Sharif Digital Repository

Apathy is a stressor and debilitating common condition for both stroke survivors and their caregivers. However, its effects on the postural control of these patients have not yet been investigated. Improved postural stability through withdrawing attention from postural control by concurrent cognitive task (i.e. dual-task condition) has been reported previously, but the effect of apathy, as a confounding factor, remains unknown. This study aimed to examine the effects of apathy and dual-task condition on postural control of chronic stroke survivors from biomechanical and neurophysiological perspectives. Twenty non-apathetic stroke survivors, 20 apathetic stroke survivors, and 20 sex-, age-,... 

Background: Longitudinal data on the association of quantitative thigh muscle MRI markers with knee osteoarthritis (KOA) outcomes are scarce. These associations are of clinical importance, with potential use for thigh muscle–directed disease-modifying interventions. Purpose: To measure KOA-associated longitudinal changes in MRI-derived muscle cross-sectional area (CSA) and adipose tissue and their association with downstream symptom worsening and knee replacement (KR). Materials and Methods: In a secondary analysis of the Osteoarthritis Initiative multicenter prospective cohort (February 2004 through October 2015), knees of participants with available good-quality thigh MRI scans at baseline... 

Aim of study: Glioblastoma Multiforme (GBM) is an aggressive brain cancer in adults that kills most patients in the first year due to ineffective treatment. Different clinical, biomedical, and image data features are needed to analyze GBM, increasing complexities. Besides, they lead to weak performances for machine learning models due to ignoring physicians' knowledge. Therefore, this paper proposes a hierarchical model based on Fuzzy C-mean (FCM) clustering, Wrapper feature selection, and twelve classifiers to analyze treatment plans. Methodology/Approach: The proposed method finds the effectiveness of previous and current treatment plans, hierarchically determining the best decision for... 

Bimodal magnetic-fluorescent materials for diagnostic imaging needs surface-engineered nanoparticles with great biosafety, pronounced colloidal stability, high magnetic moments, and strong photoluminescence (PL) emission. This work presents polymer-coated nanoparticles (PCNPs) based on manganese ferrites covered with a thin shell of nitrogen-doped carbon dots for magnetic-resonance and fluorescent dual mode imaging of cancerous tumors in vivo. An in situ thermolysis of metal oxalates and phenylenediamine in diphenyl ether allows for the facile synthesis of hybrid magneto-fluorescent nanoparticles. They possess an average size of 55 ± 5 nm with strong and excitation-independent PL emission at... 

Humans have a unique ability to use language for social communication. The neural architecture for language comprehension and production may have prominently emerged in the brain areas that were originally involved in social cognition. Here, we directly tested the fundamental link between language and social processing using functional magnetic resonance data (MRI) data from over 1,000 human subjects. Cortical activations in language and social tasks showed a striking similarity with a complementary hemispheric lateralization. Within core language areas, left-lateralized activations in the language task were mirrored by right-lateralized activations in the social task. Outside these areas,... 

Cancer is a deadly disease that has long plagued humans and has become more prevalent in recent years. The common treatment modalities for this disease have always faced many problems and complications, and this has led to the discovery of strategies for cancer diagnosis and treatment. The use of magnetic nanoparticles in the past two decades has had a significant impact on this. One of the objectives of the present study is to introduce the special properties of these nanoparticles and how they are structured to load and transport drugs to tumors. In this study, iron oxide (Fe3O4) nanoparticles with 6 nm sizes were coated with hyperbranched polyglycerol (HPG) and folic acid (FA). The... 

In the present study, first, Fe3O4nanoparticles were functionalized using glutaric acid and then composited with CQDs. Doxorubicin (DOX) drug was loaded to evaluate the performance of the nanocomposite for targeted drug delivery applications. The XRD pattern confirmed the presence of characteristic peaks of CQDs and Fe3O4. In the FTIR spectrum, the presence of carboxyl functional groups on Fe3O4/CQDs was observed; DOX (positive charge) is loaded onto Fe3O4/CQDs (negative charge) by electrostatic absorption. FESEM and AFM images showed that the particle sizes of Fe3O4and CQDs were 23-75 and 1-3 nm, respectively. The hysteresis curves showed superparamagnetic properties for Fe3O4and Fe3O4/CQDs... 

Abstract: Commercially pure titanium and titanium alloys have been among the most commonly used materials for biomedical applications since the 1950s. Due to the excellent mechanical tribological properties, corrosion resistance, biocompatibility, and antibacterial properties of titanium, it is getting much attention as a biomaterial for implants. Furthermore, titanium promotes osseointegration without any additional adhesives by physically bonding with the living bone at the implant site. These properties are crucial for producing high-strength metallic alloys for biomedical applications. Titanium alloys are manufactured into the three types of α, β, and α + β. The scientific and clinical... 

Food choice is one of the most fundamental and most frequent value-based decisions for all animals including humans. However, the neural circuitry involved in food-based decisions is only recently being addressed. Given the relatively fast dynamics of decision formation, electroencephalography (EEG)-informed fMRI analysis is highly beneficial for localizing this circuitry in humans. Here, by using the EEG correlates of evidence accumulation in a simultaneously recorded EEG-fMRI dataset, we found a significant role for the right temporal-parietal operculum (PO) and medial insula including gustatory cortex (GC) in binary choice between food items. These activations were uncovered by using the... 

Systemic chemotherapy has lost its position to treat cancer over the past years mainly due to drug resistance, side effects, and limited survival ratio. Among a plethora of local drug delivery systems to solve this issue, the combinatorial strategy of chemo-hyperthermia has recently received attention. Herein we developed a magneto-thermal nanocarrier consisted of superparamagnetic iron oxide nanoparticles (SPIONs) coated by a blend formulation of a three-block copolymer Pluronic F127 and F68 on the oleic acid (OA) in which Curcumin as a natural and chemical anti-cancer agent was loaded. The subsequent nanocarrier SPION@OA-F127/F68-Cur was designed with a controlled gelation temperature of... 

