Publications by authors named "Alireza Moradzadeh"

Objectives: Non-invasive biomarkers, for the diagnosis of celiac disease, can reduce the need for biopsy, particularly in pediatric patients. The aim of this study was to investigate the levels tissue transglutaminase antibody (tTG) and plasma citrulline and its correlation with intestinal biopsy.

Methods: In this cross-sectional descriptive study, Pediatric patients with celiac disease referred to (XXX)were included.

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Understanding ion solvation and transport under confinement is critical for a wide range of emerging technologies, including water desalination and energy storage. While molecular dynamics (MD) simulations have been widely used to study the behavior of confined ions, considerable deviations between simulation results depending on the specific treatment of intermolecular interactions remain. In the following, we present a systematic investigation of the structure and dynamics of two representative solutions, that is, KCl and LiCl, confined in narrow carbon nanotubes (CNTs) with a diameter of 1.

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Purpose: We tried to investigate the role of Schwann and satellite cells in the treatment of neurogenic bladder and bowel dysfunction; following spinal cord injury in the rabbit model.

Methods: Twelve male New Zealand rabbits underwent induction of neurogenic bladder by spinal cord injury. Rabbits underwent the fiber tractography analysis to confirm the induction of spinal cord injury.

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Background: Recurrent abdominal pain (RAP) is one of the frequent complaints in general practice, particularly in pediatrics and is among the common cause of referral to gastroenterology clinics.

Purpose: This study is designed to investigate the effects of probiotics for the treatment of RAP and desired therapeutic outcomes.

Methods: One hundred twenty-five children with the diagnosis of RAP according to Rome III criteria for irritable bowel syndrome (IBS), functional abdominal pain (FAP), functional dyspepsia (FD), and abdominal migraine (AM), were enrolled in this double-blind randomized controlled trial.

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Molecular dynamics (MD) simulation is a popularly used computational tool to compute microscopic and macroscopic properties of a variety of systems including liquids, solids, biological systems, etc. To determine properties of atomic systems to a good level of accuracy with minimal noise or fluctuation, MD simulations are performed over a long time ranging from a few nanoseconds to several tens to hundreds of nanoseconds depending on the system and the properties of interest. In this study, by considering simple liquids, we explore the feasibility of significantly reducing the MD simulation time to compute various properties of monatomic systems such as the structure, pressure, and isothermal compressibility.

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Machine learning is an attractive paradigm to circumvent difficulties associated with the development and optimization of force-field parameters. In this study, a deep neural network (DNN) is used to study the inverse problem of the liquid-state theory, in particular, to obtain the relation between the radial distribution function (RDF) and the Lennard-Jones (LJ) potential parameters at various thermodynamic states. Using molecular dynamics (MD), many observables, including RDF, are determined once the interatomic potential is specified.

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We develop coarse-grained force fields (CGFFs) for computationally efficient and accurate molecular simulation of imidazolium-based ionic liquids. To obtain CGFF parameters, we employ a systematic coarse-graining approach based on the relative entropy (RE) method to reproduce not only the structure but also the thermodynamic properties of the reference all-atom molecular model. Our systematic coarse-graining approach adds a constraint to the RE minimization using the Lagrange multiplier method in order to reproduce thermodynamic properties such as pressure.

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