Functional motor recovery is improved due to local placement of GDNF microspheres after delayed nerve repair
Bibliographic record
Abstract
The majority of bioengineering strategies to promote peripheral nerve regeneration after injury have focused on therapies to bridge large nerve defects while fewer therapies are being developed to treat other nerve injuries, such as nerve transection. We constructed delivery systems using fibrin gels containing either free GDNF or polylactide-glycolic acid (PLGA) microspheres with GDNF to treat delayed nerve repair, where ELISA verified GDNF release. We determined the formulation of microspheres containing GDNF that optimized nerve regeneration and functional recovery in a rat model of delayed nerve repair. Experimental groups underwent delayed nerve repair and treatment with GDNF microspheres in fibrin glue at the repair site or control treatments (empty microspheres or free GDNF without microspheres). Contractile muscle force, muscle mass, and MUNE were measured 12 weeks following treatment, where GDNF microspheres (2 weeks formulation) were superior compared to either no GDNF or short-term release of free GDNF to nerve. Nerve histology distal to the repair site demonstrated increased axon counts and fiber diameters due to GDNF microspheres (2 weeks formulation). GDNF microspheres partially reversed the deleterious effects of chronic nerve injury, and recovery was slightly favored with the 2 weeks formulation compared to the 4 weeks formulation.
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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".