Interactions of corneal epithelial cells and surfaces modified with cell adhesion peptide combinations
Bibliographic record
Abstract
In order to facilitate the adhesion of corneal epithelial cells to a poly dimethyl siloxane (PDMS) substrate ultimately for the development of a synthetic keratoprosthesis, PDMS surfaces were modified by covalent attachment of combinations of cell adhesion and synergistic peptides derived from laminin and fibronectin. Peptides studied included YIGSR and its synergistic peptide PDSGR from laminin and the fibronectin derived RGDS and PHSRN. Surfaces were modified with combinations of peptides determined by an experimental design. Peptide surface densities, measured using 125-I labeled tyrosine containing analogs, were on the order of pmol/cm2. Surface density varied as a linear function of peptide concentration in the reaction solution, and was different for the different peptides examined. The lowest surface density at all solution fractions was obtained with GYRGDS, while the highest density was consistently obtained with GYPDSGR. These results provide evidence that the surfaces were modified with multiple peptides. Water contact angles and XPS results provided additional evidence for differences in the chemical composition of the various surfaces. Significant differences in the adhesion of human corneal epithelial cells to the modified surfaces were noted. Statistical analysis of the experimental adhesion results suggested that solution concentration YIGSR, RGDS, and PHSRN as well as the interaction effect of YIGSR and PDSGR had a significant effect on cell interactions. Modification with multiple peptides resulted in greater adhesion than modification with single peptides only. Surface modification with a control peptide PPSRN in place of PHSRN resulted in a decrease in cell adhesion in virtually all cases. These results suggest that surface modification with appropriate combinations of cell adhesion peptides and synergistic peptides may result in improved cell surface interactions.
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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".