Stroke Recovery in Rats after 24‐Hour–Delayed Intramuscular Neurotrophin‐3 Infusion
Bibliographic record
Abstract
OBJECTIVE: Neurotrophin-3 (NT3) plays a key role in the development and function of locomotor circuits including descending serotonergic and corticospinal tract axons and afferents from muscle and skin. We have previously shown that gene therapy delivery of human NT3 into affected forelimb muscles improves sensorimotor recovery after stroke in adult and elderly rats. Here, to move toward the clinic, we tested the hypothesis that intramuscular infusion of NT3 protein could improve sensorimotor recovery after stroke. METHODS: Rats received unilateral ischemic stroke in sensorimotor cortex. To simulate a clinically feasible time to treatment, 24 hours later rats were randomized to receive NT3 or vehicle by infusion into affected triceps brachii for 4 weeks using implanted catheters and minipumps. RESULTS: Radiolabeled NT3 crossed from the bloodstream into the brain and spinal cord in rodents with or without strokes. NT3 increased the accuracy of forelimb placement during walking on a horizontal ladder and increased use of the affected arm for lateral support during rearing. NT3 also reversed sensory impairment of the affected wrist. Functional magnetic resonance imaging during stimulation of the affected wrist showed spontaneous recovery of peri-infarct blood oxygenation level-dependent signal that NT3 did not further enhance. Rather, NT3 induced neuroplasticity of the spared corticospinal and serotonergic pathways. INTERPRETATION: Our results show that delayed, peripheral infusion of NT3 can improve sensorimotor function after ischemic stroke. Phase I and II clinical trials of NT3 (for constipation and neuropathy) have shown that peripheral high doses are safe and well tolerated, which paves the way for NT3 as a therapy for stroke. ANN NEUROL 2019;85:32-46.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".