Bioengineering / Biyomühendislik
Permanent URI for this collectionhttps://hdl.handle.net/11147/4529
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Conference Object Citation - WoS: 2Citation - Scopus: 2Portatif ve Düşük Maliyetli Merceksiz Holografik Mikroskop Platformu ile Nanoparçacık Tespiti(IEEE, 2020) Delikoyun, Kerem; Keçili, Seren; Tekin, Hüseyin CumhurIn the biological and medical science, detection of biomolecule at very low concentration (<100 pg/mL) is of great importance and it is extensively used in the diagnosis of diseases, drug response monitoring and cancer research. For biomolecule detection tests, captured biomolecules generate signals (fluorescence, color, etc.), which are analyzed by trained personnel in bulky, high cost and fragile devices. However, for clinical applications, the access to these tests is very difficult at resource-limited settings. Lensless holographic microscopy provides high resolution imaging of samples without the need of expensive and fragile optical elements (mirror, lens, filter, etc.) used in traditional imaging technologies. While this technology offers a robust, portable and low-cost design, it enables fully automated processing of the sample image with digital processing schemes and this can also help eliminate user error. In this study, lensless holographic microscopy platform, which can be used in surface coverage assays for the detection of biomolecules, is proposed. It has been shown that nanoparticles (700-1200 nm) used as labels in surface coverage assays for the detection of biomolecules could be sensed on the platform. Therefore, it is anticipated that biomolecules detection could be realized rapidly and sensitively with this easy-to-use and low-cost imaging platform at the location where the high-level health institutions are not available and even at point-of-care settings.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.