Sharif Digital Repository

Extensive studies have been performed on the genetic variations involved in common neurodegenerative diseases such as Alzheimer's, macular degeneration, and Parkinson's. In most cases, no specific gene has been identified pointing to a distinct pathogenic pathway, therefore, this study mainly aims to find common genes among aforementioned diseases according to determination of a specific pathogenic protein pathway.In this study, we reached a deep understanding of the function of nervous system and the discovery of causative agents of the diseases by applying the sources of information from genome datasets in bioinformatics analysis. The utilized database comprises the classification of... 

According to the World Health Organization, about 35% of deaths worldwide are due to cardiovascular diseases, therefore the evaluation of vascular endothelial function has great prognostic and diagnostic value for cardiovascular diseases. The conventional noninvasive method for endothelial function evaluation is the measurement of flow-mediated dilation in brachial artery using ultrasound imaging (FMD-US). As the accuracy of FMD-US depends on the operator's skill and the resolution of the ultrasound images, this method has not been adopted. In this work, we propose to use a low cost, easily-accessible surrogate signal, the photoplethysmogram (PPG) to implement the FMD test. Whereas previous... 

Cardiovascular diseases are the first leading cause of death in the world also in IRAN. Early detection of such problems can decrease the costs also can help to cure the patient but it needs continuous monitoring and automated classification of hearbeats. Mobile devices and wearable gadgets are good solutions which can help patients before visiting the doctor.In this research, an algorithm is introduced which with the help of ECG signal detects dangerous myocardial problems. Our approach is using deep learning method which were not considered much before. In the proposed algorithm ECG signal is processed in order to get features and with dimensionality reduction, input of the network gets... 

Cardiovascular diseases are the leading cause of death globally so early diagnosis of them is important. Many researchers focused on this field. First signs of cardiac diseases appear in the electrocardiogram signal. This signal represents the electrical activity of the heart so it’s primarily used for the detection and classification of cardiac arrhythmias. Permanent monitoring of this signal is not possible for specialists so we should do this by means of Artificial Intelligence. In this thesis, we use recurrent neural networks to classify electrocardiogram’s arrhythmias. This deep learning method, use two sources of data to learn from. The first part of data is global for everyone and the... 

Apnea Bradycardia (AB) episodes (breathing pauses associated with a significant fall in heart rate) are the most common disease in preterm infants. Consequences associated with apnea-bradycardia episodes involve a compromise in oxygenation and tissue perfusion, a poor neuromotor prognosis at childhood and a predisposing factor to sudden-death syndrome in preterm newborns. It is therefore important that these episodes are recognized (early detected or predicted if possible), to start an appropriate treatment and to prevent the associated risks. In this thesis, we propose two Bayesian Network (BN) approaches (Markovian and Switching Kalman Filter) for the early detection of apnea bradycardia... 

Parkinson's disease is a degenerative disorder of the central nervous system. The motor symptoms of Parkinson's disease result from the death of dopamine-generating cells in the substantia nigra. The cause of this cell death is unknown. Early in the course of the disease, the most obvious symptoms are movement-related. Some of the symptoms are: shaking, rigidity, slowness of movement and difficulty with walking and gait. Modern treatments are effective at managing the early motor symptoms of the disease, mainly through the use of levodopa and dopamine agonists. As the disease progresses these drugs eventually become ineffective and produce a complication called dyskinesia, marked by... 

Dementia is one of the most common disorders among the elderly population. Statistical analyses show that among several subtypes of dementia, Alzheimer’s disease (AD) is the most frequent cause of dementia and this number is projected to increase. AD not only results in impairment of learning and memory but also in the moderate stages of illness, motor functions are profoundly disturbed, and ultimately will affect the patient's lifetime. The proposed drug treatments for this disease only reduce its progress probability. Therefore, early diagnosis for effective treatment of Alzheimer's disease is one of the critical issues in the field of dementia. This project is an effort to extract... 

An infectious disease can spread through different communities via mobility networks. In this study we address three basic questions related to this matter in the meta-population approximation: firstly, where did the disease start? Secondly, when did the disease start? Thirdly, how does it spread in the network? To answer these questions, we introduce a generic mathematical framework with appropriate physical assumptions and study the spread of diseases. Then, with analytical solutions, we bring up different algorithms in order to answer these three questions. Using these algorithms, we redefine the effective distance and arriving time and unveil the simple geometry of the disease outbreak  

SIS and SIR are common models for describing and predicting the epidemics of the contagious diseases. But these models fail to predict patterns of spreading dynamics in the case of co-infective diseases, i.e. getting infected by one disease, alters the chance of getting infected by the other one. Coinfection has been studied in the mean field approximation and on complex networks with different topologies. Another study shows temporal correlations of the underlying transmission network, play role on co-infection dynamics.In this research we investigate the effects of various temporal correlation on epidemic order parameters of independent infection and co-infection.For this purpose, we... 

This project aims to design a drug to fight leukemia and some autoimmune diseases having few side effects. These drugs are composed of natural sugar and amino acid structures. Among the various treatments available, drug structures as inhibitors of enzymes involved in cancer-related cellular processes is an important treatment. Methotrexate is one of the most widely used anticancer drugs in the last half-century and treats leukemia and many autoimmune diseases such as psoriasis. It is a derivative of folic acid (vitamin B9) and one of the drugs with specific properties. It is an anti-folate whose anti-metabolic effects can help kill cancer cells. One of the problems with using this drug to... 

