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    Expression of PIAS genes in migraine patients

    , Article Journal of Molecular Neuroscience ; Volume 71, Issue 10 , 2021 , Pages 2053-2059 ; 08958696 (ISSN) Ghafouri Fard, S ; Hesami, O ; Nazer, N ; Sayad, A ; Taheri, M ; Sharif University of Technology
    Humana Press Inc  2021
    Abstract
    Migraine is a complex disabling condition which is associated with dysregulation of several pathways particularly those being associated with immune responses. In order to assess contribution of protein inhibitor of activated STAT (PIAS) in the pathogenesis of migraine, we quantified expression levels of PIAS1–PIAS4 genes in the circulation of patients with migraine compared with controls. Expression of PIAS1 was substantially lower in total migraineurs compared with controls (ratio of mean expressions (RME) = 0.18, SE = 0.29, P value < 0.001) and in both male and female migraineurs compared with sex-matched controls. Expression of PIAS2 was lower in migraineurs without aura compared with... 

    Migraine analysis through EEG signals with classification approach

    , Article 2012 11th International Conference on Information Science, Signal Processing and their Applications, ISSPA 2012, 2 July 2012 through 5 July 2012 ; July , 2012 , Pages 859-863 ; 9781467303828 (ISBN) Sayyari, E ; Farzi, M ; Estakhrooeieh, R. R ; Samiee, F ; Shamsollahi, M. B ; Sharif University of Technology
    2012
    Abstract
    Migraine is a common type of headache with neurovascular origin. In this paper, a quantitative analysis of spontaneous EEG patterns is used to examine the migraine patients with maximum and minimum pain levels. The analysis is based on alpha band phase synchronization algorithm. The efficiency of extracted features are examined through one-way ANOVA test. we reached the P-value of 0.0001, proving that the EEG patterns are statistically discriminant in maximum and minimum pain levels. We also used a Neural Network based approach in order to classify the EEG patterns, distinguishing between minimum and maximum pain levels. We achieved the total accuracy of 90.9 %