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Simulating the cardiac valves is one of the most complex tasks in cardiovascular modeling. As fluid–structure interaction simulations are highly computationally demanding, machine-learning techniques can be considered a good alternative. Nevertheless, it is necessary to design many aortic valve geometries to generate a training set. A method for the design of a synthetic database of geometric models is presented in this study. We suggest using synthetic geometries that enable the development of several aortic valve and left ventricular models in a range of sizes and shapes. In particular, we developed 22 variations of left ventricular geometries, including one original model, seven models with varying wall thicknesses, seven models with varying heights, and seven models with varying shapes. To guarantee anatomical accuracy and physiologically acceptable fluid volumes, these models were verified using actual patient data. Numerical simulations of left ventricle contraction and aortic valve leaflet opening/closing were performed to evaluate the electro-physiological potential distribution in the left ventricle and wall shear stress distribution in aortic valve leaflets. The proposed synthetic database aims to increase the predictive power of machine-learning models in cardiovascular research and, eventually, improve patient outcomes after aortic valve surgery.

The complicated interaction between a fluid flow and a deformable structure is referred to as fluid–structure interaction (FSI). FSI plays a crucial role in the functioning of the aortic valve. Blood exerts stresses on the leaflets as it passes through the opening or shutting valve, causing them to distort and vibrate. The pressure, velocity, and turbulence of the fluid flow have an impact on these deformations and vibrations. Designing artificial valves, diagnosing and predicting valve failure, and improving surgical and interventional treatments all require the understanding and modeling of FSI in aortic valve dynamics. The most popular techniques for simulating and analyzing FSI in aortic valves are computational fluid dynamics (CFD) and finite element analysis (FEA). By studying the relationship between fluid flow and valve deformations, researchers and doctors can gain knowledge about the functioning of valves and possible pathological diseases. Overall, FSI is a complicated phenomenon that has a great impact on how well the aortic valve works. Aortic valve diseases and disorders can be better identified, treated, and managed by comprehending and mimicking this relationship. This article provides a literature review that compiles valve reconstruction methods from 1952 to the present, as well as FSI modeling techniques that can help advance valve reconstruction. The Scopus, PubMed, and ScienceDirect databases were used in the literature search and were structured into several categories. By utilizing FSI modeling, surgeons, researchers, and engineers can predict the behavior of the aortic valve before, during, and after surgery. This predictive capability can contribute to improved surgical planning, as it provides valuable insights into hemodynamic parameters such as blood flow patterns, pressure distributions, and stress analysis. Additionally, FSI modeling can aid in the evaluation of different treatment options and surgical techniques, allowing for the assessment of potential complications and the optimization of surgical outcomes. It can also provide valuable information on the long-term durability and functionality of prosthetic valves. In summary, fluid–structure interaction modeling is an effective tool for predicting the outcomes of aortic valve surgery. It can provide valuable insights into hemodynamic parameters and aid in surgical planning, treatment evaluation, and the optimization of surgical outcomes.

An algorithm providing information on the key geometric features of an aorta extracted from multi-slice computed tomography images is proposed. Using the numerical method, the aorta’s geometric characteristics, such as vessel cross-sectional areas and diameters, as well as distances between arteries, can be determined. This step is crucial for training the meta-model necessary to construct an expert system with a significantly reduced volume of data and for identifying key relationships between diagnoses and geometric and hydrodynamic features. This methodology is expected to be part of an innovative decision-making software product for clinical implementation. Based on clinical and anamnestic data as well as calculations, the software will provide the shunt parameters (in particular, its diameter) and installation position to ensure regular blood flow.

