Sharif Digital Repository

The increasing importance of nanotechnology in the field of biomedical applications has encouraged the development of new nanomaterials endowed with multiple functions. Novel nanoscale drug delivery systems with diagnostic, imaging and therapeutic properties hold many promises for the treatment of different types of diseases, including cancer, infection and neurodegenerative syndromes. Carbon nanotubes (CNTs) are both low-dimensional sp2 carbon nanomaterials exhibiting many unique physical and chemical properties that are interesting in a wide range of areas including nanomedicine. Since 2004, CNTs have been extensively explored as drug delivery carriers for the intracellular transport of... 

In this paper, distributed electroencephalographic (EEG) sources in the brain have been mapped with the objective of early diagnosis of Alzheimer's disease (AD). To this end, records from a montage of a high-density EEG from 17 early AD patients and 17 matched healthy control subjects were considered. Subjects were in eyes-closed, resting-state condition. Cortical EEG sources were modeled by the standardized low-resolution brain electromagnetic tomography (sLORETA) method. Relative logarithmic power spectral density values were obtained in the four conventional frequency bands (alpha, beta, delta, and theta) and 12 cortical regions. Results show that in the left brain hemisphere, the theta... 

This study presents applying recurrence quantification analysis (RQA) on EEG recordings and their subbands: delta, theta, alpha, beta, and gamma for epileptic seizure detection. RQA is adopted since it does not require assumptions about stationarity, length of signal, and noise. The decomposition of the original EEG into its five constituent subbands helps better identification of the dynamical system of EEG signal. This leads to better classification of the database into three groups: Healthy subjects, epileptic subjects during a seizure-free interval (Interictal) and epileptic subjects during a seizure course (Ictal). The proposed algorithm is applied to an epileptic EEG dataset provided... 

Parkinson's disease (PD) is a neurodegenerative disease, which is not easily diagnosed using clinical tests and the discovery of proper methods would be a major step towards a successful diagnosis. In the present study, we employed metabolic profiling using proton nuclear magnetic resonance spectroscopy to find metabolites in serum, which are helpful for the diagnosis of PD. Classification of PD and healthy subject was done using random forest. Serum levels of selenium measured by atomic absorption spectrometry in PD group were lower than the serum selenium levels in the control group. The metabolites causing selenium changes in PD patients were identified using random forest, and a model... 

Radio signals can induce an electric field inside the brain, which might be potentially beneficial in the treatment of neurodegenerative diseases. For instance, a new method for the treatment of Alzheimer's disease in mice through the exposure to the radiation of mobile phones has been successfully demonstrated. In the light of these results, studying the induction of an electric field in the human brain through the controlled exposure to radio signals is of paramount importance for the eventual development of similar treatment techniques in humans. In this study, the authors study the radio signals in 100-1000 MHz as a means for inducing an electric field into the human brain in a... 

Recently graph theory and complex networks have been widely used as a mean to model functionality of the brain. Among different neuroimaging techniques available for constructing the brain functional networks, electroencephalography (EEG) with its high temporal resolution is a useful instrument of the analysis of functional interdependencies between different brain regions. Alzheimer's disease (AD) is a neurodegenerative disease, which leads to substantial cognitive decline, and eventually, dementia in aged people. To achieve a deeper insight into the behavior of functional cerebral networks in AD, here we study their synchronizability in 17 newly diagnosed AD patients compared to 17 healthy... 

During the last decade, reports show that the incidence and prevalence of Alzheimer's disease (AD) and other dementias have significantly increased. AD poses an enormous escalating threat to health services and resources. Early diagnosis of AD is recognized as one of the major challenges and primary aims in scientific communities. With the arrival of nanoscience and nanotechnology to medicine, hopes for early diagnosis and treatment of AD have considerably increased. To this end, nanobioresearchers are focused on three major areas consisting of early detection and recognition, biological markers and diagnosis, and pharmacotherapy. Several efforts are in progress for the discovery of new... 

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... 

Protein fibrillation process (e.g., from amyloid beta (Aβ) and α-synuclein) is the main cause of several catastrophic neurodegenerative diseases such as Alzheimer's and Parkinson diseases. During the past few decades, nanoparticles (NPs) were recognized as one of the most promising tools for inhibiting the progress of the disease by controlling the fibrillation kinetic process; for instance, gold NPs have a strong capability to inhibit Aβ fibrillations. It is now well understood that a layer of biomolecules would cover the surface of NPs (so called "protein corona") upon the interaction of NPs with protein sources. Due to the fact that the biological species (e.g., cells and amyloidal... 

The aggregation of an amyloid protein, α-synuclein (α-Syn), has been suggested as a potential cause of Parkinson's and several other neurodegenerative diseases. To explore the possibility of using nanoparticle-based therapeutic agents for the treatment of such diseases, we investigated the influence of surface chemistry and concentration of mesoporous silica nanoparticles (MSNPs) on the fibrillation of recombinant human α-Syn protein in the present work. Bare MSNPs as well as MSNPs of different surface functionalities, including 3-(2-aminoethyl amino) propyltrimethoxysilane (AAS), succinic anhydride (carboxyl), and polyethyleneimine (PEI) were prepared and characterized by electron... 

