WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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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) Özdemir, Durmuş; Pelit, Fusun; Ozdemir, Durmus; Kazan, Aysegul; Tasdelen, Ozge; Baycan, Neval; 04.01. Department of Chemistry; 04. Faculty of Science; 01. Izmir Institute of TechnologyWith 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.Editorial A Thin Film Micro-Extraction Based Salivary Metabolomics and Chemometric Strategy for Rapid Lung Cancer Diagnosis(Galenos Publ House, 2025) Özdemir, Durmuş; Basbinar, Yasemin; Goksel, Ozlem; Goksel, Tuncay; Erbas, İlknur; Pelit, Fusun; Ozdemir, Durmus; 01. Izmir Institute of Technology; 04.01. Department of Chemistry; 04. Faculty of ScienceINTRODUCTION: Lung cancer (LC) remains one of the leading causes of cancer-related mortality worldwide, largely due to the lack of reliable biomarkers for early detection.1 Despite advances in di-agnostic imaging and targeted therapies, the five-year survival rate remains low because most cases are diagnosed at advanced stages. Consequently, the development of sensitive, non-invasive, and cost-effective diagnostic approaches is a major clinical priority. Metabolomics, the comprehensive profiling of small-molecule metabolites, has emerged as a powerful tool for uncovering cancer-associated metabolic alterations, providing insights into tumor biology and facilitating the discovery of novel biomarkers for accurate diagnosis and disease monitoring. Among biological matrices, saliva is a promising diagnostic biofluid because it can be collected non-invasively, is simple to obtain, and reflects systemic and local metabolic changes. Recent studies have demonstrated its potential for detecting various cancers, including lung cancer, highlighting its value for biomarker-based early di-agnosis.2,3 In this study, a novel thin-film microextraction (TFME) technique integrated with liquid chromatography-tandem mass spectrometry (LC-MS/MS) is introduced for the rapid, selective, and reproducible extraction of salivary metabolites. The developed TFME approach offers high throughput, reduced solvent consumption, and enhanced analytical performance, enabling the identification and quantification of key metabolic biomarkers associated with lung cancer. The objective of this workflow is to advance saliva-based metabolomics toward clinical translation, offering a promising avenue for the early and non-invasive diagnosis of lung cancer. MATERIAL AND METHODS: Synthesis of SiO2 Nanoparticles and TFME blade Preparation: SiO2 nanoparticles were synthesized using the Stöber method, followed by post-coating with tetraethyl orthosilicate, centrifugation, wash-ing with ethanol, and drying. The nanoparticles were incorporated into a polyacrylonitrile (PAN) matrix and coated onto steel TFME blades via a controlled dip-coating process to ensure uniform film thick-ness. Participants and Sample Collection: Saliva samples were collected from 40 histopathologically con-firmed lung cancer patients and 38 healthy volunteers following an overnight fast and an oral rinse. Ethical approval and informed consent were obtained (Ege University Ethics Committee, protocol: 15-11.1/46). Saliva samples were centrifuged, diluted (1:2), and stored at -80 °C until analysis. TFME Sampling and Analysis: A 96-well plate system equipped with PAN/SiO2-coated TFME blades was used for metabolite extraction (Figure 1). Blades were immersed in diluted saliva samples and rotated at 850 rpm for 150 minutes to allow analyte adsorption, followed by desorption of analytes in 0.1% formic acid for 30 minutes. Desorbed solutions were spiked with 0.5 µg/mL ornidazole as an internal standard prior to LC-MS/MS analysis. RESULTS: The TFME method was optimized to detect 18 metabolites in pre-treatment saliva samples from lung cancer patients. Chromatographic evaluation demonstrated that the Inertsil 100 column, employing isocratic elution with ornidazole as the internal standard, provided optimal separation effi-ciency and reproducibility. Extraction parameters, including desorption solution type and pH, were optimized; desorption solution type 2 at pH 8-9 yielding the highest metabolite recovery. Analytical validation indicated robust linearity (R2: 0.9841-0.9975), sensitivity (limit of detection: 0.014-0.97 μg/mL; limit of quantification: 0.046-3.20 μg/mL), precision (%relative standard deviation <20%), and accuracy (85-125% for most metabolites). Pathway analysis revealed significant alterations in the me-tabolism of phenylalanine, purine, tyrosine, histidine, and methionine. The Heatmap visualization showed increased levels of proline, hypoxanthine, phenylalanine, and tyrosine in lung cancer pa-tients. receiver operating characteristic curve analysis highlighted these metabolites as potential bi-omarkers, with proline exhibiting the highest diagnostic performance [area under the curve (AUC): 0.946], followed by hypoxanthine (AUC: 0.933) and phenylalanine (AUC: 0.905) CONCLUSION: The findings of this study demonstrate that the TFME approach is a reliable and effi-cient platform for metabolomic profiling in lung cancer. Using pre-treatment saliva samples, the method achieved a sensitivity exceeding 90% for detecting newly diagnosed histopathologically con-firmed patients. Among the metabolites analyzed, proline, hypoxanthine, and phenylalanine showed strong diagnostic potential, consistent with the pathway analyses implicating purine and phenylala-nine metabolism. These results underscore the potential of salivary metabolomics as a non-invasive screening alternative in the absence of validated early lung cancer biomarkers. Additionally, TFME’s high-throughput capacity, cost-effectiveness, and environmental sustainability support its feasibility for routine clinical application.Article Geographical Classification and Characterization of Turkish Gemlik Virgin Olive Oils From Two Locations (Salihli - Manisa and Gemlik - Bursa) Based on Their Glyceridic Profiles(Innovhub SSI - Stazioni Sperimentali per l'Industria, 2025) Özdemir, Durmuş; Ozdemir, Durmus; 01. Izmir Institute of Technology; 04. Faculty of Science; 04.01. Department of ChemistryThe Gemlik olive cultivar (which is grown for its fruit and oil, also known as the Trilya or Tirilye olive) is the major domestic cultivar of the Marmara region and originated in Bursa province on the Gulf of Gemlik. It has also been cultivated widely for over twenty years in other olive growing regions in Turkey and is the source of speculative claims by the domestic sector about the properties of its oil. In this study, VOO samples produced from Gemlik olive cultivar grown over two crop years in the two main locations (Salihli-Manisa n=10 and Gemlik -Bursa n=14) and reference samples from the Olive Research Institute-Borova/Izmir (n=2) were analysed using the common and approved capillary GC (Fatty Acid Composition-FA) and HPLC (Triacylglycerol Profile-TAG) methods. All data from both methods were classified with the most popular chemometrics methods (Principal Component Analysis, PCA and Hierarchical Cluster Analysis, HCA). The results of the glyceridic data from the PCA indicated that the changes of cumulative percentage were the reason for variance levels (based on PC1 and PC2) in VOO samples of between 61.75 and 77.93% for all data over the two crop years. According to the PCA biplot analysis for the two crop years, some major-minor compounds and calculated parameters from FAs and TAGs data played an effective role in the geographical characterisation and classification of Gemlik VOO from two different locations, Manisa and Bursa. Consequently, the FA and TAG profiles could be promising in determining the correct geographical classification of monocultivar Gemlik VOOs.