Design of barrier coatings on kink-resistant peripheral nerve conduits
Bibliographic record
Abstract
Here, we report on the design of braided peripheral nerve conduits with barrier coatings. Braiding of extruded polymer fibers generates nerve conduits with excellent mechanical properties, high flexibility, and significant kink-resistance. However, braiding also results in variable levels of porosity in the conduit wall, which can lead to the infiltration of fibrous tissue into the interior of the conduit. This problem can be controlled by the application of secondary barrier coatings. Using a critical size defect in a rat sciatic nerve model, the importance of controlling the porosity of the nerve conduit walls was explored. Braided conduits without barrier coatings allowed cellular infiltration that limited nerve recovery. Several types of secondary barrier coatings were tested in animal studies, including (1) electrospinning a layer of polymer fibers onto the surface of the conduit and (2) coating the conduit with a cross-linked hyaluronic acid-based hydrogel. Sixteen weeks after implantation, hyaluronic acid-coated conduits had higher axonal density, displayed higher muscle weight, and better electrophysiological signal recovery than uncoated conduits or conduits having an electrospun layer of polymer fibers. This study indicates that braiding is a promising method of fabrication to improve the mechanical properties of peripheral nerve conduits and demonstrates the need to control the porosity of the conduit wall to optimize functional nerve recovery.
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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) | 0.001 | 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".