Sharif Digital Repository

Problems with the mitral valve can make the heart less efficient at pumping blood around the body. Severe problems can lead to heart failure if the valve is not surgically repaired or replaced. A simple approximation of the heart geometry is used and the valve dimensions are based on reported measurements. The primary objective for this study is to design and simulate the opening and closing behavior of the mitral valve using 2D fluid-structure interaction (FSI) model using ADINA software in order to evaluate and investigate the hemodynamic performance and problems of mitral valve, in which the blood is described as a viscous incompressible fluid, and the mitral valve is described as an... 

Obstruction of Left Anterior Descending artery (LAD) due to thrombosis or atherosclerotic plaques is the leading cause of death world–wide. Targeted delivery of drugs through micro- Nano-particles holds noteworthy promise which can make clot busting or the restenosis treatment with minimal toxicity possible. In this work, Fluid-Structure Interaction (FSI) simulations of blood flow through a patient-specific reconstructed geometry of LAD artery have been conducted using a non-Newtonian hematocrit (HCT) dependent model for blood. Based on physiological facts, 25, 45 and 65 percentages of HCT were assumed as acceptable representations of anemic, healthy and diabetic blood conditions,... 

Cardiovascular diseases (CVDs) have ranked first cause of deaths globally. In 2016, about 17.7 million people died from CVDs representing 31% of all world deaths. So, early intelligent detection of cardiovascular disease could help to save many lives in worldwide. There are several methods to analyze heart activity and to detect any abnormalities including Electrocardiogram, Stress test, Echocardiography, cardiac catheterization and coronary angiography.Among all methods, Electrocardiogram (ECG) is the most common and convenient type where it measures heart electrical activity and records it as a series of pulses. Analyzing these pulses would provide useful information about normal and... 

Predicting the evolution of viruses is vital in controlling, preventing, and treating diseases. Mutations that evade the host immune system can propagate and persist through generations, making it crucial to anticipate and combat them effectively. The 1918 H1N1 pandemic serves as an example of the devastating impact of pandemics caused by viral mutations. By predicting mutations in advance, we can identify potential future pandemics and take effective preventative measures to mitigate their impact. Proteins play a vital role in the functioning of viruses. They are involved in various processes, such as replication, transcription, and host cell invasion. Any changes in the protein sequence... 

In the last few years, the most important problem to find a solution for the COVID-19 disease, and tests show that many factors are effective in the health and recovery of people with this disease. Therefore, scientists all over the world are looking to prevent the spread of this disease by identifying the effective factors in the recovery of corona patients and finding solutions for their health. The proposed algorithm is hybrid deep learning model CNN+GRU and appeal them to the laboratory test data from hospital. In this research, the goal is to be able to diagnose this disease in time with routine laboratory test, which consist of three main stages. In the first stage: initial... 

In this comprehensive study, we explore the influence of general health innovation outputs on the burden of diseases across 70 nations spanning two decades, from 2000 to 2019, marking the first investigation of its kind. Our investigation takes into account associations among health-related patents, scholarly publications pertaining to health, and disability-adjusted life years (DALYs), a measure used to assess overall disease burden. Employing a robust fixed effect panel data model, our findings illuminate the notable impact health-related publications exert in mitigating disease burden universally across all countries examined. However, we did not find a similar association between patent... 

The purpose of this study is to model people's behavior on both sides of the kidney market. The healthy person decides to donate a live kidney (for a certain amount) on the supply side. On the demand side, the patient chooses between the two existing kidney queues, the living kidney and the cadaveric kidney, based on the individual's income and belief from the waiting time in each of the two queues. The sick person faces two critical risks in life: risk of death and kidney failure. To model these two risks, it is assumed that each person has a level of health that consists of two deterministic and stochastic parts. In this way, with a deterministic rate, a person's health level will decrease... 

In this study we developed an analytical model to delve into the behavior of participants in the market of kidney in Iran. We get health as a good which not only elevates the utility of any individual, it add more years to each individual longevity. We find out that ESRD patients who are in serious need of receiving a new kidney will never postpone buying kidney in case of having the price fixed in all periods.We found out that the waiting time in queue of receiving market is a u-shape function in which there is a minimum. We proved that the price making the queue minimum is definitely bigger than the market clearance market. Accordingly, despite all the unfair and immoral view toward... 

In this paper evaluate the short-term effects of air pollution on the mortality rate caused by cardiovascular problems, respiratory problems, digestive problems and tumors and cancers. This is done by using daily mortality data of Tehran, Tabriz, Mashad, Shiraz, Ahwaz and Isfahan during the years 1390 to 1394, we also use daily measurements of pollutant levels including Carbon-monooxcide, Sulfure-dioxide, Oxides of Nitrogen, Ozone and Particulate matters in the same period. The effects are estimad by looking at the relation of daily mortality in each city along with its pollution level. By controling for the weather conditions and city and month fixed effects, we find significant positive... 

This paper studies and compares the effect of "Dutch disease" in Iranian economy in two periods of rising oil price (1973-1978) and (2004-2010). The study presents a DSGE model to analysis the behavior of economic variable in both periods and to address this question that whether or not policy maker has learning behavior. The model consists of households, final goods producers who divided in tradable goods and non-tradable goods sector and the government. I use calibration methods to estimate models parameters.Comparison of simulated results and the actual macroeconomic variables in Iran during 1338-1390 indicates that, first of all Dutch disease effect occur in both periods of oil shock so... 

