Researcher: Bilgin, Buse
Loading...
Name Variants
Bilgin, Buse
Email Address
Birth Date
4 results
Search Results
Now showing 1 - 4 of 4
Publication Metadata only Machine learning-based approach to identify formalin-fixed paraffin-embedded glioblastoma and healthy brain tissues(Spie-Int Soc Optical Engineering, 2022) N/A; Department of Electrical and Electronics Engineering; N/A; N/A; N/A; N/A; N/A; Department of Electrical and Electronics Engineering; Torun, Hülya; Batur, Numan; Bilgin, Buse; Esengür, Ömer Tarık; Baysal, Kemal; Kulaç, İbrahim; Solaroğlu, İhsan; Onbaşlı, Mehmet Cengiz; PhD Student; Undergraduate Student; PhD Student; Undergraduate Student; Faculty Member; Faculty Member; Faculty Member; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; Graduate School of Sciences and Engineering; School of Medicine; School of Medicine; School of Medicine; School of Medicine; College of Engineering; N/A; N/A; N/A; N/A; 119184; 170305; 102059; 258783Glioblastoma is the most malignant and common high-grade brain tumor with a 14-month overall survival length. According to recent World Health Organization Central Nervous System tumor classification (2021), the diagnosis of glioblastoma requires extensive molecular genetic tests in addition to the traditional histopathological analysis of Formalin-Fixed Paraffin-Embedded (FFPE) tissues. Time-consuming and expensive molecular tests as well as the need for clinical neuropathology expertise are the challenges in the diagnosis of glioblastoma. Hence, an automated and rapid analytical detection technique for identifying brain tumors from healthy tissues is needed to aid pathologists in achieving an error-free diagnosis of glioblastoma in clinics. Here, we report on our clinical test results of Raman spectroscopy and machine learning-based glioblastoma identification methodology for a cohort of 20 glioblastoma and 18 white matter tissue samples. We used Raman spectroscopy to distinguish FFPE glioblastoma and white matter tissues applying our previously reported protocols about optimized FFPE sample preparation and Raman measurement parameters. One may analyze the composition and identify the subtype of brain tumors using Raman spectroscopy since this technique yields detailed molecule-specific information from tissues. We measured and classified the Raman spectra of neoplastic and non-neoplastic tissue sections using machine learning classifiers including support vector machine and random forest with 86.6% and 83.3% accuracies, respectively. These proof-of-concept results demonstrate that this technique might be eventually used in the clinics to assist pathologists once validated with a larger and more diverse glioblastoma cohort and improved detection accuracies.Publication Metadata only Clinical validation of SERS metasurface SARS-CoV-2 biosensor(Spie-Int Soc Optical Engineering, 2022) İlgu, Müslüm; Yanık, Cenk; Çelik, Süleyman; Öztürk, Meriç; N/A; N/A; Department of Electrical and Electronics Engineering; N/A; N/A; N/A; N/A; Department of Electrical and Electronics Engineering; Bilgin, Buse; Torun, Hülya; Doğan, Özlem; Ergönül, Önder; Solaroğlu, İhsan; Can, Füsun; Onbaşlı, Mehmet Cengiz; PhD Student; PhD Student; Undergraduated Student; Faculty Member; Faculty Member; Faculty Member; Faculty Member; Faculty Member; Department of Electrical and Electronics Engineering; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; School of Medicine; School of Medicine; School of Medicine; School of Medicine; College of Engineering; Koç University Hospital; N/A; N/A; N/A; 170418; 110398; 102059; 103165; 258783The real-time polymerase chain reaction (RT-PCR) analysis using nasal swab samples is the gold standard approach for COVID-19 diagnosis. However, due to the high false-negative rate at lower viral loads and complex test procedure, PCR is not suitable for fast mass screening. Therefore, the need for a highly sensitive and rapid detection system based on easily collected fluids such as saliva during the pandemic has emerged. In this study, we present a surface-enhanced Raman spectroscopy (SERS) metasurface optimized with genetic algorithm (GA) to detect SARS-CoV-2 directly using unprocessed saliva samples. During the GA optimization, the electromagnetic field profiles were used to calculate the field enhancement of each structure and the fitness values to determine the performance of the generated substrates. The obtained design was fabricated using electron beam lithography, and the simulation results were compared with the test results using methylene blue fluorescence dye. After the performance of the system was validated, the SERS substrate was tested with inactivated SARS-CoV-2 virus for virus detection, viral load analysis, cross-reactivity, and variant detection