Epidermal Growth Factor and Erythropoietin Infusion Accelerate Functional Recovery in Combination With Rehabilitation
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Rehabilitation is the only treatment option for chronic stroke deficits, but unfortunately, it often provides incomplete recovery. In this study, a novel combination of growth factor administration and rehabilitation therapy was used to facilitate functional recovery in a rat model of cortical stroke. METHODS: Ischemia was induced via injection of endothelin-1 into the sensorimotor cortex. This was followed by either a 2-week infusion of epidermal growth factor and erythropoietin or artificial cerebrospinal fluid into the ipsilateral lateral ventricle. Two weeks after ischemia, animals began an 8-week enriched rehabilitation program. Functional recovery was assessed after ischemia using the Montoya staircase-reaching task, beam-traversing, and cylinder test of forelimb asymmetry. RESULTS: The combination of growth factor infusion and rehabilitation led to a significant acceleration in recovery in the staircase task. When compared with controls, animals receiving the combination treatment attained significant recovery of function at 4 weeks after stroke, whereas those receiving rehabilitation alone did not recover until 10 weeks. Significant recovery was also observed on the beam-traversing and cylinder tasks. CONCLUSIONS: Combining behavioral rehabilitation with growth factor infusion accelerates motor recovery. These data suggest a promising new avenue of combination therapies that may have the potential to reduce the rehabilitation time necessary to recover from sensorimotor deficits arising from stroke.
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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 | 0.001 | 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".