The aim of this study is to develop two-dimensional cellular automata model of HIV infection that depicts the dynamics involved in the interactions between acquired immune system and HIV infection in the peripheral blood stream. The appropriate biological rules of cellular automata model have been extracted from expert knowledge and the model has been simulated with determined initial conditions. Obtained results have been validated through comparing with the accepted AIDS reference curve. The new rules and states were added to the proposed model to show the effects of applying combined antiretroviral therapy. Our results showed that by applying RTI and PI drugs with maximum drug effectiveness, comparing with cases in which no treatment was applied, the steady state concentrations of healthy (infected) CD 4 + T cells were increased (decreased) 53% (41%). Also, the use of cART with maximum drug effectiveness led to a 69% reduction in the steady state level of viral load. At this time, obtained results have been validated through comparing with available clinical data. Our results showed good agreement with both reference curve and the clinical data. In the second phase of this study, by applying genetic algorithms, a therapeutic schedule has been provided that its use, while maintaining the quality of the treatment, leads to a 47% reduction in both drug dosage and the side effects of antiretroviral drugs.
Accurate and computationally efficient means of electrocardiography (ECG) arrhythmia detec-tion has been the subject of considerable re-search efforts in recent years. Intelligent com-puting tools such as artificial neural network (ANN) and fuzzy logic approaches are demon-strated to be competent when applied individu-ally to a variety of problems. Recently, there has been a growing interest in combining both of these approaches, and as a result, adaptive neural fuzzy filters (ANFF) [1] have been evolved. This study presents a comparative study of the classification accuracy of ECG signals using (MLP) with back propagation training algorithm, and a new adaptive neural fuzzy filter architec-ture (ANFF) for early diagnosis of ECG ar-rhythmia. ANFF is inherently a feed forward multilayered connectionist network which can learn by itself according to numerical training data or expert knowledge represented by fuzzy if-then rules [1]. In this paper we used an adap-tive neural fuzzy filter as an ECG beat classifier. We combined 3 famous wavelet transforms and used them mid 4 the order AR model coefficient as features. Our results suggest that a new proposed classifier (ANFF) with these features can generalize better than ordinary MLP archi-tecture and also learn better and faster. The results of proposed method show high accu-racy in ECG beat classification (97.6%) with 100% specificity and high sensitivity
displayed special advantages on tissue blood perfusion. This paper explores the theoretical background of laser Doppler and speckle techniques. Four applications in measurement of tissue blood perfusion are introduced which are: Laser Doppler flowmetry (LDF) , Laser Doppler Perfusion Monitor (LDPM) , Laser Doppler Perfusion Imager (LDPI) , Laser Speckle Contrast Imaging . In this paper we will describe theses four techniques in brief separately and then we will compare them with each other and with other methods for assessment of microvascular blood perfusion. First , we will explain LDF technique in brief. Then basic principles and components of the LDPM and LDPI instrumentation are discussed. At the end Laser Speckle Contrast Imaging Technique will be introduced. Some image examples are given to compare the commercialized laser Doppler technique and prototype laser Doppler contrast imager techniques Keywords-component; laser Doppler perfusion imager, Laser Doppler perfusion monitor, Laser Speckle contrast imaging, Tissue blood flow, biomedical instrumentation.
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