Person: ILDIZ, GÜLCE ÖĞRÜÇ
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ILDIZ
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GÜLCE ÖĞRÜÇ
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Publication Restricted 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, RuiSchizophrenia (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.Publication Open Access Micro-Raman Spectroscopy and X-ray Diffraction Analyses of the Core and Shell Compartments of an Iron-Rich Fulgurite(MDPI, 2022) KARADAĞ, AHMET; Kaygısız, Ersin; Nikitin, Timur; Öngen, Sinan; ILDIZ, GÜLCE ÖĞRÜÇ; Aysal, Namık; Yılmaz, Ayberk; Fausto, RuiFulgurites are naturally occurring structures that are formed when lightning discharges reach the ground. In this investigation, the mineralogical compositions of core and shell compartments of a rare, iron-rich fulgurite from the Mongolian Gobi Desert were investigated by X-ray diffraction and micro-Raman spectroscopy. The interpretation of the Raman data was helped by chemometric analysis, using both multivariate curve resolution (MCR) and principal component analysis (PCA), which allowed for the fast identification of the minerals present in each region of the fulgurite. In the core of the fulgurite, quartz, microcline, albite, hematite, and barite were first identified based on the Raman spectroscopy and chemometrics analyses. In contrast, in the shell compartment of the fulgurite, the detected minerals were quartz, a mixture of the K-feldspars orthoclase and microcline, albite, hematite, and goethite. The Raman spectroscopy results were confirmed by X-ray diffraction analysis of powdered samples of the two fulgurite regions, and are consistent with infrared spectroscopy data, being also in agreement with the petrographic analysis of the fulgurite, including scanning electron microscopy with backscattering electrons (SEM-BSE) and scanning electron microscopy with energy dispersive X-ray (SEM-EDX) data. The observed differences in the mineralogical composition of the core and shell regions of the studied fulgurite can be explained by taking into account the effects of both the diffusion of the melted material to the periphery of the fulgurite following the lightning and the faster cooling at the external shell region, together with the differential properties of the various minerals. The heavier materials diffused slower, leading to the concentration in the core of the fulgurite of the iron and barium containing minerals, hematite, and barite. They first underwent subsequent partial transformation into goethite due to meteoric water within the shell of the fulgurite. The faster cooling of the shell region kinetically trapped orthoclase, while the slower cooling in the core area allowed for the extensive formation of microcline, a lower temperature polymorph of orthoclase, thus justifying the prevalence of microcline in the core and a mixture of the two polymorphs in the shell. The total amount of the K-feldspars decreases only slightly in the shell, while quartz and albite appeared in somewhat larger amounts in this compartment of the fulgurite. On the other hand, at the surface of the fulgurite, barite could not be stabilized due to sulfate lost (in the form of SO2 plus O-2 gaseous products). The conjugation of the performed Raman spectroscopy experiments with the chemometrics analysis (PCA and, in particular, MCR analyses) was shown to allow for the fast identification of the minerals present in the two compartments (shell and core) of the sample. This way, the XRD experiments could be done while knowing in advance the minerals that were present in the samples, strongly facilitating the data analysis, which for compositionally complex samples, such as that studied in the present investigation, would have been very much challenging, if possible.Publication Metadata only FT-IR spectroscopy and multivariate analysis as an auxiliary tool for diagnosis of mental disorders: Bipolar and schizophrenia cases(Pergamon-Elsevier Science Ltd, The Boulevard, Langford Lane, Kidlington, Oxford Ox5 1Gb, England, 2016-01-05) Arslan, M.; Ünsalan, Ozan; Araujo-Andrade, C.; Kurt, Ethan; Karatepe, Hasan Turan; Yılmaz, Ayberk; Bölükbaşı Yalçınkaya, Olcay; Herken, Hasan; ILDIZ, GÜLCE ÖĞRÜÇ; 107326; 106111; 242682; 175748; 123240In this study, a methodology based on Fourier-transform infrared spectroscopy and principal component analysis and partial least square methods is proposed for the analysis of blood plasma samples in order to identify spectral changes correlated with some biomarkers associated with schizophrenia and bipolarity. Our main goal was to use the spectral information for the calibration of statistical models to discriminate and classify blood plasma samples belonging to bipolar and schizophrenic patients. IR spectra of 30 samples of blood plasma obtained from each, bipolar and schizophrenic patients and healthy control group were collected. The results obtained from principal component analysis (PCA) show a clear discrimination between the bipolar (BP), schizophrenic (SZ) and control group' (CG) blood samples that also give possibility to identify three main regions that show the major differences correlated with both mental disorders (biomarkers). Furthermore, a model for the classification of the blood samples was calibrated using partial least square discriminant analysis (PLS-DA), allowing the correct classification of BP, SZ and CG samples. The results obtained applying this methodology suggest that it can be used as a complimentary diagnostic tool for the detection and discrimination of these mental diseases. (C) 2015 Elsevier B.V. All rights reserved.Publication Metadata only Raman spectroscopic and chemometric investigation of lipid-protein ratio contents of soybean mutants(2020) Yılmaz, Ayberk; Kabuk, Hayrunnisa Nur; Kaygısız, Ersin; Fausto, Rui; MERİÇ, SİNAN; AYAN, ALP; ATAK, ÇİMEN; ÇELİK, ÖZGE; ILDIZ, GÜLCE ÖĞRÜÇSeeds belonging to fourth generation mutants (M-4) of Ataem-7 cultivar (A7) variety and S04-05 (S) breeding line salt-tolerant