Milos Radovic

Computer simulation of three-dimensional plaque formation and progression in the carotid artery

Atherosclerosis is becoming the number one cause of death worldwide. In this study, three-dimensional computer model of plaque formation and development for human carotid artery is developed. The three-dimensional blood flow is described by the Navier–Stokes equation, together with the continuity equation. Mass transfer within the blood lumen and through the arterial wall is coupled with the blood flow and is modeled by a convection–diffusion equation. The low-density lipoproteins transports in lumen of the vessel and through the vessel tissue are coupled by Kedem–Katchalsky equations. The inflammatory process is modeled using three additional reaction–diffusion partial differential equations. Fluid–structure interaction is used to estimate effective wall stress analysis. Plaque growth functions for volume progression are correlated with shear stress and effective wall stress distribution. We choose two specific patients from MRI study with significant plaque progression. Plaque volume progression using three time points for baseline, 3- and 12-month follow up is fitted. Our results for plaque localization correspond to low shear stress zone and we fitted parameters from our model using nonlinear least-square method. Determination of plaque location and composition, and computer simulation of progression in time for a specific patient shows a potential benefit for the prediction of disease progression. The proof of validity of three-dimensional computer modeling in the evaluation of atherosclerotic plaque burden may shift the clinical information of MRI from morphological assessment toward a functional tool. Understanding and prediction of the evolution of atherosclerotic plaques either into vulnerable or stable plaques are major tasks for the medical community.

Mining data from hemodynamic simulations for generating prediction and explanation models

One of the most common causes of human death is stroke, which can be caused by carotid bifurcation stenosis. In our work, we aim at proposing a prototype of a medical expert system that could significantly aid medical experts to detect hemodynamic abnormalities (increased artery wall shear stress). Based on the acquired simulated data, we apply several methodologies for1) predicting magnitudes and locations of maximum wall shear stress in the artery, 2) estimating reliability of computed predictions, and 3) providing user-friendly explanation of the models decision. The obtained results indicate that the evaluated methodologies can provide a useful tool for the given problem domain.

Minimum redundancy maximum relevance feature selection approach for temporal gene expression data

BackgroundFeature selection, aiming to identify a subset of features among a possibly large set of features that are relevant for predicting a response, is an important preprocessing step in machine learning. In gene expression studies this is not a trivial task for several reasons, including potential temporal character of data. However, most feature selection approaches developed for microarray data cannot handle multivariate temporal data without previous data flattening, which results in loss of temporal information.We propose a temporal minimum redundancy - maximum relevance (TMRMR) feature selection approach, which is able to handle multivariate temporal data without previous data flattening. In the proposed approach we compute relevance of a gene by averaging F-statistic values calculated across individual time steps, and we compute redundancy between genes by using a dynamical time warping approach.ResultsThe proposed method is evaluated on three temporal gene expression datasets from human viral challenge studies. Obtained results show that the proposed method outperforms alternatives widely used in gene expression studies. In particular, the proposed method achieved improvement in accuracy in 34 out of 54 experiments, while the other methods outperformed it in no more than 4 experiments.ConclusionWe developed a filter-based feature selection method for temporal gene expression data based on maximum relevance and minimum redundancy criteria. The proposed method incorporates temporal information by combining relevance, which is calculated as an average F-statistic value across different time steps, with redundancy, which is calculated by employing dynamical time warping approach. As evident in our experiments, incorporating the temporal information into the feature selection process leads to selection of more discriminative features.

Electromagnetic field investigation on different cancer cell lines

BackgroundThere is a strong interest in the investigation of extremely low frequency Electromagnetic Fields (EMF) in the clinic. While evidence about anticancer effects exists, the mechanism explaining this effect is still unknown.MethodsWe investigated in vitro, and with computer simulation, the influence of a 50 Hz EMF on three cancer cell lines: breast cancer MDA-MB-231, and colon cancer SW-480 and HCT-116. After 24 h preincubation, cells were exposed to 50 Hz extremely low frequency (ELF) radiofrequency EMF using in vitro exposure systems for 24 and 72 h. A computer reaction-diffusion model with the net rate of cell proliferation and effect of EMF in time was developed. The fitting procedure for estimation of the computer model parameters was implemented.ResultsExperimental results clearly showed disintegration of cells treated with a 50 Hz EMF, compared to untreated control cells. A large percentage of treated cells resulted in increased early apoptosis after 24 h and 72 h, compared to the controls. Computer model have shown good comparison with experimental data.ConclusionUsing EMF at specific frequencies may represent a new approach in controlling the growth of cancer cells, while computer modelling could be used to predict such effects and make optimisation for complex experimental design. Further studies are required before testing this approach in humans.

Application of data mining algorithms for mammogram classification

One of the leading causes of cancer death among women is breast cancer. In our work we aim at proposing a prototype of a medical expert system (based on data mining techniques) that could significantly aid medical experts to detect breast cancer. This paper presents the CAD (computer aided diagnosis) system for the detection of normal and abnormal pattern in the breast. The proposed system consists of four major steps: the image preprocessing, the feature extraction, the feature selection and the classification process that classifies mammogram into normal (without tumor) and abnormal (with tumor) pattern. After removing noise from mammogram using the Discrete Wavelet Transformation (DWT), first is selected the region of interest (ROI). By identifying the boundary of the breast, it is possible to remove any artifact present outside the breast area, such as patient markings. Then, a total of 20 GLCM features are extracted from the ROI, which were used as inputs for classification algorithms. In order to compare the classification results, we used seven different classifiers. Normal breast images and breast image with masses (total 322 images) used as input images in this study are taken from the mini-MIAS database.

