Constraint‐induced movement therapy promotes motor recovery after neonatal stroke in the absence of neural precursor activation
Bibliographic record
Abstract
Neonatal stroke is a leading cause of long-term disability and currently available rehabilitation treatments are insufficient to promote recovery. Activating neural precursor cells (NPCs) in adult rodents, in combination with rehabilitation, can accelerate functional recovery following stroke. Here, we describe a novel method of constraint-induced movement therapy (CIMT) in a rodent model of neonatal stroke that leads to improved functional outcomes, and we asked whether the recovery was correlated with expansion of NPCs. A hypoxia/ischemia (H/I) injury was induced on postnatal day 8 (PND8) via unilateral carotid artery ligation followed by systemic hypoxia. One week and two weeks post-H/I, CIMT was administered in the form of 3 botulinum toxin (Botox) injections, which induced temporary paralysis in the unaffected limb. Functional recovery was assessed using the foot fault task. NPC proliferation was assessed using the neurosphere assay and EdU immunohistochemistry. We found that neonatal H/I injury alone expands the NPC pool by >2.5-fold relative to controls. We determined that using Botox injections as a method to provide CIMT results in significant functional motor recovery after H/I. However, CIMT does not lead to enhanced NPC activation or migration into the injured parenchyma in vivo. At the time of functional recovery, increased numbers of proliferating inflammatory cells were found within the injured motor cortex. Together, these findings suggest that NPC activation following CIMT does not account for the observed functional improvement and suggests that CIMT-mediated modification of the CNS inflammatory response may play a role in the motor recovery.
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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) | 0.001 | 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) | 0.001 | 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".