Comparison of the Patency and Regenerative Potential of Biodegradable Vascular Prostheses of Different Polymer Compositions in an Ovine Model.

The lack of suitable autologous grafts and the impossibility of using synthetic prostheses for small artery reconstruction make it necessary to develop alternative efficient vascular grafts. In this study, we fabricated an electrospun biodegradable poly(ε-caprolactone) (PCL) prosthesis and poly(3-hydroxybutyrate-co-3-hydroxyvalerate)/poly(ε-caprolactone) (PHBV/PCL) prosthesis loaded with iloprost (a prostacyclin analog) as an antithrombotic drug and cationic amphiphile with antibacterial activity. The prostheses were characterized in terms of their drug release, mechanical properties, and hemocompatibility. We then compared the long-term patency and remodeling features of PCL and PHBV/PCL prostheses in a sheep carotid artery interposition model. The research findings verified that the drug coating of both types of prostheses improved their hemocompatibility and tensile strength. The 6-month primary patency of the PCL/Ilo/A prostheses was 50%, while all PHBV/PCL/Ilo/A implants were occluded at the same time point. The PCL/Ilo/A prostheses were completely endothelialized, in contrast to the PHBV/PCL/Ilo/A conduits, which had no endothelial cells on the inner layer. The polymeric material of both prostheses degraded and was replaced with neotissue containing smooth-muscle cells; macrophages; proteins of the extracellular matrix such as type I, III, and IV collagens; and vasa vasorum. Thus, the biodegradable PCL/Ilo/A prostheses demonstrate better regenerative potential than PHBV/PCL-based implants and are more suitable for clinical use.

ML-driven segmentation of microvascular features during histological examination of tissue-engineered vascular grafts.

Introduction: The development of next-generation tissue-engineered medical devices such as tissue-engineered vascular grafts (TEVGs) is a leading trend in translational medicine. Microscopic examination is an indispensable part of animal experimentation, and histopathological analysis of regenerated tissue is crucial for assessing the outcomes of implanted medical devices. However, the objective quantification of regenerated tissues can be challenging due to their unusual and complex architecture. To address these challenges, research and development of advanced ML-driven tools for performing adequate histological analysis appears to be an extremely promising direction. Methods: We compiled a dataset of 104 representative whole slide images (WSIs) of TEVGs which were collected after a 6-month implantation into the sheep carotid artery. The histological examination aimed to analyze the patterns of vascular tissue regeneration in TEVGs in situ. Having performed an automated slicing of these WSIs by the Entropy Masker algorithm, we filtered and then manually annotated 1,401 patches to identify 9 histological features: arteriole lumen, arteriole media, arteriole adventitia, venule lumen, venule wall, capillary lumen, capillary wall, immune cells, and nerve trunks. To segment and quantify these features, we rigorously tuned and evaluated the performance of six deep learning models (U-Net, LinkNet, FPN, PSPNet, DeepLabV3, and MA-Net). Results: After rigorous hyperparameter optimization, all six deep learning models achieved mean Dice Similarity Coefficients (DSC) exceeding 0.823. Notably, FPN and PSPNet exhibited the fastest convergence rates. MA-Net stood out with the highest mean DSC of 0.875, demonstrating superior performance in arteriole segmentation. DeepLabV3 performed well in segmenting venous and capillary structures, while FPN exhibited proficiency in identifying immune cells and nerve trunks. An ensemble of these three models attained an average DSC of 0.889, surpassing their individual performances. Conclusion: This study showcases the potential of ML-driven segmentation in the analysis of histological images of tissue-engineered vascular grafts. Through the creation of a unique dataset and the optimization of deep neural network hyperparameters, we developed and validated an ensemble model, establishing an effective tool for detecting key histological features essential for understanding vascular tissue regeneration. These advances herald a significant improvement in ML-assisted workflows for tissue engineering research and development.