Experimental and Clinical Evidence for Use of Decellularized Nerve Allografts in Peripheral Nerve Gap Reconstruction
Bibliographic record
Abstract
Despite the inherent capability for axonal regeneration, recovery following severe peripheral nerve injury remains unpredictable and often very poor. Surgeons typically use autologous nerve grafts taken from the patient's own body to bridge long nerve gaps. However, the amount of suitable nerve available from a given patient is limited, and using autologous grafts leaves the patient with scars, numbness, and other forms of donor-site morbidity. Therefore, surgeons and engineers have sought off-the-shelf alternatives to the current practice of autologous nerve grafting. Decellularized nerve allografts have recently become available as an alternative to traditional nerve autografting. In this review, we provide a critical analysis comparing the advantages and limitations of the three major experimental models of decellularized nerve allografts: cold preserved, freeze-thawed, and chemical detergent based. Current tissue engineering-based techniques to optimize decellularized nerve allografts are discussed. We also evaluate studies that supplement decellularized nerve grafts with exogenous factors such as Schwann cells, stem cells, and growth factors to both support and enhance axonal regeneration through the decellularized allografts. In examining the advantages and disadvantages of the studies of decellularized allografts, we suggest that experimental methods, including the animal model, graft length, follow-up time, and outcome measures of regenerative progress and success be consolidated. Finally, all clinical studies in which decellularized nerve allografts have been used to bridge nerve gaps in patients are reviewed.
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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 | 0.007 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".