Epilepsy is the most common disorder of human brain. The goal of this paper is to present a new method for classification of epileptic phases based on the sub-bands of electroencephalogram (EEG) signals obtained from the Stockwell transform (ST). ST is a time-frequency analysis that not only covers the advantages of both short-time Fourier transform (FT) and wavelet transform (WT), but also overcomes their shortcomings. In the proposed method, at first, EEG signal is transformed into time-frequency domain using ST and all operations are performed in the new domain. Then, the amplitudes of ST in five sub-bands, namely delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ), are computed. In... 

Techniques available in graph theory can be applied to signals recorded from human brain. In network analysis of EEG signals, the individual nodes are EEG sensor locations and the edges correspond to functional relations between them that are extracted from EEG time series. In this paper, we study EEG-based directed functional networks in Alzheimer's disease (AD). To this end, directed connectivity matrices of 25 AD patients and 26 healthy subjects are processed and a number of meaningful graph theory metrics are studied. Our data show that functional networks of AD brains have significantly reduced global connectivity in alpha and beta bands (P < 0.05). The AD brains have significantly... 

Studies have shown that balance and mobility in people with Parkinson's disease (PD) can improve through rehabilitation interventions. However, until now no quantitative method investigated how these patients improve their balance control. In this study, a single inverted pendulum model with PID controller was used to describe the improvement of forty PD patients after a 12-session therapy program, and to compare their balance with twenty healthy subjects. The Center of Pressure (COP) data were recorded in seven sensory conditions - on rigid and foam surface, each with eyes open and closed, and with visual disturbance; and stance on rigid surface with attached vibrator to the Achilles... 

Parkinson's disease (PD) is the second most common and progressive neurological disorder. Parkinson's signs are caused by dysfunction in PD patient's brain network. Newly, resting state functional magnetic resonance imaging has been utilized to assess the altered functional connectivity in PD patients. In this study, we investigated the properties of the brain network topology in 19 PD patients compared to 17 normal healthy group by means of graph theory. In addition, we used four different graph formation methods to explore linear and nonlinear relationships between fMRI signals. Each correlation measure created a weighted graph for each subject. Different graph characteristics have been... 

Monitoring the ratio of 40- and 42-residue amyloid β peptides (i.e., Aβ40 and Aβ42) in human plasma is considered one of the hallmarks of detection of the early stage of Alzheimer's disease (AD). Therefore, development of a specific, yet non-antibody-based method for simultaneous detection of Aβ40 and Aβ42 may have considerable clinical applications. Here, we developed a 'nanoparticle-based colorimetric sensor array' utilizing label-free gold and silver nanoparticles for visual detection of Aβ42 and Aβ40. Different aggregation behaviors of nanoparticles through their conjugation with Aβ42 and Aβ40 followed by the coordination of Aβ42 and Aβ40 with Cu(ii) led to diverse spectral and color... 

Alzheimer’s disease (AD) is an irreversible neurological disorder that progresses gradually and can cause severe cognitive and behavioral impairments. This disease is currently considered a social and economic incurable issue due to its complicated and multifactorial characteristics. Despite decades of extensive research, we still lack definitive AD diagnostic and effective therapeutic tools. Consequently, one of the most challenging subjects in modern medicine is the need for the development of new strategies for the treatment of AD. A large body of evidence indicates that amyloid-β (Aβ) peptide fibrillation plays a key role in the onset and progression of AD. Recent studies have reported... 

Aggregation of the natively unfolded protein α-synuclein (α-syn) is key to the development of Parkinson's disease (PD). Some nanoparticles (NPs) can inhibit this process and in turn be used for treatment of PD. Using simulation strategies, we show here that α-syn self-assembly is electrostatically driven. Dimerization by head-to-head monomer contact is triggered by dipole-dipole interactions and subsequently stabilized by van der Waals interactions and hydrogen bonds. Therefore, we hypothesized that charged nano-objects could interfere with this process and thus prevent α-syn fibrillation. In our simulations, positively and negatively charged graphene sheets or superparamagnetic iron oxide... 

Background: Fear of falling (FoF) is defined as a lasting concern about falling that causes a person to limit or even stop the daily activities that he/she is capable of. Seventy percent of Parkinson's disease (PD) patients report activity limitations due to FoF. Timely identification of FoF is critical to prevent its additional adverse effects on the quality of life. Self-report questionnaires are commonly used to evaluate the FoF, which may be prone to human error. Objectives: In this study, we attempted to identify a new postural stability-indicator to objectively predict the intensity of FoF and its related behavior(s) in PD patients. Methods: Thirty-eight PD patients participated in the... 

Background: Balance training improves postural control in Parkinson's disease (PD). However, a systematic approach for the development of individualized, optimal training programs is still lacking, as the learning dynamics of the postural control in PD, over a training program, are poorly understood. Objectives: We investigated the learning dynamics of the postural control in PD, during a balance-training program, in terms of the clinical, posturographic, and novel model-based measures. Methods: Twenty patients with PD participated in a balance-training program, 3 days a week, for 6 weeks. Clinical tests assessed functional balance and mobility pre-training, mid-training, and post-training.... 

Blind source separation is an important field of study in signal processing, in which the goal is to estimate source signals by having mixed observations. There are some conventional methods in this field that aim to estimate source signals by considering certain assumptions on sources. One of the most popular assumptions is the non-Gaussianity of sources which is the basis of many popular blind source separation methods. These methods may fail to estimate sources when the distribution of two or more sources is Gaussian. Hence, this study aims to introduce a new approach in blind source separation for nonlinear and chaotic signals by using a dynamical similarity measure and relaxing...