المؤلفون: Viacheslav V. Danilov, Vladislav V. Laptev, Kirill Yu. Klyshnikov, Alexander D. Stepanov, Leo A. Bogdanov, Larisa V. Antonova, Evgenia O. Krivkina, Anton G. Kutikhin, Evgeny A. Ovcharenko

المصدر: Frontiers in Bioengineering and Biotechnology, Vol 12 (2024)

مصطلحات موضوعية: histological segmentation, deep learning, vascular tissue engineering, digital pathology, tissue-engineered vascular grafts, Biotechnology, TP248.13-248.65

الوصف: IntroductionThe 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.MethodsWe 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).ResultsAfter 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.ConclusionThis 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.

وصف الملف: electronic resource

العلاقة: https://www.frontiersin.org/articles/10.3389/fbioe.2024.1411680/fullTest; https://doaj.org/toc/2296-4185Test

الوصول الحر: https://doaj.org/article/53202ee167604aba83882a48ba4491ebTest

المؤلفون: Kirill Yu. Klyshnikov, Maria A. Rezvova, Nikita V. Belikov, Tatiana V. Glushkova, Evgeny A. Ovcharenko

المصدر: Frontiers in Cardiovascular Medicine, Vol 10 (2023)

مصطلحات موضوعية: biocompatible polymers, PVDF, PCL, external reinforcement, vascular scaffold, thermal extrusion, Diseases of the circulatory (Cardiovascular) system, RC666-701

الوصف: BackgroundDecellularized xenogenic scaffolds represent a promising substrate for tissue-engineered vascular prostheses, particularly those with smaller diameters (

وصف الملف: electronic resource

العلاقة: https://www.frontiersin.org/articles/10.3389/fcvm.2023.1257812/fullTest; https://doaj.org/toc/2297-055XTest

الوصول الحر: https://doaj.org/article/113d62f1bdf64e1e9dff319aecf93227Test

المؤلفون: Viacheslav V. Danilov, Kirill Y. Klyshnikov, Pavel S. Onishenko, Alex Proutski, Yuriy Gankin, Farid Melgani, Evgeny A. Ovcharenko

المصدر: Frontiers in Bioengineering and Biotechnology, Vol 11 (2023)

مصطلحات موضوعية: generative design, heart valve prosthesis, prosthetic heart valve, machine learning, optimization, gradient methods, Biotechnology, TP248.13-248.65

الوصف: 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_designTest.

وصف الملف: electronic resource

العلاقة: https://www.frontiersin.org/articles/10.3389/fbioe.2023.1238130/fullTest; https://doaj.org/toc/2296-4185Test

الوصول الحر: https://doaj.org/article/6b235ba6e7ad464799cbc8fe83c6d581Test

المؤلفون: Alexander E. Kostyunin, Tatiana V. Glushkova, Arseniy A. Lobov, Evgeny A. Ovcharenko, Bozhana R. Zainullina, Leo A. Bogdanov, Daria K. Shishkova, Victoria E. Markova, Maksim A. Asanov, Rinat A. Mukhamadiyarov, Elena A. Velikanova, Tatiana N. Akentyeva, Maria A. Rezvova, Alexander N. Stasev, Alexey V. Evtushenko, Leonid S. Barbarash, Anton G. Kutikhin

المصدر: Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, Vol 12, Iss 1 (2023)

مصطلحات موضوعية: bacterial invasion, bioprosthetic heart valves, matrix metalloproteinases, neutrophil infiltration, structural valve degeneration, Diseases of the circulatory (Cardiovascular) system, RC666-701

الوصف: Background Whereas the risk factors for structural valve degeneration (SVD) of glutaraldehyde‐treated bioprosthetic heart valves (BHVs) are well studied, those responsible for the failure of BHVs fixed with alternative next‐generation chemicals remain largely unknown. This study aimed to investigate the reasons behind the development of SVD in ethylene glycol diglycidyl ether–treated BHVs. Methods and Results Ten ethylene glycol diglycidyl ether–treated BHVs excised because of SVD, and 5 calcified aortic valves (AVs) replaced with BHVs because of calcific AV disease were collected and their proteomic profile was deciphered. Then, BHVs and AVs were interrogated for immune cell infiltration, microbial contamination, distribution of matrix‐degrading enzymes and their tissue inhibitors, lipid deposition, and calcification. In contrast with dysfunctional AVs, failing BHVs suffered from complement‐driven neutrophil invasion, excessive proteolysis, unwanted coagulation, and lipid deposition. Neutrophil infiltration was triggered by an asymptomatic bacterial colonization of the prosthetic tissue. Neutrophil elastase, myeloblastin/proteinase 3, cathepsin G, and matrix metalloproteinases (MMPs; neutrophil‐derived MMP‐8 and plasma‐derived MMP‐9), were significantly overexpressed, while tissue inhibitors of metalloproteinases 1/2 were downregulated in the BHVs as compared with AVs, together indicative of unbalanced proteolysis in the failing BHVs. As opposed to other proteases, MMP‐9 was mostly expressed in the disorganized prosthetic extracellular matrix, suggesting plasma‐derived proteases as the primary culprit of SVD in ethylene glycol diglycidyl ether–treated BHVs. Hence, hemodynamic stress and progressive accumulation of proteases led to the extracellular matrix degeneration and dystrophic calcification, ultimately resulting in SVD. Conclusions Neutrophil‐ and plasma‐derived proteases are responsible for the loss of BHV mechanical competence and need to be thwarted to prevent SVD.

