Yazar "Yenerel, Mustafa Nuri" seçeneğine göre listele

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    Dual-beam optical manipulation of red blood cells for the investigation of paroxysmal nocturnal hemoglobinuria
    (The Optical Society, 2021) Parlatan, Uğur; Soysal, Kaan Batu; Parlatan, Şeyma; Mastanzade, Metban; Özbalak, Murat; Yenerel, Mustafa Nuri; Ünlü, Bürçin M.
    Paroxysmal nocturnal hemoglobinuria was studied using dual-beam optical tweezers. Force measurements show a moderate change between disease and healthy states. PCA/LDA method was also used for classification.
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    A raman tweezer study with single red blood cells for the diagnosis of paroxysmal nocturnal hemoglobinuria
    (The Optical Society, 2021) Parlatan, Şeyma; Parlatan, Uğur; Soysal, Kaan Batu; Mastanzade, Metban; Özbalak, Murat; Yenerel, Mustafa Nuri; Başar, Günay; Ünlü, Bürçin M.
    The surface structure of individual erythrocytes changes in the state of paroxysmal nocturnal hemoglobinuria. We proposed a diagnostic model by Raman tweezers which provided optical immobilization and chemical interrogation
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    Raman tweezers as an alternative diagnostic tool for paroxysmal nocturnal hemoglobinuria
    (2021) Soysal, Kaan Batu; Parlatan, Şeyma; Mastanzade, Metban; Özbalak, Murat; Yenerel, Mustafa Nuri; Ünlü, Mehmet Burçin; Başar, Günay; Parlatan, Uğur
    Paroxysmal nocturnal hemoglobinuria (PNH) is a rare disease characterized by hemolysis of red blood cells (RBC) and venous thrombosis. The gold standard method for the diagnosis of this disease is flow cytometry. Here, we propose a combined optical tweezers and Raman spectral (Raman tweezers) approach to analyze blood samples from volunteers with or without PNH conditions. Raman spectroscopy is a well-known method for investigating a material's chemical structure and is also used in molecular analysis of biological compounds. In this study, we trap individual RBCs found in whole blood samples drawn from PNH patients and the control group. Evaluation of the Raman spectra of these cells by band component analysis and machine learning shows a significant difference between the two groups. The specificity and the sensitivity of the training performed by support vector machine (SVM) analysis were found to be 81.8% and 78.3%, respectively. This study shows that an immediate and high accuracy test result is possible for PNH disease by employing Raman tweezers and machine learning.