A New Model for Facial Nerve Research
Bibliographic record
Abstract
To introduce a Thy1-GFP transgenic rat model, whose axons constitutively express green fluorescent protein (GFP), in order to study facial nerve regeneration. Facial nerve injury can cause devastating physical and social sequelae. The functional recovery of the facial nerve can result in synkinesis and permanent axonal misrouting. Facial nerve research has been hindered by the lack of available animal models and reliable outcome measures. Transgenic Thy1-GFP rats underwent a proximal facial nerve crush injury and were imaged at 0, 1, 2, 4, and 8 weeks after injury. Nerve regeneration was assessed via confocal imaging and fluorescence microscopy. Uninjured animals reliably demonstrated facial nerve fluorescence and had predictable anatomical landmarks. Fluorescence microscopy demonstrated the loss and reappearance of fluorescence with regeneration of axons following injury. This was confirmed with the visualization of denervation and reinnervation of zygomaticus muscle motor end plates using confocal microscopy. The Thy1-GFP rat is a novel transgenic tool that enables direct visualization of facial nerve regeneration after injury. The utility of this model extends to a variety of clinical facial nerve injury paradigms.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".