A comparison of adsorbed and grafted fibronectin coatings under static and dynamic conditions
Bibliographic record
Abstract
Coatings for medical devices are expected to improve their surface biocompatibility mainly by being bioactive, i.e. stimulating healing-oriented interactions with living cells, tissues and organs. In particular, for stent applications, coatings are often designed to enhance the endothelialization process. The coating strategy will be primarily responsible for the interfacial properties between the substrate and the coating, which must show high stability. Therefore, the present work aims at comparing the stability of adsorbed and grafted fibronectin, a protein well-known to promote endothelialization. Fibronectin coatings were deposited on fluorocarbon films generated by a plasma-based process on stainless steel substrates. Then, deformation tests were performed in order to simulate the stenting procedure and stability tests were completed under static and under-flow conditions. Coatings were characterized by XPS, AFM, water contact angle, immunostaining and ToF-SIMS analyses. The results show higher stability for the grafted coatings; indeed, the integrity of the protein simply adsorbed was strongly compromised especially after under-flow tests. Both coatings exhibited similar behavior after deformation and static tests. These results clearly show the impact of the coating strategy on the overall stability of the coatings as well as the importance of under-flow investigations.
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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".