وصف الملف: electronic resource

العلاقة: https://doaj.org/toc/2047-9980Test

الوصول الحر: https://doaj.org/article/ed68d24de8844d5994cf0c6ce895ff9fTest

المؤلفون: Miraslau I. Makarevich, Pavel A. Nikishau, Ivan A. Berezianko, Tatiana V. Glushkova, Maria A. Rezvova, Evgeny A. Ovcharenko, Giyjaz E. Bekmukhamedov, Dmitry G. Yakhvarov, Sergei V. Kostjuk

المصدر: Macromol, Vol 1, Iss 4, Pp 243-255 (2021)

مصطلحات موضوعية: cationic polymerization, living polymerization, isobutylene, styrene, polyisobutylene, poly(styrene-block-isobutylene-block-styrene), Chemical technology, TP1-1185, Biochemistry, QD415-436

الوصف: The cationic polymerization of isobutylene and its block copolymerization with styrene using DiCumCl/TiCl4/2,6-lutidine initiating system has been studied in open conditions. It was shown that a higher concentration of proton trap is required in open conditions as compared to the glove box technique in order to have good control over molecular weight and polydispersity. Polyisobutylenes with Mn ≤ 50,000 g mol−1 and low polydispersity (Đ ≤ 1.2) were prepared at [Lu] = 12 mM. The synthesis of poly(styrene-block-isobutylene-block-styrene) triblock copolymer (SIBS) in open conditions required the addition of proton trap into two steps, half at the beginning of the reaction and the second half together with styrene. Following this protocol, a series of triblock copolymers with different length of central polyisobutylene block (from Mn = 20,000 g mol−1 to 50,000 g mol−1) and side polystyrene blocks (Mn = 4000 g mol−1–9000 g mol−1) with low polydispersity (Đ ≤ 1.25) were synthesized. High molecular SIBS (Mn > 50,000 g mol−1) with low polydispersity (Đ < 1.3) containing longer polystyrene blocks (Mn > 6000 g mol−1) demonstrated higher tensile strength (~13.5 MPa).

وصف الملف: electronic resource

العلاقة: https://www.mdpi.com/2673-6209/1/4/17Test; https://doaj.org/toc/2673-6209Test

الوصول الحر: https://doaj.org/article/c58f3518e4ea47d0b1be781878d7a845Test

المؤلفون: Maria A. Rezvova, Kirill Y. Klyshnikov, Aleksander A. Gritskevich, Evgeny A. Ovcharenko

المصدر: International Journal of Molecular Sciences, Vol 24, Iss 4, p 3963 (2023)

مصطلحات موضوعية: biomaterials, durability, heart valve replacement, hemocompatibility, immune response, mechanical heart valves, Biology (General), QH301-705.5, Chemistry, QD1-999

الوصف: 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.

وصف الملف: electronic resource

العلاقة: https://www.mdpi.com/1422-0067/24/4/3963Test; https://doaj.org/toc/1661-6596Test; https://doaj.org/toc/1422-0067Test

الوصول الحر: https://doaj.org/article/f2dcad14689e446b9e321c90412e3883Test

المؤلفون: Viacheslav V. Danilov, Kirill Yu. Klyshnikov, Olga M. Gerget, Anton G. Kutikhin, Vladimir I. Ganyukov, Alejandro F. Frangi, Evgeny A. Ovcharenko

المصدر: Scientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)

مصطلحات موضوعية: Medicine, Science

الوصف: Abstract 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.

وصف الملف: electronic resource

العلاقة: https://doaj.org/toc/2045-2322Test

الوصول الحر: https://doaj.org/article/af0e99a2f2bf46a59aad110982b117f1Test

المؤلفون: Viacheslav V. Danilov, Kirill Yu. Klyshnikov, Olga M. Gerget, Igor P. Skirnevsky, Anton G. Kutikhin, Aleksandr A. Shilov, Vladimir I. Ganyukov, Evgeny A. Ovcharenko

المصدر: Frontiers in Cardiovascular Medicine, Vol 8 (2021)

مصطلحات موضوعية: keypoint tracking, multi-task learning, transcatheter aortic valve replacement, deep learning—CNN, medical image analysis, aortography, Diseases of the circulatory (Cardiovascular) system, RC666-701

الوصف: 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.

