Constraint-Induced Movement Therapy (CIMT) and Neural Precursor Cell (NPC) Transplantation Synergistically Promote Anatomical and Functional Recovery in a Hypoxic-Ischemic Mouse Model
Bibliographic record
Abstract
Cerebral palsy (CP) is a common neurodevelopmental disorder characterized by pronounced motor dysfunction and resulting in physical disability. Neural precursor cells (NPCs) have shown therapeutic promise in mouse models of hypoxic-ischemic (HI) perinatal brain injury, which mirror hemiplegic CP. Constraint-induced movement therapy (CIMT) enhances the functional use of the impaired limb and has emerged as a beneficial intervention for hemiplegic CP. However, the precise mechanisms and optimal application of CIMT remain poorly understood. The potential synergy between a regenerative approach using NPCs and a rehabilitation strategy using CIMT has not been explored. We employed the Rice-Vannucci HI model on C57Bl/6 mice at postnatal day (PND) 7, effectively replicating the clinical and neuroanatomical characteristics of hemiplegic CP. NPCs were transplanted in the corpus callosum (CC) at PND21, which is the age corresponding to a 2-year-old child from a developmental perspective and until which CP is often not formally diagnosed, followed or not by Botulinum toxin injections in the unaffected forelimb muscles at PND23, 26, 29 and 32 to apply CIMT. Both interventions led to enhanced CC myelination and significant functional recovery (as shown by rearing and gait analysis testing), through the recruitment of endogenous oligodendrocytes. The combinatorial treatment indicated a synergistic effect, as shown by newly recruited oligodendrocytes and functional recovery. This work demonstrates the mechanistic effects of CIMT and NPC transplantation and advocates for their combined therapeutic potential in addressing hemiplegic CP.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".