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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.)

Background: Decellularized xenogenic scaffolds represent a promising substrate for tissue-engineered vascular prostheses, particularly those with smaller diameters (<6 mm). Despite their benefits, a notable limitation presents itself during decellularization, namely, the diminished mechanical strength that introduces the risk of aneurysmal dilations in the early post-implantation period. This study introduces a strategy for modification the mechanical properties of these biological scaffolds through the forming of an external polymeric reinforcement via thermal extrusion., Methods: The study utilized scaffolds fabricated from bovine internal mammary arteries through decellularization and preservation. The scaffolds were divided into subgroups and reinforced with polymeric helices made of Polyvinylidene fluoride (PVDF) and Polycaprolactone (PCL), n  = 5 for each. An experimental setup for external reinforcement coating was designed. Computed microtomography was employed to obtain accurate 3D models of the scaffolds. Mechanical properties were evaluated through in vitro uniaxial tension tests (Z50, Zwick/Roell, Germany), compliance evaluation and numerical simulations (Abaqus/CAE, Dassault Systemes, France) to investigate the effect of external reinforcement on aneurysm growth., Results: Using a double-layer helix for the reinforcement significantly enhanced the radial tensile strength of the scaffolds, increasing it up to 2.26 times. Yet, the comparison of vessel's compliance between two reinforced and the Control scaffolds within the physiological pressures range did not reveal any significant differences. Numerical simulation of aneurysm growth showed that thin-walled regions of the Control scaffold developed aneurysmal-type protrusions, bulging up to 0.7 mm, with a substantial degradation of mechanical properties. In contrast, both PVDF and PCL reinforced scaffolds did not exhibit significant property degradation, with deformations ranging 0.1-0.13 mm depending on the model, and a maximum decrease in the modulus of elasticity of 23%., Conclusion: The results of the study demonstrated that the external polymer helical reinforcement of decellularized scaffolds via thermal extrusion enables a controlled modification of mechanical properties, notably enhancing radial strength while maintaining sufficient compliance within the physiological pressure range. A series of in vitro tests demonstrated the consistency and potential of this approach for decellularized xenogenic scaffolds, a concept that had not been explored before., 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., (© 2023 Klyshnikov, Rezvova, Belikov, Glushkova and Ovcharenko.)

Majority of modern techniques for creating and optimizing the geometry of medical devices are based on a combination of computer-aided designs and the utility of the finite element method This approach, however, is limited by the number of geometries that can be investigated and by the time required for design optimization. To address this issue, we propose a generative design approach that combines machine learning (ML) methods and optimization algorithms. We evaluate eight different machine learning methods, including decision tree-based and boosting algorithms, neural networks, and ensembles. For optimal design, we investigate six state-of-the-art optimization algorithms, including Random Search, Tree-structured Parzen Estimator, CMA-ES-based algorithm, Nondominated Sorting Genetic Algorithm, Multiobjective Tree-structured Parzen Estimator, and Quasi-Monte Carlo Algorithm. In our study, we apply the proposed approach to study the generative design of a prosthetic heart valve (PHV). The design constraints of the prosthetic heart valve, including spatial requirements, materials, and manufacturing methods, are used as inputs, and the proposed approach produces a final design and a corresponding score to determine if the design is effective. Extensive testing leads to the conclusion that utilizing a combination of ensemble methods in conjunction with a Tree-structured Parzen Estimator or a Nondominated Sorting Genetic Algorithm is the most effective method in generating new designs with a relatively low error rate. Specifically, the Mean Absolute Percentage Error was found to be 11.8% and 10.2% for lumen and peak stress prediction respectively. Furthermore, it was observed that both optimization techniques result in design scores of approximately 95%. From both a scientific and applied perspective, this approach aims to select the most efficient geometry with given input parameters, which can then be prototyped and used for subsequent in vitro experiments. By proposing this approach, we believe it will replace or complement CAD-FEM-based modeling, thereby accelerating the design process and finding better designs within given constraints. The repository, which contains the essential components of the study, including curated source code, dataset, and trained models, is publicly available at https://github.com/ViacheslavDanilov/generative&#95;design., Competing Interests: VD, AP, and YG were employed by the Company 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 © 2023 Danilov, Klyshnikov, Onishenko, Proutski, Gankin, Melgani and Ovcharenko.)

Currently, transcatheter aortic valve implantation (TAVI) represents the most efficient treatment option for patients with aortic stenosis, yet its clinical outcomes largely depend on the accuracy of valve positioning that is frequently complicated when routine imaging modalities are applied. Therefore, existing limitations of perioperative imaging underscore the need for the development of novel visual assistance systems enabling accurate procedures. In this paper, we propose an original multi-task learning-based algorithm for tracking the location of anatomical landmarks and labeling critical keypoints on both aortic valve and delivery system during TAVI. In order to optimize the speed and precision of labeling, we designed nine neural networks and then tested them to predict 11 keypoints of interest. These models were based on a variety of neural network architectures, namely MobileNet V2, ResNet V2, Inception V3, Inception ResNet V2 and EfficientNet B5. During training and validation, ResNet V2 and MobileNet V2 architectures showed the best prediction accuracy/time ratio, predicting keypoint labels and coordinates with 97/96% accuracy and 4.7/5.6% mean absolute error, respectively. Our study provides evidence that neural networks with these architectures are capable to perform real-time predictions of aortic valve and delivery system location, thereby contributing to the proper valve positioning during TAVI., 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 © 2021 Danilov, Klyshnikov, Gerget, Skirnevsky, Kutikhin, Shilov, Ganyukov and Ovcharenko.)