An Exercise Mimetic Approach to Reduce Poststroke Deconditioning and Enhance Stroke Recovery
Bibliographic record
Abstract
Evidence supports early rehabilitation after stroke to limit disability. However, stroke survivors are typically sedentary and experience significant cardiovascular and muscular deconditioning. Despite growing consensus that preclinical and clinical stroke recovery research should be aligned, there have been few attempts to incorporate cardiovascular and skeletal muscle deconditioning into animal models of stroke. Here, we demonstrate in rats that a hindlimb sensorimotor cortex stroke results in both cardiovascular and skeletal muscle deconditioning and impairments in gait akin to those observed in humans. To reduce poststroke behavioral, cardiovascular, and skeletal muscle perturbations, we then used a combinatorial intervention consisting of aerobic and resistance exercise in conjunction with administration of resveratrol (RESV), a drug with exercise mimetic properties. A combination of aerobic and resistance exercise mitigated decreases in cardiovascular fitness and attenuated skeletal muscle abnormalities. RESV, beginning 24 hours poststroke, reduced acute hindlimb impairments, improved recovery in hindlimb function, increased vascular density in the perilesional cortex, and attenuated skeletal muscle fiber changes. Early RESV treatment and aerobic and resistance exercise independently provided poststroke benefits, at a time when individuals are rapidly becoming deconditioned as a result of inactivity. Although no additive effects were observed in these experiments, this approach represents a promising strategy to reduce poststroke behavioral impairments and minimize deconditioning. As such, this treatment regime has potential for enabling patients to engage in more intensive rehabilitation at an earlier time following stroke when mechanisms of neuroplasticity are most prevalent.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".