Carpal tunnel syndrome (CTS) is the most common pathology among disorders of the peripheral nervous system related to median nerve compression. To our knowledge, there are limited data on the effect of tendon movement on median nerve compression. This study focuses on the understanding of the carpal syndrome by simulating the impact of tendons movement caused by fingers flexion by Finite Element Analysis. Therefore, such modeling is the step toward the development of a personalized technique for value determining median nerve compression. Open-source MRI of the human right hand was used to build patient-specific phalanges of the fingers. Carpal tunnel soft tissues were considered as hyper-elastic materials, while bone structures were considered as elastic ones. The final finite-element model had 40 solid bodies which contacted the joint. Results were obtained for four cases of wrist movements: finger flexion, hand flexion/extension, and wrist extension with subsequent by finger flexion. Compression of the median nerve ranged from 129 Pa to 227 Pa. The results show that compression of the median nerve occurs faster during wrist flexion than during wrist extension or finger flexion. A decrease in compression during finger flexion was noticed with wrist extension followed by finger flexion.

Long-term fiber tissue remodeling and the progressive thickening of the aortic valve leaflets called calcific aortic stenosis lead to cardiac blood outflow obstruction. This disease is the most prevalent heart valve pathology in developed countries. Surgeons can perform aortic valve replacement through traditional open-heart surgery involving a cut (incision) in the chest or use minimally invasive methods such as transcatheter aortic valve implantation (TAVI). These types of surgery have numerous advantages and limitations. Recently, the Ozaki operation for aortic valve replacement using tissue from the autologous pericardium has been proposed. Despite being a promising technique for aortic valve pathology treatment, there is a lack of long-term results and optimal selection of leaflet sizing. Numerical fluid simulations can help surgeons predict operation outcomes for each patient. Nevertheless, the description of the material model for leaflet mechanics leaves an open question. Furthermore, selecting the most suitable model to describe the different conditions of the aortic valve is difficult. We performed a numerical analysis of aortic valve leaflet material models to describe the hemodynamics in normal, pathological, and Ozaki cases. We also reveal wall shear stress, von Mises stress, and displacement distributions. Based on the parameters mentioned above, we found that the Ozaki case model behaved similarly to the mathematical model describing the normal case. Numerical simulations also provide information on the mechanisms of aortic valve work in different states of the heart cycle.

In this work, a new approach to solving problems of optimal control of manufacture procedures for the production of silica optical fiber are proposed. The procedure of silica tubes alloying by the Modified Chemical Vapor Deposition (MCVD) method and optical fiber drawing from a preform are considered. The problems of optimal control are presented as problems of controlling distributed systems with objective functionals and controls of different types. Two problems are formulated and solved. The first of them is the problem of the temperature field optimizing in the silica tubes alloying process in controlling the consumption of the oxygen–hydrogen gas mixture (in the one- and two-dimensional statements), the second problem is the geometric optimization of fiber shape in controlling the drawing velocity of the finished fiber. In both problems, while using an analog to the method of Lagrange, the optimality systems in the form of differential problems in partial derivatives are obtained, as well as formulas for finding the optimal control functions in an explicit form. To acquire optimality systems, the qualities of lower semicontinuity, convexity, and objective functional coercivity are applied. The numerical realization of the obtained systems is conducted by using Comsol Multiphysics.

Background and Objectives: To propose a new classification of diabetic foot syndrome adapted for inclusion in the ICD-10 (the ICD-10 is the 10th revision of the International Statistical Classification of Diseases) and providing more reliable data on the number of clinical cases. Materials and Methods: A randomized controlled trial was performed. A total of 180 patients (36.6%) discharged from the hospital after surgical treatment and 312 patients (63.4%) who applied independently were observed and analysed. All patients had type 2 diabetes and were comparable in gender, age, duration of diabetes, area and nature of the wound defect. Results: We proposed to add the following to the existing ICD-10 and the emerging ICD-11 codes: Edf10.0—insulin-dependent diabetes mellitus with diabetic foot syndrome and Edf11.0—non-insulin-dependent diabetes mellitus with diabetic foot syndrome, where “df” is an acronym for diabetic foot. The new classification designates the seven most frequent areas of the lesion and five degrees of depth of soft tissue lesions. Conclusions: The proposed classification adapted for ICD-10 will enable the standardisation of diagnosis, providing a complete picture of this complication of diabetes mellitus, determining the number of amputations and their validity. Accurate statistics will allow for objective funding and timely preventive measures.