Micropattern Printing of Adhesion, Spreading, and Migration Peptides on Poly(tetrafluoroethylene) Films To Promote Endothelialization
Bibliographic record
Abstract
We report here the development of an original multistep micropatterning technique for printing peptides on surfaces, based on the ink-jet printer technology. Contrary to most micropatterning methods used nowadays, this technique is advantageous because it allows displaying 2D-arrays of multiple biomolecules. Moreover, this low cost procedure allies the advantages of computer-aided design with high flexibility and reproducibility. A Hewlett-Packard printer was modified to print peptide solutions, and Adobe Illustrator was used as the graphic-editing software to design high-resolution checkerboard-like micropatterns. In a first step, PTFE films were treated with ammonia plasma to introduce amino groups on the surface. These chemical functionalities were reacted with heterobifunctional cross-linker sulfo-succinimidyl 4-(N-maleimidomethyl)cycloexane-1-carboxylate (S-SMCC) to allow the subsequent surface covalent conjugation of various cysteine-modified peptides to the polymer substrate. These peptidic molecules containing RGD and WQPPRARI sequences were selected for their adhesive, spreading, and migrational properties toward endothelial cells. On one hand, our data demonstrated that the initial cell adhesion does not depend on the chemical structure and combination of the peptides covalently bonded either through conventional conjugation or micropatterning. On the other hand, spreading and migration of endothelial cells is clearly enhanced while coconjugating the GRGDS peptide in conjunction with WQPPRARI. This behavior is further improved by micropatterning these peptides on specific areas of the polymer surface.
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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 | 0.001 |
| 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".