Epsin Mimetic UPI Peptide Delivery Strategies to Improve Endothelization of Vascular Grafts

Changizi, Shirin; Sameti, Mahyar; Bazemore, Gabby; Chen, Hong; Bashur, Chris A.

doi:10.1002/mabi.202300073

Cited by 3 publications

(1 citation statement)

References 60 publications

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“…Constructing a confluent functional endothelium has been considered an attractive strategy to improve the biocompatibility of blood-contacting biomaterials, especially for small-diameter vascular grafts (<6 mm in diameter), which remain challenged with low long-term patency rates due to endothelial loss-induced thrombosis and intimal hyperplasia . Numerous strategies have been developed to improve endothelialization of these blood-contacting surfaces, among which growth factors such as vascular endothelial growth factor (VEGF) played a crucial role in vascularized tissue regeneration due to its potent angiogenic activity and promotion of endothelialization. − However, the use of growth factors usually faces issues related to safety, stability, and cost-effectiveness. , Alternatively, biomimetic peptides composed of natural amino acids hold great promise for their relatively low cost and good stability, ease of synthesis, high bioactivity, and minimized immunogenicity. − …”

Section: Introductionmentioning

confidence: 99%

Surface Functionalization of Electrospun Scaffolds by QK-AG73 Peptide for Enhanced Interaction with Vascular Endothelial Cells

Bai,

Zhang,

Gao

et al. 2023

Langmuir

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Rapid endothelialization still remains challenging for blood-contacting biomaterials, especially for long-term, functional, small-diameter vascular grafts. The vascular endothelial growth factor (VEGF)-mimicking QK peptide holds great promise in promoting vascular endothelial cellular activities such as adhesion, spreading, proliferation, and migration. Syndecans are transmembrane proteoglycans that are highly expressed on cell surfaces, including vascular endothelial cells, which can act as docking receptors to provide binding sites for a variety of cellular growth and signaling molecules. Herein, a novel peptide QK-AG73 that coupled the QK domain with the syndecan binding peptide AG73 was proposed, aiming to synergistically enhance the interaction with vascular endothelial cells. In addition, mechanically matched bioactive scaffolds based on poly(L-lactide-co-εcaprolactone) were successfully prepared by surface functionalization of the covalently combined QK-AG73 peptide. The result showed that the adhesion of human umbilical vein endothelial cells (HUVECs) was increased by approximately 2-fold on QK-AG73-modified surface compared with those modified with a single QK or AG73 peptide. Moreover, surface functionalization of electrospun scaffolds by this QK-AG73 peptide was more efficient in specifically promoting the proliferation of HUVECs and allowing them to grow with an elongated cobblestone-like cell morphology. It was hypothesized that both VEGF receptors and transmembrane syndecan receptors were involved in cellular regulation by the QK-AG73 peptide, which resulted in synergistic improvement of the interactions with vascular endothelial cells and provided a promising strategy to promote endothelialization of small-diameter vascular grafts.

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Section: Introductionmentioning

confidence: 99%

Surface Functionalization of Electrospun Scaffolds by QK-AG73 Peptide for Enhanced Interaction with Vascular Endothelial Cells

Bai,

Zhang,

Gao

et al. 2023

Langmuir

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A comparative analysis of coronary bypass implants: Experimental and fluid-structure interaction analysis

Changizi,

Afrasiabian,

Rassoli

et al. 2024

Biocybernetics and Biomedical Engineering

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Deep Learning-Based Prediction of Stress and Strain Maps in Arterial Walls for Improved Cardiovascular Risk Assessment

Shokrollahi,

Dong,

Zhou

et al. 2023

Applied Sciences

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Conducting computational stress-strain analysis using finite element methods (FEM) is a common approach when dealing with the complex geometries of atherosclerosis, which is a leading cause of global mortality and complex cardiovascular disease. The considerable expense linked to FEM analysis encourages the substitution of FEM with a considerably faster data-driven machine learning (ML) approach. This study investigated the potential of end-to-end deep learning tools as a more effective substitute for FEM in predicting stress-strain fields within 2D cross sections of arterial walls. We first proposed a U-Net-based fully convolutional neural network (CNN) to predict the von Mises stress and strain distribution based on the spatial arrangement of calcification within arterial wall cross-sections. Further, we developed a conditional generative adversarial network (cGAN) to enhance, particularly from the perceptual perspective, the prediction accuracy of stress and strain field maps for arterial walls with various calcification quantities and spatial configurations. On top of U-Net and cGAN, we also proposed their ensemble approaches to improve the prediction accuracy of field maps further. Our dataset, consisting of input and output images, was generated by implementing boundary conditions and extracting stress-strain field maps. The trained U-Net models can accurately predict von Mises stress and strain fields, with structural similarity index scores (SSIM) of 0.854 and 0.830 and mean squared errors of 0.017 and 0.018 for stress and strain, respectively, on a reserved test set. Meanwhile, the cGAN models in a combination of ensemble and transfer learning techniques demonstrate high accuracy in predicting von Mises stress and strain fields, as evidenced by SSIM scores of 0.890 for stress and 0.803 for strain. Additionally, mean squared errors of 0.008 for stress and 0.017 for strain further support the model’s performance on a designated test set. Overall, this study developed a surrogate model for finite element analysis, which can accurately and efficiently predict stress-strain fields of arterial walls regardless of complex geometries and boundary conditions.

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Epsin Mimetic UPI Peptide Delivery Strategies to Improve Endothelization of Vascular Grafts

Cited by 3 publications

References 60 publications

Surface Functionalization of Electrospun Scaffolds by QK-AG73 Peptide for Enhanced Interaction with Vascular Endothelial Cells

Surface Functionalization of Electrospun Scaffolds by QK-AG73 Peptide for Enhanced Interaction with Vascular Endothelial Cells

A comparative analysis of coronary bypass implants: Experimental and fluid-structure interaction analysis

Deep Learning-Based Prediction of Stress and Strain Maps in Arterial Walls for Improved Cardiovascular Risk Assessment

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