Levodopa [L-3, 4-dihydroxyphenylalanine, or L-DOPA] is an important neurotransmitter used for the treatment of neural disorders such as Parkinson’s disease. Abnormal L-Dopa concentrations in biological fluids can be used for evaluation of diseases such as Parkinson’s diseases. In the first part of this thesis, a rapid and sensitive method for levodopa detection was reported which is based on in situ formation of polylevodopa nanoparticles. The assay is very simple and low cost and uses only NaOH and HCl as reagents. Under alkaline conditions, levodopa is spontaneously oxidized to its quinone derivative and shows the fluorescence properties. The fluorescence signal of the oxidation product of... 

In the present work we report a quantitative structure-activity relationship (QSAR) study on S1P1 receptor’s agonists that have therapeutic potential for autoimmune disorders such as Multiple Sclerosis (MS). Such studies play an important role in drug design and lead optimization by developing a mathematical relationship between the chemical structures of compounds and their biological activities.
We used both linear and nonlinear techniques such as MLR and ANN respectively to model these compounds together with techniques such as Stepwise-MLR, GA-MLR and GA-ANN in the variable selection step as it is an important step in every QSAR study. Since topological descriptors are well... 

Metabonomics is a quantitative measurement of time dependent metabolic interactions for living systems in response to the pathological or genetic variations. NMR spectroscopy has emerged as a key tool for understanding metabolic processes in living systems. In this project, the study of metabolomics was performed as classification and regression on samples of parkinson’s disease, multiple sclerosis disease, celiac disease and crohn’s disease. In classification part, various methods were applied using optimal parameters.classification methods in Parkinson’s disease, multiple sclerosis disease, celiac disease and crohn’s disease were RF, CART, PLS-DA, RF and RF respectively. Based on the... 

Dengue is one of the most important infectious diseases in the world. This disease is a viral infection that is transmitted to humans through the bite of a mosquito called Aedes aegypti. For this reason, geographical regions infected with this type of mosquito are at risk of Dengue outbreak. In this thesis, we first present a mathematical model describing the geographical spread of Dengue disease, which includes the movement of both the human population and the winged mosquito population. This model is derived from a mixed system of partial and ordinary differential equations. Our proposed model has the ability to consider the possibility of asymptomatic infection, so that the presence of... 

The field of Genome-Wide Asocciation Studies (GWAS) plays a vital role in understanding the genetic basis of complex traits and diseases. In this thesis, the focus is on investigating the effectiveness of two approaches combining Differential Evolution (DE) with Random Forest (RF) and support vector machine (SVM) for feature selection in the context of GWAS. Arabidopsois Thaliana dataset is used as experimental dataset for comparative analysis. The main goal is to achieve more efficient feature selection while maintaining competitive accuracy compared to RF and SVM without using DE. This research includes conducting experiments using DE with RF and DE with SVM followed by a comprehensive... 

In season 1, we introduce a mathematical model of the in vivo progression of Alzheimer’s disease with focus on the role of prions in memory impairment. Our model consists of differential equations that describe the dynamic formation of Aβ -amyloid plaques based on the concentrations of Aβ oligomers, PrPC proteins, and the Aβ-×-PrPC complex, which are hypothesized to be responsible for synaptic tox- icity. We prove the well posedness of the model and provided stability results for its unique equilibrium, when the polymerization rate of β-amyloid is constant and also when it is described by a power law. In seson 2, We consider the existence and uniqueness of solutions of an initial-boundary... 

Networks and the epidemiology of directly transmitted infectious diseases are fun-damentally linked. The foundations of epidemiology and early epidemiological models were based on population wide random-mixing, but in practice each individual has a finite set of contacts to whom they can pass infection; the ensemble of all such contacts forms a network. Knowledge of the structure of the network allows models to compute the epidemic dynamics at the population scale from the individual-level behaviour of infections.Motivated by the analysis of social networks, we study a model of random net-works that has both a given degree distribution and a tunable clustering coefficient.We consider two... 

We consider a system of nonlinear partial differential equations corresponding to a generalization of a mathematical model for geographical spreading of dengue disease. the mosquito population is divided into subpopulations: winged form (mature female mosquitoes) and aquatic form (comprising eggs, larvae and pupae); the human population is divided into the subpopulations:susceptible, infected and removed (or immune). On the other hand we allow higher spatial dimensions and also parameters depending on space and time. is last generalization is done to cope with possible abiotic effects as variations in temperature, humidity, wind velocity, carrier capacities, and so on; thus, the results... 

DNA microarrays consist of DNA microscopic points that are attached to a solid surface such as glass, plastic or silicon chip and formed as an array. The fixed pieces of DNA are considered as searchers. In an experiment, we can use thousands of searchers. Therefore, any microarray consists of the same number of genetic tests as the experiment performed on all of them in parallel. Whit this ability, arrays have speeded up the biological investigations. Microarray technology can be seen as a continued development of southern blotting. However, the most important stage in this technology, analysis of data, requires reliable bioinformatics tools achieving high reliabilities. Infectious diseases,... 

Despite the significant progress of machine learning models in the health domain, current advanced methods usually produce non-transparent and black-box models, and for this reason, they are not widely used in medical decision-making. To address the issue of non-transparency in black-box models, interpretable machine learning models have been developed. In the health domain, counterfactual scenarios can provide personalized explanations for predictions and suggest necessary changes to transition from an undesirable outcome class to a desirable one for physicians. The aim of this study is to present an interpretable machine learning framework in the health domain that, in addition to having...