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    The metabolic network model of primed/naive human embryonic stem cells underlines the importance of oxidation-reduction potential and tryptophan metabolism in primed pluripotency

    , Article Cell and Bioscience ; Volume 9, Issue 1 , 2019 ; 20453701 (ISSN) Yousefi, M ; Marashi, S. A ; Sharifi Zarchi, A ; Taleahmad, S ; Sharif University of Technology
    BioMed Central Ltd  2019
    Background: Pluripotency is proposed to exist in two different stages: Naive and Primed. Conventional human pluripotent cells are essentially in the primed stage. In recent years, several protocols have claimed to generate naive human embryonic stem cells (hESCs). To the best of our knowledge, none of these protocols is currently recognized as the gold standard method. Furthermore, the consistency of the resulting cells from these diverse protocols at the molecular level is yet to be shown. Additionally, little is known about the principles that govern the metabolic differences between naive and primed pluripotency. In this work, using a computational approach, we tried to shed light on... 

    A metabonomics study on crohn's disease using nuclear magnetic resonance spectroscopy

    , Article HealthMED ; Volume 6, Issue 11 , July , 2012 , Pages 3577-3583 ; 18402291 (ISSN) Fathi, F ; Kyani, A ; Nejad, M. R ; Rezaye Tavirani, M ; Naderi, N ; Zali, M. R ; Tafazzoli, M ; Oskouie, A. A ; Sharif University of Technology
    Objective: Crohn's disease (CD) is one the important illnesses can affect any part of the gastrointestinal tract. CD is not easily diagnosed using the clinical tests. Thus, the discovery of proper methods would be a major step towards CD diagnosis. The aim of this study was to seek the metabolic biomarkers causes of CD compare to control group. Materials and Methods: In present study, we employed metabolic profiling using proton nuclear magnetic resonance spectroscopy (1HNMR) to find metabolites in serum which are helpful for the diagnosis of CD. Classification of CD and healthy subject was done using classification and regression trees (CART). The metabolites that caused changes in people... 

    Metabolomic biomarkers in the diagnosis of non-alcoholic fatty liver disease

    , Article Hepatitis Monthly ; Volume 19, Issue 9 , 2019 ; 1735143X (ISSN) Chashmniam, S ; Ghafourpour, M ; Rezaei Farimani, A ; Gholami, A ; Nobakht Motlagh Ghoochani, B. F ; Sharif University of Technology
    Kowsar Medical Publishing Company  2019
    Background: Nonalcoholic fatty liver disease (NAFLD) is the most abundant chronic liver disorder, because racial and ethnic differences may influence prevalence and severity of NAFLD. Objectives: This metabolomic study was conducted to identify the metabolic biomarkers and determine the mechanism of progress of NAFLD in Iranian patients. Methods: Serum samples were collected from 75 participants (37 healthy controls and 38 patients with NAFLD) after an overnight fast. The metabolome of all samples were determined by nuclear magnetic resonance (NMR) and were compared by multivariate statistical analysis. Results: Totally, 19 metabolomic biomarkers were identified by NMR. Compared to healthy... 

    The metabolome profiling of obese and non-obese individuals: Metabolically healthy obese and unhealthy non-obese paradox

    , Article Iranian Journal of Basic Medical Sciences ; Volume 23, Issue 2 , 2020 , Pages 186-194 Chashmniam, S ; Madani, N. H ; Ghoochani, B. F. N. M ; Safari Alighiarloo, N ; Khamseh, M. E ; Sharif University of Technology
    Mashhad University of Medical Sciences  2020
    Objective(s): The molecular basis of “metabolically healthy obese” and “metabolically unhealthy non-obese” phenotypes is not fully understood. Our objective was to identify metabolite patterns differing in obese (metabolically healthy vs unhealthy (MHO vs MUHO)) and non-obese (metabolically healthy vs unhealthy (MHNO vs MUHNO)) individuals. Materials and Methods: This case-control study was performed on 86 subjects stratified into four groups using anthropometric and clinical measurements: MHO (21), MUHO (21), MHNO (22), and MUHNO (22). Serum metabolites were profiled using nuclear magnetic resonance (NMR). Multivariate analysis was applied to uncover discriminant metabolites, and enrichment...