Calculation of femoral cortical bone elasticity modulus from computed tomography scans

The results obtained from the finite element analysis, apart from precise geometry, depend on the applied boundary conditions and material properties. The use of patient specific geometries and patient’s material properties leads to more accurate results. The aim of this paper was to determine elasticity modulus of a human femoral bone when the bone is considered as an isotropic material. Elasticity modulus for the femoral bone was calculated from a computed tomography images and obtained values showed how the modulus changes along the femoral bone. The images were additionally used to create a patient specific 3D model of the femoral bone. Additionally, the effect of a change in elasticity modulus values was demonstrated by comparison of maximum displacement and von Mises stress obtained from the finite element analyses of the femoral bone model. The numerical simulations showed the influence of the elastic modulus on the displacement and stress values and importance of the appropriate elasticity modulus

Walking at speeds close to the preferred transition speed as an approach to obesity treatment

Introduction. Increasing energy expenditure through certain exercise is an important component of effective interventions to enhance initial weight loss and prevent weight regain. Objective. The purpose of this study was to determine the effect of a 16-week weight loss exercise programme on morpho-functional changes in female adults and to examine the programme effects on two subpopulations with different levels of obesity. Methods. Fifty-six middle-aged women were divided into 2 groups according to their body mass index (BMI): 25-29.9 kg/m2 - overweight (OW) and ≥30 kg/m2 - obese (OB). The exercise protocol included a walking technique based on hip rotation at horizontal plane at speeds close to the preferred transition speed (PTS). At the initiation of the study and after 16 weeks of the programme, anthropometric, morphological and cardiovascular parameters of all subjects were assessed. The main effects of Group (OW and OB) and Time and the interaction effect of Group by Time were tested by time repeated measures General Linear Model (mixed between-within subjects ANOVA). Results. Mean weight loss during the programme was 10.3 kg and 20.1 kg in OW and OB, respectively. The average fat mass (FM) loss was 9.4 kg in OW and 16.9 kg in OB. The Mixed ANOVA revealed a significant Group by Time interaction effects for waist circumference, body weight, body water, fat free mass, FM, %FM and BMI (p<0.05). Conclusion. The applied exercise protocol has proved as beneficial in the treatment of obesity, since it resulted in a significant weight loss and body composition changes. The reduction in body weight was achieved mainly on account of the loss of fat mass. © 2012. Centre for Evaluation in Education and Science

Big Data and machine learning: new frontier in lung cancer care

This review paper gives state-of-the-art in the field of lung cancer modelling. Due to the increasing amount of data available, research in lung cancer comes into the era of Big Data. New algorithms and methods are developed and coupled with machine learning techniques in order to improve the prediction of lung cancer development, determine the adequate therapy and increase the patient survival. We first give the overview of the current situation in the field of lung cancer, then investigate the role of ‘omics’ data in prevention and treatment of lung cancer, only to reach the explanations of the new available methods in lung cancer research—computational modelling and machine learning methods

Sexual Dimorphism in the Dimensions of Teeth in a Serbian Population

The study of teeth is of great interest to anthropologists, biologists, orthodontists and forensic scientists. The existence of sexual dimorphism in permanent teeth is a known phenomenon. Aim of this study was to analyze the presence of sexual dimorphism in the mesiodistal and vestibulolingual diameter of permanent teeth in the sample of Serbian population. Measurements were taken on plaster casts of 201 individuals of both sexes, ages between 18-25 years, using a digital caliper with 0.01 mm precision. The mesiodistal and vestibulolingual diameter of each permanent tooth was determined. A Student’s t-test and a Mann-Whitney U test were used to statistically analyze the obtained results. There were no statistically significant differences in the teeth crown diameter between the right and left side of the same dental arch. Majority of the teeth examined were larger in male than in female patients. Statistically significant difference in the mesiodistal diameter of male and female maxillary and mandibular canines was found. The results of this study indicate that there are significant differences in teeth size between sexes in Serbian population. Males have larger diameters in teeth crowns than females. Canines show the greatest dimorphism

Automatic Detection of Cardiomyopathy in Cardiac Left Ventricle Ultrasound Images

