Raman tweezers as an alternative diagnostic tool for paroxysmal nocturnal hemoglobinuria. Issue 35 (19th August 2021)

• Record Type: Journal Article
• Authors: Soysal, Kaan Batu; Parlatan, Seyma; Mastanzade, Metban; Ozbalak, Murat; Yenerel, Mustafa Nuri; Unlu, Mehmet Burcin; Basar, Gunay; Parlatan, Ugur
• Is Part Of: Analytical methods. Volume 13:Issue 35(2021)
• Page Start: 3963
• Page End: 3969
• Publication Date: 2021-08-19
• Subjects: Chemistry, Analytic -- Periodicals; Analytical biochemistry -- Periodicals; Chemical laboratories -- Standards -- Periodicals; 543.1905
• DOI: 10.1039/d1ay01116b
• Languages: English
• ISSNs: 1759-9660

Abstract

Abstract : Paroxysmal nocturnal hemoglobinuria (PNH) is a rare disease. Combination of Raman tweezers and machine learning is a candidate to be the next diagnosis method. Band component analysis reveals the chemical structure of the RBCs. Abstract : 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.