Welcome to the Bezmialem Academic Repository
Bezmialem Vakıf University's Institutional Academic Archive System aims to ensure that the scientific knowledge and research outputs produced by our university are made available openly and sustainably for the benefit of society and all stakeholders. Our university considers it a fundamental responsibility to contribute to the advancement of science and the dissemination of academic knowledge, in line with the principles of transparency and reliability. Within the system, various academic outputs, such as articles, theses, books, book chapters, reports, and presentations, are made accessible.
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.
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.
A cross-sectional study on ChatGPT's alignment with clinical practice guidelines in musculoskeletal rehabilitation
(2025-12-01) SAFRAN E.; YILDIRIM S.
BackgroundAI models like ChatGPT have the potential to support musculoskeletal rehabilitation by providing clinical insights. However, their alignment with evidence-based guidelines needs evaluation before integration into physiotherapy practice.ObjectiveTo evaluate the performance of ChatGPT (GPT-4 model) in generating responses to musculoskeletal rehabilitation queries by comparing its recommendations with evidence-based clinical practice guidelines (CPGs).DesignThis study was designed as a cross-sectional observational study.MethodsTwenty questions covering disease information, assessment, and rehabilitation were developed by two experienced physiotherapists specializing in musculoskeletal disorders. The questions were distributed across three anatomical regions: upper extremity (7 questions), lower extremity (9 questions), and spine (4 questions). ChatGPT\"s responses were obtained and evaluated independently by two raters using a 5-point Likert scale assessing relevance, accuracy, clarity, completeness, and consistency. Weighted kappa values were calculated to assess inter-rater agreement and consistency within each category.ResultsChatGPT\"s responses received the highest average score for clarity (4.85), followed by accuracy (4.62), relevance (4.50), and completeness (4.20). Consistency received the lowest score (3.85). The highest agreement (weighted kappa = 0.90) was observed in the disease information category, whereas rehabilitation displayed relatively lower agreement (weighted kappa = 0.56). Variability in consistency and moderate weighted kappa values in relevance and clarity highlighted areas requiring improvement.ConclusionsThis study demonstrates ChatGPT\"s potential in providing guideline-aligned information in musculoskeletal rehabilitation. However, due to observed limitations in consistency, completeness, and the ability to replicate nuanced clinical reasoning, its use should remain supplementary rather than as a primary decision-making tool. While it performed better in disease information, as evidenced by higher inter-rater agreement and scores, its performance in the rehabilitation category was comparatively lower, highlighting challenges in addressing complex, nuanced therapeutic interventions. This variability in consistency and domain-specific reasoning underscores the need for further refinement to ensure reliability in complex clinical scenarios.Clinical trial numberNot applicable.
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.)