soybeans were investigated by Raman spectroscopy, complemented by chemometrics methods, in order to evaluate changes induced by mutations in the relative lipid-protein contents, and to find fast, efficient strategies for discrimination of the mutants and the control groups based on their Raman spectra. It was concluded that gamma irradiation caused an increase in the lipid to protein ratio of the studied Ataem-7 variety mutants, while it led to a decrease of this ratio in the investigated S04-05 breeding line mutants. These results were found to be in agreement with data obtained by reflectance spectrum analysis of the seeds in the full ultraviolet to near-infrared spectral region and suggest the possibility of developing strategies where gamma irradiation can be used as a tool to improve mutant soybean plants targeted to different applications, either enriched in proteins or in lipids. Ward's clustering and principal component analysis showed a clear discrimination between mutants and controls and, in the case of the studied S-type species, discrimination between the different mutants. The grouping scheme is also found to be in agreement with the compositional information extracted from the analysis of the lipid-protein contents of the different samples.Publication 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, RuiAutism 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.Publication Open Access PLS-DA Model for the Evaluation of Attention Deficit and Hyperactivity Disorder in Children and Adolescents through Blood Serum FTIR Spectra(MDPI, 2021) ILDIZ, GÜLCE ÖĞRÜÇ; KARADAĞ, AHMET; Kaygısız, Ersin; Fausto, RuiAttention deficit and hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders of childhood. It affects similar to 10% of the world's population of children, and about 30-50% of those diagnosed in childhood continue to show ADHD symptoms later, with 2-5% of adults having the condition. Current diagnosis of ADHD is based on the clinical evaluation of the patient, and on interviews performed by clinicians with parents and teachers of the children, which, together with the fact that it shares common symptoms and frequent comorbidities with other neurodevelopmental disorders, makes the accurate and timely diagnosis of the disorder a difficult task. Despite the large effort to identify reliable biomarkers that can be used in a clinical environment to support clinical diagnosis, this goal has never been achieved hitherto. In the present study, infrared spectroscopy was used together with multivariate statistical methods (hierarchical clustering and partial least-squares discriminant analysis) to develop a model based on the spectra of blood serum samples that is able to distinguish ADHD patients from healthy individuals. The developed model used an approach where the whole infrared spectrum (in the 3700-900 cm(-1) range) was taken as a holistic imprint of the biochemical blood serum environment (spectroscopic biomarker), overcoming the need for the search of any particular chemical substance associated with the disorder (molecular biomarker). The developed model is based on a sensitive and reliable technique, which is cheap and fast, thus appearing promising to use as a complementary diagnostic tool in the clinical environment.Publication Restricted Investigation of Menopause-Induced Changes on Hair by Raman Spectroscopy and Chemometrics(Pergamon-Elsevier Science Ltd., 2022) Brito, Anna Luiza B.; Brueggen, Carlotta; ILDIZ, GÜLCE ÖĞRÜÇ; Fausto, RuiThe ending of estrogen production in the ovaries after menopause results in a series of important physiologic changes, including hair texture and growth. In this study we demonstrate that Raman spectroscopy can be used successfully as a tool to probe menopause-induced changes on hair, in particular when coupled with suitable chemometrics approaches. The detailed analysis of the average Raman spectra (in particular of the Amide I and vS-S stretching spectral regions) of the hair samples of women pre- and post-menopause allowed to estimate that absence of estrogen in post-menopause women leads to an average reduction of similar to 12% in the thickness of the hair cuticle, compared to that of pre-menopause women, and revealed the strong prevalence of disulphide bonds in the most stable gauche-gauche-gauche conformation in the hair cuticle. From the analysis of the vS-S stretching spectral region it could also be concluded that the amount of alpha-helix keratin is slightly higher for post-menopause than for pre-menopause women. A series of statistical models were developed in order to classify the hair samples. Outperforming the traditional PCA-LDA (principal component analysis - linear discriminant analysis) approach, in the present study a GA-LDA (genetic algorithm - linear discriminant analysis) strategy was used for variable reduction/selection and samples' classification. This strategy allowed to develop of a statistical model (L16), which has exceptional prediction capability (total accuracy of 96.6%, with excellent sensitivity and selectivity) and can be used as an efficient instrument for the hair samples' classification. In addition, a new chemometrics approach is here presented, which allows to overcome the intrinsic limitations of the GA algorithm and that can be used to develop statistical models that use GA as the variable reduction/selection method, but superseding its stochastic nature. Three suitable models for classification of the hair samples according to the menopause status of the women were developed using this novel approach (LV17, BLV20 and PLS7 models), which are based on the Fisher's and Bayers' LDA approaches and the PLS-DA method. The followed new chemometrics approach uses the results of a large set of GA-LDA runs over the full data matrix for the selection of the reduced data matrices. The criterion for the selection of the variables is their statistical significance in terms of number of occurrences as solutions of the whole set of GA-LDA runs. (C) 2022 Elsevier B.V. All rights reserved.