Salivary Detection of Zika Virus Infection Using ATR-FTIR Spectroscopy Coupled with Machine Learning Algorithms and Univariate Analysis: A Proof-of-Concept Animal Study

Bibliographic Details
Main Author: Oliveira, Stephanie Wutke
Publication Date: 2023
Other Authors: Cardoso-Sousa, Leia, Georjutti, Renata Pereira, Shimizu, Jacqueline Farinha [UNESP], Silva, Suely [UNESP], Caixeta, Douglas Carvalho, Guevara-Vega, Marco, Cunha, Thúlio Marquez, Carneiro, Murillo Guimarães, Goulart, Luiz Ricardo, Jardim, Ana Carolina Gomes [UNESP], Sabino-Silva, Robinson
Format: Article
Language: eng
Source: Repositório Institucional da UNESP
Download full: http://dx.doi.org/10.3390/diagnostics13081443
http://hdl.handle.net/11449/247259
Summary: Zika virus (ZIKV) diagnosis is currently performed through an invasive, painful, and costly procedure using molecular biology. Consequently, the search for a non-invasive, more cost-effective, reagent-free, and sustainable method for ZIKV diagnosis is of great relevance. It is critical to prepare a global strategy for the next ZIKV outbreak given its devastating consequences, particularly in pregnant women. Attenuated total reflection–Fourier transform infrared (ATR-FTIR) spectroscopy has been used to discriminate systemic diseases using saliva; however, the salivary diagnostic application in viral diseases is unknown. To test this hypothesis, we intradermally challenged interferon-gamma gene knockout C57/BL6 mice with ZIKV (50 µL,105 FFU, n = 7) or vehicle (50 µL, n = 8). Saliva samples were collected on day three (due to the peak of viremia) and the spleen was also harvested. Changes in the salivary spectral profile were analyzed by Student’s t test (p < 0.05), multivariate analysis, and the diagnostic capacity by ROC curve. ZIKV infection was confirmed by real-time PCR of the spleen sample. The infrared spectroscopy coupled with univariate analysis suggested the vibrational mode at 1547 cm−1 as a potential candidate to discriminate ZIKV and control salivary samples. Three PCs explained 93.2% of the cumulative variance in PCA analysis and the spectrochemical analysis with LDA achieved an accuracy of 93.3%, with a specificity of 87.5% and sensitivity of 100%. The LDA-SVM analysis showed 100% discrimination between both classes. Our results suggest that ATR-FTIR applied to saliva might have high accuracy in ZIKV diagnosis with potential as a non-invasive and cost-effective diagnostic tool.
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spelling Salivary Detection of Zika Virus Infection Using ATR-FTIR Spectroscopy Coupled with Machine Learning Algorithms and Univariate Analysis: A Proof-of-Concept Animal StudyATR-FTIRdiagnosismicesalivaZika virusZika virus (ZIKV) diagnosis is currently performed through an invasive, painful, and costly procedure using molecular biology. Consequently, the search for a non-invasive, more cost-effective, reagent-free, and sustainable method for ZIKV diagnosis is of great relevance. It is critical to prepare a global strategy for the next ZIKV outbreak given its devastating consequences, particularly in pregnant women. Attenuated total reflection–Fourier transform infrared (ATR-FTIR) spectroscopy has been used to discriminate systemic diseases using saliva; however, the salivary diagnostic application in viral diseases is unknown. To test this hypothesis, we intradermally challenged interferon-gamma gene knockout C57/BL6 mice with ZIKV (50 µL,105 FFU, n = 7) or vehicle (50 µL, n = 8). Saliva samples were collected on day three (due to the peak of viremia) and the spleen was also harvested. Changes in the salivary spectral profile were analyzed by Student’s t test (p < 0.05), multivariate analysis, and the diagnostic capacity by ROC curve. ZIKV infection was confirmed by real-time PCR of the