Development of a Spinal Cord Injury Model Permissive to Study the Cardiovascular Effects of Rehabilitation Approaches Designed to Induce Neuroplasticity
Bibliographic record
Abstract
As primary medical care for spinal cord injury (SCI) has improved over the last decades there are more individuals living with neurologically incomplete (vs. complete) cervical injuries. For these individuals, a number of promising therapies are being actively researched in pre-clinical settings that seek to strengthen the remaining spinal pathways with a view to improve motor function. To date, few, if any, of these interventions have been tested for their effectiveness to improve autonomic and cardiovascular (CV) function. As a first step to testing such therapies, we aimed to develop a model that has sufficient sparing of descending sympathetic pathways for these interventions to target yet induces robust CV impairment. Twenty-six Wistar rats were assigned to SCI (n = 13) or naïve (n = 13) groups. Animals were injured at the T3 spinal segment with 300 kdyn of force. Fourteen days post-SCI, left ventricular (LV) and arterial catheterization was performed to assess in vivo cardiac and hemodynamic function. Spinal cord lesion characteristics along with sparing in catecholaminergic and serotonergic projections were determined via immunohistochemistry. SCI produced a decrease in mean arterial pressure of 17 ± 3 mmHg (p < 0.001) and left ventricular contractility (end-systolic elastance) of 0.7 ± 0.1 mmHg/µL (p < 0.001). Our novel SCI model produced significant decreases in cardiac and hemodynamic function while preserving 33 ± 9% of white matter at the injury epicenter, which we believe makes it a useful pre-clinical model of SCI to study rehabilitation approaches designed to induce neuroplasticity.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".