The brain is one of the most critical parts of the body with a lot of complexity. The treatment of brain diseases has always been in an aura of uncertainty due to its high sensitivity. In the meantime, Parkinson's disease has become the second most frequent brain disease after Alzheimer's, involving more than two percent of the population over 65 years of age. One of the biggest questions in this field is how the Parkinson's process is formed. This question has already received much attention from the pathophysiological point of view but has not been answered from the functional and structural brain network's point of view. This research compares healthy macaque monkeys' functional and... 

Recent studies of brain activities indicate that Phase-Amplitude Coupling (PAC) between several regions of the brain, are meaningfully related to Parkinson’s Disease. In this research, we have studied PAC as a statistical measure in Parkinsonian patients’ brains while Deep Brain Stimulation treatment with different stimulation frequencies are being applied. In order to do so, we have investigated patients’ brain signals, and estimated PAC between regions of interest; afterwards, by using the estimated PAC values, we have found significant effects of the treatments on parkinsonian brains; furthermore, two treatments that are using 130 Hz and 340 Hz stimuli signals are compared. In this... 

The highly developed biological data collection methods enable scientists to capture protein-protein interaction (PPI) in the human body, which could be analyzed as biological networks such as protein-protein interaction networks. These networks reveal essential information about the biological process in human cells and can be used to identify genes associated with cancers. Effectively identifying disease-related genes would contribute to improving the treatment and diagnosis of various diseases. Current methods for identifying disease-related genes mainly focus on the hypothesis of guilt-by-association and do not consider the global information in the PPI network. Besides, most methods pay... 

We present a deep neural network to classify dermatological disease from patient images. Using self-supervised learning method we have utilized large amount of unlabeled data. We have pre-trained our model on 27000 dermoscopic images gathered from razi hospital, the best dermatological hospital in Iran, along with 33000 images from ISIC 2020 dataset. We have evaluated our model performance in semi-supervised and transfer learning approaches. Our experiments show that using this approach can improve model accuracy and PRC up to 20 percent on semi-supervised setting. The results also show that pretraining can improve classification PRC up to 20 percent on transfer learning task on HAM10000... 

Due to the overall progress in the processing of imaging tissue cells, the identification and diagnosis of complex diseases using machine learning methods has become very important. Recognizing cell characteristics such as size, shape, and chromatin design is essential in determining cell type, which can be achieved through learning methods such as deep network training. Finding the nucleus or cytoplasm of cells in medical images is a small but significant part of a long process of diagnosing and treating diseases. Today, artificial intelligence has rushed to the aid of experts in this field and has increased the speed and accuracy of experts in finding these cells and their nuclei. This... 

Parkinson’s disease (PD) is a progressive debilitating neurological disorder that causes motor and cognitive impairment. Administration of dopaminergic medication (Levodopa) has been reported to be effective in attenuating the excessive pathological synchronization in basal ganglia. However, long term levodopa therapy has its pitfalls. High frequency deep brain stimulation (DBS) has been suggested as an effective alternative for reducing motor symptoms in PD. In this method, distinct brain regions involved in the pathophysiology of the disease are stimulated electrically at high frequencies (i.e. at 130 Hz). While several studies have been carried out on the effects of DBS and its clinical... 

Cardiovascular diseases are very common nowadays and are one of the main reasons of death. Being among the major types of these diseases, correct and in time diagnosis of Coronary Artery Disease (CAD) is very important. The best and most accurate CAD diagnosis method by now is recognized as Angiography, which has many side effects and is costly. Thus researchers are seeking for inexpensive, though still accurate, methods. Existing studies have used several features in collecting data from patients, while applying different data mining algorithms to increase accuracy. In this thesis, a data set is introduced which utilizes several new and effective features for CAD diagnosis, as well as a... 

Parkinson's is a disease caused by a disorder in the central nervous system of the body. There is no definite cure for this disease, but one of the ways to prevent the progress of this disease is to use movement therapy. One of the goals of designing wearable systems consisting of inertial sensors is to make it possible to perform this movement therapy from a distance. The purpose of the present study and research is to use the approach of simple and complex activities in order to increase the accuracy in the detection of activities and also to solve the problems of the previous system, with the help of creating the ability to detect complex meaningful activities for Parkinson's patients. In... 

Researchers working at Mowafaghian Rehabilitation Research Center have decided to develop a telerehabilitation system named SEPANTA, especially designed for activity recognition of Parkinson's Disease patients. In this regard, the system uses 34 mobility exercises, including 20 LSVT-BIG activities (especially designed for PD patients) and 14 functional daily activities. Human Activity Recognition (HAR) systems faces various challenges e.g., intra-class variabilities, meaning differences in an activity performance by different persons or a person. Data augmentation and utilizing deep learning models are the most common solutions for the risen challenges. However, deep structures require an... 

Human Activity Recognition (HAR) has had tremendous improvements in the field of elderly monitoring and telerehabilitation. An anchor point for HAR systems in telerehabilitation is supervising rehabilitative excercises. For Parkinson’s disease (PD) patients, a group of rehabilitative activities, known as Lee Silverman Voice Treatment-BIG, or LSVT-BIG, have shown to be effective in improving motor performance. Similar to any rehabilitative measure, delivering these activities requires the supervision of an expert or clinician, so that the patient receives proper feedbacks. HAR systems can replace human experts. They can recognize activities and provide the user with proper feedback. HAR...