A Vascular Graft On‐a‐Chip Platform for Assessing the Thrombogenicity of Vascular Prosthesis and Coatings with Tuneable Flow and Surface Conditions
Bibliographic record
Abstract
Abstract Vascular grafts are essential for the management of cardiovascular disease. However, the lifesaving potential of these devices is undermined by thrombosis arising from material and flow interactions on the blood contacting surface. To combat this issue, antithrombogenic coatings have emerged as a promising strategy for modulating blood and graft interaction in vivo. Although an important determinant of graft performance, hemodynamics are frequently overlooked for in vitro testing of coatings and their translatability remains poorly understood. Herein, this limitation is addressed with a microscale graft on‐a‐chip platform incorporating vascular prosthesis and coatings with tuneable flow and surface conditions in vitro. As a proof of concept, the platform is used to test the thrombogenicity of a novel class of lubricant‐infused surface (LIS) and antibody lubricant‐infused (anti‐CD34 LIS) coated expanded polytetrafluoroethylene (ePTFE) vascular grafts in the presence of arterial wall shear stress with and without endothelial cells. The findings suggest LIS ePTFE is thromboresistant under flow with significantly reduced fibrin(ogen) deposition, thrombin activity, and blood cell adhesion compared to uncoated controls. It is moreover apparent that the microscale properties of the device are advantageous for the testing and translation of novel antithrombogenic coatings and blood‐contacting materials in general.
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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".