Bioactive Coatings of Endovascular Stents Based on Polyelectrolyte Multilayers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it