Biosynthetic Corneal Implants for Replacement of Pathologic Corneal Tissue: Performance in a Controlled Rabbit Alkali Burn Model
Bibliographic record
Abstract
PURPOSE: To evaluate the performance of structurally reinforced, stabilized recombinant human collagen-phosphorylcholine (RHCIII-MPC) hydrogels as corneal substitutes in a rabbit model of severe corneal damage. METHODS: One eye each of 12 rabbits received a deep corneal alkali wound. Four corneas were implanted with RHCIII-MPC hydrogels. The other eight control corneas were implanted with either allografts or a simple cross-linked RHCIII hydrogel. In all cases, 6.25 mm diameter, 350 μm thick buttons were implanted by anterior lamellar keratoplasty to replace damaged corneal tissue. Implants were followed for nine months by clinical examination and in vivo confocal microscopy, after which implanted corneas were removed and processed for histopathological and ultrastructural examination. RESULTS: Alkali exposure induced extensive central corneal scarring, ocular surface irregularity, and neovascularization in one case. All implants showed complete epithelial coverage by four weeks postoperative, but with accompanying suture-induced vascularization in 6 out of 12 cases. A stable, stratified epithelium with hemidesmosomal adhesion complexes regenerated over all implants, and subbasal nerve regeneration was observed in allograft and RHCIII-MPC implants. Initially acellular biosynthetic implants were populated with host-derived keratocytes as stromal haze subsided and stromal collagen was remodeled. Notably, RHCIII-MPC implants exhibited resistance to vascular ingrowth while supporting endogenous cell and nerve repopulation. CONCLUSIONS: Biosynthetic implants based on RHC promoted cell and nerve repopulation in alkali burned rabbit eyes. In RHCIII-MPC implants, evidence of an enhanced resistance to neovascularization was additionally noted.
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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) | 0.001 | 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 | 0.000 |
| 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".