In Vitro Topographical Model of Fuchs Dystrophy for Evaluation of Corneal Endothelial Cell Monolayer Formation
Bibliographic record
Abstract
A common indication for corneal transplantation, which is the most transplanted tissue, is a dysfunctional corneal endothelium due to Fuchs' endothelial dystrophy (FED). FED is diagnosed by the presence of in vivo pathological microtopography on the Descemet membrane, which is called corneal guttata. Minimally invasive corneal endothelial cell regenerative procedures such as endothelial cell injection therapy and Rho kinase inhibitor pharmacotherapy have been proposed as alternatives to conventional corneal transplantation for FED patients. However, the effect of guttata on monolayer reformation following such therapies is unknown and there is no equivalent in vitro or animal model to study monolayer reformation. Using a synthetic guttata FED disease model, the formation of the monolayer is investigated to evaluate the efficacy of both therapies. Results obtained suggest that guttata dimensions, density, and spacing greatly affect the fate of corneal endothelial cells in terms of migratory behavior and monolayer reformation. Densely packed synthetic guttata mimicking late-stage FED hinders monolayer reformation, while synthetic guttata of lower height and density show improved monolayer formation. These results suggest that severity of the FED, as determined by height and density of existing guttata, can potentially attenuate corneal endothelial monolayer formation of corneal cell injection therapy and pharmacotherapy.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".