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    Öğe
    Electrospun TiO2 Nanofibers in the Presence of Avocado Seed Extract
    (2023) Temiz, Kubra; Yurtsever, Merve Çapkın
    The electrospinning technique is particularly prominent in membrane production techniques due to its easy applicability and ability to obtain nanofibers in various diameters with high surface area. Titanium dioxide (TiO2) is often used in biomaterial technologies due to their high biocompatibility, high processability and their photocatalytic activities in waste water treatment. In this study, TTIP (Titanium tetra isopropoxide) was used as a TiO2 precursor and PVP polymer was used as a carrier polymer for electrospinning. Avocado seed extract (ASE) which is a new and valuable source of phenolic compounds was used for the coordination and reduction of TTIP. TiO2-PVP-Avocado seed extract (T/P/A) composite nanofibers were produced at different voltages, distances and polymer concentrations. Crystalline TiO2 formation was not observed in as-spun nanofibers, thus selected nanofibers were heat treated at 500oC for 3 h. Smooth and integrated TiO2 nanofibers prepared by using 5 w% PVP, at 15 kV and 15 cm distance with or without ASE were imaged by Scanning Electron Microscopy (SEM). X-ray Diffraction (XRD) patterns of heat-treated TiO2 nanofibers prepared in the presence of ASE were crystallized mainly in anatase form. However, both anatase and rutile phases were obtained in the crystalline structure of TiO2 nanofibers when ASE was not used. Specific IR vibrations of TiO2 were shown by Fourier Transform Infrared Spectroscopy (FTIR). These green synthesized electrospun TiO2 nanofibers may have potential to be used in biomaterial and engineering applications.
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    Öğe
    Network medicine approaches for identification of novel prognostic systems biomarkers and drug candidates for papillary thyroid carcinoma
    (Wiley, 2023) Kori, Medi; Temiz, Kubra; Gov, Esra
    Papillary thyroid carcinoma (PTC) is one of the most common endocrine carcinomas worldwide and the aetiology of this cancer is still not well understood. Therefore, it remains important to understand the disease mechanism and find prognostic biomarkers and/or drug candidates for PTC. Compared with approaches based on single-gene assessment, network medicine analysis offers great promise to address this need. Accordingly, in the present study, we performed differential co-expressed network analysis using five transcriptome datasets in patients with PTC and healthy controls. Following meta-analysis of the transcriptome datasets, we uncovered common differentially expressed genes (DEGs) for PTC and, using these genes as proxies, found a highly clustered differentially expressed co-expressed module: a 'PTC-module'. Using independent data, we demonstrated the high prognostic capacity of the PTC-module and designated this module as a prognostic systems biomarker. In addition, using the nodes of the PTC-module, we performed drug repurposing and text mining analyzes to identify novel drug candidates for the disease. We performed molecular docking simulations, and identified: 4-demethoxydaunorubicin hydrochloride, AS605240, BRD-A60245366, ER 27319 maleate, sinensetin, and TWS119 as novel drug candidates whose efficacy was also confirmed by in silico analyzes. Consequently, we have highlighted here the need for differential co-expression analysis to gain a systems-level understanding of a complex disease, and we provide candidate prognostic systems biomarker and novel drugs for PTC.