A functional tacrolimus-releasing nerve wrap for enhancing nerve regeneration following surgical nerve repair
Bibliographic record
Abstract
JOURNAL/nrgr/04.03/01300535-202501000-00036/figure1/v/2024-05-14T021156Z/r/image-tiff Axonal regeneration following surgical nerve repair is slow and often incomplete, resulting in poor functional recovery which sometimes contributes to lifelong disability. Currently, there are no FDA-approved therapies available to promote nerve regeneration. Tacrolimus accelerates axonal regeneration, but systemic side effects presently outweigh its potential benefits for peripheral nerve surgery. The authors describe herein a biodegradable polyurethane-based drug delivery system for the sustained local release of tacrolimus at the nerve repair site, with suitable properties for scalable production and clinical application, aiming to promote nerve regeneration and functional recovery with minimal systemic drug exposure. Tacrolimus is encapsulated into co-axially electrospun polycarbonate-urethane nanofibers to generate an implantable nerve wrap that releases therapeutic doses of bioactive tacrolimus over 31 days. Size and drug loading are adjustable for applications in small and large caliber nerves, and the wrap degrades within 120 days into biocompatible byproducts. Tacrolimus released from the nerve wrap promotes axon elongation in vitro and accelerates nerve regeneration and functional recovery in preclinical nerve repair models while off-target systemic drug exposure is reduced by 80% compared with systemic delivery. Given its surgical suitability and preclinical efficacy and safety, this system may provide a readily translatable approach to support axonal regeneration and recovery in patients undergoing nerve surgery.
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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) | 0.002 | 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".