Sharif Digital Repository

Cardiovascular diseases have been known as one of the main reasons of mortality all around the world. Nevertheless, this disease is preventable if it can be diagnosed in an early stage. Therefore, it is crucial to develop Clinical Decision Support Systems (CDSSs) that are able to help physicians diagnose the disease and its related risks. This study focuses on cardiovascular disease diagnosis in an Iranian community by developing a CDSS, based on Support Vector Machine (SVM) combined with Binary Particle Swarm Optimisation (BPSO). We used SVM as the classifier and benefited enormously from optimisation capabilities of BPSO in model development as well as feature selection. Finally,... 

In this paper we study how the meso-scale and micro-scale electroencephalography (EEG) synchronization measures can be used for discriminating patients suffering from Alzheimer's disease (AD) from normal control subjects. To this end, two synchronization measures, namely power spectral density and multivariate phase synchronization, are considered and the topography of the changes in patients vs. Controls is shown. The AD patients showed increased power spectral density in the frontal area in theta band and widespread decrease in the higher frequency bands. It was also characterized with decreased multivariate phase synchronization in the left fronto-temporal and medial regions, which was... 

Normal human breathing exhibits complex variability in both respiratory rhythm and volume. Analyzing such nonlinear fluctuations may provide clinically relevant information in patients with complex illnesses such as asthma. We compared the cycle-by-cycle fluctuations of inter-breath interval (IBI) and lung volume (LV) among healthy volunteers and patients with various types of asthma. Continuous respiratory datasets were collected from forty agematched men including 10 healthy volunteers, 10 patients with controlled atopic asthma, 10 patients with uncontrolled atopic asthma, and 10 patients with uncontrolled non-atopic asthma during 60 min spontaneous breathing. Complexity of breathing... 

Metabonomics and other "omic" fields are essential science in analytical chemistry. Modern analytical instruments such as proton nuclear magnetic resonance (1H-NMR) can provide the great quantity of analytical information. In order to assign unknown samples, chemometric methods recognition build classification model based on experimental data. Firstly, some current strategies regarding disease diagnosis are exhibited in metabonomic studies. Some diseases such as crohn's disease can be difficult to diagnose since its signs and symptoms may be similar to other medical problems or often mimic other symptoms. Applications of NMR and supervised pattern recognition in the field of metabonomics are... 

Aim: The aim of this study was to search for metabolic biomarkers of Crohn's disease (CD). Background: Crohn's disease (CD) is a type of inflammatory bowel disease that causes a wide variety of symptoms. CD can influence any part of the gastrointestinal tract from mouth to anus. CD is not easily diagnosed because monitoring tools are currently insufficient. Thus, the discovery of proper methods is needed for early diagnosis of CD. Patients and methods: We utilized metabolic profiling using proton nuclear magnetic resonance spectroscopy (1HNMR) to find the metabolites in serum. Classification of CD and healthy subject was done using partial least squares discriminant analysis (PLS-DA).... 

Aim: The aim of this study is to look for the proper methods that would be a major step towards untreated CD diagnosis and seek the metabolic biomarkers causes of CD and compare them to control group. Background: Celiac disease (CD) is a common autoimmune disorder that is not easily diagnosed using the clinical tests. Patients and methods: Thirty cases and 30 controls were entered into this study. Metabolic profiling was obtained using proton nuclear magnetic resonance spectroscopy (1HNMR) to seek metabolites that are helpful for the detection of CD. Classification of CD and healthy subject was done using random forest (RF). Results: The obtained classification model showed an 89% correct... 

In this paper we use fractal method for detection and diagnosis of abnormalities in mammograms. We have used 168 images that were carefully selected by a radiologist and their abnormalities were also confirmed by biopsy. These images included asymmetric lesions, architectural distortion, normal tissue and mass lesion where in case of mass lesion they included circumscribed benign, ill-defined and spiculated malignant masses. At first, by using wavelet transform and piecewise linear coefficient mapping, image enhancement were done. Secondly detection of lesions was done by fractal method as a ROI. Since in investigation of breast cancer, it is important that fibroglandular tissues in both... 

Alzheimer’s disease (AD) is currently ranked as the sixth leading cause of death in the United States and recent estimates indicate that the disorder may rank third, just behind heart disease and cancer, as a cause of death for older people. Clearly, predicting this disease in the early stages and preventing it from progressing is of great importance. The diagnosis of Alzheimer’s disease (AD) requires a variety of medical tests, which leads to huge amounts of multivariate heterogeneous data. It can be difficult and exhausting to manually compare, visualize, and analyze this data due to the heterogeneous nature of medical tests, therefore, an efficient approach for accurate prediction of the... 

Acute and chronic inflammatory demyelinating polyradiculoneuropathies (AIDP and CIDP) are two immune-related conditions in the peripheral nervous system. In the current study, we assessed expression levels of Beta-secretase (BACE1), brain-derived neurotrophic factor (BDNF) and their antisense transcripts in the peripheral blood of AIDP and CIDP patients compared with age- and sex-matched controls to assess their potential as biomarkers for these conditions. Expressions of BACE1 and BACE1-AS were down-regulated in CIDP cases compared with controls (Ratios of mean expressions=0.01 and 0.03; P values= 1.07E-08, respectively). On the other hand, expressions of BDNF and BDNF-AS were up-regulated... 

This paper aims to propose a high-speed and accurate fully-automated method to detect COVID-19 from the patient's chest CT scan images. We introduce a new dataset that contains 48,260 CT scan images from 282 normal persons and 15,589 images from 95 patients with COVID-19 infections. At the first stage, this system runs our proposed image processing algorithm that analyzes the view of the lung to discard those CT images that inside the lung is not properly visible in them. This action helps to reduce the processing time and false detections. At the next stage, we introduce a novel architecture for improving the classification accuracy of convolutional networks on images containing small...