Comparison of the linking arm effect on the biological performance of a CD31 agonist directly grafted on L605 CoCr alloy by a plasma-based multistep strategy
Bibliographic record
Abstract
Stents are cardiovascular implants deployed on atherosclerotic arteries that aid in reopening, sustaining, and avoiding their collapse. Nevertheless, postimplantation complications exist, and the risk of the renewal of the plaque subsists. Therefore, enhanced properties are mandatory requirements for clinics. For that purpose, a novel approach allowing the direct-grafting of bioactive molecules on cobalt-chromium devices (L605) has been developed. This original strategy involves the direct plasma functionalization of metallic surfaces with primary amines (–NH2). These groups act as anchor points to covalently graft biomolecules of interest, herein a peptide derived from CD31 (P23) with proendothelialization and antithrombotic properties. However, the biological activity of the grafted peptide could be impacted by its conformation. For this study, glutaric anhydride (GA), a short chain spacer, and polyethylene glycol (PEG) with antifouling properties were used as linking arms (LAs). The covalent grafting of the CD31 agonist on L605 by different LAs (GA-P23 and PEG-P23) was confirmed by XPS and ToF-SIMS analyses. The biological performance of these functionalized surfaces showed that, compared to the electropolished (EP) alloy, grafting the P23 with both LA increases adhesion and proliferation of endothelial cells (ECs) since day 1: EP = 68 ± 10%, GA-P23 = 101 ± 7%, and PEG-P23 = 106 ± 5% of cell viability. Moreover, ECs formed a complete monolayer at the surface, preventing clot formation (hemoglobin-free >80%). The potential of this plasma-based strategy for cardiovascular applications was confirmed by promoting a fast re-endothelialization, by improving the hemocompatibility of the alloy when coupled with the CD31 agonist and by its transfer onto commercial L605 stents, as confirmed by ToF-SIMS.
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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".