TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14365/4
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Article Upregulated Acute Systemic Inflammation-Related Genes Based on Endotoxin Exposure Provide ‘Survival Benefit’ or Create ‘High Risk of Death’ in Leukaemia and Colon Cancer(Istanbul University, 2024-07-10) Duran, Gizem Ayna; Duran, Assist. Prof. Dr. Gizem Ayna; Ayna Duran, GizemObjective: Although endotoxin exposure has been shown to trigger innate immune responses and promote cancer, it has also been shown to prevent cancer formation. In our study, survival analysis was performed to determine whether the upregulated genes triggered by endotoxins have hazardous effects on cancers or provide a survival benefit. Materials and Methods: Gene intensity values of control and bacterial endotoxin-administered individuals were obtained from the Gene Expression Omnibus database. Using the R "Linear Models for Microarray Data" package, differentially expressed gene analyses were conducted to determine genes that differ between healthy and bacterial endotoxin-administered samples. "ShinyGo 0.80" web-based tool was used to determine the disease types indicated by these genes. The "Kaplan-Meier Plotter" web-based tool was used to conduct survival analysis. Results: Genes that create an innate immune response to bacterial endotoxin exposure and are upregulated differently than in individuals without exposure were identified. According to gene enrichment analyses, the two main types of cancer identified were leukaemia/lymoma and colon cancer. We detected that MLF1, STAT5B, and BCL3 genes led to poor survival; however, the ARHGAP26 gene was protective for acute myeloid leukaemia patients. In the case of colon cancer, SMAD7 and TLR2 genes were determined as leading to "high risk of death". Conclusion: Once the systemic inflammation-related genes identified in our study are confirmed through laboratory experiments in samples taken from solid tissue in the case of colon cancer and at the level of genes obtained from blood samples in leukemias, genetically targeted treatments will also be possible.Article Citation - WoS: 5Citation - Scopus: 7Detection of Olfactory Stimulus in Electroencephalogram Signals Using Machine and Deep Learning Methods(Istanbul University, 2024-01-30) Akbuğday, Burak; Akbugday, S.P.; Sadikzade, R.; Akan, A.; Unal, S.; Sadighzadeh, RezaThe investigation of olfactory stimuli has become more prominent in the context of neuromarketing research over the last couple of years. Although a few studies suggest that olfactory stimuli are linked with consumer behavior and can be observed in various ways, such as via electroencephalogram (EEG), a universal method for the detection of olfactory stimuli has not been established yet. In this study, 14-channel EEG signals acquired from participants while they were presented with 2 identical boxes, scented and unscented, were processed to extract several linear and nonlinear features. Two approaches are presented for the classification of scented and unscented cases: i) using machine learning (ML) methods utilizing extracted features; ii) using deep learning (DL) methods utilizing relative sub-band power topographic heat map images. Experimental results suggest that the olfactory stimulus can be successfully detected with up to 92% accuracy by the proposed method. Furthermore, it is shown that topographic heat maps can accurately depict the response of the brain to olfactory stimuli. © 2024 Istanbul University. All rights reserved.Article Fluorescence Microscopy Denoizing Via Neighbor Linear Embedding(Istanbul University, 2024-01-31) Kırmızıay, Çağatay; Aydeniz, Burhan; Türkan, MehmetOne of the difficulties in studying fluorescence imaging of biological structures is the presence of noise corruption. Even though hardware- and software-related technologies have undergone continual improvement, the unavoidable effect of Poisson–Gaussian mixture type is generally encountered in fluorescence microscopy images. This noise should be mitigated to allow the extraction of valuable information from fluorescence images for various types of biological analysis. Thus, this study introduces a new and efficient learning-based denoizing approach for fluorescence microscopy. The proposed approach is based mainly on linear transformations between noise-free and noisy submanifold structures of patch spaces, benefiting from linear neighbor embeddings of local image patches. According to visual and statistical results, the developed algorithm called "neighbor linear-embedding denoizing" algorithm has a highly competitive and generally superior performance in comparison with the other algorithms used for fluorescence microscopy image denoizing in the literature. © 2024 Istanbul University. All rights reserved.