Sustained delivery of bioactive neurotrophin-3 to the injured spinal cord
Bibliographic record
Abstract
Spinal cord injury is a debilitating condition that currently lacks effective clinical treatment. Neurotrophin-3 (NT-3) has been demonstrated in experimental animal models to induce axonal regeneration and functional improvements, yet its local delivery remains challenging. For ultimate clinical translation, a drug delivery system is required for localized, sustained, and minimally invasive release. Here, an injectable composite drug delivery system (DDS) composed of biodegradable polymeric nanoparticles dispersed in a hyaluronan/methyl cellulose hydrogel was injected into the intrathecal space to achieve acute local delivery to the spinal cord after a thoracic clip compression injury. NT-3 was encapsulated in the DDS and released in vitro for up to 50 d. With a single injection of the DDS into the intrathecal space of the injured spinal cord, NT-3 diffused ventrally through the cord and was detectable in the spinal cord for at least 28 d therein. Delivery of NT-3 resulted in significant axon growth with no effect on the astroglial response to injury in comparison with vehicle and injury controls. NT-3 treatment promoted functional improvements at 21 d according to the Basso Beattie Bresnahan locomotor scale in comparison with the DDS alone. The sustained delivery of bioactive NT-3 to the injured spinal cord achieved in this study demonstrates the promise of this DDS for central nervous system repair.
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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) | 0.000 | 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 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".