Current Issue : January - March Volume : 2018 Issue Number : 1 Articles : 5 Articles
Models that describe the trace element status formation in the human organism are essential for a correction of micromineral\n(trace elements) deficiency. A direct trace element retention assessment in the body is difficult due to the many internal\nmechanisms. The trace element retention is determined by the amount and the ratio of incoming and excreted substance.\nSo, the concentration of trace elements in drinking water characterizes the intake, whereas the element concentration in\nurine characterizes the excretion. This system can be interpreted as three interrelated elements that are in equilibrium.\nSince many relationships in the system are not known, the use of standard mathematical models is difficult. The artificial\nneural network use is suitable for constructing a model in the best way because it can take into account all dependencies\nin the system implicitly and process inaccurate and incomplete data. We created several neural network models to describe\nthe retentions of trace elements in the human body. On the model basis, we can calculate the microelement levels in the\nbody, knowing the trace element levels in drinking water and urine. These results can be used in health care to provide\nthe population with safe drinking water....
The paper presents results of machine learning approach accuracy applied analysis of cardiac activity. The study evaluates the\ndiagnostics possibilities of the arterial hypertension by means of the short-term heart rate variability signals. Two groups were\nstudied: 30 relatively healthy volunteers and 40 patients suffering from the arterial hypertension of II-III degree. The following\nmachine learning approaches were studied: linear and quadratic discriminant analysis, k-nearest neighbors, support vector\nmachine with radial basis, decision trees, and naive Bayes classifier. Moreover, in the study, different methods of feature\nextraction are analyzed: statistical, spectral, wavelet, and multifractal. All in all, 53 features were investigated. Investigation\nresults show that discriminant analysis achieves the highest classification accuracy. The suggested approach of noncorrelated\nfeature set search achieved higher results than data set based on the principal components....
Background: Studies have identified hemodynamic shear stress as an important\ndeterminant of endothelial function and atherosclerosis. In this study, we assess the\ninfluences of hemodynamic shear stress on carotid plaques.\nMethods: Carotid stenosis phantoms with three severity (30, 50, 70%) were made\nfrom 10% polyvinyl alcohol (PVA) cryogel. The phantoms were placed in a pulsatile flow\nloop with the same systolic/diastolic phase (35/65) and inlet flow rate (16 L/h). Ultrasonic\nparticle imaging velocimetry (Echo PIV) and computational fluid dynamics (CFD)\nwere used to calculate the velocity profile and shear stress distribution in the carotid\nstenosis phantoms. Inlet/outlet boundary conditions used in CFD were extracted from\nEcho PIV experiments to make sure that the results were comparable.\nResults: Echo PIV and CFD results showed that velocity was largest in 70% than those\nin 30 and 50% at peak systole. Echo PIV results indicated that shear stress was larger\nin the upper wall and the surface of plaque than in the center of vessel. CFD results\ndemonstrated that wall shear stress in the upstream was larger than in downstream of\nplaque. There was no significant difference in average velocity obtained by CFD and\nEcho PIV in 30% (p = 0.25). Velocities measured by CFD in 50% (93.01 cm/s) and in 70%\n(115.07 cm/s) were larger than those by Echo PIV in 50% (60.26 �± 5.36 cm/s) and in\n70% (89.11 �± 7.21 cm/s).\nConclusions: The results suggested that Echo PIV and CFD could obtain hemodynamic\nshear stress on carotid plaques. Higher WSS occurred in narrower arteries, and\nthe shoulder of plaque bore higher WSS than in bottom part....
Background: The immunotoxicity of engine exhausts is of high concern to human\nhealth due to the increasing prevalence of immune-related diseases. However, the\nevaluation of immunotoxicity of engine exhausts is currently based on expensive and\ntime-consuming experiments. It is desirable to develop efficient methods for immunotoxicity\nassessment.\nMethods: To accelerate the development of safe alternative fuels, this study proposed\na computational method for identifying informative features for predicting proinflammatory\npotentials of engine exhausts. A principal component regression (PCR)\nalgorithm was applied to develop prediction models. The informative features were\nidentified by a sequential backward feature elimination (SBFE) algorithm.\nResults: A total of 19 informative chemical and biological features were successfully\nidentified by SBFE algorithm. The informative features were utilized to develop a computational\nmethod named FS-CBM for predicting proinflammatory potentials of engine\nexhausts. FS-CBM model achieved a high performance with correlation coefficient values\nof 0.997 and 0.943 obtained from training and independent test sets, respectively.\nConclusions: The FS-CBM model was developed for predicting proinflammatory\npotentials of engine exhausts with a large improvement on prediction performance\ncompared with our previous CBM model. The proposed method could be further\napplied to construct models for bioactivities of mixtures....
The behavior of the 2006 ten Tusscher-Panfilov model of human ventricular myocytes under\nthe impact of periodic excitation impulses was studied in the BeatBox simulation environment.\nThe cardiomyocyte model has a limited susceptibility to an forced higher frequency\nexcitation rhythm. A high-frequency excitation rhythm can be forced by gradually\nincreasing the frequency of excitation impulses. The mechanism of defibrillation pulse impact\nconsists of presumably prolonging the refractoriness of cardiomyocytes which undermines\ntheir susceptibility for a long time to a forced high-frequency rhythm of fibrillation,\nas a result for which they hinder the propagation of a fibrillation wave. This is the only\nmechanism of defibrillation that was identified during the simulation. The threshold energy\nof a depolarizing defibrillation pulse prolonging the refractoriness of the cardiomyocyte varies\ndepending on a delay relative to the excitation impulse (the excitation cycle phase) in a\nwide range (the maximum value exceeds the minimum by several thousand times). The results\nshow differences in the mechanisms of impact on a cardiomyocyte between an excitation\nimpulse and a monophasic defibrillation pulse....
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