WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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Article Chloroaluminum Phthalocyanine Loaded Bovine Serum Albumin Nanoparticles as a Dual-Functional Nanoplatform for Sono-Photodynamic Cancer Therapy(Elsevier, 2026) Akdoğan, Yaşar; Nartas, Eylem Doga; Calibasi-Kocal, Gizem; Akdogan, Yasar; 03.09. Department of Materials Science and Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyChloroaluminum phthalocyanine (ClAlPc) loaded bovine serum albumin (BSA) nanoparticles (NPs) were synthesized as a dual-functional platform for photodynamic and sonodynamic therapies (PDT and SDT). ClAlPc loading did not disturb the morphology of the BSA NPs. Their spherical structure, with a size around 200 nm, was preserved upon ClAlPc loading (1 %w/w). Singlet oxygen productions in the presence of ClAlPc loaded BSA NPs or free ClAlPc were determined by ultraviolet absorption (UV-vis) spectroscopy and electron paramagnetic resonance (EPR) spectroscopy. While a slower rate of singlet oxygen formation rate after both PDT and SDT was detected by UV-vis measurements in the presence of ClAlPc loaded BSA NPs, EPR results showed a similar rate of singlet oxygen formation for both ClAlPc loaded BSA NPs and free ClAlPc. Confocal microscopy confirmed the efficient cellular uptake and perinuclear localization of the ClAlPc loaded BSA NPs in HCT-116 cancer cells. In vitro cytotoxicity studies demonstrated a dose and time dependent photo-and sonotoxic effects in the presence of ClAlPc loaded BSA. In particular, simultaneous application of light and ultrasound as sono-photodynamic therapy (SPDT) resulted in 15 % cell viability in the presence of ClAlPc loaded BSA NPs, which is much lower than individual PDT and SDT results, confirming the effect of the combination therapy on cell viability. In comparison, free ClAlPc reduced cell viability to 27 %. These findings suggest that ClAlPc loaded BSA NPs is a promising "one-for-two" nanoplatform for combined cancer therapy to reduce the limitations of both methods.Article Ai-Assisted Survival Prediction in Colorectal Cancer: a Clinical Decision Support Tool(Dokuz Eylul Univ inst Health Sciences, 2024) Misirlioglu, Huseyin Koray; Leblebici, Asım; Leblebici, Asim; Calibasi-Kocal, Gizem; Ellidokuz, Hulya; Basbinar, Yasemin; 01.01. Units Affiliated to the Rectorate; 01. Izmir Institute of TechnologyPurpose: This study was planned to determine the problems and affecting factors that children encounter Purpose: Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide. Accurate survival prediction is crucial for advanced-stage patients to optimize treatment strategies and improve clinical outcomes. This study aimed to develop an artificial intelligence-assisted clinical decision support system (CDSS) for survival prediction in CRC patients using clinical and genomic data from the Cancer Genome Atlas Colon Adenocarcinoma Collection (TCGA-COAD) dataset. Methods: Machine learning algorithms, including C4.5 Decision Tree, Support Vector Machines (SVM), Random Forest, and Naive Bayes, were employed to create survival prediction models. Clinical parameters and genomic data from key pathways, such as glycolysis/gluconeogenesis and mTORC1, were integrated into the models. The models were evaluated based on accuracy and performance. Results: The Random Forest algorithm achieved the highest accuracy (82.3%) when only clinical parameters were used. When clinical data were combined with gene expression data, the model's accuracy increased further. The resulting models were incorporated into a user-friendly web interface, SurvCOCA, for clinical use. Conclusions: This study demonstrates the potential of AI-based tools to improve prognosis predictions in CRC patients. Further research is needed, with larger datasets and additional machine learning algorithms, to enhance clinical decision-making and optimize treatment strategies.