وصف الملف: electronic resource

العلاقة: https://www.frontiersin.org/articles/10.3389/fcvm.2021.697737/fullTest; https://doaj.org/toc/2297-055XTest

الوصول الحر: https://doaj.org/article/c254ad5e6fc340d0a4f96d3ddce2179eTest

المؤلفون: Maria A. Rezvova, Pavel A. Nikishau, Miraslau I. Makarevich, Tatiana V. Glushkova, Kirill Yu. Klyshnikov, Tatiana N. Akentieva, Olga S. Efimova, Andrey P. Nikitin, Valentina Yu. Malysheva, Vera G. Matveeva, Evgeniia A. Senokosova, Mariam Yu. Khanova, Viacheslav V. Danilov, Dmitry M. Russakov, Zinfer R. Ismagilov, Sergei V. Kostjuk, Evgeny A. Ovcharenko

المصدر: Nanomaterials, Vol 12, Iss 5, p 733 (2022)

مصطلحات موضوعية: single-walled carbon nanotubes, polymer nanocomposites, biocompatible polymers, interphase interaction, SIBS, cardiovascular medical devices, Chemistry, QD1-999

الوصف: Nanocomposites based on poly(styrene-block-isobutylene-block-styrene) (SIBS) and single-walled carbon nanotubes (CNTs) were prepared and characterized in terms of tensile strength as well as bio- and hemocompatibility. It was shown that modification of CNTs using dodecylamine (DDA), featured by a long non-polar alkane chain, provided much better dispersion of nanotubes in SIBS as compared to unmodified CNTs. As a result of such modification, the tensile strength of the nanocomposite based on SIBS with low molecular weight (Mn = 40,000 g mol–1) containing 4% of functionalized CNTs was increased up to 5.51 ± 0.50 MPa in comparison with composites with unmodified CNTs (3.81 ± 0.11 MPa). However, the addition of CNTs had no significant effect on SIBS with high molecular weight (Mn~70,000 g mol−1) with ultimate tensile stress of pure polymer of 11.62 MPa and 14.45 MPa in case of its modification with 1 wt% of CNT-DDA. Enhanced biocompatibility of nanocomposites as compared to neat SIBS has been demonstrated in experiment with EA.hy 926 cells. However, the platelet aggregation observed at high CNT concentrations can cause thrombosis. Therefore, SIBS with higher molecular weight (Mn~70,000 g mol−1) reinforced by 1–2 wt% of CNTs is the most promising material for the development of cardiovascular implants such as heart valve prostheses.

وصف الملف: electronic resource

العلاقة: https://www.mdpi.com/2079-4991/12/5/733Test; https://doaj.org/toc/2079-4991Test

الوصول الحر: https://doaj.org/article/02840033f1e04083948e16b3a6205a17Test

المؤلفون: Maria A. Rezvova, Arseniy E. Yuzhalin, Tatiana V. Glushkova, Miraslau I. Makarevich, Pavel A. Nikishau, Sergei V. Kostjuk, Kirill Yu. Klyshnikov, Vera G. Matveeva, Mariam Yu. Khanova, Evgeny A. Ovcharenko

المصدر: Polymers, Vol 12, Iss 9, p 2158 (2020)

مصطلحات موضوعية: nanocomposites, carbon nanotubes, biocompatible materials, electrical conductivity, triblock copolymers, Organic chemistry, QD241-441

الوصف: In this study, we incorporated carbon nanotubes (CNTs) into poly(styrene-block-isobutylene-block-styrene) (SIBS) to investigate the physical characteristics of the resulting nanocomposite and its cytotoxicity to endothelial cells. CNTs were dispersed in chloroform using sonication following the addition of a SIBS solution at different ratios. The resultant nanocomposite films were analyzed by X-ray microtomography, optical and scanning electron microscopy; tensile strength was examined by uniaxial tension testing; hydrophobicity was evaluated using a sessile drop technique; for cytotoxicity analysis, human umbilical vein endothelial cells were cultured on SIBS–CNTs for 3 days. We observed an uneven distribution of CNTs in the polymer matrix with sporadic bundles of interwoven nanotubes. Increasing the CNT content from 0 wt% to 8 wt% led to an increase in the tensile strength of SIBS films from 4.69 to 16.48 MPa. The engineering normal strain significantly decreased in 1 wt% SIBS–CNT films in comparison with the unmodified samples, whereas a further increase in the CNT content did not significantly affect this parameter. The incorporation of CNT into the SIBS matrix resulted in increased hydrophilicity, whereas no cytotoxicity towards endothelial cells was noted. We suggest that SIBS–CNT may become a promising material for the manufacture of implantable devices, such as cardiovascular patches or cusps of the polymer heart valve.

وصف الملف: electronic resource

العلاقة: https://www.mdpi.com/2073-4360/12/9/2158Test; https://doaj.org/toc/2073-4360Test

الوصول الحر: https://doaj.org/article/ddf18a70408c452d9ad5e975cca3447bTest