Browsing by Author "Bayari, Sevgi"

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Auxiliary Differential Diagnosis of Schizophrenia and Phases of Bipolar Disorder Based on the Blood Serum Raman Spectra
(Wiley, 2020) ILDIZ, GÜLCE ÖĞRÜÇ; Bayari, Sevgi; Aksoy, Umut M.; Yorguner, Neşe; Bulut, Hüseyin; Yılmaz, Sultan S.; Halimoğlu, Gökhan; Kabuk, Hayrunnisa Nur; YAVUZ, GİZEM; Fausto, Rui
Schizophrenia (SZ) and bipolar disorder (BP) are severe psychiatric disorders that are characterized by an extensive spectrum of symptoms and affect approximately 2% of the world population. BP exhibits three well-distinct phases, which are classified as manic and depressive episodes and euthymic phase. These disorders are of difficult differential clinical diagnosis due to the similarity of their symptoms. Diagnostic approaches for SZ and BP are based on constructed patient interviews and subjective evaluations of clinical symptoms, and there are still no molecular-based auxiliary diagnostic tools to support the clinical diagnosis. In this study, an analytical model for auxiliary differential diagnosis of SZ and BP, based on the analysis of patients' blood serum Raman spectra, is developed, which is able to account for the different BP phases and can also differentiate SZ and BP patients from healthy individuals. The model is based on a hierarchical sequence of four two-class PLS-DA steps where the Raman spectra are theX-predictor variables. It is concluded that the full 400-3,100 cm(-1)Raman spectroscopic range is a sensitive probe for the disorders, thus working as a general spectroscopic biomarker for the illnesses. The proposed methodology is reliable, fast, cheap, essentially minimal-invasive, and might be implemented easily in the clinical environment.
Open Access
Fourier Transform Infrared Spectroscopy Based Complementary Diagnosis Tool for Autism Spectrum Disorder in Children and Adolescents
(MDPI, 2020) ILDIZ, GÜLCE ÖĞRÜÇ; Bayari, Sevgi; Karadağ, Ahmet; Kaygısız, Ersin; Fausto, Rui
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that begins early in life and continues lifelong with strong personal and societal implications. It affects about 1%-2% of the children population in the world. The absence of auxiliary methods that can complement the clinical evaluation of ASD increases the probability of false identification of the disorder, especially in the case of very young children. In this study, analytical models for auxiliary diagnosis of ASD in children and adolescents, based on the analysis of patients' blood serum ATR-FTIR (Attenuated Total Reflectance-Fourier Transform Infrared) spectra, were developed. The models use chemometrics (either Principal Component Analysis (PCA) or Partial Least Squares Discriminant Analysis (PLS-DA)) methods, with the infrared spectra being the X-predictor variables. The two developed models exhibit excellent classification performance for samples of ASD individuals vs. healthy controls. Interestingly, the simplest, unsupervised PCA-based model results to have a global performance identical to the more demanding, supervised (PLS-DA)-based model. The developed PCA-based model thus appears as the more economical alternative one for use in the clinical environment. Hierarchical clustering analysis performed on the full set of samples was also successful in discriminating the two groups.