Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Time Distributed Classification of Alzheimer’s Disease on MRI Scans(John Wiley and Sons Ltd, 2026) Dundar, Mehmet Sait; Yilmaz, BulentThe diagnosis of Alzheimer's disease (AD) has progressively depended on sophisticated neuroimaging methods alongside cognitive assessments. This study combines volumetric feature analysis with computational modeling techniques, focusing on spatial and temporal analysis, to categorize individuals as cognitively normal (CN), mild cognitive impairment (MCI), or AD using magnetic resonance imaging (MRI) data. In the initial phase, volumetric changes, comprising cortical thickness, white matter, grey matter, cerebrospinal fluid, and total intracranial volume, were derived from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset utilizing the CAT12 toolbox in statistical parametric mapping (SPM). Linear regression was utilized on these variables over time to create slopes that reflect volumetric change rates, which then served as inputs for machine learning classifiers. The slopes of cortical thickness exhibited the greatest classification accuracy, reaching 82.5% with a random forest model for differentiating AD from CN individuals. During the second phase, a deep learning methodology was utilized, relying solely on the MRI scans and excluding the outcomes from the first phase. A pre-trained 3D ResNet-101 convolutional neural network (CNN) model extracted spatial characteristics from MRI volumes, whereas long short-term memory (LSTM) networks recorded temporal dynamics across subsequent annual scans. This hybrid CNN-LSTM design markedly improved classification performance, attaining 96.7% accuracy for AD against CN and enhancing the distinction of MCI cases. Nonetheless, discrepancies in MCI categorization were chiefly ascribed to the restricted access to annual MRI data and the model's pre-training on CN and AD cohorts. These findings highlight the potential of integrating volumetric statistical analysis with deep learning for automated AD categorization. This work enhances neuroimaging diagnostic methods by utilizing both spatial and temporal MRI data, enabling early diagnosis and better evaluation of disease development.Article Citation - Scopus: 6Amelioration Potential of Synthetic Oxime Chemical Cores Against Multiple Sclerosis and Alzheimer's Diseases: Evaluation in Aspects of in Silico and in Vitro Experiments(Elsevier B.V., 2024-12) Yilmaz, Anil; Koca, Murat; Ercan, Selami; Acar, Özden Ozgun; Boǧa, Mehmet; Sen, Alaattin; Kurt, AdnanAlzheimer disease (AD) and multiple sclerosis (MS) are inflammatory neurological disorders. The main symptom of AD is dementia, and the main symptoms of MS are vertigo, sexual dysfunction, cognitive problems, and fatigue. Today, millions of people are affected by AD and MS, and the number is growing day by day. However, there are not any accurate remedies for both disorders. For this reason, discovering novel drug molecules against neurological disorders such as AD and MS is essential and precious. Oximes and benzofurans exhibit many pharmacological effects including anti-inflammatory and neurological activities. Thus, several novel compounds bearing oxime and benzofuran chemical cores were designed and synthesized, and their in vitro anticholinesterase activities were investigated in our previous study. A number of the synthesized molecules showed excellent anticholinesterase activity against both AChE and BChE enzymes. The mentioned study constituted a background for this study. In this study, we picked different chemical skeletons among all the synthesized molecules to conduct further in silico and in vitro experiments. In order to support our in vitro anticholinesterase findings, we also examined in silico anti-Alzheimer activity of the selected molecules. In addition, in silico and in vitro activities against MS disease of the synthesized molecules were investigated. Molecule 4 extraordinarily showed outstanding activity against AD disease both in silico and in vitro, as well as in silico activity against MS disease. This feature makes molecule 4 a possible drug lead molecule which is very limited in the market. On the other hand, molecule 1, a less substituted oxime skeleton, demonstrated the strongest in vitro activity against MS disease through in vitro anti-inflammatory effect. As an observation, molecule 4 was determined to be the most promising molecule to focus on in the further steps. © 2024 Elsevier B.V., All rights reserved.