Bioactive Coatings of Endovascular Stents Based on Polyelectrolyte Multilayers
Bibliographic record
Abstract
Layer-by-layer self-assembly of two polysaccharides, hyaluronan (HA) and chitosan (CH), was employed to engineer bioactive coatings for endovascular stents. A polyethyleneimine (PEI) primer layer was adsorbed on the metallic surface to initiate the sequential adsorption of the weak polyelectrolytes. The multilayer growth was monitored using a radiolabeled HA and shown to be linear as a function of the number of layers. The chemical structure, interfacial properties, and morphology of the self-assembled multilayer were investigated by time-of-flight secondary ions mass spectrometry (ToF-SIMS), contact angle measurements, and atomic force microscopy (AFM), respectively. Multilayer-coated NiTi disks presented enhanced antifouling properties, compared to unmodified NiTi disks, as demonstrated by a decrease of platelet adhesion in an in vitro assay (38% reduction; p = 0.036). An ex vivo assay on a porcine model indicated that the coating did not prevent fouling by neutrophils. To assess whether the multilayers may be exploited as in situ drug delivery systems, the nitric-oxide-donor sodium nitroprusside (SNP) was incorporated within the multilayer. SNP-doped multilayers were shown to further reduce platelet adhesion, compared to standard multilayers (40% reduction). When NiTi wires coated with a multilayer containing a fluorescently labeled HA were placed in intimate contact with the vascular wall, the polysaccharide translocated on the porcine aortic samples, as shown by confocal microscopy observation of a treated artery. The enhanced thromboresistance of the self-assembled multilayer together with the antiinflammatory and wound healing properties of hyaluronan and chitosan are expected to reduce the neointimal hyperplasia associated with stent implantation.
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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) | 0.001 | 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".