Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Analytical Methodology for Monitoring and Distribution Pattern Analysis of Polycyclic Aromatic Hydrocarbons in River Basins Based on Chemometrics(Wiley, 2025) Yildirim, Ebru calkan; Pelit, Fusun; Ozdemir, Durmus; Kazan, Aysegul; Tasdelen, Ozge; Baycan, NevalWith the increase in urbanization and industrialization, the environmental quality of river basins, which serve as a crucial source of irrigation for agricultural activities, has been deteriorating progressively. Thus, monitoring persistent toxic substances in urban water resources is crucial for maintaining ecological stability and protecting human health. In recent years, particular attention has been directed toward the prevention of polyaromatic hydrocarbons (PAHs), highlighting the importance of analyzing these compounds in water samples through more environmentally sustainable techniques. In this study, we report a green, rapid, cost-effective and simple dispersive liquid-liquid extraction (DLLME) method to monitor PAHs in river waters taken from 21 stations located within the geographical boundaries of the Gediz River Basin in Izmir Province, T & uuml;rkiye. Methodological parameters were optimized by chemometric techniques including Plackett-Burman (PBD) and Box-Behnken design. The method's accuracy was tested upon spiked river samples, and the recoveries ranged from 80% to 102%. The calibration curves were linear, with correlation coefficients greater than 0.98. The limit of detection values were between 0.01 and 0.05 ng mL-1. The reproducibility (RSD%) varied from 4.0% to 19%. Multivariate classification methods such as principal component analysis (PCA) and hierarchical cluster analysis (HCA), along with the supervised classification method partial least squares discriminant analysis (PLS-DA) were applied to elucidate the general distribution patterns of individual PAHs in the basin water samples. The chemometric evaluation conducted across four seasons revealed that PAH contamination was higher in the fall and winter months, resulting in a clear separation from spring and summer samples by using the first two principal components.Article Citation - WoS: 2Citation - Scopus: 2A Novel Approach Utilizing Rapid Thin-Film Microextraction Method for Salivary Metabolomics Studies in Lung Cancer Diagnosis(Elsevier, 2024) Pelit, Fusun; Erbas, Ilknur; Ozupek, Nazli Mert; Gul, Merve; Sakrak, Esra; Ocakoglu, Kasim; Goksel, Ozlem; Özdemir, DurmuşThis study investigated the potential of targeted salivary metabolomics as a convenient diagnostic tool for lung cancer (LC), utilizing a rapid TFME-based method. It specifically examines TFME blades modified with SiO2 nanoparticles, which were produced using a custom-made coating system. Validation of the metabolite biomarker analysis was performed by these blades using liquid chromatography-tandem mass spectroscopy (LCMS/MS). The extraction efficiencies of SiO2 nanoparticle/polyacrylonitrile (PAN) composite-coated blades were compared for 18 metabolites. Response surface methodology (RSM) was used to optimize the analysis conditions. Linear calibration plots were obtained for all metabolites at concentrations between 0.025 to 4.0 mu g/mL in the presence of internal standard, with correlation coefficients (R-2) ranging from 0.9975 to 0.9841. The limit of detection (LOD) and limit of quantitation (LOQ) were in the range of 0.014 to 0.97 mu g mL(-1) and 0.046 to 3.20 mu gmL(-1), respectively. The %RSD values for all analytes were within the acceptable range (less than 20 %) for the proposed method. The method was applied to the saliva samples of 40 patients with LC and 38 healthy controls. The efficacy of metabolites for LC diagnosis was determined by in silico methods and the results reveal that phenylalanine and purine metabolism metabolites (e.g., hypoxanthine) are of great importance for LC diagnosis. Furthermore, potentially significant biomarker analysis results from the ROC curve data reveal that proline, hypoxanthine, and phenylalanine were identified as potential biomarkers for LC diagnosis.Article Citation - WoS: 4Citation - Scopus: 4Identification of Volatile Biomarkers in Exhaled Breath by Polythiophene Solid Phase Microextraction Fiber for Disease Diagnosis Using Gc-Ms(Elsevier, 2024) Pelit, Fusun; Goksel, Ozlem; Dizdas, Tugberk Nail; Arin, Aycan; Ozgur, Su; Erbas, Ilknur; Pelit, LeventThe diagnosis of diseases through monitoring of volatile organic compounds (VOCs) in exhaled breath (EB) holds great potential for clinical applications. However, a standardized method for VOC analysis in EB yet to be proposed. The present study presents an untargeted method for screening and identifying potential volatile biomarkers in EB by a lab-made solid phase microextraction (SPME) fiber. A polythiophene-based SPME fiber was produced by an electrochemical method and VOC sampling was performed under dynamic and controlled conditions. Following the sampling step, the adsorbed VOCs on the SPME fiber were analyzed using gas chromatography-mass spectrometry (GC-MS). The VOCs in EB were screened by the MS detector in selected ion monitoring (SIM) mode within the mass/charge (m/z) range of 13-94 values. Potential biomarkers among all detected VOCs in each subject's EB sample were identified through machine learning algorithms, employing a comparative analysis of distinctive retention times (RT) and peak areas between the lung cancer (LC) and control groups in two stages. In the initial stage of the study, the areas of all peaks observed in the SIM-GC-MS chromatograms of 25 LC and 51 control group subjects were integrated, and the resulting retention times and peak areas were recorded for subsequent analysis to identify potential biomarkers. A total of 1.346 distinct compounds were detected among the 76 subjects in this step, and statistical analysis using the LightGBM algorithm revealed the potential biomarkers for LC diagnosis. The PTh-SPME fibre successfully identified four novel cancer biomarkers in breath matrix: 4-heptenal, 4-methyl-1-octene, 1,2,3,4-tetrahydro-5,8-dimethyl-1-octylnaphthalene and tetrahydro-2-(2,5-undecadiynyloxy)-2H-pyran. In the second step of the study, the efficacy of the top ten selected biomarkers was evaluated in a cohort of 166 subjects, including 70 individuals with LC and 96 in the control group. The model achieved accuracy, area under the curve (AUC), and F Score values of 0.818, 0.816, and 0.817, respectively. The test model correctly predicted 27 out of 33 subjects between LC and control groups.