Rapid and Efficient Assembly of Functional Silicone Surfaces Protected by PEG: Cell Adhesion to Peptide-Modified PDMS
Bibliographic record
Abstract
While silicone elastomers generally have excellent biomaterials properties, their hydrophobicity can elicit undesired local biological responses through adsorption and denaturation of proteins. Surface-bound poly(ethylene glycol) (PEG) can ameliorate the situation by preventing contact between the external biology and the silicone elastomer. It is further possible to manipulate the biocompatibility of the surface by linking peptides, proteins or other biological entities to the PEG. Previous synthetic approaches to PEG-protected surfaces are compromised by issues of reproducibility. We describe two rapid and efficient approaches to silicone surface modification by PEG-linked adhesion peptides that overcome this problem: SiH groups are introduced throughout a silicone elastomer during elastomer synthesis or only at the surface after cure; then, in either case, protein-repellent PEG brushes at the surface are introduced by hydrosilylation to give surfaces that can be stored for extensive periods of time without degradation. Activation of the free alcohol with an NSC group followed by immediate conjugation to relevant biological molecules occurs in high yields, as shown for RGDS and GYRGDS. High surface grafting density of the peptides was demonstrated using radiolabeling techniques. Biological activity was demonstrated by a 5-fold increase in cell adhesion on the peptide-modified surfaces when compared to unmodified PDMS control surfaces.
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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".