PubMed İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/397
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Article Citation - Scopus: 23Synthesis and Comprehensive in Vivo Activity Profiling of Olean-12-en-28-ol, 3β-Pentacosanoate in Experimental Autoimmune Encephalomyelitis: A Natural Remyelinating and Anti-Inflammatory Agent(American Chemical Society, 2023-01-04) Şenol, Halil; Ozgun-Acar, Özden; Daǧ, Aydan; Eken, Ahmet; Guner, Hüseyin; Aykut, Zaliha Gamze; Sen, AlaattinMultiple sclerosis (MS) treatment has received much attention, yet there is still no certain cure. We herein investigate the therapeutic effect of olean-12-en-28-ol, 3β-pentacosanoate (OPCA) on a preclinical model of MS. First, OPCA was synthesized semisynthetically and characterized. Then, the mice with MOG<inf>35-55</inf>-induced experimental autoimmune/allergic encephalomyelitis (EAE) were given OPCA along with a reference drug (FTY720). Biochemical, cellular, and molecular analyses were performed in serum and brain tissues to measure anti-inflammatory and neuroprotective responses. OPCA treatment protected EAE-induced changes in mouse brains maintaining blood-brain barrier integrity and preventing inflammation. Moreover, the protein and mRNA levels of MS-related genes such as HLD-DR1, CCL5, TNF-α, IL6, and TGFB1 were significantly reduced in OPCA-treated mouse brains. Notably, the expression of genes, including PLP, MBP, and MAG, involved in the development and structure of myelin was significantly elevated in OPCA-treated EAE. Furthermore, therapeutic OPCA effects included a substantial reduction in pro-inflammatory cytokines in the serum of treated EAE animals. Lastly, following OPCA treatment, the promoter regions for most inflammatory regulators were hypermethylated. These data support that OPCA is a valuable and appealing candidate for human MS treatment since OPCA not only normalizes the pro- and anti-inflammatory immunological bias but also stimulates remyelination in EAE. © 2023 Elsevier B.V., All rights reserved.Article Citation - Scopus: 302Molecular Mechanisms of Drug Resistance and Its Reversal in Cancer(Taylor and Francis Ltd healthcare.enquiries@informa.com, 2015-03-11) Kartal Yandim, Melis; Adan Gökbulut, Aysun; Baran, Yusuf; Adan-Gokbulut, Aysun; Kartal-Yandim, MelisChemotherapy is the main strategy for the treatment of cancer. However, the main problem limiting the success of chemotherapy is the development of multidrug resistance. The resistance can be intrinsic or acquired. The resistance phenotype is associated with the tumor cells that gain a cross-resistance to a large range of drugs that are structurally and functionally different. Multidrug resistance arises via many unrelated mechanisms, such as overexpression of energy-dependent efflux proteins, decrease in uptake of the agents, increase or alteration in drug targets, modification of cell cycle checkpoints, inactivation of the agents, compartmentalization of the agents, inhibition of apoptosis and aberrant bioactive sphingolipid metabolism. Exact elucidation of resistance mechanisms and molecular and biochemical approaches to overcome multidrug resistance have been a major goal in cancer research. This review comprises the mechanisms guiding multidrug resistance in cancer chemotherapy and also touches on approaches for reversing the resistance. © 2017 Elsevier B.V., All rights reserved.Article Citation - Scopus: 24Analyzing the Nexus Between Environmental Sustainability and Clean Energy for the USA(Springer, 2024-03-22) Dogan, Eyup; Si Mohammed, Kamel; Khan, Zeeshan Anis; BinSaeed, Rima Hassan; Mohammed, Kamel SiEnvironmental sustainability is a key target to achieve sustainable development goals (SDGs). However, achieving these targets needs tools to pave the way for achieving SDGs and COP28 targets. Therefore, the primary objective of the present study is to examine the significance of clean energy, research and development spending, technological innovation, income, and human capital in achieving environmental sustainability in the USA from 1990 to 2022. The study employed time series econometric methods to estimate the empirical results. The study confirmed the long-run cointegrating relationship among CO<inf>2</inf> emissions, human capital, income, R&D, technological innovation, and clean energy. The results are statistically significant in the short run except for R&D expenditures. In the long run, the study found that income and human capital contribute to further aggravating the environment via increasing CO<inf>2</inf> emissions. However, R&D expenditures, technological innovation, and clean energy help to promote environmental sustainability by limiting carbon emissions. The study recommends investment in technological innovation, clean energy, and increasing R&D expenditures to achieve environmental sustainability in the USA. © 2024 Elsevier B.V., All rights reserved.Article Citation - Scopus: 15An Effective Colorectal Polyp Classification for Histopathological Images Based on Supervised Contrastive Learning(Elsevier Ltd, 2024-04) Yengec-Tasdemir, Sena Busra; Aydin, Zafer; Akay, Ebru; Doǧan, Serkan; Yilmaz, BulentEarly detection of colon adenomatous polyps is pivotal in reducing colon cancer risk. In this context, accurately distinguishing between adenomatous polyp subtypes, especially tubular and tubulovillous, from hyperplastic variants is crucial. This study introduces a cutting-edge computer-aided diagnosis system optimized for this task. Our system employs advanced Supervised Contrastive learning to ensure precise classification of colon histopathology images. Significantly, we have integrated the Big Transfer model, which has gained prominence for its exemplary adaptability to visual tasks in medical imaging. Our novel approach discerns between in-class and out-of-class images, thereby elevating its discriminatory power for polyp subtypes. We validated our system using two datasets: a specially curated one and the publicly accessible UniToPatho dataset. The results reveal that our model markedly surpasses traditional deep convolutional neural networks, registering classification accuracies of 87.1% and 70.3% for the custom and UniToPatho datasets, respectively. Such results emphasize the transformative potential of our model in polyp classification endeavors. © 2024 Elsevier B.V., All rights reserved.