Predegenerating donor nerve for grafting using focused ultrasound neurotomy
Bibliographic record
Abstract
Early experimental evidence suggests that predegenerated nerve graft enhance axonal growth compared to freshly harvested nerve graft in nerve gap repair. However, the practicality of surgically pre-conditioning nerves for grafting remains a major obstacle. Herein, we aim to investigate the feasibility of focal lesioning of nerve tissue using high-intensity focused ultrasound (HIFU) and compare the regenerative effects of predegenerated nerve grafts to freshly harvested grafts in a rat sciatic nerve transection and repair model. As a proof of principle, sciatic nerves were exposed for HIFU lesioning to increase targeting accuracy. HIFU induced focal thermal lesioning to the sciatic nerve, resulting in p75 and cJun up-regulation at the distal segment observed at 7 days post-sonication. All behavioural outcome metrics including von Frey sensory test and walking gait were similar between animals with freshly harvested and predegenerated nerve grafts except soleus muscle weight was significantly higher in the latter group. As well, similar axon histomorphometric results were found in 2-week survival animals. Our findings showed that it is possible to induce Wallerian degeneration in distal nerve for grafting using non-invasive HIFU. However, limited beneficial effects of predegenerated grafts were obtained compared to freshly harvested grafts in nerve gap repair.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".