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: 6Citation - Scopus: 9BRCA Variations Risk Assessment in Breast Cancers Using Different Artificial Intelligence Models(MDPI, 2021-11-09) Senturk, Niyazi; Tuncel, Gulten; Dogan, Berkcan; Aliyeva, Lamiya; Dundar, Mehmet Sait; Ozemri Sag, Sebnem; Ergoren, Mahmut Cerkez; Sag, Sebnem OzemriArtificial intelligence provides modelling on machines by simulating the human brain using learning and decision-making abilities. Early diagnosis is highly effective in reducing mortality in cancer. This study aimed to combine cancer-associated risk factors including genetic variations and design an artificial intelligence system for risk assessment. Data from a total of 268 breast cancer patients have been analysed for 16 different risk factors including genetic variant classifications. In total, 61 BRCA1, 128 BRCA2 and 11 both BRCA1 and BRCA2 genes associated breast cancer patients' data were used to train the system using Mamdani's Fuzzy Inference Method and Feed-Forward Neural Network Method as the model softwares on MATLAB. Sixteen different tests were performed on twelve different subjects who had not been introduced to the system before. The rates for neural network were 99.9% for training success, 99.6% for validation success and 99.7% for test success. Despite neural network's overall success was slightly higher than fuzzy logic accuracy, the results from developed systems were similar (99.9% and 95.5%, respectively). The developed models make predictions from a wider perspective using more risk factors including genetic variation data compared with similar studies in the literature. Overall, this artificial intelligence models present promising results for BRCA variations' risk assessment in breast cancers as well as a unique tool for personalized medicine software.