Epoxy Cross-Linked Collagen and Collagen-Laminin Peptide Hydrogels as Corneal Substitutes
Bibliographic record
Abstract
A bi-functional epoxy-based cross-linker, 1,4-Butanediol diglycidyl ether (BDDGE), was investigated in the fabrication of collagen based corneal substitutes. Two synthetic strategies were explored in the preparation of the cross-linked collagen scaffolds. The lysine residues of Type 1 porcine collagen were directly cross-linked using l,4-Butanediol diglycidyl ether (BDDGE) under basic conditions at pH 11. Alternatively, under conventional methodology, using both BDDGE and 1-Ethyl-3-(3-dimethyl aminopropyl) carbodiimide (EDC)/N-hydroxysuccinimide (NHS) as cross-linkers, hydrogels were fabricated under acidic conditions. In this latter strategy, Cu(BF4)2·XH2O was used to catalyze the formation of secondary amine bonds. To date, we have demonstrated that both methods of chemical cross-linking improved the elasticity and tensile strength of the collagen implants. Differential scanning calorimetry and biocompatibility studies indicate comparable, and in some cases, enhanced properties compared to that of the EDC/NHS controls. In vitro studies showed that human corneal epithelial cells and neuronal progenitor cell lines proliferated on these hydrogels. In addition, improvement of cell proliferation on the surfaces of the materials was observed when neurite promoting laminin epitope, IKVAV, and adhesion peptide, YIGSR, were incorporated. However, the elasticity decreased with peptide incorporation and will require further optimization. Nevertheless, we have shown that epoxy cross-linkers should be further explored in the fabrication of collagen-based hydrogels, as alternatives to or in conjunction with carbodiimide cross-linkers.
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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) | 0.001 | 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".