Designing and testing anti‐scarring and anti‐inflammatory biomaterials for corneal implants: A promising alternative to human donors
Bibliographic record
Abstract
Purpose: Corneal blindness is mainly treated by cornea transplantation. However, there is a shortage of cornea donors for surgeries in many countries. Even with accessible donors, patients suffering from severe pathologies causing inflammation may not benefit as they are at high risk for graft rejection. The Griffith laboratory is the first to use acellular human collagen implants to regenerate high‐risk corneas in a group of patients. For broader clinical applications, scalable collagen‐like peptide (CLP) synthetic hydrogels are being developed. It is important to identify design patterns to ensure the implants do not cause inflammation. Methods: The growth of stromal fibroblasts cultured on CLP hydrogels with and without RGD groups are compared, with collagen gels and tissue culture plastic as controls. Cellular growth and viability on the different substrates were compared using the AmarBlue assay. Next, TGF‐β was added to the media to stimulate fibroblast differentiation into myofibroblasts. The latter has been linked to corneal scarring. The proportion of resulting myofibroblasts was determined by immunostaining using anti‐smooth muscle actin (SMA) antibody. Stromal cells seeded onto the hydrogels were also stained against fibronectin to study its implication due to RGD. Furthermore, the CLP‐based hydrogel was tested in vivo on alkali burnt BALB/c mice to investigate their practical biocompatibility and efficacy. Results: α‐SMA expression was observed in cells grown on CLP‐RGD containing hydrogels but not on tissue culture plastic or CLP‐only control. There was a positive fibronectin expression by cells cultured on CLP‐RGD hydrogels, on 5% collagen hydrogels and on the issue culture control. The CLP‐RGD hydrogel is a smooth and soft gel with desirable water content. The in vivo test showed that there was no sign of inflammation or angiogenesis in the cornea after three months, which can prove the great biocompatibility of the prepared CLP‐based hydrogel. Conclusions: This project allowed for the testing of cell growth on RGD‐containing CLP hydrogels, which has not been done before. Early results suggest that RGD groups within CLPs induced myofibroblast differentiation and potential pro‐inflammatory responses.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".