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    Effect of axonal fiber architecture on mechanical heterogeneity of the white matter—a statistical micromechanical model

    , Article Computer Methods in Biomechanics and Biomedical Engineering ; 2021 ; 10255842 (ISSN) Hoursan, H ; Farahmand, F ; Ahmadian, M. T ; Sharif University of Technology
    Taylor and Francis Ltd  2021
    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... 

    A viscoelastic model for axonal microtubule rupture

    , Article Journal of Biomechanics ; Volume 48, Issue 7 , 2015 , Pages 1241-1247 ; 00219290 (ISSN) Shamloo, A ; Manuchehrfar, F ; Rafii Tabar, H ; Sharif University of Technology
    Elsevier Ltd  2015
    Axon is an important part of the neuronal cells and axonal microtubules are bundles in axons. In axons, microtubules are coated with microtubule-associated protein tau, a natively unfolded filamentous protein in the central nervous system. These proteins are responsible for cross-linking axonal microtubule bundles. Through complimentary dimerization with other tau proteins, bridges are formed between nearby microtubules creating bundles. Formation of bundles of microtubules causes their transverse reinforcement and has been shown to enhance their ability to bear compressive loads. Though microtubules are conventionally regarded as bearing compressive loads, in certain circumstances during... 

    A three-dimensional statistical volume element for histology informed micromechanical modeling of brain white matter

    , Article Annals of Biomedical Engineering ; Volume 48, Issue 4 , 2020 , Pages 1337-1353 Hoursan, H ; Farahmand, F ; Ahmadian, M. T ; Sharif University of Technology
    Springer  2020
    This study presents a novel statistical volume element (SVE) for micromechanical modeling of the white matter structures, with histology-informed randomized distribution of axonal tracts within the extracellular matrix. The model was constructed based on the probability distribution functions obtained from the results of diffusion tensor imaging as well as the histological observations of scanning electron micrograph, at two structures of white matter susceptible to traumatic brain injury, i.e. corpus callosum and corona radiata. A simplistic representative volume element (RVE) with symmetrical arrangement of fully alligned axonal fibers was also created as a reference for comparison. A... 

    Analysis and Evaluation of Heterogeneous Diffusion Model in Accordance with DWI Data

    , M.Sc. Thesis Sharif University of Technology Vafaei, Amin (Author) ; Hosseini, Abolfazl (Supervisor) ; Jahed, Mehran (Co-Advisor)
    Conventional MRI has been used to diagnose different types of brain injuries. However these methods have generally failed to diagnose mild types of injury. We are working on the specification of mild traumatic brain injury, using diffusion MRI data, based on a multi compartment simulation of white matter tissue. This effort is essential for better understanding of underlying tissue micro-structure changes in patients with trauma. Some studies have been used in similar data fitting approaches in order to estimate axon diameter distribution. Specifically, a comparative study between different Compartment Models has shown that “ActiveAx” model has the best agreement with underlying tissue... 

    Constitutive Modeling and Numerical Simulation of Axonal Swelling at Large Deformation and Its Effects on Action Potential Propagation

    , Ph.D. Dissertation Sharif University of Technology Dehghany Dahaj, Mohammad (Author) ; Naghdabadi, Reza (Supervisor) ; Sohrabpour, Saeed (Supervisor)
    Brain diseases like Parkinson’s, ischemia and trauma are among major causes of death worldwide. Axonal swelling is the hallmark of most of these diseases. It is frequently observed that the axonal swelling will not remain homogeneous and shape transformations like forming multiple beads along the axon (or beading) may happen. These focal large swellings can alter or even block the passing action potentials along the axon and hence may have serious cognitive consequences. This research deals with theoretical and numerical modeling of axonal swellings. The proposed model has two parts: the central axoplasm and the surrounding cortical membrane. Instable large deformations, water diffusion,... 

    A thermodynamically consistent electro-chemo-mechanical theory for modeling axonal swelling

    , Article Journal of the Mechanics and Physics of Solids ; Volume 145 , 2020 Dehghany, M ; Naghdabadi, R ; Sohrabpour, S ; Li, Y ; Hu, Y ; Sharif University of Technology
    Elsevier Ltd  2020
    In the present study, for the first time, a thermodynamically consistent large deformation theory is developed to model the multi physics problem of axonal swelling which is the hallmark of most of the brain diseases. To this end, the relevant axonal compartments are first explained and the corresponding model parts are introduced. Next, the problem is formulated as an open thermodynamic system and the corresponding constitutive and evolution equations are extracted utilizing the balance laws. Here, a multiplicative decomposition of the deformation gradient is used to capture the active behavior of the axonal actin cortex. Specific free energy functions are given for the model parts to... 

