Dendrimer‐grafted cell adhesion peptide–modified PDMS
Bibliographic record
Abstract
Surface concentration of cell adhesion peptides is thought to play a role in the interactions between biomaterials and cells. The high density of functional groups at the periphery of dendrimers has been exploited in various applications, but their full potential for generating surfaces with high functional group concentrations has not yet been realized. Poly(dimethylsiloxane) elastomers were surface modified with both polyethylene oxide (PEO) and generation 3 diaminobutane dendrimers. PEO and the dendrimers were subsequently used as linker molecules for surface grafting of cell adhesion peptides. ATR-FTIR, X-ray photoelectron spectroscopy, and water contact angle results confirmed the successful attachment of the polymer linkers and peptides. Peptide grafting density was quantified by means of (125)I radiolabeling. Maximum surface peptide grafting density on dendrimer-modified surfaces was twofold greater than the maximum peptide grafting density achieved via the PEO linker. However, vascular endothelial cell adhesion was significantly greater on surfaces modified with the PEO linker, presumably due to the highly flexible PEO spacer making the peptide more accessible for binding with the cell surface receptors. These results suggest that, although peptide surface density may be important, optimizing surface density may not be sufficient for improving biological 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) | 0.003 | 0.001 |
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".