using machine learning models. After the inactivated virus tests are completed, with 36 PCR positive and 33 negative clinical samples, we were able to detect the SARS-CoV-2 positive samples from Raman spectra with 95.2% sensitivity and specificity.Publication Metadata only Genetic algorithm-driven design of SERS-active surfaces for early detection of diseases(Spie-Int Soc Optical Engineering, 2020) N/A; Department of Electrical and Electronics Engineering; N/A; N/A; Department of Electrical and Electronics Engineering; Bilgin, Buse; Türkmen, Berkay; Baysal, Kemal; Solaroğlu, İhsan; Onbaşlı, Mehmet Cengiz; PhD Student; Undergraduate Student; Faculty Member; Faculty Member; Faculty Member; Department of Electrical and Electronics Engineering; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); Graduate School of Sciences and Engineering; College of Engineering; School of Medicine; School of Medicine; College of Engineering; N/A; N/A; 119184; 102059; 258783Surface-enhanced Raman spectroscopy (SERS) enables the surface plasmon-based amplification and detection of Raman signals from biomarkers, which emerge at ultralow concentrations in the early phases of diseases. Thus, SERS chips could be used for early detection of diseases from their biomarkers obtained from liquid or tissue biopsies. While this surface enhancement capability of nanoscale gold or silver layers on different substrates were demonstrated in previous experiments and electromagnetic models, the position of the biomarker molecules on the SERS chips cannot be known or estimated a priori. As a result, SERS chips must be designed over millimeter-scale areas such that the signal amplification must be large (10(6) times or higher with respect to no SERS) and must span the entire slide. Simultaneous surface-enhancement of Raman signals and distributing this enhancement factor (EF) over the sample surface requires an iterative and \learning" design procedure for the geometries of nanoscale metallic features that could maximize both EF and its area simultaneously. In this study, we develop genetic algorithms and use finite-difference time-domain (FDTD) modeling to optimize the geometry of gold nanostructures (NS) on glass microscope slides to functionalize these slides as SERS-active surfaces for SERS-based enhancement of Raman spectra. By using FDTD models, we calculated the enhancement factors in 3D on glass surface for 785 nm laser for Raman spectrum measurements and used genetic algorithms (GA) to iterate on the metal NS geometry to maximize the average and the hot spot EF over the periodic patterns on the slide. Field enhancement factors as high as 10(17) and 10(15) were calculated for hot-spots and for whole-slide averages, respectively. The optimized structures indicate that GA could help maximize label-free and whole-slide Raman signal enhancement factors for single-cell SERS detection.Publication Open Access Genetic algorithm-driven surface-enhanced Raman spectroscopy substrate optimization(Multidisciplinary Digital Publishing Institute (MDPI), 2021) Yanık, Cenk; Department of Electrical and Electronics Engineering; N/A; Onbaşlı, Mehmet Cengiz; Bilgin, Buse; Torun, Hülya; Faculty Member; Department of Electrical and Electronics Engineering; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); College of Engineering; Graduate School of Sciences and Engineering; 258783; N/A; N/ASurface-enhanced Raman spectroscopy (SERS) is a highly sensitive and molecule-specific detection technique that uses surface plasmon resonances to enhance Raman scattering from analytes. In SERS system design, the substrates must have minimal or no background at the incident laser wavelength and large Raman signal enhancement via plasmonic confinement and grating modes over large areas (i.e., squared millimeters). These requirements impose many competing design constraints that make exhaustive parametric computational optimization of SERS substrates pro-hibitively time consuming. Here, we demonstrate a genetic-algorithm (GA)-based optimization method for SERS substrates to achieve strong electric field localization over wide areas for recon-figurable and programmable photonic SERS sensors. We analyzed the GA parameters and tuned them for SERS substrate optimization in detail. We experimentally validated the model results by fabricating the predicted nanostructures using electron beam lithography. The experimental Raman spectrum signal enhancements of the optimized SERS substrates validated the model predictions and enabled the generation of a detailed Raman profile of methylene blue fluorescence dye. The GA and its optimization shown here could pave the way for photonic chips and components with arbitrary design constraints, wavelength bands, and performance targets.