    Whole-genome analysis of de novo somatic point mutations reveals novel mutational biomarkers in pancreatic cancer

    , Article Cancers ; Volume 13, Issue 17 , 2021 ; 20726694 (ISSN) Ghareyazi, A ; Mohseni, A ; Dashti, H ; Beheshti, A ; Dehzangi, A ; Rabiee, H. R ; Alinejad Rokny, H ; Sharif University of Technology
    MDPI  2021
    It is now known that at least 10% of samples with pancreatic cancers (PC) contain a causative mutation in the known susceptibility genes, suggesting the importance of identifying cancer-associated genes that carry the causative mutations in high-risk individuals for early detection of PC. In this study, we develop a statistical pipeline using a new concept, called gene-motif, that utilizes both mutated genes and mutational processes to identify 4211 3-nucleotide PC-associated gene-motifs within 203 significantly mutated genes in PC. Using these gene-motifs as distinguishable features for pancreatic cancer subtyping results in identifying five PC subtypes with distinguishable phenotypes and... 

    Micromechanics of brain white matter tissue: a fiber-reinforced hyperelastic model using embedded element technique

    , Article Journal of the Mechanical Behavior of Biomedical Materials ; Volume 80 , April , 2018 , Pages 194-202 ; 17516161 (ISSN) Yousefsani, S. A ; Shamloo, A ; Farahmand, F ; Sharif University of Technology
    Elsevier Ltd  2018
    A transverse-plane hyperelastic micromechanical model of brain white matter tissue was developed using the embedded element technique (EET). The model consisted of a histology-informed probabilistic distribution of axonal fibers embedded within an extracellular matrix, both described using the generalized Ogden hyperelastic material model. A correcting method, based on the strain energy density function, was formulated to resolve the stiffness redundancy problem of the EET in large deformation regime. The model was then used to predict the homogenized tissue behavior and the associated localized responses of the axonal fibers under quasi-static, transverse, large deformations. Results... 

    A three-dimensional micromechanical model of brain white matter with histology-informed probabilistic distribution of axonal fibers

    , Article Journal of the Mechanical Behavior of Biomedical Materials ; Volume 88 , 2018 , Pages 288-295 ; 17516161 (ISSN) Yousefsani, S. A ; Farahmand, F ; Shamloo, A ; Sharif University of Technology
    Elsevier Ltd  2018
    This paper presents a three-dimensional micromechanical model of brain white matter tissue as a transversely isotropic soft composite described by the generalized Ogden hyperelastic model. The embedded element technique, with corrected stiffness redundancy in large deformations, was used for the embedment of a histology-informed probabilistic distribution of the axonal fibers in the extracellular matrix. The model was linked to a multi-objective, multi-parametric optimization algorithm, using the response surface methodology, for characterization of material properties of the axonal fibers and extracellular matrix in an inverse finite element analysis. The optimum hyperelastic... 

    Bioinspired nanofiber scaffold for differentiating bone marrow-derived neural stem cells to oligodendrocyte-like cells: Design, fabrication, and characterization

    , Article International Journal of Nanomedicine ; Volume 15 , 2020 , Pages 3903-3920 Boroojeni, F. R ; Mashayekhan, S ; Abbaszadeh, H. A ; Ansarizadeh, M ; Khoramgah, M. S ; Rahimi Movaghar, V ; Sharif University of Technology
    Dove Medical Press Ltd  2020
    Background: Researchers are trying to study the mechanism of neural stem cells (NSCs) differentiation to oligodendrocyte-like cells (OLCs) as well as to enhance the selective differentiation of NSCs to oligodendrocytes. However, the limitation in nerve tissue acces-sibility to isolate the NSCs as well as their differentiation toward oligodendrocytes is still challenging. Purpose: In the present study, a hybrid polycaprolactone (PCL)-gelatin nanofiber scaffold mimicking the native extracellular matrix and axon morphology to direct the differentiation of bone marrow-derived NSCs to OLCs was introduced. Materials and Methods: In order to achieve a sustained release of T3, this factor was...