Collagen Analogs Promote Tissue Regeneration in HSV-1-Infected Corneas in Animal Models
Bibliographic record
Abstract
Herpes simplex virus type 1 (HSV-1) is a leading cause of infectious corneal blindness worldwide. Human donor corneal transplantation remains the primary treatment for scarred corneas resulting from herpes simplex keratitis (HSK), a severe inflammatory corneal disease caused by HSV-1 infection, despite a high risk of re-infection or immune rejection of the allografts. As possible alternatives to donor grafting for HSK, we developed cell-free, regeneration-stimulating corneal implants designed to work even under adverse inflammatory situations such as severe infections. The implants comprised short, fully synthetic collagen-like peptides conjugated to polyethylene glycol (CLP-PEG) and crosslinked using carbodiimide chemistry. Being cell-free, they lacked the cellular targets that an already activated immune system would encounter in these inflamed corneas. We tested the performance of these implants in guinea pig and rabbit models of HSK. Three different HSV-1 strains were used to create experimental HSK in rabbits and guinea pigs. There were no overall statistically significant species differences or species-strain differences in virus-induced mortality. At three months post-operation, all treated corneas showed tissue regeneration, but with haze or neovascularization. The initially cell-free CLP-PEG implants allowed for repopulation by ingrowing cells to regenerate neocorneal tissue, despite the inflammation. However, they did not prevent HSV-1 reactivation nor re-infection, as neovascularization and disorganization were observed within the neocorneas. A detailed histopathological examination revealed viral strain differences, but only KOS infection showed interspecies neovascularization differences. A more detailed examination with larger numbers of animals is merited to fully elucidate the effects of the different viral strains on rabbits versus guinea pigs.
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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 | 0.001 | 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 | 0.001 |
| 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".