Abstract 352: Using Extracellular Matrix Biomolecules to Modify Poly(vinyl alcohol) Vascular Graft Surface Properties
Bibliographic record
Abstract
The demand for biosynthetic vascular grafts is increasing due to the growing prevalence of occlusive arterial disease in the population. Autologous vessels are often used for treatment but are limited due to a patient’s pre-existing condition or previous surgeries. There is a clinical need for engineered small diameter blood vessel grafts with mechanical and physiologic properties that mimic native arteries. Poly(vinyl alcohol) hydrogel (PVA) has been previously shown to be a suitable biomaterial for grafting. However, its hydrophilic and bioinert properties prevent in vivo endothelialization. Previously, our work has shown that modification of the PVA surface with different biomolecules can alter endothelial growth and function. Coating with gelatin exhibited a pro-endothelial environment for tissue growth but displayed pro-thrombotic properties. When cyclicRGD (cRGD) peptide was incorporated in the PVA graft, we observed increased endothelial cell viability without an increase in platelet adhesion. We hypothesize that short peptide sequences of the basement membrane extracellular matrix will produce peak endothelial cell growth without compromising the hemocompatibility of the graft. Planar PVA underwent surface modification with different basement membrane proteins and peptides including collagen, laminin, cRGD, and YIGSR. To characterize the hydrophobicity of the material, we measured the static water contact angle on the surface of the PVA. Cell attachment and proliferation of primary endothelial cells were quantified with immunohistochemistry by staining for nuclei, actin, and VE-cadherin. Initial results showed increased hydrophobicity and cell attachment with the surface modifications. Currently, we are assessing thrombotic potential by obtaining plasma clotting time, and platelet attachment and activation during incubation with platelet rich plasma. In the future, we will measure cell migration and retention under flow and characterize the cell phenotype under pro-thrombotic and anti-thrombotic flow conditions using real time PCR. This work is a significant step toward advancing the fabrication of biosynthetic vascular grafts for the treatment of cardiovascular disease
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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) | 0.002 | 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".