Bioactive Hydrogel-Filament Scaffolds for Nerve Repair and Regeneration
Bibliographic record
Abstract
The design of novel biomaterials is crucial for the advancement of tissue engineering in nerve regeneration. In this study we developed and evaluated novel biosynthetic scaffolds comprising collagen crosslinked with a terpolymer of poly(N-isopropylacrylamide) (PNiPAAm) as conduits for nerve growth. These collagen-terpolymer (collagen-TERP) scaffolds grafted with the laminin pentapeptide YIGSR were previously used as corneal substitutes in pigs and demonstrated enhanced nerve regeneration compared to allografts. The purpose of this project was to enhance neuronal growth on the collagen-TERP scaffolds through the incorporation of supporting fibers. Neuronal growth on these matrices was assessed in vitro using isolated dorsal root ganglia as a nerve source. Statistical significance was assessed using a one-way ANOVA. The incorporation of fibers into the collagen-TERP scaffolds produced a significant increase in neurite extension (p<0.05). The growth habit of the nerves varied with the type of fiber and included directional growth of the neurites along the surface of certain fiber types. Furthermore, the presence of fibers in the collagen-TERP scaffolds appeared to influence neurite morphology and function; neurites grown on fibers-incorporated collagen-TERP scaffolds expressed higher levels of Na channels compared to the scaffolds without fiber. Overall, our results suggest that incorporation of supporting fibers enhanced neurite outgrowth and that surface properties of the scaffold play an important role in promoting and guiding nerve regeneration. More importantly, this study demonstrates the potential value of tissue engineered collagen-TERP hybrid scaffolds as conduits in peripheral nerve repair.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".