Corneal Neurotization Improves Ocular Surface Health in a Novel Rat Model of Neurotrophic Keratopathy and Corneal Neurotization
Bibliographic record
Abstract
Purpose: Corneal neurotization is a novel surgical procedure to reinnervate the cornea in patients with neurotrophic keratopathy (NK). We developed a rat model of NK and corneal neurotization to further investigate corneal neurotization as a treatment to improve maintenance and healing of the corneal epithelium. Methods: Thy1-GFP+ Sprague Dawley rats were used to develop the model. Corneal denervation was performed via stereotactic electrocautery of the ophthalmomaxillary branch of the trigeminal nerve. Corneal neurotization was performed by guiding donor sensory axons from the contralateral infraorbital nerve into the cornea via two nerve grafts. Corneal imaging, including nerve density measurements and retrograde labeling were performed to validate the model. In vivo assays of corneal maintenance and repair were used to examine whether treatment with corneal neurotization improved healing in rats with NK. Results: Corneal neurotization significantly increased corneal axon density in rats with NK (P < 0.01). Retrograde labeling of the cornea in rats with corneal neurotization labeled 206 ± 82 neurons in the contralateral trigeminal ganglion, confirming axons reinnervating the cornea derived from the contralateral infraorbital nerve. Corneal reinnervation after corneal neurotization improved corneal epithelial maintenance and corneal healing after injury (P < 0.01). Conclusions: Donor nerve fibers reinnervate the insensate cornea after corneal neurotization and significantly improve corneal maintenance and repair. This model can be used to further investigate how corneal neurotization influences epithelial maintenance and repair in the context of NK.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".