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.
Original language | English |
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Pages (from-to) | 3963-3969 |
Number of pages | 7 |
Journal | Analytical Methods |
Volume | 13 |
Issue number | 35 |
DOIs | |
Publication status | Published - 21 Sept 2021 |
Bibliographical note
Publisher Copyright:© The Royal Society of Chemistry.
Funding
This study was supported by grants from the Republic of Turkey Ministry of Industry and Technology (Project Number: 2009K120520) and Scientic and Technological Research Council of Turkey (TUBITAK Project Number: 118S113). The authors thank all the volunteers for donating their blood used in this project.
Funders | Funder number |
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Scientic and Technological Research Council of Turkey | |
TUBITAK | 118S113 |