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search.filters.applied.f.language: search.filters.language.enSubject: search.filters.subject.Alzheimer's disease

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    Categorization Of Alzheimer's Disease Stages Using Deep Learning Approaches With Mcnemar's Test
    (Başkent Üniversitesi Mühendislik Fakültesi, 2024-03-13) Sener, Begum; Acici, Koray; Sumer, Emre
    Early diagnosis is crucial in Alzheimer's disease both clinically and for preventing the rapid progression of the disease. Early diagnosis with awareness studies of the disease is of great importance in terms of controlling the disease at an early stage. Additionally, early detection can reduce treatment costs associated with the disease. A study has been carried out on this subject to have the great importance of detecting Alzheimer's disease at a mild stage and being able to grade the disease correctly. This study's dataset consisting of MRI images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) was split into training and testing sets, and deep learning -based approaches were used to obtain results. The dataset consists of three classes: Alzheimer's disease (AD), Cognitive Normal (CN), and Mild Cognitive Impairment (MCI). The achieved results showed an accuracy of 98.94% for CN vs AD in the one vs one (1 vs 1) classification with the EfficientNetB0 model and 99.58% for AD vs CNMCI in the one vs All (1 vs All) classification with AlexNet model. In addition, in the study, an accuracy of 98.42% was obtained with the EfficientNet121 model in MCI vs CN classification. These results indicate the significant potential for mild stage Alzheimer's disease detection of Alzheimer's disease. Early detection of the disease in the mild stage is a critical factor in preventing the progression of Alzheimer's disease. In addition, a variant of the non -parametric statistical McNemar's Test was applied to determine the statistical significance of the results obtained in the study. Statistical significance of 1 vs 1 and 1 vs all classifications were obtained for EfficientNetB0, DenseNet, and AlexNet models.
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    Cerliponase Alfa Decreases Aβ Load And Alters Autophagy- Related Pathways In Mouse Hippocampal Neurons Exposed To Faβ1-42
    (LIFE SCIENCES, 2024-11-15) Kose, Selma; Cinar, Elif; Akyel, Hilal; Cakir-Aktas, Canan; Tel, Banu Cahide; Karatas, Hulya; Kelicen-Ugur, Pelin
    Extracellular aggregation of amyloid-beta (A beta) in the brain plays a central role in the onset and progression of Alzheimer's disease (AD). Moreover, intraneuronal accumulation of A beta via oligomer internalization might play an important role in the progression of AD. Deficient autophagy, which is a lysosomal degradation process, occurs during the early stages of AD. Tripeptidyl peptidase-1 (TPP1) functions as a lysosomal enzyme, and TPP1 gene mutations are associated with type 2 late infantile neuronal ceroid lipofuscinosis (LINCL). Nevertheless, there is little information about the role of TPP1 in the pathogenesis of AD; therefore, the present study aimed to measure the decrease in intraneuronal A beta accumulation by a recombinant analog of the TPP1 enzyme, cerliponase alfa (CER) (Brineura (R)), and to determine whether autophagy pathways play a role in this decrease. In this study, endogenous A beta accumulation was induced by fA beta(1-42) (a toxic fragment of full-length A beta) exposure, and mouse hippocampal neuronal cells (HT-22) were treated with CER (human recombinant rhTPP1 1 mg mL-1). Soluble A beta, TPP1, and the proteins involved in autophagy, including mammalian target of rapamycin (p-mTOR/ mTOR), p62/sequestosome-1 (p62/SQSTM1), and microtubule-associated protein 1 A/1B-light chain 3 (LC3), were evaluated using western blotting. The sirtuin-1, beclin-1, and Atg5 genes were also studied using RT-PCR. A beta and TPP1 localizations were observed via immunocytochemistry. CER reduced the A beta load in HT-22 cells by inducing TPP1 expression and converting pro-TPP1 into the mature form. Furthermore, exposure to CER and fA beta(1-42) induced the autophagy-regulatory/related pathways in HT-22 cells and exposure to CER alone increased sirtuin-1 activity. Based on the present findings, we suggest that augmentation of TPP1 with enzyme replacement therapy may be a potential therapeutic option for the treatment of AD.