Browsing by Author "Dundar, Mehmet Sait"

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3D Sampling of K-Space With Non-Cartesian Trajectories in MR Imaging
(Gazi Univ, Fac Engineering Architecture, 2025) Dundar, Mehmet Sait; Gumus, Kazim Z.; Yilmaz, Bulent
This study presents an innovative approach to 3D k-space sampling in MR imaging using non-Cartesian concentric shell trajectories. The method involves 32 concentric shells of varying radii, allowing for rapid data acquisition through undersampling techniques. Simulations using IDEA software demonstrate that this approach can fill the k-space in less than one second, a significant time reduction compared to traditional FLASH sequences that can take 3-4 minutes. The concentric shell model enhances imaging efficiency by minimizing artifacts and ensuring uniform k-space filling, leading to higher resolution and faster scans. This technique shows promise for clinical applications, particularly in dynamic imaging scenarios such as acute stroke and pediatric radiology, where speed and precision are critical. As illustrated in Figure A, the concentric shell trajectories enable uniform k-space filling, significantly reducing scan times and improving image quality. These results are based on the simulations conducted with IDEA software.
Citation - Scopus: 1
Alzheimer Disease Associated Loci: APOE Single Nucleotide Polymorphisms in Marmara Region
(MDPI, 2024) Ismail, Aya Badeea; Dundar, Mehmet Sait; Erguzeloglu, Cemre Ornek; Ergoren, Mahmut Cerkez; Alemdar, Adem; Sag, Sebnem Ozemri; Temel, Sehime Gulsun
Alzheimer's disease (AD) is a major global health challenge, especially among individuals aged 65 or older. According to population health studies, Turkey has the highest AD prevalence in the Middle East and Europe. To accurately determine the frequencies of common and rare APOE single nucleotide polymorphisms (SNPs) in the Turkish population residing in the Marmara Region, we conducted a retrospective study analyzing APOE variants in 588 individuals referred to the Bursa Uludag University Genetic Diseases Evaluation Center. Molecular genotyping, clinical exome sequencing, bioinformatics analysis, and statistical evaluation were employed to identify APOE polymorphisms and assess their distribution. The study revealed the frequencies of APOE alleles as follows: epsilon 4 at 9.94%, epsilon 2 at 9.18%, and epsilon 3 at 80.68%. The gender-based analysis in our study uncovered a tendency for females to exhibit a higher prevalence of mutant genotypes across various SNPs. The most prevalent haplotype observed was epsilon 3/epsilon 3, while rare APOE SNPs were also identified. These findings align with global observations, underscoring the significance of genetic diversity and gender-specific characteristics in comprehending health disparities and formulating preventive strategies.
Citation - WoS: 3
Citation - Scopus: 3
Artificial Cells: A Potentially Groundbreaking Field of Research and Therapy
(Sciendo, 2024) Dundar, Mehmet Sait; Yildirim, A. Baki; Yildirim, Duygu T.; Akalin, Hilal; Dundar, Munis
Artificial cells are synthetic constructs that mimic the architecture and functions of biological cells. Artificial cells are designed to replicate the fundamental principles of biological systems while also have the ability to exhibit novel features and functionalities that have not been achieved before. Mainly, Artificial cells are made up of a basic structure like a cell membrane, nucleus, cytoplasm and cellular organelles. Nanotechnology has been used to make substances that possess accurate performance in these structures. There are many roles that artificial cells can play such as drug delivery, bio-sensors, medical applications and energy storage. An additional prominent facet of this technology is interaction with biological systems. The possibility of synthetic cells being compatible with living organisms opens up the potential for interfering with specific biological activities. This element is one of the key areas of research in medicine, aimed at developing novel therapies and comprehending life processes. Nevertheless, artificial cell technology is not exempt from ethical and safety concerns. The interplay between these structures and biological systems may give rise to questions regarding their controllability and safety. Hence, the pursuit of artificial cell research seeks to reconcile ethical and safety concerns with the potential advantages of this technology.
Citation - WoS: 1
Automatic Blurry Colon Image Detection Using Laplacian Operator-Based Features
(Elsevier Science Bv, 2018) Yilmaz, Bulent; Kacmaz, Rukiye Nur; Dundar, Mehmet Sait
Citation - WoS: 6
Citation - Scopus: 6
BRCA Variations Risk Assessment in Breast Cancers Using Different Artificial Intelligence Models
(MDPI, 2021) Senturk, Niyazi; Tuncel, Gulten; Dogan, Berkcan; Aliyeva, Lamiya; Dundar, Mehmet Sait; Ozemri Sag, Sebnem; Ergoren, Mahmut Cerkez
Artificial intelligence provides modelling on machines by simulating the human brain using learning and decision-making abilities. Early diagnosis is highly effective in reducing mortality in cancer. This study aimed to combine cancer-associated risk factors including genetic variations and design an artificial intelligence system for risk assessment. Data from a total of 268 breast cancer patients have been analysed for 16 different risk factors including genetic variant classifications. In total, 61 BRCA1, 128 BRCA2 and 11 both BRCA1 and BRCA2 genes associated breast cancer patients' data were used to train the system using Mamdani's Fuzzy Inference Method and Feed-Forward Neural Network Method as the model softwares on MATLAB. Sixteen different tests were performed on twelve different subjects who had not been introduced to the system before. The rates for neural network were 99.9% for training success, 99.6% for validation success and 99.7% for test success. Despite neural network's overall success was slightly higher than fuzzy logic accuracy, the results from developed systems were similar (99.9% and 95.5%, respectively). The developed models make predictions from a wider perspective using more risk factors including genetic variation data compared with similar studies in the literature. Overall, this artificial intelligence models present promising results for BRCA variations' risk assessment in breast cancers as well as a unique tool for personalized medicine software.
