Growth factor enhancement of peripheral nerve regeneration through a novel synthetic hydrogel tube
Bibliographic record
Abstract
OBJECT: The authors' long-term goal is repair of peripheral nerve injuries by using synthetic nerve guidance devices that improve both regeneration and functional outcome relative to an autograft. They report the in vitro processing and in vivo application of synthetic hydrogel tubes that are filled with collagen gel impregnated with growth factors. METHODS: Poly(2-hydroxyethyl methacrylate-co-methyl methacrylate) (PHEMA-MMA) porous 12-mm-long tubes with an inner diameter of 1.3 mm and an outer diameter of 1.8 mm were used to repair surgically created 10-mm gaps in the rat sciatic nerve. The inner lumen of the tubes was filled with collagen matrix alone or matrix supplemented with either neurotropin-3 at 1 microg/ml, brain-derived neurotrophic factor at 1 microg/ml, or acidic fibroblast growth factor (FGF-1) at 1 or 10 microg/ml. Nerve regeneration through the growth factor-enhanced tubes was assessed at 8 weeks after repair by histomorphometric analysis at the midgraft level and in the nerve distal to the tube repair. The tubes were biostable and biocompatible, and supported nerve regeneration in more than 90% of cases. Nerve regeneration was improved in tubes in which growth factors were added, compared with empty tubes and those containing collagen gel alone (negative controls). Tubes filled with 10 microg/ml of FGF-1 dispersed in collagen demonstrated regeneration comparable to autografts (positive controls) and showed significantly better regeneration than the other groups. CONCLUSIONS: The PHEMA-MMA tubes augmented with FGF-1 in their lumens appear to be a promising alternative to autografts for repair of nerve injuries. Studies are in progress to assess the long-term biocompatibility of these implants and to enhance regeneration further.
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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".