Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Comparison of Rapamycin and 3-Methyladenine in Cisplatin-Induced Experimental Cardiotoxicity(Wiley, 2026) Kaymak, Emin; Karabulut, Derya; Yalcin, Betul; Guner, Serife Ayaz; Ozturk, Emel; Findik, Fatma; Boyvat, DuduIn this study, we evaluated how cisplatin cardiotoxicity affects the histological and endocrine functions of the heart and autophagy while using Rapamycin(Rapa) and 3-methyladenine(3-MA) as autophagy activators and inhibitors. Control, Cisplatin (Cis), 3-methyladenine + Cisplatin (3-MA + Cis) and Rapamycin + Cisplatin (Rapa + Cis). Rapa and 3-MA were administered for 15 days, while a single dose of cisplatin was administered on the 7th day. Natriuretic peptide receptor-A(NPR-A), receptor-B(NPR-B) and biochemically atrial natriuretic peptide(ANP) and brain natriuretic peptide(BNP) levels were evaluated in heart tissue. Cis caused a statistically significant increase in NPR-A and NPR-B expression, as well as ANP and BNP levels. However, the levels of Beclin-1 and LC3B were not statistically significant. Rapa was more effective than 3-MA + Cis on NPR-A and NPR-B expressions, but did not show the same effect on ANP and NT-proBNP levels. Cis caused an increase in Beclin-1 and LC3B levels, while a decrease was observed in both 3-MA + Cis and Rapa + Cis groups. Our results revealed that Cis cardiotoxicity disrupts autophagy and endocrine function of the heart. It was concluded that by continuing the activator and inhibitor substances after Cis application, more effective results can be obtained in Beclin-1 expression than LC3B expression and that they can be effective in eliminating the toxicity of Cis.Conference Object Citation - WoS: 23Citation - Scopus: 52Evaluation of Classification Algorithms, Linear Discriminant Analysis and a New Hybrid Feature Selection Methodology for the Diagnosis of Coronary Artery Disease(Institute of Electrical and Electronics Engineers Inc., 2018-12) Kolukisa, Burak; Hacilar, Hilal; Göy, Gökhan; Kus, Mustafa; Bakir-Güngör, Burcu; Aral, Atilla; Güngör, Vehbi ÇağrıAccording to the World Health Organization (WHO), 31% of the world's total deaths in 2016 (17.9 million) was due to cardiovascular diseases (CVD). With the development of information technologies, it has become possible to predict whether people have heart diseases or not by checking certain physical and biochemical values at a lower cost. In this study, we have evalated a set of different classification algorithms, linear discriminant analysis and proposed a new hybrid feature selection methodology for the diagnosis of coronary heart diseases (CHD). Throughout this research effort, using three publicly available Heart Disease diagnosis datasets (UCI Machine Learning Repository), we have conducted comparative performance evaluations in terms of accuracy, sensitivity, specificity, F-measure, AUC and running time. © 2023 Elsevier B.V., All rights reserved.