WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 8Citation - Scopus: 7Raft-Synthesized Poegma-B Block Copolymers: Preparation of Nanosized Micelles for Anticancer Drug Release(Springer, 2021-11-14) Bayram, Nazende Nur; Topuzogullari, Murat; Isoglu, Ismail Alper; Isoglu, Sevil Dincer; Dinçer İşoğlu, SevilTo achieve high stability and biocompatibility in physiological environment, oligoethyleneglycol methacrylate (OEGMA) and 4-vinylpyridine (4VP)-based amphiphilic block copolymers were prepared as micellar carriers to deliver doxorubicin into tumor cells. First, macroinitiator of OEGMA was synthesized by RAFT polymerization at [M](0)/[CTA](0)/[I](0) ratio of 100/1/0.2 in dimethylformamide (DMF) at 70 degrees C, in the presence of 4,4'-azobis(4-cyanovaleric acid) (ACVA) as initiator and 4-cyano-4-(thiobenzoylthio)pentanoic acid (CTA) as chain transfer agent, respectively. It was followed by copolymerization with 4-VP at similar conditions. The formation of RAFT-mediated polymers was approved by FTIR, H-1-NMR and GPC. For the preparation of drug-loaded micelles, a dialysis method was applied and hydrophobic doxorubicin, as a model drug, was entrapped into the micelles. Size distributions and morphologies of drug-loaded micelles were investigated by light scattering and scanning electron microscopy, respectively. Critical micelle concentration was estimated as 0.0019 mg/mL by measuring light scattering intensity in different polymer concentrations. Also, drug loading and entrapment efficiencies were calculated as 4.41% and 17.65% by measuring the DOX amount in the micelles, spectrophotometrically. At last, the drug-loaded micelles were applied to SKBR-3 breast cancer cell lines and revealed up to %40 cell inhibition at 48 and 72 h. As a result, these nanosized and biocompatible micelles can be used for the delivery of hydrophobic drugs, and they can also be modified for further targeting and imaging applications toward specific cancer cells. [GRAPHICS] .Conference Object Peptide Targeted Core Cross-Linked Micelles for Dox Delivery to HER2 Expressing Cancer Cells(Mary Ann Liebert, inc, 2022) Bayram, Nazende Nur; Ulu, Gizem Tugce; Gurdap, Seda; Isoglu, Ismail Alper; Baran, Yusuf; Isoglu, Sevil DincerConference Object Leveraging MicroRNA-Gene Associations With Mirgedinet: An Intelligent Approach for Enhanced Classification of Breast Cancer Molecular Subtypes(Springer International Publishing AG, 2025) Qumsiyeh, Emma; Bakir-Gungor, Burcu; Yousef, MalikUnderstanding the molecular subtypes of breast cancer is crucial for advancing targeted therapies and precision medicine. For the BRCA molecular subtype prediction problem, this study employs miRGediNET, a machinelearning approach that integrates data from miRTarBase, DisGeNET, and HMDD databases to investigate shared gene associations between microRNA (miRNA) activity and disease mechanisms. Using the BRCA LumAB_Her2Basal dataset, we evaluate miRGediNET's performance against traditional feature selection methods, including CMIM, mRmR, Information Gain (IG), SelectKBest (SKB), Fast Correlation-Based Filter (FCBF), and XGBoost (XGB). These feature selection techniques were assessed using various classification algorithms including Random Forest (RF), Support Vector Machine (SVM), LogitBoost, Decision Tree, and AdaBoost, all executed with default parameters. The feature selection methods were tested using Monte Carlo Cross-Validation, where performance metrics obtained for each iteration were averaged to ensure robustness. Our findings reveal that miRGediNET outperforms traditional methods in accuracy and Area Under the Curve (AUC), emphasizing its superior capability to identify key genes that bridge miRNA interactions and breast cancer mechanisms. Notably, both miRGediNET and Information Gain (IG) feature selection consistently identified ESR1, a critical biomarker frequently reported in recent research associated with breast cancer prognosis and resistance to endocrine therapies. This integrative approach provides deeper biological insights into miRNA-disease interactions, paving the way for enhanced patient stratification, biomarker discovery, and personalized medicine strategies. The miRGediNET tool, developed on the KNIME platform, offers a practical resource for further exploration in the field of bioinformatics and oncology.Article Citation - WoS: 13Citation - Scopus: 14HER2-Targeted, Degradable Core Cross-Linked Micelles for Specific and Dual pH-Sensitive Dox Release(Wiley-VCH Verlag GmbH, 2021-11-09) Bayram, Nazende Nur; Ulu, Gizem Tugce; Topuzogullari, Murat; Baran, Yusuf; Isoglu, Sevil Dincer; Dinçer İşoğlu, SevilHere, a targeted, dual-pH responsive, and