Biomimetic Architectures for Peripheral Nerve Repair: A Review of Biofabrication Strategies
Bibliographic record
Abstract
Biofabrication techniques have endeavored to improve the regeneration of the peripheral nervous system (PNS), but nothing has surpassed the performance of current clinical practices. However, these current approaches have intrinsic limitations that compromise patient care. The "gold standard" autograft provides the best outcomes but requires suitable donor material, while implantable hollow nerve guide conduits (NGCs) can only repair small nerve defects. This review places emphasis on approaches that create structural cues within a hollow NGC lumen in order to match or exceed the regenerative performance of the autograft. An overview of the PNS and nerve regeneration is provided. This is followed by an assessment of reported devices, divided into three major categories: isotropic hydrogel fillers, acting as unstructured interluminal support for regenerating nerves; fibrous interluminal fillers, presenting neurites with topographical guidance within the lumen; and patterned interluminal scaffolds, providing 3D support for nerve growth via structures that mimic native PNS tissue. Also presented is a critical framework to evaluate the impact of reported outcomes. While a universal and versatile nerve repair strategy remains elusive, outlined here is a roadmap of past, present, and emerging fabrication techniques to inform and motivate new developments in the field of peripheral nerve regeneration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".