Preclinical evaluations of acellular biological conduits for peripheral nerve regeneration
Bibliographic record
Abstract
Various types of natural biological conduits have been investigated as alternatives to the current surgical standard approach for peripheral nerve injuries. Autologous nerve graft, the current gold standard for peripheral nerve damage, is limited by clinical challenges such as donor-site morbidity and limited availability. The purpose of this study was to evaluate the efficacy of using acellular xenographic conduits (nerve, artery, and dermis) for the repair of a 1.2 cm critical size defect of peripheral nerve in a rodent model. Four months post surgery, the animal group receiving acellular artery as a nerve conduit showed excellent physiological outcome in terms of the prevention of muscle atrophy and foot ulcer. Histological assessment of the bridged site revealed excellent axon regeneration, as opposed to the nonrepaired control group or the group receiving dermal conduit. Finally, the study evaluated the potential improvement via the addition of undifferentiated mesenchymal stem cells into the artery conduit during the bridging procedure. The mesenchymal stem cell-dosed artery conduit group resulted in significantly higher concentration of regenerated axons over artery conduit alone, and exhibited accelerated muscle atrophy rescue. Our results demonstrated that xenographic artery conduits promoted excellent axonal regeneration with highly promising clinical relevance.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".