Chondroitin Sulfate Coatings Display Low Platelet but High Endothelial Cell Adhesive Properties Favorable for Vascular Implants
Bibliographic record
Abstract
This study highlights the advantages of chondroitin sulfate (CS) as a sublayer combining selective low-fouling properties, low-platelet adhesion and pro-adhesive properties on endothelial cells, making CS promising for vascular graft applications. These properties were evaluated by comparing CS with well-known low-fouling coatings such as poly(ethylene glycol) (PEG) and carboxymethylated dextran (CMD), which were covalently grafted on primary amine-rich plasma polymerized (LP) films. Protein adsorption studies by quartz crystal microbalance with dissipation monitoring (QCM-D) and fluorescence measurements showed that CS is as effective as PEG in reducing fibrinogen adsorption (~90% reduction). CS also largely reduced adsorption of bovine serum albumin (BSA) as well as fetal bovine serum (FBS) but to a lower extent than PEG and CMD surfaces (72% vs 85% for BSA and 66% vs 89% for FBS). Whole blood perfusion assays indicated that, while LP surfaces were highly reactive with platelets, PEG, CMD, and CS grafted surfaces drastically decreased platelet adhesion and activation to levels significantly lower than polyethylene terephthalate (PET) surfaces. Finally, while human umbilical vein endothelial cell (HUVEC) adhesion and growth were found to be very limited on PEG and CMD, they were significantly increased on CS compared to that on bare PET and reached similar values as those for tissue culture polystyrene positive controls. Interestingly, HUVEC retention during perfusion with blood was found to be excellent on CS but poor on PET. Overall, our results suggest that the CS surface has the advantage of promoting HUVEC growth and resistance to flow-induced shear stress while preventing fibrinogen and platelet attachment. Such a nonthrombogenic but endothelial-cell adhesive surface is thus promising to limit vascular graft occlusion.
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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".