Local Delivery of Brain-Derived Neurotrophic Factor Enables Behavioral Recovery and Tissue Repair in Stroke-Injured Rats
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Bibliographic record
Abstract
We induced tissue repair following ischemic stroke in rats by delivering brain-derived neurotrophic factor (BDNF) directly to the brain. This protein is a potent modulator of plasticity and neuroprotection in the developing and adult central nervous system; however, the therapeutic potential of BDNF has been largely thwarted by its inability to cross the blood–brain barrier at an effective concentration. Herein, we demonstrate enhanced acute recovery of forepaw dexterity and enhanced hindlimb function at 7 weeks postinjury by delivering BDNF locally, with sustained release in vivo for up to 21 days. Using an encapsulation-free methodology, BDNF was dispersed in a hydrogel composed of hyaluronan and methylcellulose with poly(lactic-co-glycolic acid) nanoparticles and this composite was deposited epicortically, directly above the stroke lesion. BDNF delivery augmented plasticity, as evidenced by synaptophysin staining in the contralesional hemisphere of BDNF-treated rats, and presence of the vehicle reduced the lesion volume and prevented neuron loss in perilesional tissue. With local, sustained delivery directly to the brain, we demonstrate the benefit of BDNF in the treatment of stroke injury in a rodent model. Impact Statement We developed a biocomposite that can be mixed with brain-derived neurotrophic factor (BDNF) and dispensed onto the surface of the brain to provide sustained, local release of the protein using a procedure that avoids additional damage to neural tissue. The composite is simple to fabricate, and provides sustained release without nanoparticle encapsulation of BDNF, preserving material and protein bioactivity. We demonstrate that when delivered epicortically to a rat model of stroke, this composite allows BDNF to diffuse into the brain, resulting in enhanced behavioral recovery and synaptic plasticity in the contralesional hemisphere.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it