Reconstruction of a human cornea by the self-assembly approach of tissue engineering using the three native cell types
Bibliographic record
Abstract
Purpose: The purpose of this study was to produce and characterize human tissue-engineered corneas reconstructed using all three corneal cell types (epithelial, stromal, and endothelial cells) by the self-assembly approach.\n\nMethods: Fibroblasts cultured in medium containing serum and ascorbic acid secreted their own extracellular matrix and formed sheets that were superposed to reconstruct a stromal tissue. Endothelial and epithelial cells were seeded on each side of the reconstructed stroma. After culturing at the air-liquid interface, the engineered corneas were fixed for histology and transmission electron microscopy (TEM). Immunofluorescence labeling of epithelial keratins, basement membrane components, Na+/K+-ATPase α1, and collagen type I was also performed.\n\nResults: Epithelial and endothelial cells adhered to the reconstructed stroma. After 10 days at the air-liquid interface, the corneal epithelial cells stratified (4 to 5 cell layers) and differentiated into well defined basal and wing cells that also expressed Na+/K+-ATPase α1 protein, keratin 3/12, and basic keratins. Basal epithelial cells from the reconstructed epithelium formed many hemidesmosomes and secreted a well defined basement membrane rich in laminin V and collagen VII. Endothelial cells formed a monolayer of tightly-packed cells and also expressed the function related protein Na+/K+-ATPase α1.\n\nConclusions: This study demonstrates the feasibility of producing a complete tissue-engineered human cornea, similar to native corneas, using untransformed fibroblasts, epithelial and endothelial cells, without the need for exogenous biomaterial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".