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Bezmialem Vakıf University is committed to contributing to the advancement of science and technology, and to the broad dissemination of knowledge for the benefit of society and all external stakeholders by adopting open, repeatable and reliable research outputs and practices.
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Recent Submissions
Novel triazole-urea hybrids as promising EGFR inhibitors: Synthesis, molecular modeling and antiproliferative activity studies against breast cancer
(2025-12-15) TÜRE A.; Gülcan M. M.; DİNGİŞ BİRGÜL S. İ.; Erdoğan O.; Erdoğan Ö.; ÖZ TUNCAY F.; Çakmak Ü.; KOLCUOĞLU Y.; Cevik O.; Akdemir A.; et al.
Breast cancer is the second leading cause of mortality among women globally. In this study, novel promising urea derivatives containing a 4-phenyl-5-sulphanylidene-4,5-dihydro-1H-1,2,4-triazole group were synthesized and evaluated for their biological activities against breast cancer. The cytotoxicity and apoptotic profiles of these compounds were assessed on the MCF7 breast cancer cell line and the L929 fibroblast cell line. Compound 5c exhibited the strongest anticancer activity against MCF7 cells with an IC50 value of 56.97±4.22 µM, while it showed significantly lower cytotoxicity against L929 cells (IC50 = 1651±18.39 µM). Compound 5c also induced early apoptosis in MCF7 cells, with an apoptosis rate of 18.40% and 5.28%, respectively. Additionally, the EGFR inhibitory activities of the synthesized compounds were evaluated, with compound 5i demonstrating the most potent EGFR inhibition, showing an IC50 value of 35.1 nM. These results suggest that compound 5c likely exerts its anticancer effects through mechanisms other than EGFR inhibition, while compound 5i has significant potential as an effective EGFR inhibitor. Molecular modeling studies were conducted to suggest putative binding interactions of compounds 5d, 5e and 5i with wildtype hEGFR. Further studies are warranted to explore their activity against other cancer types.
Comparison of surgical approaches to the hippocampal formation with artificial intelligence
(2025-12-01) DÜNDAR T. T.; KURT PEHLİVANOĞLU M.; EKER A. G.; Albayrak N. B.; Mutluer A. S.; YURTSEVER İ.; DOĞAN İ.; Duru N.; Ture U.
The relatively complex functional anatomy of the mediobasal temporal region makes surgical approaches to this area challenging. Several studies describe various surgical approaches, along with their combinations and modifications, to reach lesions of this region. Some of these surgical approaches have been compared using artificial intelligence-based approaches that can be predicted, classified, and analyzed for complex data. Several surgical approaches, such as anterior transsylvian, trans-superior temporal sulcus, trans-middle temporal gyrus, subtemporal-transparahippocampal, presigmoid-retrolabyrinthine, supratentorial-infraoccipital, and paramedian supracerebellar-transtentorial, were selected for comparison. Magnetic resonance images (MRIs) were taken according to the criteria specified by the Radiology Department. With an open-source software tool, volumetric data from cranial MRIs were segmented and anatomical structures in the main regions were reconstructed. The Q-learning algorithm was used to find pathways similar to these standard surgical pathways. The Q-learning scores among the selected pathways are as follows: anterior transsylvian (Q_A) = 31.01, trans-superior temporal sulcus (Q_B) = 25.00, trans-middle temporal gyrus (Q_C) = 28.92, subtemporal-transparahippocampal (Q_D) = 23.51, presigmoid- retrolabyrinthine (Q_E) = 27.54, supratentorial-infraoccipital (Q_F) = 27.2, and paramedian supracerebellar-transtentorial (Q _G) = 21.04. The Q-value score for the supracerebellar transtentorial approach was the highest among the examined approaches and therefore optimal. A difference was also found between the total risk score of all points with pathways drawn by clinicians and the total risk scores of the pathways formed and followed by Q-learning. Artificial intelligence-based approaches may significantly contribute to the success of the surgical approaches examined. Furthermore, artificial intelligence can contribute to clinical outcomes in both preoperative surgical planning and intraoperative technical equipment-assisted neurosurgery. However, further studies with more detailed data are needed for more sensitive results.
ADHD, social skills and risky internet use among elementary school children
(2025-12-01) DERİN S.; Celik S.; Selman S. B.
Background: Previous studies have established a link between Attention-Deficit/Hyperactivity Disorder (ADHD) and risky internet use (RIU); however, the processes underlying this association remain unclear. This study examines whether a proportion of the association between ADHD and RIU was shared with social skills. Methods: The sample included 142 children aged 6–12 years (65% female, M = 8.5, SD = 1.7), comprising 71 children diagnosed with ADHD and 71 controls without ADHD. Standardized assessments were administered to measure RIU and social skills. Path analysis was employed to evaluate the association among ADHD, social skills, and RIU. Key demographic variables, including gender, birth timing, age of speech onset, household income, parental education, and number of siblings, were controlled for in the analyses. Results: An ADHD diagnosis was significantly associated with reduced social skills (β = − 1.68, p < 0.001), and reduced social skills was strongly linked to higher levels of RIU (β = − 0.57, p = 0.004). The direct association between ADHD and RIU was not statistically significant (β = − 0.52, p = 0.169). However, a significant indirect effect was observed, indicating that ADHD-RIU link was shared with reduced social skills (β = 0.96, p = 0.004). Conclusions: The findings indicate that a significant proportion of the association between ADHD and RIU was shared with social skills, emphasizing the importance of social skills as a potential factor for RIU risk in children with ADHD. Interventions that focus on enhancing social skills may support efforts to address RIU in this population.
Machine learning based CAGIB score predicts in-hospital mortality of cirrhotic patients with acute gastrointestinal bleeding
(2025-12-01) Bai Z.; Lin S.; Sun M.; Yuan S.; Marcondes M. B.; Ma D.; Zhu Q.; Li Y.; He Y.; Philips C. A.; et al.
Acute gastrointestinal bleeding (AGIB) is a potentially lethal complication in cirrhosis. In this prospective international multi-center study, the performance of CAGIB score for predicting the risk of in-hospital death in 2467 cirrhotic patients with AGIB was validated. Machine learning (ML) models were established based on CAGIB components, and their area under curves (AUCs) were calculated and compared. Gray zone approach was employed to further stratify the risk of death. In training cohort, the AUC of CAGIB score was 0.789. Among the ML models, the least square support vector machine regression (LS-SVMR) model had the best predictive performance (AUC = 0.986). Patients were further divided into low- (LS-SVMR score 0.160) groups with in-hospital mortality of 0.38%, 2.22%, and 64.37%, respectively. Statistical results were retained in validation cohort. LS-SVMR model has an excellent predictive performance for in-hospital death in cirrhotic patients with AGIB (ClinicalTrials.gov; NCT04662918). (Figure presented.)