Specific Adsorption via Peptide Tags: Oriented Grafting and Release of Growth Factors for Tissue Engineering
Bibliographic record
Abstract
Numerous strategies have been proposed to decorate biomaterials with growth factors (GFs) for tissue engineering applications; their practicability as clinical tools, however, remains uncertain. We previously presented two complementary amphipathic peptides, namely, E5 and K5, which could be utilized as tags to direct GF capture onto organic materials via E5/K5 coiled-coil interactions. We here investigated their potential as mediators of GF physical adsorption. Enzyme-linked immunosorbent assays highlighted that both electrostatic and hydrophobic interactions could contribute to the adsorption process, without interfering with the peptides propensity for coiled-coil interactions. E5-tagged vascular endothelial growth factor, in particular, was efficiently adsorbed to poly(allylamine)-functionalized polystyrene, was maintained in a bioactive state and was steadily liberated over several days with little initial burst. This simple immobilization procedure was successfully applied to poly(ethylene terephthalate) films. Altogether, our data demonstrated that coil-tag-directed adsorption is a tunable, versatile and straightforward strategy to decorate biomaterials with GFs.
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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) | 0.000 | 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".