Strategic Design and Fabrication of Nerve Guidance Conduits for Peripheral Nerve Regeneration
Bibliographic record
Abstract
Nerve guidance conduits (NGCs) have been drawing considerable attention as an aid to promote regeneration of injured axons across damaged peripheral nerves. Ideally, NGCs should include physical and topographic axon guidance cues embedded as part of their composition. Over the past decades, much progress has been made in the development of NGCs that promote directional axonal regrowth so as to repair severed nerves. This paper briefly reviews the recent designs and fabrication techniques of NGCs for peripheral nerve regeneration. Studies associated with versatile design and preparation of NGCs fabricated with either conventional or rapid prototyping (RP) techniques have been examined and reviewed. The effect of topographic features of the filler material as well as porous structure of NGCs on axonal regeneration has also been examined from the previous studies. While such strategies as macroscale channels, lumen size, groove geometry, use of hydrogel/matrix, and unidirectional freeze-dried surface are seen to promote nerve regeneration, shortcomings such as axonal dispersion and wrong target reinnervation still remain unsolved. On this basis, future research directions are identified and discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".