Cellular and nerve regeneration within a biosynthetic extracellular matrix for corneal transplantation
Bibliographic record
Abstract
Our objective was to determine whether key properties of extracellular matrix (ECM) macromolecules can be replicated within tissue-engineered biosynthetic matrices to influence cellular properties and behavior. To achieve this, hydrated collagen and N-isopropylacrylamide copolymer-based ECMs were fabricated and tested on a corneal model. The structural and immunological simplicity of the cornea and importance of its extensive innervation for optimal functioning makes it an ideal test model. In addition, corneal failure is a clinically significant problem. Matrices were therefore designed to have the optical clarity and the proper dimensions, curvature, and biomechanical properties for use as corneal tissue replacements in transplantation. In vitro studies demonstrated that grafting of the laminin adhesion pentapeptide motif, YIGSR, to the hydrogels promoted epithelial stratification and neurite in-growth. Implants into pigs' corneas demonstrated successful in vivo regeneration of host corneal epithelium, stroma, and nerves. In particular, functional nerves were observed to rapidly regenerate in implants. By comparison, nerve regeneration in allograft controls was too slow to be observed during the experimental period, consistent with the behavior of human cornea transplants. Other corneal substitutes have been produced and tested, but here we report an implantable matrix that performs as a physiologically functional tissue substitute and not simply as a prosthetic device. These biosynthetic ECM replacements should have applicability to many areas of tissue engineering and regenerative medicine, especially where nerve function is required.
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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 | 0.000 | 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) | 0.000 | 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".