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Percutaneous coronary intervention, a common treatment for atherosclerotic coronary artery lesions, occasionally results in perforations associated with increased mortality rates. Stents coated with a bioresorbable polymer membrane may offer an effective solution for sealing coronary artery perforations. Additionally, such coatings could be effective in mitigating neointimal hyperplasia within the vascular lumen and correcting symptomatic aneurysms. This study examines polymer membranes fabricated by electrospinning of polycaprolactone, polydioxanone, polylactide-co-caprolactone, and polylactide-co-glycolide. In uniaxial tensile tests, all the materials appear to surpass theoretically derived elongation thresholds necessary for stent deployment, albeit polydioxanone membranes are found to disintegrate during the experimental balloon expansion. As revealed by in vitro hemocompatibility testing, polylactide-co-caprolactone membranes exhibit higher thrombogenicity compared to other evaluated polymers, while polylactide-co-glycolide samples fail within the first day post-implantation into the abdominal aorta in rats. The PCL membrane exhibited significant water leakage in the permeability test. Comprehensive evaluation of mechanical testing, bio- and hemocompatibility, as well as biodegradation dynamics shows the advantage of membranes based on and the mixture of polylactide-co-caprolactone and polydioxanone over other polymer groups. These findings lay a foundational framework for conducting preclinical studies on stent configurations in large laboratory animals, emphasizing that further investigations under conditions closely mimicking clinical use are imperative for making definitive conclusions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Rezvova, Ovcharenko, Klyshnikov, Glushkova, Kostyunin, Shishkova, Matveeva, Velikanova, Shabaev and Kudryavtseva.)

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., Competing Interests: VD was employed by Quantori. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Danilov, Laptev, Klyshnikov, Stepanov, Bogdanov, Antonova, Krivkina, Kutikhin and Ovcharenko.)