and Stroke Foundation of Canada Research Fellowship, Michael Smith Foundation for Health Research Postdoctoral Fellowship AVK: Canadian Institute of Health Research, Craig H. Neilsen Foundation.
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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 | 0.000 | 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) | 0.002 | 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 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".