WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 3Citation - Scopus: 3HER2-Specific Peptide (LTWWYSPY) and Antibody (Herceptin) Targeted Core Cross-Linked Micelles for Breast Cancer: A Comparative Study(MDPI, 2023-02-22) Bayram, Nazende Nur; Ulu, Gizem Tugce; Abdulhadi, Nusaibah Abdulsalam; Guerdap, Seda; Isoglu, Ismail Alper; Baran, Yusuf; Isoglu, Sevil Dincer; Gürdap, SedaThis study aims to prepare a novel breast cancer-targeted micelle-based nanocarrier, which is stable in circulation, allowing intracellular drug release, and to investigate its cytotoxicity, apoptosis, and cytostatic effects, in vitro. The shell part of the micelle is composed of zwitterionic sulfobetaine ((N-3-sulfopropyl-N,N-dimethylamonium)ethyl methacrylate), while the core part is formed by another block, consisting of AEMA (2-aminoethyl methacrylamide), DEGMA (di(ethylene glycol) methyl ether methacrylate), and a vinyl-functionalized, acid-sensitive cross-linker. Following this, a targeting agent (peptide (LTVSPWY) and antibody (Herceptin((R)))), in varying amounts, were coupled to the micelles, and they were characterized by H-1 NMR, FTIR (Fourier-transform infrared spectroscopy), Zetasizer, BCA protein assay, and fluorescence spectrophotometer. The cytotoxic, cytostatic, apoptotic, and genotoxic effects of doxorubicin-loaded micelles were investigated on SKBR-3 (human epidermal growth factor receptor 2 (HER2)-positive) and MCF10-A (HER2-negative). According to the results, peptide-carrying micelles showed a higher targeting efficiency and better cytostatic, apoptotic, and genotoxic activities than antibody-carrying and non-targeted micelles. Also, micelles masked the toxicity of naked DOX on healthy cells. In conclusion, this nanocarrier system has great potential to be used in different drug-targeting strategies, by changing targeting agents and drugs.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 > Classification