Biosynthetic Collagen-Analog Hydrogels Stimulate Endogenous Regrowth of Rabbit Corneas: A Pilot Study
Bibliographic record
Abstract
Pro-regenerative corneal implants are being developed to improve corneal healing for companion animals in clinical practice. This pilot study evaluated early corneal tissue and nerve regeneration using biosynthetic collagen-analog hydrogels (CAH) in liquid and solid forms. Their efficacy was compared to each other and to allografts on nine white New Zealand rabbits, divided in three groups of three. Each rabbit cornea underwent keratectomy followed by grafting with either a control allograft cornea, liquid injectable, or solid CAH implant. Corneal healing was assessed over 16 weeks using clinical exams, esthesiometry, in vivo confocal microscopy, and optical coherence tomography. One rabbit per group was euthanized at 3, 10, and 16 weeks for histopathological analysis. Both liquid and solid implants enabled corneal re-epithelialization and regeneration of stromal tissue and corneal nerves. Esthesiometric values indicated faster nerve regeneration in rabbits grafted with biosynthetic implants compared to allografts (p < 0.005). By 16 weeks, regenerated neocorneas achieved transparency comparable to allografts. Solid and liquid CAH implants supported complete corneal tissue and nerve regeneration in the studied rabbits. These results suggest that with further research and development, the current gold standard for corneal transplantation could be replaced by high-performing, easily produced biosynthetic alternatives.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".