A Liquid Hydrogel to Restore Long Term Corneal Integrity After Perforating and Non-Perforating Trauma in Feline Eyes
Bibliographic record
Abstract
Purpose: To evaluate long-term in vivo functionality of corneas regenerated using a cell-free, liquid hydrogel filler (LiQD Cornea) after deep corneal trauma in the feline model. Methods: Two healthy cats underwent 4 mm diameter stepwise 250/450 µm deep surgical corneal ablation with and without needle perforation. The filler comprising 10% (w/w) collagen-like peptide conjugated to polyethylene glycol (CLP-PEG) and 1% fibrinogen and crosslinked with 2% (w/w) 4-(4,6-dimethoxy-1,3,5-triazin-2-yl)-4-methylmorpholinium chloride (DMTMM), was applied to the wound bed previously coated with thrombin (250 U/ml). In situ gelation occurred within 5 min, and a temporary tarsorrhaphy was performed. Eyes were examined weekly for 1 month, then monthly over 12 months. Outcome parameters included slit-lamp, Scheimpflug tomography, optical coherence tomography, confocal and specular microscopy, and immunohistochemistry studies. Results: The gelled filler was seamlessly incorporated, supporting smooth corneal re-epithelialization. Progressive in-growth of keratocytes and nerves into the filler corresponding to the mild haze observed faded with time. The regenerated neo-cornea remained stably integrated throughout the 12 months, without swelling, inflammation, infection, neovascularization, or rejection. The surrounding host stroma and endothelium remained normal at all times. Tomography confirmed restoration of a smooth surface curvature. Conclusion: Biointegration of this hydrogel filler allowed stable restoration of corneal shape and transparency in the feline model, with less inflammation and no neovascularization compared to previous reports in the minipig and rabbit models. It offers a promising alternative to cyanoacrylate glue and corneal transplantation for ulcerated and traumatized corneas in human patients.
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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 | 0.000 |
| 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".