Reactive Ion Plasma Modification of Poly(Vinyl‐Alcohol) Increases Primary Endothelial Cell Affinity and Reduces Thrombogenicity
Bibliographic record
Abstract
Abstract Bulk material properties and luminal surface interaction with blood determine the clinical viability of vascular grafts, and reducing intimal hyperplasia is necessary to improve their long‐term patency. Here, the authors report that the surface of a biocompatible hydrogel material, poly(vinyl alcohol) (PVA) can be altered by exposing it to reactive ion plasma (RIP) in order to increase primary endothelial cell attachment. The power and the carrier gas of the RIP treatment are varied and the resultant surface nitrogen, water contact angle, as well as the ability of the RIP‐treated surfaces to support primary endothelial colony forming cells is characterized. Additionally, in a clinically relevant shunt model, the amounts of platelet and fibrin attachment to the surface were quantified during exposure to non‐anticoagulated blood. Treatments with all carrier gases resulted in an increase in the surface nitrogen. Treating PVA with O 2 , N 2 , and Ar RIP increased affinity to primary endothelial colony forming cells. The RIP treatments did not increase the thrombogenicity compared to untreated PVA and had significantly less platelet and fibrin attachment compared to the current clinical standard of expanded polytetrafluoroethylene (ePTFE). These findings indicate that RIP‐treatment of PVA could lead to increased patency in synthetic vascular grafts.
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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".