Gelatin-methacrylamide gel loaded with microspheres to deliver GDNF in bilayer collagen conduit promoting sciatic nerve growth
Bibliographic record
Abstract
In this study, we fabricated glial cell-line derived neurotrophic factor (GDNF)-loaded microspheres, then seeded the microspheres in gelatin-methacrylamide hydrogel, which was finally integrated with the commercial bilayer collagen membrane (Bio-Gide(®)). The novel composite of nerve conduit was employed to bridge a 10 mm long sciatic nerve defect in a rat. GDNF-loaded gelatin microspheres had a smooth surface with an average diameter of 3.9±1.8 μm. Scanning electron microscopy showed that microspheres were uniformly distributed in both the GelMA gel and the layered structure. Using enzyme-linked immunosorbent assay, in vitro release studies (pH 7.4) of GDNF from microspheres exhibited an initial burst release during the first 3 days (18.0%±1.3%), and then, a prolonged-release profile extended to 32 days. However, in an acidic condition (pH 2.5), the initial release percentage of GDNF was up to 91.2%±0.9% within 4 hours and the cumulative release percentage of GDNF was 99.2%±0.2% at 48 hours. Then the composite conduct was implanted in a 10 mm critical defect gap of sciatic nerve in a rat. We found that the nerve was regenerated in both conduit and autograft (AG) groups. A combination of electrophysiological assessment and histomorphometry analysis of regenerated nerves showed that axonal regeneration and functional recovery in collagen tube filled with GDNF-loaded microspheres (GM + CT) group were similar to AG group (P>0.05). Most myelinated nerves were matured and arranged densely with a uniform structure of myelin in a neat pattern along the long axis in the AG and GM + CT groups, however, regenerated nerve was absent in the BLANK group, left the 10 mm gap empty after resection, and the nerve fiber exhibited a disordered arrangement in the collagen tube group. These results indicated that the hybrid system of bilayer collagen conduit and GDNF-loaded gelatin microspheres combined with gelatin-methacrylamide hydrogels could serve as a new biodegradable artificial nerve guide for nerve tissue engineering.
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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".