spleen sample. The infrared spectroscopy coupled with univariate analysis suggested the vibrational mode at 1547 cm−1 as a potential candidate to discriminate ZIKV and control salivary samples. Three PCs explained 93.2% of the cumulative variance in PCA analysis and the spectrochemical analysis with LDA achieved an accuracy of 93.3%, with a specificity of 87.5% and sensitivity of 100%. The LDA-SVM analysis showed 100% discrimination between both classes. Our results suggest that ATR-FTIR applied to saliva might have high accuracy in ZIKV diagnosis with potential as a non-invasive and cost-effective diagnostic tool.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)Innovation Center in Salivary Diagnostic and Nanobiotechnology Department of Physiology Institute of Biomedical Sciences Federal University of UberlandiaCollege of Dentistry University Center of Triangle (UNITRI)Laboratory of Antiviral Research Institute of Biomedical Science Federal University of UberlandiaInstitute of Biosciences Humanities and Exact Sciences São Paulo State UniversitySchool of Medicine Federal University of Uberlandia (UFU)Faculty of Computing Federal University of Uberlandia (UFU)Institute of Biotechnology Federal University of UberlandiaInstitute of Biosciences Humanities and Exact Sciences São Paulo State UniversityCNPq: 409157/2022-8FAPEMIG: APQ-00476-20FAPEMIG: APQ-01487-22FAPEMIG: APQ-02148-21FAPEMIG: APQ-03613-17FAPEMIG: APQ-04686-22Universidade Federal de Uberlândia (UFU)University Center of Triangle (UNITRI)Universidade Estadual Paulista (UNESP)Oliveira, Stephanie WutkeCardoso-Sousa, LeiaGeorjutti, Renata PereiraShimizu, Jacqueline Farinha [UNESP]Silva, Suely [UNESP]Caixeta, Douglas CarvalhoGuevara-Vega, MarcoCunha, Thúlio MarquezCarneiro, Murillo GuimarãesGoulart, Luiz RicardoJardim, Ana Carolina Gomes [UNESP]Sabino-Silva, Robinson2023-07-29T13:11:08Z2023-07-29T13:11:08Z2023-04-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.3390/diagnostics13081443Diagnostics, v. 13, n. 8, 2023.2075-4418http://hdl.handle.net/11449/24725910.3390/diagnostics130814432-s2.0-85153961605Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengDiagnosticsinfo:eu-repo/semantics/openAccess2025-04-03T19:03:44Zoai:repositorio.unesp.br:11449/247259Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestrepositoriounesp@unesp.bropendoar:29462025-04-03T19:03:44Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Salivary Detection of Zika Virus Infection Using ATR-FTIR Spectroscopy Coupled with Machine Learning Algorithms and Univariate Analysis: A Proof-of-Concept Animal Study
title Salivary Detection of Zika Virus Infection Using ATR-FTIR Spectroscopy Coupled with Machine Learning Algorithms and Univariate Analysis: A Proof-of-Concept Animal Study
spellingShingle Salivary Detection of Zika Virus Infection Using ATR-FTIR Spectroscopy Coupled with Machine Learning Algorithms and Univariate Analysis: A Proof-of-Concept Animal Study
Oliveira, Stephanie Wutke
ATR-FTIR
diagnosis
mice
saliva
Zika virus
title_short Salivary Detection of Zika Virus Infection Using ATR-FTIR Spectroscopy Coupled with Machine Learning Algorithms and Univariate Analysis: A Proof-of-Concept Animal Study
title_full Salivary Detection of Zika Virus Infection Using ATR-FTIR Spectroscopy Coupled with Machine Learning Algorithms and Univariate Analysis: A Proof-of-Concept Animal Study
title_fullStr Salivary Detection of Zika Virus Infection Using ATR-FTIR Spectroscopy Coupled with Machine Learning Algorithms and Univariate Analysis: A Proof-of-Concept Animal Study
title_full_unstemmed Salivary Detection of Zika Virus Infection Using ATR-FTIR Spectroscopy Coupled with Machine Learning Algorithms and Univariate Analysis: A Proof-of-Concept Animal Study