Parameter optimization of a computer-aided diagnosis system for detection of masses on digitized mammograms

BACKGROUND Reading mammograms is a difficult task and for this reason any development that may improve the performance in breast cancer screening is of great importance. OBJECTIVE We proposed optimized computer aided diagnosis (CAD) system, equipped with reliability estimate module, for mass detection on digitized mammograms. METHODS Proposed CAD system consists of four major steps: preprocessing, segmentation, feature extraction and classification. We propose a simple regression function as a threshold function for extraction of potential masses. By running optimization procedure we estimate parameters of the preprocessing and segmentation steps thus ensuring maximum mass detection sensitivity. In addition to the classification, where we tested seven different classifiers, the CAD system is equipped with reliability estimate module. RESULTS By performing segmentation 91.3% of masses were correctly segmented with 4.14 false positives per image (FPpi). This result is improved in the classification phase where, among the seven tested classifiers, multilayer perceptron neural network achieved the best result including 77.4% sensitivity and 0.49 FPpi. CONCLUSION By using the proposed regression function and parameter optimization we were able to improve segmentation results comparing to the literature. In addition, we showed that CAD system has high potential for being equipped with reliability estimate module.

ELECTROCHEMICAL OXYPHENYLSELENATION OF DIENES

Summary. The electrochemical oxidation of diphenyl diselenide in the presence of dienes affords the corresponding cyclic β-phenylselenoethers via an oxyphenylselenation process. The yields of ethers depend on the nature of the diene and on the reaction temperature..Zusammenfassung. Die elektrochemische Oxidation von Diphenyldiselenid in Gegenwart von Dienen ergibt über einen Oxyphenylselenierungsprozeß die entsprechenden cyclischen β-Phenylselenoether. Die Ausbeuten an Ethern hängen von der Natur des Diens und der Reaktionstemperatur ab..

Machine Learning Approach for Predicting Wall Shear Distribution for Abdominal Aortic Aneurysm and Carotid Bifurcation Models

Computer simulations based on the finite element method represent powerful tools for modeling blood flow through arteries. However, due to its computational complexity, this approach may be inappropriate when results are needed quickly. In order to reduce computational time, in this paper, we proposed an alternative machine learning based approach for calculation of wall shear stress (WSS) distribution, which may play an important role in mechanisms related to initiation and development of atherosclerosis. In order to capture relationships between geometric parameters, blood density, dynamic viscosity and velocity, and WSS distribution of geometrically parameterized abdominal aortic aneurysm (AAA) and carotid bifurcation models, we proposed multivariate linear regression, multilayer perceptron neural network and Gaussian conditional random fields (GCRF). Results obtained in this paper show that machine learning approaches can successfully predict WSS distribution at different cardiac cycle time points. Even though all proposed methods showed high potential for WSS prediction, GCRF achieved the highest coefficient of determination (0.930–0.948 for AAA model and 0.946–0.954 for carotid bifurcation model) demonstrating benefits of accounting for spatial correlation. The proposed approach can be used as an alternative method for real time calculation of WSS distribution.

Real-time monitoring of cytotoxic effects of electroporation on breast and colon cancer cell lines.

PURPOSE To study the effects of electroporation on different cell lines. MATERIAL The effects of electroporation on human breast cancer (MDA-MB-231), human colon cancer (SW-480 and HCT-116), human fibroblast cell line (MRC-5), primary human aortic smooth muscle cells (hAoSMC) and human umbilical vein endothelial cells (HUVEC) were studied. Real-time technology was used for cell viability monitoring. Acridine orange/ethidium bromide assay was applied for cell death type determination. A numerical model of electroporation has been proposed. RESULTS Electroporation induced inhibition of cell viability on dose (voltage) dependent way. The electroporation treatment 375-437.5Vcm-1 caused irreversible electroporation of cancer cells and reversible electroporation of healthy cells. The application of lower voltage rating (250Vcm-1) led to apoptosis as the predominant type of cell death, whereas the use of higher voltage (500Vcm-1) mainly caused necrosis. CONCLUSION Electroporation represents a promising method in cancer treatment. Different cancer cell lines had different response to the identical electroporation treatment. Electroporation 375-437.5Vcm-1 selectively caused permanent damage of cancer cells (SW-480), while healthy cells (MRC-5, hAoSM and HUVEC) recovered after 72h. The type of cell death is dependent of electroporation conditions. The proposed numerical model is useful for the analysis of phenomena related to electroporation treatment.

Neural network based approach for predicting maximal wall shear stress in the artery

This paper describes the use of artificial neural networks in predicting value and position maximal wall shear stress in aneurysm. For the purpose of neural network training, back propagation algorithm was used. Input data in the network are geometric parameters of aneurysm model. Obtained results indicate the possibility of a successful application of neural networks in the problems of predicting certain parameters of arteries. Future work relates to the creation of a web-based application that allows users to display the results.