Artificial Human Corneas
Bibliographic record
Abstract
PURPOSE: To review the development of artificial corneas (prostheses and tissue equivalents) for transplantation, and to provide recent updates on our tissue-engineered replacement corneas. METHODS: Modified natural polymers and synthetic polymers were screened for their potential to replace damaged portions of the human cornea or the entire corneal thickness. These polymers, combined with cells derived from each of the three main corneal layers or stem cells, were used to develop artificial corneas. Functional testing was performed in vitro. Trials of biocompatibility and immune and inflammatory reactions were performed by implanting the most promising polymers into rabbit corneas. RESULTS: Collagen-based biopolymers, combined with synthetic crosslinkers or copolymers, formed effective scaffolds for developing prototype artificial corneas that could be used as tissue replacements in the future. We have previously developed an artificial cornea that mimicked key morphologic and functional properties of the human cornea. The addition of synthetic polymers increased its toughness as it retained transparency and low light scattering, making the matrix scaffold more suitable for transplantation. These new composites were implanted into rabbits without causing any acute inflammation or immune response. We have also fabricated full-thickness composites that can be fully sutured. However, the long-term effects of these artificial corneas need to be evaluated. CONCLUSIONS: Novel tissue-engineered corneas that comprise composites of natural and synthetic biopolymers together with corneal cell lines or stem cells will, in the future, replace portions of the cornea that are damaged. Our results provide a basis for the development of both implantable temporary and permanent corneal replacements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".