stable micelle nanocarrier is designed, which specifically selects an HER2 receptor on breast cancer cells. Intracellularly degradable and stabilized micelles are prepared by core cross-linking via reversible addition-fragmentation chain-transfer (RAFT) polymerization with an acid-sensitive cross-linker followed by the conjugation of maleimide-doxorubicin to the pyridyl disulfide-modified micelles. Multifunctional nanocarriers are obtained by coupling HER2-specific peptide. Formation of micelles, addition of peptide and doxorubicin (DOX) are confirmed structurally by spectroscopical techniques. Size and morphological characterization are performed by Zetasizer and transmission electron microscope (TEM). For the physicochemical verification of the synergistic acid-triggered degradation induced by acetal and hydrazone bond degradation, Infrared spectroscopy and particle size measurements are used. Drug release studies show that DOX release is accelerated at acidic pH. DOX-conjugated HER2-specific peptide-carrying nanocarriers significantly enhance cytotoxicity toward SKBR-3 cells. More importantly, no selectivity toward MCF-10A cells is observed compared to HER2(+) SKBR-3 cells. Formulations cause apoptosis depending on Bax and Caspase-3 and cell cycle arrest in G2 phase. This study shows a novel system for HER2-targeted therapy of breast cancer with a multifunctional nanocarrier, which has higher stability, dual pH-sensitivity, selectivity, and it can be an efficient way of targeted anticancer drug delivery.Article Citation - WoS: 15Citation - Scopus: 14A Review of Mammographic Region of Interest Classification(Wiley Periodicals, inc, 2020-02-18) Yengec Tasdemir, Sena B.; Tasdemir, Kasim; Aydin, ZaferEarly detection of breast cancer is important and highly valuable in clinical practice. X-ray mammography is broadly used for prescreening the breast and is also attractive due to its noninvasive nature. However, experts can misdiagnose a significant proportion of the cases, which may either cause redundant examinations or cancer. In order to reduce false positive and negative rates of mammography screening, computer-aided breast cancer detection has been studied for more than 30 years and many methods have been proposed by the researchers. In this review, region of interest (ROI) classification methods, which operate on a predefined or segmented ROIs with a focus on mass classification are surveyed. A total of 72 high quality journal and conference papers are selected from the Web of Science (WOS) database that meet several inclusion criteria. A comparative analysis is provided based on ROI extraction methods, data sets and machine learning techniques employed, the prediction accuracies, and usage frequency statistics. Based on the performances obtained on publicly available data sets, the ROI classification problem from mammogram images can be considered as approaching to be solved. Nonetheless, it can still be used as complementary information in breast cancer detection from the whole mammograms, which has room for improvement. This article is categorized under: Application Areas > Science and Technology Technologies > Machine Learning Technologies > ClassificationConference Object Citation - WoS: 8Citation - Scopus: 9Meme Kanseri Histopatolojik Görüntülerinin Bilgisayar Destekli Sınıflandırılması(Institute of Electrical and Electronics Engineers Inc., 2017-10) Aksebzeci, Bekir Hakan; Kayaaltı, ÖmerNowadays, one of the most common types of cancer is breast cancer. The early and accurate diagnosis of breast cancer has great importance in the treatment of the disease. In the diagnosis of breast cancer, histopathological analysis of cell and tissue specimens taken by biopsy is considered as the gold standard. Histopathological analysis is a tedious process that is highly dependent on the knowledge and experience of the pathologists. In this study; it is aimed to develop a computer-Aided system that can reduce the workload of pathologists and help them in their diagnosis. An image set containing benign and malignant tumor images of breast cancer has been studied. To perform texture analysis on tumor images; first order statistics, Gabor and gray-level co-occurrence matrix (GLCM) feature extraction methods have been applied. Then, various classifiers were applied to the obtained feature matrices and their performances were compared. The highest classification accuracy was achieved 82.06% by Random Forests classifier with feature combination of Gabor and GLCM methods. The results presented here show that computer-Assisted diagnosis of breast cancer is a promising field. © 2018 Elsevier B.V., All rights reserved.