An in vitro 3-dimensional Collagen-based Corneal Construct with Innervation Using Human Corneal Cell Lines
Bibliographic record
Abstract
Purpose: To develop a 3-dimensional corneal construct suitable for in vitro studies of disease conditions and therapies. Design: In vitro human corneal constructs were created using chemically crosslinked collagen and chondroitin sulfate extracellular matrix and seeded with 3 human corneal cell types (epithelial, stromal, and endothelial) together with neural cells. The neural cells were derived from hybrid neuroblastoma cells and the other cells used from immortalized human corneal cell lines. To check the feasibility and characterize the constructs, cytotoxicity, cell proliferation, histology, and protein expression studies were performed. Results: Optimized culture condition permitted synchronized viability across the cell types within the construct. The construct showed a typical appearance for different cellular layers, including healthy appearing, phenotypically differentiated neurons. The expected protein expression profiles for specific cell types within the construct were confirmed with western blotting. Conclusions: An in vitro corneal construct was successfully developed with maintenance of individual cell phenotypes with anatomically correct cellular loci. The construct may be useful in evaluation of specific corneal disorders and in developing different corneal disease models. Additionally, the construct can be used in evaluating drug targeting and/or penetration to individual corneal layers, testing novel therapeutics for corneal diseases, and potentially reducing the necessity for animals in corneal research at the early stages of investigation. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".