This paper presents development of an automatic diagnostic tool based on machine learning that analyses cardiac ultrasound images of patients with cardiomyopathy in several views (4 chamber apical,2 chamber apical and M mode view). The main aim of the developed tool is to perform automatic left ventricle (LV) segmentation and to extract relevant parameters in order to estimate the severeness of cardiomyopathy in patients. Dataset included 1809 images with apical view and 53 images with M view from real patients collected at three Clinical Centers in UK and Serbia. Separate methodologies have been implemented for analyzing apical and M mode view,including U-net for segmentation,after which parameters such as left ventricular length (LVL),internal dimension (LVID),posterior wall thickness (LVPW) and interventricular septum thickness (IVS) are calculated,both in systole and diastole. The tool has also been implemented on the platform with a user-friendly interface,which allows these two modules to be used either separately or combined. In order to validate the model and compare the results between gold standard and developed methodology,two cardiology specialists have independently manually annotated LV and measured relevant parameters. The results show that the model achieves dice coefficient of 92.091% for segmentation and average root mean square error (RMSE) of 0.3052cm for parameter extraction in apical view images and average RMSE of 1.3548cm for parameter extraction in M mode view. Fully automatic detection of cardiomyopathy in cardiac LV ultrasound images can help clinicians in supporting diagnostic decision making and prescribing adequate therapy

Computational modeling of shear forces and experimental validation of endothelial cell responses in an orbital well shaker system

Vascular endothelial cells are continuously exposed to hemodynamic shear stress. Intensity and type of shear stress are highly relevant to vascular physiology and pathology. Here, we modeled shear stress distribution in a tissue culture well (R = 17.5 mm, fill volume 2 ml) under orbital translation using computational fluid dynamics with the finite element method. Free surface distribution, wall shear stress, inclination angle, drag force, and oscillatory index on the bottom surface were modeled. Obtained results predict nonuniform shear stress distribution during cycle, with higher oscillatory shear index, higher drag force values, higher circular component, and larger inclination angle of the shear stress at the periphery of the well compared with the center of the well. The oscillatory index, inclination angle, and drag force are new quantitative parameters modeled in this system, which provide a better understanding of the hydrodynamic conditions experienced and reflect the pulsatile character of blood flow in vivo. Validation experiments revealed that endothelial cells at the well periphery aligned under flow and increased Kruppel-like Factor 4 (KLF-4), cyclooxygenase-2 (COX-2) expression and endothelial nitric oxide synthase (eNOS) phosphorylation. In contrast, endothelial cells at the center of the well did not show clear directional alignment, did not induce the expression of KLF-4 and COX-2 nor increased eNOS phosphorylation. In conclusion, this improved computational modeling predicts that the orbital shaker model generates different hydrodynamic conditions at the periphery versus the center of the well eliciting divergent endothelial cell responses. The possibility of generating different hydrodynamic conditions in the same well makes this model highly attractive to study responses of distinct regions of the same endothelial monolayer to different types of shear stresses thereby better reflecting in vivo conditions

Comparison of Different Neural Network Training Algorithms with Application to Face Recognition Problem

Research in the field of face recognition has been popular for several decades. With advances in technology,approaches to solving this problems haves changed. Main goal of this paper was to compare different training algorithms for neural networks and to apply them for face recognition as it is a nonlinear problem. Algorithm that we have used for face recognition problem was the Eigenface algorithm that belongs to the Principal Component Analysis (PCA) algorithms. Percentage of recognition for all the used training functions is above 90%

Artificial Neural Network for Prediction of Seat-to-Head Frequency Response Function During Whole Body Vibrations in the Fore-and-Aft Direction

Vibrations while driving, regardless of their intensity and shape, have the most obvious effect of reducing driving comfort. Seat-to-head frequency response function (STHT) is a complex relationship resulting from the movement of the head due to the action of excitation on the seat in the form of vibrations in the seat/head interface. In this research, an artificial neural network model was developed, which aims to simulate the STHT function through the body of the subjects based on the data obtained experimentally. The experiments were conducted with twenty healthy male volunteers, who were exposed to single-axis fore-and-aft random broadband vibration. All the results of the experiment were recorded on the basis of which the artificial neural network (ANN) was trained. The developed ANN model has the ability to predict STHT values in the range of trained values both when changing the anthropometric measures of the subjects and changes in the input characteristics of vibrations. The mathematical models based on recurrent neural networks (RNN) used in this paper show with high accuracy STHT values in case there exists prior information about the anthropometric measures of the subjects and the input characteristics of vibrations. The results show that the expensive real-time simulations could be avoided by using reliable neural network models