title_sort Salivary Detection of Zika Virus Infection Using ATR-FTIR Spectroscopy Coupled with Machine Learning Algorithms and Univariate Analysis: A Proof-of-Concept Animal Study
author Oliveira, Stephanie Wutke
author_facet Oliveira, Stephanie Wutke
Cardoso-Sousa, Leia
Georjutti, Renata Pereira
Shimizu, Jacqueline Farinha [UNESP]
Silva, Suely [UNESP]
Caixeta, Douglas Carvalho
Guevara-Vega, Marco
Cunha, Thúlio Marquez
Carneiro, Murillo Guimarães
Goulart, Luiz Ricardo
Jardim, Ana Carolina Gomes [UNESP]
Sabino-Silva, Robinson
author_role author
author2 Cardoso-Sousa, Leia
Georjutti, Renata Pereira
Shimizu, Jacqueline Farinha [UNESP]
Silva, Suely [UNESP]
Caixeta, Douglas Carvalho
Guevara-Vega, Marco
Cunha, Thúlio Marquez
Carneiro, Murillo Guimarães
Goulart, Luiz Ricardo
Jardim, Ana Carolina Gomes [UNESP]
Sabino-Silva, Robinson
author2_role author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Federal de Uberlândia (UFU)
University Center of Triangle (UNITRI)
Universidade Estadual Paulista (UNESP)
dc.contributor.author.fl_str_mv Oliveira, Stephanie Wutke
Cardoso-Sousa, Leia
Georjutti, Renata Pereira
Shimizu, Jacqueline Farinha [UNESP]
Silva, Suely [UNESP]
Caixeta, Douglas Carvalho
Guevara-Vega, Marco
Cunha, Thúlio Marquez
Carneiro, Murillo Guimarães
Goulart, Luiz Ricardo
Jardim, Ana Carolina Gomes [UNESP]
Sabino-Silva, Robinson
dc.subject.por.fl_str_mv ATR-FTIR
diagnosis
mice
saliva
Zika virus
topic ATR-FTIR
diagnosis
mice
saliva
Zika virus
description Zika virus (ZIKV) diagnosis is currently performed through an invasive, painful, and costly procedure using molecular biology. Consequently, the search for a non-invasive, more cost-effective, reagent-free, and sustainable method for ZIKV diagnosis is of great relevance. It is critical to prepare a global strategy for the next ZIKV outbreak given its devastating consequences, particularly in pregnant women. Attenuated total reflection–Fourier transform infrared (ATR-FTIR) spectroscopy has been used to discriminate systemic diseases using saliva; however, the salivary diagnostic application in viral diseases is unknown. To test this hypothesis, we intradermally challenged interferon-gamma gene knockout C57/BL6 mice with ZIKV (50 µL,105 FFU, n = 7) or vehicle (50 µL, n = 8). Saliva samples were collected on day three (due to the peak of viremia) and the spleen was also harvested. Changes in the salivary spectral profile were analyzed by Student’s t test (p < 0.05), multivariate analysis, and the diagnostic capacity by ROC curve. ZIKV infection was confirmed by real-time PCR of the spleen sample. The infrared spectroscopy coupled with univariate analysis suggested the vibrational mode at 1547 cm−1 as a potential candidate to discriminate ZIKV and control salivary samples. Three PCs explained 93.2% of the cumulative variance in PCA analysis and the spectrochemical analysis with LDA achieved an accuracy of 93.3%, with a specificity of 87.5% and sensitivity of 100%. The LDA-SVM analysis showed 100% discrimination between both classes. Our results suggest that ATR-FTIR applied to saliva might have high accuracy in ZIKV diagnosis with potential as a non-invasive and cost-effective diagnostic tool.
publishDate 2023
dc.date.none.fl_str_mv 2023-07-29T13:11:08Z
2023-07-29T13:11:08Z
2023-04-01
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.3390/diagnostics13081443
Diagnostics, v. 13, n. 8, 2023.
2075-4418
http://hdl.handle.net/11449/247259
10.3390/diagnostics13081443
2-s2.0-85153961605
url http://dx.doi.org/10.3390/diagnostics13081443
http://hdl.handle.net/11449/247259
identifier_str_mv Diagnostics, v. 13, n. 8, 2023.
2075-4418
10.3390/diagnostics13081443
2-s2.0-85153961605
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Diagnostics
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv repositoriounesp@unesp.br
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