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First Report of the Detection of DENV1 in Human Blood Plasma with Near-Infrared Spectroscopy
Dengue virus (DENV) is the world’s most common arboviral infection, with an estimated 3.9 million people at risk of the infection, 100 million symptomatic cases and 10,000 deaths per year. Current diagnosis for DENV includes the use of molecular methods, such as polymerase chain reaction, which can...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9609583/ https://www.ncbi.nlm.nih.gov/pubmed/36298803 http://dx.doi.org/10.3390/v14102248 |
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author | Goh, Brendon Visendi, Paul Lord, Anton R. Ciocchetta, Silvia Liu, Wenjun Sikulu-Lord, Maggy T. |
author_facet | Goh, Brendon Visendi, Paul Lord, Anton R. Ciocchetta, Silvia Liu, Wenjun Sikulu-Lord, Maggy T. |
author_sort | Goh, Brendon |
collection | PubMed |
description | Dengue virus (DENV) is the world’s most common arboviral infection, with an estimated 3.9 million people at risk of the infection, 100 million symptomatic cases and 10,000 deaths per year. Current diagnosis for DENV includes the use of molecular methods, such as polymerase chain reaction, which can be costly for routine use. The near-infrared spectroscopy (NIR) technique is a high throughput technique that involves shining a beam of infrared light on a biological sample, collecting a reflectance spectrum, and using machine learning algorithms to develop predictive algorithms. Here, we used NIR to detect DENV1 artificially introduced into whole blood, plasma, and serum collected from human donors. Machine learning algorithms were developed using artificial neural networks (ANN) and the resultant models were used to predict independent samples. DENV in plasma samples was detected with an overall accuracy, sensitivity, and specificity of 90% (N = 56), 88.5% (N = 28) and 92.3% (N = 28), respectively. However, a predictive sensitivity of 33.3% (N = 16) and 80% (N = 10) and specificity of 46.7% (N = 16) and 32% (N = 10) was achieved for detecting DENV1 in whole blood and serum samples, respectively. DENV1 peaks observed at 812 nm and 819 nm represent C-H stretch, peaks at 1130–1142 nm are related to methyl group and peaks at 2127 nm are related to saturated fatty groups. Our findings indicate the potential of NIR as a diagnostic tool for DENV, however, further work is recommended to assess its sensitivity for detecting DENV in people naturally infected with the virus and to determine its capacity to differentiate DENV serotypes and other arboviruses. |
format | Online Article Text |
id | pubmed-9609583 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96095832022-10-28 First Report of the Detection of DENV1 in Human Blood Plasma with Near-Infrared Spectroscopy Goh, Brendon Visendi, Paul Lord, Anton R. Ciocchetta, Silvia Liu, Wenjun Sikulu-Lord, Maggy T. Viruses Article Dengue virus (DENV) is the world’s most common arboviral infection, with an estimated 3.9 million people at risk of the infection, 100 million symptomatic cases and 10,000 deaths per year. Current diagnosis for DENV includes the use of molecular methods, such as polymerase chain reaction, which can be costly for routine use. The near-infrared spectroscopy (NIR) technique is a high throughput technique that involves shining a beam of infrared light on a biological sample, collecting a reflectance spectrum, and using machine learning algorithms to develop predictive algorithms. Here, we used NIR to detect DENV1 artificially introduced into whole blood, plasma, and serum collected from human donors. Machine learning algorithms were developed using artificial neural networks (ANN) and the resultant models were used to predict independent samples. DENV in plasma samples was detected with an overall accuracy, sensitivity, and specificity of 90% (N = 56), 88.5% (N = 28) and 92.3% (N = 28), respectively. However, a predictive sensitivity of 33.3% (N = 16) and 80% (N = 10) and specificity of 46.7% (N = 16) and 32% (N = 10) was achieved for detecting DENV1 in whole blood and serum samples, respectively. DENV1 peaks observed at 812 nm and 819 nm represent C-H stretch, peaks at 1130–1142 nm are related to methyl group and peaks at 2127 nm are related to saturated fatty groups. Our findings indicate the potential of NIR as a diagnostic tool for DENV, however, further work is recommended to assess its sensitivity for detecting DENV in people naturally infected with the virus and to determine its capacity to differentiate DENV serotypes and other arboviruses. MDPI 2022-10-13 /pmc/articles/PMC9609583/ /pubmed/36298803 http://dx.doi.org/10.3390/v14102248 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Goh, Brendon Visendi, Paul Lord, Anton R. Ciocchetta, Silvia Liu, Wenjun Sikulu-Lord, Maggy T. First Report of the Detection of DENV1 in Human Blood Plasma with Near-Infrared Spectroscopy |
title | First Report of the Detection of DENV1 in Human Blood Plasma with Near-Infrared Spectroscopy |
title_full | First Report of the Detection of DENV1 in Human Blood Plasma with Near-Infrared Spectroscopy |
title_fullStr | First Report of the Detection of DENV1 in Human Blood Plasma with Near-Infrared Spectroscopy |
title_full_unstemmed | First Report of the Detection of DENV1 in Human Blood Plasma with Near-Infrared Spectroscopy |
title_short | First Report of the Detection of DENV1 in Human Blood Plasma with Near-Infrared Spectroscopy |
title_sort | first report of the detection of denv1 in human blood plasma with near-infrared spectroscopy |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9609583/ https://www.ncbi.nlm.nih.gov/pubmed/36298803 http://dx.doi.org/10.3390/v14102248 |
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