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Invasive coronary angiography remains the gold standard for diagnosing coronary artery disease, which may be complicated by both, patient-specific anatomy and image quality. Deep learning techniques aimed at detecting coronary artery stenoses may facilitate the diagnosis. However, previous studies have failed to achieve superior accuracy and performance for real-time labeling. Our study is aimed at confirming the feasibility of real-time coronary artery stenosis detection using deep learning methods. To reach this goal we trained and tested eight promising detectors based on different neural network architectures (MobileNet, ResNet-50, ResNet-101, Inception ResNet, NASNet) using clinical angiography data of 100 patients. Three neural networks have demonstrated superior results. The network based on Faster-RCNN Inception ResNet V2 is the most accurate and it achieved the mean Average Precision of 0.95, F1-score 0.96 and the slowest prediction rate of 3 fps on the validation subset. The relatively lightweight SSD MobileNet V2 network proved itself as the fastest one with a low mAP of 0.83, F1-score of 0.80 and a mean prediction rate of 38 fps. The model based on RFCN ResNet-101 V2 has demonstrated an optimal accuracy-to-speed ratio. Its mAP makes up 0.94, F1-score 0.96 while the prediction speed is 10 fps. The resultant performance-accuracy balance of the modern neural networks has confirmed the feasibility of real-time coronary artery stenosis detection supporting the decision-making process of the Heart Team interpreting coronary angiography findings.

The fabrication of ultra-high-temperature ceramics using the sintering method requires maintaining high temperatures of around 1500 °C for several hours. In contrast, this study demonstrated an alternative method for uniform formation of the corresponding microphases, which could reduce production costs in the future. The essence of the reactive hot pressing method lies in initiating a chemical reaction at an adiabatic temperature, which constitutes 60-80 % of the precursors' melting temperature, with the application of external pressure. The combination of these conditions significantly accelerates the densification process of the powder batch. The HfB2-SiC-C heteromodulus ceramics with different content of carbon platelets were manufactured via the reactive hot pressing of HfC-B4C-Si precursors at 1850 °C and 30 MPa for 4 minutes. Thus, the microhardness of the synthesized ceramics with specific chemical compositions reached 17.3 GPa, while the fracture toughness was 6.9 MPa/m2. The reactively pressed materials were compared to non-reactively pressed ones with the same compositions. X-Ray Diffraction (XRD) and Scanning Electron Microscopy (SEM) have been used for the composite characterization. Carbon inclusions were shown affecting the HfB2-SiC hardness while improving thermal shock resistance. The stratification of reactively pressed materials has been identified with silicon-depleted inner areas of the samples.

Actinobacteria are involved into multilateral relationships between insects, their food sources, infectious agents, etc. Antibiotics and related natural products play an essential role in such systems. The literature from the January 2016–August 2022 period devoted to insect-associated actinomycetes with antagonistic and/or enzyme-inhibiting activity was selected. Recent progress in multidisciplinary studies of insect–actinobacterial interactions mediated by antibiotics is summarized and discussed.

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_design .

The development of a novel artificial heart valve with outstanding durability and safety has remained a challenge since the first mechanical heart valve entered the market 65 years ago. Recent progress in high-molecular compounds opened new horizons in overcoming major drawbacks of mechanical and tissue heart valves (dysfunction and failure, tissue degradation, calcification, high immunogenic potential, and high risk of thrombosis), providing new insights into the development of an ideal artificial heart valve. Polymeric heart valves can best mimic the tissue-level mechanical behavior of the native valves. This review summarizes the evolution of polymeric heart valves and the state-of-the-art approaches to their development, fabrication, and manufacturing. The review discusses the biocompatibility and durability testing of previously investigated polymeric materials and presents the most recent developments, including the first human clinical trials of LifePolymer. New promising functional polymers, nanocomposite biomaterials, and valve designs are discussed in terms of their potential application in the development of an ideal polymeric heart valve. The superiority and inferiority of nanocomposite and hybrid materials to non-modified polymers are reported. The review proposes several concepts potentially suitable to address the above-mentioned challenges arising in the R&D of polymeric heart valves from the properties, structure, and surface of polymeric materials. Additive manufacturing, nanotechnology, anisotropy control, machine learning, and advanced modeling tools have given the green light to set new directions for polymeric heart valves.

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. 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 . 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). 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. 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.

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.

The work relates to the study of the internal multi-layered structure of optically inhomogeneous objects by the X-ray phase contrast imaging method, which is an urgent issue of modern science, technology, nuclear physics and medicine. X-ray phase-contrast imaging makes it possible to examine weakly absorbing objects, reduce the radiation dose and increase the spatial resolution. Computer modeling of X-ray diffraction within the Fresnel-Kirchhoff scalar diffraction theory was used as the research method. An algorithm was developed for creating a model of a multilayer object with different values of the refraction decrement, which allows to specify the shape, size, number and thickness of layers, and the refractive index of each layer of the object. X-ray phase-contrast image was obtained for a multilayer object with a proportional decrease in the refraction decrement from the center to the edges by the free propagation approach. A comparative analysis was carried out with an X-ray image of a homogeneous object of the same size and shape. It is shown that the modeling result contains quantitative information about the internal structure of the object and its multi-layered nature.

The unique properties of hydrogels have enabled their widespread use in various biomedical applications, including heart valve and blood vessel replacements. However, their current applications are limited by poor mechanical properties, including low strength, susceptibility to plastic deformation, and inadequate wear resistance, which are critical for load-bearing tissue replacements. Nanocomposite cryogels composed of polyvinyl alcohol and carbon nanotubes prepared using a DMSO/H 2 O solvent mixture, present a promising solution to address these limitations. Addition of nanoparticles up to 0.5% of the polymer mass showed a remarkable 59% increase in mechanical strength compared to single component cryogels. The reinforcement effect was also pronounced in strain hardening at large deformations. CNTs addition also enhanced the adhesion of Ea.hy 926 cells. However, the overall cell coverage on the cryogel surface was lower compared to control culture plastic, suggesting selective adhesion behavior. Comprehensive hemocompatibility testing showed minimal adsorption of protein molecules such as albumin and fibrinogen and no platelet adhesion when exposed to platelet rich plasma. Post contact platelet aggregation was same as untreated plasma. The studied materials also elicited minimal inflammatory response and no calcification unlike polytetrafluoroethylene which is clinically used, further proving biocompatibility. These findings suggest that PVA-based nanocomposite cryogels synthesized with dispersed CNTs hold significant promise for applications in cardiovascular surgery, particularly in the development of mechanically robust and biocompatible vascular grafts and heart valve prostheses.

Phase contrast is widely used in all fields for visualization of the internal structure of objects using X-ray radiation. The paper proposes a new approach to modeling a phase-contrast X-ray image by the method of free propagation using the Fresnel-Kirchhoff diffraction theory. A simple calculation model was developed, which makes it possible to determine the value of the change in the intensity of X-rays on three-dimensional models of objects of arbitrary shape with macroscopic dimensions. It also allows you to establish the conditions for observing a contrast image with known characteristics of the detector system and the intensity of the radiation source. The possibility of obtaining clear images of objects with small decrements of refraction of matter, determining their geometric dimensions and thickness was shown. A method of calculating the optical properties of metal alloys in the X-ray range has been developed. The approaches presented in the paper can be useful to developers of compact devices for detecting structural inhomogeneities inside the studied objects by a non-destructive method.