An engineered biocompatible drug delivery system enhances nerve regeneration after delayed repair
Bibliographic record
Abstract
Localized drug delivery strategies could greatly benefit patients with peripheral nerve injury and could be easy for surgeons to implement. We developed a local drug delivery system (DDS) using drug-loaded poly(lactic-co-glycolic acid) (PLGA) microspheres (MS) embedded in a fibrin gel. In an in vitro study, we investigated the biocompatibility of this DDS by performing a toxicity assay in which we incubated PC-12 cells with the medium released from the DDS in vitro. In an in vivo study, this DDS was applied at the rat common peroneal (CP) nerve injury site to deliver exogenous glial cell line-derived neurotrophic factor (GDNF) to the regenerating axons after delayed nerve repair. In vitro, PC-12 cells incubated with released media samples from the DDS had similar viability to control cells cultured with normal media, demonstrating that the DDS was not toxic. In vivo, the numbers of motor and sensory neurons that regenerated their axons with empty MS treatment were the same as when there was no MS treatment. The DDS increased the numbers of regenerating motor- and sensory neurons to levels indistinguishable from those observed with immediate nerve repair. The DDS increased neuron regeneration to levels double those observed with negative control groups. This biocompatible, nontoxic, fibrin gel-based DDS enhances outcomes following severe peripheral nerve injuries.
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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) | 0.001 | 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".