WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 18Citation - Scopus: 21Parameter Investigation of Topological Data Analysis for EEG Signals(Elsevier Sci Ltd, 2021-01) Altindis, Fatih; Yilmaz, Bulent; Borisenok, Sergey; Icoz, KutayTopological data analysis (TDA) methods have become appealing in EEG signal processing, because they may help the scientists explore new features of complex and large amount of data by simplifying the process from a geometrical perspective. Time delay embedding is a common approach to embed EEG signals into the state space. Parameters of this embedding method are variable and the structure of the state space can be entirely different depending on their selection. Additionally, extracted persistent homologies of the state spaces depend on filtration level and the number of points used. In this study, we showed how to adapt false nearest neighbor (FNN) test to find out the suitable/optimal time embedding parameters (i.e., time delay and embedding dimension) for EEG signals, and compared their effects on different types of artefacts and motor intention waves that are commonly used in brain-computer interfaces. We extracted and compared persistent homologies of state spaces that were reconstructed with four different sets of parameters. Later, the effect of filtration level on extracted persistent homologies was compared, and statistical significance levels were computed between leftand right-hand movement imaginations. Finally, computational cost of the discussed methods was found, and the adaptability of this method to a real-time application was evaluated. We demonstrated that the discussed parameters of the TDA approach were highly crucial to extract true topological features of the EEG signals, and the adapted testing approaches depicted the applicability of this approach on real-time analysis of EEG signals.Article Citation - WoS: 12Citation - Scopus: 13Numerical Analysis and Experimental Verification of Optical Scattering From Microplastics(Royal Soc, 2023-08) Genc, Sinan; Icoz, Kutay; Erdem, TalhaAccurate and fast characterization of the micron-sized plastic particles in aqueous media requires an in-depth understanding of light interaction with these particles. Due to the complexity of Mie scattering theory, the features of the scattered light have rarely been related to the physical properties of these tiny objects. To address this problem, we reveal the relation of the wavelength-dependent optical scattering patterns with the size and refractive index of the particles by numerically studying the angular scattering features. We subsequently present a low-cost setup to measure the optical scattering of the particles. Theoretical investigation shows that the angular distribution of the scattered light by microplastics carries distinct signatures of the particle size and the refractive index. The results can be used to develop a portable, low-cost setup to detect microplastics in water.Article Citation - WoS: 36Citation - Scopus: 43Molecular Separation by Using Active and Passive Microfluidic Chip Designs: A Comprehensive Review(Wiley, 2023-10-27) Ebrahimi, Aliakbar; Icoz, Kutay; Didarian, Reza; Shih, Chih-Hsin; Tarim, E. Alperay; Nasseri, Behzad; Avci, HuseyinSeparation and identification of molecules and biomolecules such as nucleic acids, proteins, and polysaccharides from complex fluids are known to be important due to unmet needs in various applications. Generally, many different separation techniques, including chromatography, electrophoresis, and magnetophoresis, have been developed to identify the target molecules precisely. However, these techniques are expensive and time consuming. "Lab-on-a-chip" systems with low cost per device, quick analysis capabilities, and minimal sample consumption seem to be ideal candidates for separating particles, cells, blood samples, and molecules. From this perspective, different microfluidic-based techniques have been extensively developed in the past two decades to separate samples with different origins. In this review, "lab-on-a-chip" methods by passive, active, and hybrid approaches for the separation of biomolecules developed in the past decade are comprehensively discussed. Due to the wide variety in the field, it will be impossible to cover every facet of the subject. Therefore, this review paper covers passive and active methods generally used for biomolecule separation. Then, an investigation of the combined sophisticated methods is highlighted. The spotlight also will be shined on the elegance of separation successes in recent years, and the remainder of the article explores how these permit the development of novel techniques. This review is about the microfludic-based methods that have been used in the past two decades for the separation of different biomolecules like protein, DNA, and RNA. In this regard, passive, active, and hybrid microfludic methods that are used for biomolecules separation are disscused and reviewed in this paper.imageArticle Citation - WoS: 16Citation - Scopus: 18Microfluidic Chip Based Direct Triple Antibody Immunoassay for Monitoring Patient Comparative Response to Leukemia Treatment(Springer, 2020-07-13) Icoz, Kutay; Akar, Unal; Unal, EkremWe report a time and cost-efficient microfluidic chip for screening the leukemia cells having three specific antigens. In this method, the target blast cells are double sorted with immunomagnetic beads and captured by the 3rd antibody immobilized on the gold surface in a microfluidic chip. The captured blast cells in the chip were imaged using a bright-field optical microscope and images were analyzed to quantify the cells. First sorting was performed with nano size immunomagnetic beads and followed by 2nd sorting where micron size immunomagnetic beads were used. The low-cost microfluidic platform is made of PMMA and glass including micro size gold pads. The developed microfluidic platform was optimized with cultured B type lymphoblast cells and tested with the samples of leukemia patients. The 8 bone marrow samples of 4 leukemia patients on the initial diagnosis and on the 15th day after the start of the chemotherapy treatment were tested both with the developed microfluidic platform and the flow cytometry. A 99% statistical agreement between the two methods shows that the microfluidic chip is able to monitor the decrease in the number of blast cells due to the chemotherapy. The experiments with the patient samples demonstrate that the developed system can perform relative measurements and have a potential to monitor the patient response to the applied therapy and to enable personalized dose adjustment.Article Citation - WoS: 6Citation - Scopus: 7Improved Senescent Cell Segmentation on Bright-Field Microscopy Images Exploiting Representation Level Contrastive Learning(Wiley, 2024-03) Celebi, Fatma; Boyvat, Dudu; Ayaz-Guner, Serife; Tasdemir, Kasim; Icoz, KutayMesenchymal stem cells (MSCs) are stromal cells which have multi-lineage differentiation and self-renewal potentials. Accurate estimation of total number of senescent cells in MSCs is crucial for clinical applications. Traditional manual cell counting using an optical bright-field microscope is time-consuming and needs an expert operator. In this study, the senescence cells were segmented and counted automatically by deep learning algorithms. However, well-performing deep learning algorithms require large numbers of labeled datasets. The manual labeling is time consuming and needs an expert. This makes deep learning-based automated counting process impractically expensive. To address this challenge, self-supervised learning based approach was implemented. The approach incorporates representation level contrastive learning component into the instance segmentation algorithm for efficient senescent cell segmentation with limited labeled data. Test results showed that the proposed model improves mean average precision and mean average recall of downstream segmentation task by 8.3% and 3.4% compared to original segmentation model.Article Citation - WoS: 5Citation - Scopus: 9Image Processing and Cell Phone Microscopy to Analyze the Immunomagnetic Beads on Micro-Contact Printed Gratings(MDPI Ag, 2016-09-28) Icoz, KutayIn this paper we report an ultra-low-cost spherical ball lens based cell phone microscopy and image processing algorithms to analyze the amount of immunomagnetic beads on micro-contact printed gratings. The spherical ball lens provides approximately 100x magnification but the recorded images are not clear and are noisy. By using the image-processing algorithms, the noise can be reduced and the images can be enhanced to quantify the amount of immunomagnetic beads on micro-contact printed lines. This method, which is portable and low-cost, can be an alternative read out mechanism for biosensing applications using immunomagnetic beads on micro-contact printed surface receptors. Further, 0.0335 mg/mL was the lowest magnetic bead concentration that could be detected above the inherent noise level of the spherical ball lens.Article Citation - WoS: 12Citation - Scopus: 14Detection of Proteins Using Nano Magnetic Particle Accumulation-Based Signal Amplification(MDPI, 2016-11-29) Icoz, Kutay; Mzava, OmaryWe report a biosensing method based on magnetic particles where coated magnetic particles are used for immunomagnetic separation, and uncoated magnetic particles are used for signal enhancement. To quantify the signal amplification, optical micrographs are analyzed to measure changes in pixel area and pixel intensity. Microcontact-printed surface receptors are arranged in alternating lines on gold chips, enabling differential calculations. In a model experiment, target molecules-streptavidin-are first captured and separated by biotin-coated magnetic particles, and then exposed to a gold surface functionalized with biotin-coupled bovine serum albumin, forming a sandwich assay. Applying a magnetic field and introducing uncoated magnetic particles resulted in accumulation around magnetic particles in the sandwich assay and enhancement of the contrast to noise ratio at least by eight-fold in a range of 0.1-100 mu M.Article Citation - WoS: 10Citation - Scopus: 13Deep Learning Based Semantic Segmentation and Quantification for MRD Biochip Images(Elsevier Sci Ltd, 2022-08) Celebi, Fatma; Tasdemir, Kasim; Icoz, KutayMicrofluidic platforms offer prominent advantages for the early detection of cancer and monitoring the patient response to therapy. Numerous microfluidic platforms have been developed for capturing and quantifying the tumor cells integrating several readout methods. Earlier, we have developed a microfluidic platform (MRD Biochip) to capture and quantify leukemia cells. This is the first study which employs a deep learning-based segmentation to the MRD Biochip images consisting of leukemic cells, immunomagnetic beads and micropads. Implementing deep learning algorithms has two main contributions; firstly, the quantification performance of the readout method is improved for the unbalanced dataset. Secondly, unlike the previous classical computer vision -based method, it does not require any manual tuning of the parameters which resulted in a more generalized model against variations of objects in the image in terms of size, color, and noise. As a result of these benefits, the proposed system is promising for providing real time analysis for microfluidic systems. Moreover, we compare different deep learning based semantic segmentation algorithms on the image dataset which are acquired from the real patient samples using a bright-field microscopy. Without cell staining, hyper-parameter optimized, and modified U-Net semantic segmentation algorithm yields 98.7% global accuracy, 86.1% mean IoU, 92.2% mean precision, 92.2% mean recall and 92.2% mean F-1 score measure on the patient dataset. After segmentation, quantification result yields 89% average precision, 97% average recall on test images. By applying the deep learning algorithms, we are able to improve our previous results that employed conventional computer vision methods.Article Citation - WoS: 22Citation - Scopus: 29Automated Quantification of Immunomagnetic Beads and Leukemia Cells from Optical Microscope Images(Elsevier Sci Ltd, 2019-03) Uslu, Fatma; Icoz, Kutay; Tasdemir, Kasim; Yilmaz, BulentQuantification of tumor cells is crucial for early detection and monitoring the progress of cancer. Several methods have been developed for detecting tumor cells. However, automated quantification of cells in the presence of immunomagnetic beads has not been studied. In this study, we developed computer vision based algorithms to quantify the leukemia cells captured and separated by micron size immunomagnetic beads. Color, size based object identification and machine learning based methods were implemented to quantify targets in the images recorded by a bright field microscope. Images acquired by a 40x or a 20x objective were analyzed, the immunomagnetic beads were detected with an error rate of 0.0171 and 0.0384 respectively. Our results reveal that the proposed method attains 91.6% precision for the 40x objective and 79.7% for the 20x objective. This algorithm has the potential to be the signal readout mechanism of a biochip for cell detection. (C) 2019 Elsevier Ltd. All rights reserved.