Citation - Scopus: 6
Genetic Variants in Genes Correlated to the PI3K/AKT Pathway: The Role of ARAP3, CDH5, KIF and RELN Primary Lymphedema
(International Society of Lymphology, 2023) Dundar, Mehmet Sait; Belanová, I.; Bonetti, Gabriele; Gelanová, V.; Kozáčiková, R.; Vešelényiová, Dominika; Donato, Kevin
Genetic anomalies affecting lymphatic development and function can lead to lymphatic dysfunction, which could manifest as lymphedema- Understanding the signaling pathways governing lymphatics function is crucial for developing targeted diagnostic and therapeutic interventions. This study aims to characterize genetic variants in genes involved in the PUKIAKT signaling pathway, which plays a critical role in lymphangiogenesis. 408 patients diagnosed with primary lymphedema were sequenced usinga next-generation sequencing (NGS) gene panel composed of 28 diagnostic genes and 71 candidate genes. The analysis revealed six variants in genes RFLN, ARAP3,CDHS and K1F11. Five of these variants have never been reported in the literature. All these genes have been correlated to lymphatic activity and are involved in the P13K/AKT pathway. As the P13K/AKT signaling pathway plays an essential role in lymphangiogenesis and lymphatic function, genetic variants in genes correlated to this pathway could lead to lymphedema. Our findings underscore the potential of the P13K/AKT pathway in lymphedema pathogenesis, supporting the role of RELN,ARAP3,CDH5,and KIF11 as diagnostic and therapeutic targets. © 2024 Elsevier B.V., All rights reserved.
Citation - WoS: 8
Citation - Scopus: 11
The Impact and Future of Artificial Intelligence in Medical Genetics and Molecular Medicine: An Ongoing Revolution
(Springer Heidelberg, 2024) Ozcelik, Firat; Dundar, Mehmet Sait; Yildirim, A. Baki; Henehan, Gary; Vicente, Oscar; Sanchez-Alcazar, Jose A.; Dundar, Munis
Artificial intelligence (AI) platforms have emerged as pivotal tools in genetics and molecular medicine, as in many other fields. The growth in patient data, identification of new diseases and phenotypes, discovery of new intracellular pathways, availability of greater sets of omics data, and the need to continuously analyse them have led to the development of new AI platforms. AI continues to weave its way into the fabric of genetics with the potential to unlock new discoveries and enhance patient care. This technology is setting the stage for breakthroughs across various domains, including dysmorphology, rare hereditary diseases, cancers, clinical microbiomics, the investigation of zoonotic diseases, omics studies in all medical disciplines. AI's role in facilitating a deeper understanding of these areas heralds a new era of personalised medicine, where treatments and diagnoses are tailored to the individual's molecular features, offering a more precise approach to combating genetic or acquired disorders. The significance of these AI platforms is growing as they assist healthcare professionals in the diagnostic and treatment processes, marking a pivotal shift towards more informed, efficient, and effective medical practice. In this review, we will explore the range of AI tools available and show how they have become vital in various sectors of genomic research supporting clinical decisions.
Citation - WoS: 1
Motion Artifact Detection in Colonoscopy Images
(Sciendo, 2018) Kacmaz, Rukiye Nur; Yilmaz, Bulent; Dundar, Mehmet Sait; Dogan, Serkan
Computer-aided detection is an integral part of medical image evaluation process because examination of each image takes a long time and generally experts' do not have enough time for the elimination of images with motion artifact (blurred images). Computer-aided detection is required for both increasing accuracy rate and saving experts' time. Large intestine does not have straight structure thus camera of the colonoscopy should be moved continuously to examine inside of the large intestine and this movement causes motion artifact on colonoscopy images. In this study, images were selected from open-source colonoscopy videos and obtained at Kayseri Training and Research Hospital. Totally 100 images were analyzed half of which were clear. Firstly, a modified version of histogram equalization was applied in the pre-processing step to all images in our dataset, and then, used Laplacian, wavelet transform (WT), and discrete cosine transform-based (DCT) approaches to extract features for the discrimination of images with no artifact (clear) and images with motion artifact. The Laplacian-based feature extraction method was used for the first time in the literature on colonoscopy images. The comparison between Laplacian-based features and previously used methods such as WT and DCT has been performed. In the classification phase of our study, support vector machines (SVM), linear discriminant analysis (LDA), and k nearest neighbors (k-NN) were used as the classifiers. The results showed that Laplacian-based features were more successful in the detection of images with motion artifact when compared to popular methods used in the literature. As a result, a combination of features extracted using already existing approaches (WT and DCT) and the Laplacian-based methods reached 85% accuracy levels with SVM classification approach.
What Does the Water Inside the Brain Tell Us? Diffusion Tensor Imaging
(Sciendo, 2018) Acer, Niyazi; Dundar, Mehmet Sait; Bastepe-Gray, Serap
The brain consist of about 75 percent water. Diffusion tensor imaging (DTI) is an advanced magnetic resonance (MR) technique imaging that has been developed for diagnostic and research in medicine. It can be use DTI tractography to better understand degenerating axons of white matter lesions in some neurological diseases such as MS, AD, trauma, cerebral ischemia, epilepsy, brain tumors and metabolic disorders.