Progress in the development of a corneal replacement: keratoprostheses and tissue-engineered corneas
Bibliographic record
Abstract
Rapid progress has been made in the past 5 years in the development of corneal replacements. Traditionally they are divided into two categories, keratoprostheses and tissue-engineered corneal equivalents, as replacement tissues are increasingly in demand worldwide. There are currently several different keratoprosthesis models in clinical use around the world. The most popular and most widely publicized is the AlphaCor model, which has enjoyed significant clinical success. However, improvements remain to be made, and the aim of most of the current research is to better understand the interactions between a synthetic material and the surrounding biology on a more fundamental level. This improved understanding will no doubt lead to improvements in current models and to the development of new models in the near future. While tissue-engineered corneal equivalents have been under investigation for considerably less time, there is growing evidence to suggest that a tissue-engineered corneal equivalent comprised of primarily natural materials will exist in the not too distant future. Research groups have reported strong in vitro and in vivo results. The strength of the collagen matrix and its ability to support cell infiltration have been the primary avenues of research. Various collagen crosslinking techniques have been used. Infiltration of three major cells of the cornea has been observed. Most importantly, the ability of these materials to support nerve ingrowth has been demonstrated. While challenges remain with both types of corneal replacements, the considerable progress in the recent past suggests that reliable implants for the treatment of a variety of corneal diseases will be available. This review will provide an overview of recent results, and will provide insight into the future of research on 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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".