Molecular imaging (MI) can provide not only structural images utilizing temporal imaging techniques, but also functional and molecular data using a variety of newly developed imaging techniques. Nanotechnology’s application in MI has commanded a lot of attention in recent decades, and it has provided tremendous potential for imaging living subjects. In this study, D-glucosamine conjugated functionalized magnetic iron oxide nanoparticles (Fe3O4-PEG-DG NPs) were prepared and studied as magnetic resonance imaging (MRI) contrast agents. To evaluate their distribution, single-photon emission computed tomography (SPECT) is performed. Fe3O4 NPs are made using a well-known co-precipitation process... 

Asthma is a chronic inflammatory disease associated with a high prevalence of psychiatric disorders. There are specific brain networks responsible for emotional processes, including two important networks associated with psychiatric problems: the default mode network (DMN), which is more active in the resting state, and the salience network (SN), which is structurally connected to DMN. Although previous studies suggested that neuro-phenotypes of asthma may be recognizable by the neural activity of brain circuits, an association between the brain's functional alterations and psychiatric impairments induced by asthma remains unknown. We aimed to assess DMN and SN activity and its association... 

In recent years, the regression dynamic causal modelling (rDCM) method was introduced as a new version of dynamic causal modelling (DCM) to derive effective connectivity in whole-brain networks for functional magnetic resonance imaging (fMRI) data. In this research, we used data obtained while applying the stimulation of audio movie comprised different emotional states. We applied this method to two networks consisting of ten auditory and forty-four regions, respectively. This method was used to study effective connections between emotional states and represent the distinction between emotions. Finally, significant effective connections were found in emotional processing and auditory... 

A diffusion tensor imaging (DTI) -based statistical micromechanical model was developed to study the effect of axonal fiber architecture on the inter- and intra-regional mechanical heterogeneity of the white matter. Three characteristic regions within the white matter, i.e., corpus callosum, brain stem, and corona radiata, were studied considering the previous observations of locations of diffuse axonal injury. The embedded element technique was used to create a fiber-reinforced model, where the fiber was characterized by a Holzapfel hyperelastic material model with variable dispersion of axonal orientations. A relationship between the fractional anisotropy and the dispersion parameter of... 

Purpose: Alzheimer's disease (AD) starts years before its signs and symptoms including the dementia become apparent. Diagnosis of the AD in the early stages is important to reduce the speed of brain decline. Aim of the study: Identifying the alterations in the functional connectivity of the brain during the disease stages is among the main important issues in this regard. Therefore, in this study, the changes in the functional connectivity during the AD stages were analyzed. Materials and methods: By employing the functional magnetic resonance imaging (fMRI) data and graph theory, weighted undirected graphs of the whole-brain and default mode network (DMN) network were investigated... 

Viral diseases have long been among the biggest challenges for healthcare systems around the world. The recent Coronavirus Disease 2019 (COVID-19) pandemic is an example of how complicated the situation can get if we are not prepared to combat a viral outbreak in time, which brings up the need for quick and affordable biosensing platforms and vast knowledge of potential antiviral effects and drug/gene delivery opportunities. The same challenges have also existed for nonviral immunogenic disorders. Nanomedicine is considered a novel candidate for effectively overcoming these worldwide challenges. Among the versatile nanomaterials commonly used in biomedical applications, graphene has recently... 

Spatially-varying intensity noise is a common source of distortion in medical images and is often associated with reduced accuracy in medical image registration. In this paper, we propose two multi-resolution image registration algorithms based on Empirical Mode Decomposition (EMD) that are robust against additive spatially-varying noise. EMD is a multi-resolution tool that decomposes a signal into several principle patterns and residual components. Our first proposed algorithm (LR-EMD) is based on the registration of EMD feature maps from both floating and reference images in various resolutions. In the second algorithm (AFR-EMD), we first extract a single average feature map based on EMD... 

Simultaneous recording of activity across brain regions can contain additional information compared to regional recordings done in isolation. In particular, multivariate pattern analysis (MVPA) across voxels has been interpreted as evidence for distributed coding of cognitive or sensorimotor processes beyond what can be gleaned from a collection of univariate effects (UVE) using functional magnetic resonance imaging (fMRI). Here, we argue that regardless of patterns revealed, conventional MVPA is merely a decoding tool with increased sensitivity arising from considering a large number of ‘weak classifiers’ (i.e., single voxels) in higher dimensions. We propose instead that ‘real’ multivoxel... 

Glioma is a common type of tumor that develops in the brain. Due to many differences in the shape and appearance, accurate segmentation of glioma for identifying all parts of the tumor and its surrounding tissues in cancer detection is a challenging task in cancer detection. In recent researches, the combination of atlas-based segmentation and machine learning methods have presented superior performance over other automatic brain MRI segmentation algorithms. To overcome the side effects of limited existing information on atlas-based segmentation, and the long training and the time consuming phase of learning methods, we proposed a semi-supervised learning framework by introducing a... 

Anatomical image segmentation is one of the foundations for medical planning. Recently, convolutional neural networks (CNN) have achieved much success in segmenting volumetric (3D) images when a large number of fully annotated 3D samples are available. However, rarely a volumetric medical image dataset containing a sufficient number of segmented 3D images is accessible since providing manual segmentation masks is monotonous and time-consuming. Thus, to alleviate the burden of manual annotation, we attempt to effectively train a 3D CNN using a sparse annotation where ground truth on just one 2D slice of the axial axis of each training 3D image is available. To tackle this problem, we propose...