Enhancement of Facial Nerve Motoneuron Regeneration through Cross-Face Nerve Grafts by Adding End-to-Side Sensory Axons
Bibliographic record
Abstract
BACKGROUND: In unilateral facial palsy, cross-face nerve grafts are used for emotional facial reanimation. Facial nerve regeneration through the grafts takes several months, and the functional results are sometimes inadequate. Chronic denervation of the cross-face nerve graft results in incomplete nerve regeneration. The authors hypothesize that donor axons from regional sensory nerves will enhance facial motoneuron regeneration, improve axon regeneration, and improve the amplitude of facial muscle movement. METHODS: In the rat model, a 30-mm nerve graft (right common peroneal nerve) was used as a cross-face nerve graft. The graft was coapted to the proximal stump of the transected right buccal branch of the facial nerve and the distal stumps of the transected left buccal and marginal mandibular branches. In one group, sensory occipital nerves were coapted end-to-side to the cross-face nerve graft. Regeneration of green fluorescent protein-positive axons was imaged in vivo in transgenic Thy1-green fluorescent protein rats, in which all neurons express green fluorescence. After 16 weeks, retrograde labeling of regenerated neurons and histomorphometric analysis of myelinated axons was performed. Functional outcomes were assessed with video analysis of whisker motion. RESULTS: "Pathway protection" with sensory axons significantly enhanced motoneuron regeneration, as assessed by retrograde labeling, in vivo fluorescence imaging, and histomorphometry, and significantly improved whisker motion during video analysis. CONCLUSION: Sensory pathway protection of cross-face nerve grafts counteracts chronic denervation in nerve grafts and improves regeneration and functional outcomes.
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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) | 0.001 | 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) | 0.002 | 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".