The Human Tissue-Engineered Cornea (hTEC): Recent Progress
Bibliographic record
Abstract
Each day, about 2000 U.S. workers have a job-related eye injury requiring medical treatment. Corneal diseases are the fifth cause of blindness worldwide. Most of these diseases can be cured using one form or another of corneal transplantation, which is the most successful transplantation in humans. In 2012, it was estimated that 12.7 million people were waiting for a corneal transplantation worldwide. Unfortunately, only 1 in 70 patients received a corneal graft that same year. In order to provide alternatives to the shortage of graftable corneas, considerable progress has been achieved in the development of living corneal substitutes produced by tissue engineering and designed to mimic their in vivo counterpart in terms of cell phenotype and tissue architecture. Most of these substitutes use synthetic biomaterials combined with immortalized cells, which makes them dissimilar from the native cornea. However, studies have emerged that describe the production of tridimensional (3D) tissue-engineered corneas using untransformed human corneal epithelial cells grown on a totally natural stroma synthesized by living corneal fibroblasts, that also show appropriate histology and expression of both extracellular matrix (ECM) components and integrins. This review highlights contributions from laboratories working on the production of human tissue-engineered corneas (hTECs) as future substitutes for grafting purposes. It overviews alternative models to the grafting of cadaveric corneas where cell organization is provided by the substrate, and then focuses on their 3D counterparts that are closer to the native human corneal architecture because of their tissue development and cell arrangement properties. These completely biological hTECs are therefore very promising as models that may help understand many aspects of the molecular and cellular mechanistic response of the cornea toward different types of diseases or wounds, as well as assist in the development of novel drugs that might be promising for therapeutic purposes.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".