Bioengineering / Biyomühendislik
Permanent URI for this collectionhttps://hdl.handle.net/11147/4529
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Conference Object Size-Based Microparticle Seperation Using Negative Magnetophoresis(Chemical and Biological Microsystems Society, 2021) Solmaz Özçelik, Özge; Öksüz, Cemre; Tekin, Hüseyin CumhurWe present a new size-based microparticle separation device using negative magnetophoresis. Microparticles spiked in the paramagnetic medium were filtered with respect to their sizes in a microfluidic channel placed between two magnets. Negative magnetophoresis allows large microparticles to be captured before the magnets, while small microparticles pass through the magnets under a constant flow. With this method, we reached 84.2% capturing efficiency of large microparticles (44 µm diameter) and capturing purity of 80.3% in the presence of small microparticles (17 µm diameter) at 3 µL/min flow rate. The capturing purity could further improve up to 99% by increasing the flow rate.Conference Object Citation - Scopus: 2Label-Free Detection of Rare Cancer Cells Using Deep Learning and Magnetic Levitation Principle(SPIE, 2021) Delikoyun, Kerem; Demir, Ali Aslan; Tekin, Hüseyin CumhurMagnetic levitation is an effective tool for separating target cells within a heterogeneous solution by utilizing density differences among cell lines. However, magnetic levitation cannot be used to identify target cells which have similar density profile as the other cells in the solution. Therefore, accuracy of cell identification can dramatically reduce. In this study, we introduce, for the first time, the use of deep learning-based object detection approach for label-free identification of rare cancer cells within levitated cells. As a result, our novel and hybrid detection strategy could be used to identify circulating tumor cells for diagnosis and prognosis of cancer. © 2021 SPIE.Conference Object Deep Convolutional Neural Networks for Viability Analysis Directly From Cell Holograms Captured Using Lensless Holographic Microscopy(The Chemical and Biological Microsystems Society (CBMS), 2019) Delikoyun, Kerem; Çine, Ersin; Anıl İnevi, Müge; Özçivici, Engin; Özuysal, Mustafa; Tekin, Hüseyin CumhurCell viability analysis is one of the most widely used protocols in the fields of biomedical sciences. Traditional methods are prone to human error and require high-cost and bulky instrumentations. Lensless digital inline holographic microscopy (LDIHM) offers low-cost and high resolution imaging. However, recorded holograms should be digitally reconstructed to obtain real images, which requires intense computational work. We introduce a deep transfer learning-based cell viability classification method that directly processes the hologram without reconstruction. This new model is only trained once and viability of each cell can be predicted from its hologram. © 2019 CBMS-0001.Conference Object Citation - Scopus: 2Magnetic Levitation-Based Protein Detection Using Lensless Digital Inline Holographic Microscopy(The Chemical and Biological Microsystems Society (CBMS), 2019) Yaman, Sena; Delikoyun, Kerem; Tekin, Hüseyin CumhurWe present a portable protein detection platform based on magnetic levitation principle integrated with a lensless imaging system. In the platform, polymer microspheres are used to capture selectively target proteins and magnetic nanoparticle labels. The imaging system monitors the levitation height change of polymer microspheres with respect to the presence of target protein on their surfaces. This system enables the detection of target proteins down to ng/mL levels in a short time. © 2019 CBMS-0001.Conference Object Electromechanical Lab-On Platform for Creatinine Analysis Using Automated Elisa Protocols(Chemical and Biological Microsystems Society, 2020) Karakuzu, Betül; Tarım, Ergün Alperay; Öksüz, Cemre; Tekin, Hüseyin CumhurWe present an electromechanical lab-on-a-chip (LOC) platform for the automated serum creatinine detection applying enzyme-linked immunosorbent assay (ELISA) principle. In the platform, antibody covered bar selectively captures the creatinine in the sample and the electromechanical system allows automatic movement between the designed reservoirs containing assay solutions. At the end of the protocol, the absorbance value of the appeared color is measured to determine creatinine concentration in the sample. Since this system allows measuring automatically creatinine levels with minimum time and cost, it can be utilized for point-of-care monitoring of chronic kidney diseases (CKD) for the future. © 2020 CBMS-0001