Metabolomics and Machine Learning Approaches Combined in Pursuit for More Accurate Paracoccidioidomycosis Diagnoses

ABSTRACT Brazil and many other Latin American countries are areas of endemicity for different neglected diseases, and the fungal infection paracoccidioidomycosis (PCM) is one of them. Among the clinical manifestations, pneumopathy associated with skin and mucosal lesions is the most frequent. PCM de...

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Autores principales: Estela de Oliveira Lima, Luiz Claudio Navarro, Karen Noda Morishita, Camila Mika Kamikawa, Rafael Gustavo Martins Rodrigues, Mohamed Ziad Dabaja, Diogo Noin de Oliveira, Jeany Delafiori, Flávia Luísa Dias-Audibert, Marta da Silva Ribeiro, Adriana Pardini Vicentini, Anderson Rocha, Rodrigo Ramos Catharino
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Publicado: American Society for Microbiology 2020
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spelling oai:doaj.org-article:ac85687c0ff94853a2b955f83d53a6a62021-12-02T18:23:16ZMetabolomics and Machine Learning Approaches Combined in Pursuit for More Accurate Paracoccidioidomycosis Diagnoses10.1128/mSystems.00258-202379-5077https://doaj.org/article/ac85687c0ff94853a2b955f83d53a6a62020-06-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00258-20https://doaj.org/toc/2379-5077ABSTRACT Brazil and many other Latin American countries are areas of endemicity for different neglected diseases, and the fungal infection paracoccidioidomycosis (PCM) is one of them. Among the clinical manifestations, pneumopathy associated with skin and mucosal lesions is the most frequent. PCM definitive diagnosis depends on yeast microscopic visualization and immunological tests, but both present ambiguous results and difficulty in differentiating PCM from other fungal infections. This research has employed metabolomics analysis through high-resolution mass spectrometry to identify PCM biomarkers in serum samples in order to improve diagnosis for this debilitating disease. To upgrade the biomarker selection, machine learning approaches, using Random Forest classifiers, were combined with metabolomics data analysis. The proposed combination of these two analytical methods resulted in the identification of a set of 19 PCM biomarkers that show accuracy of 97.1%, specificity of 100%, and sensitivity of 94.1%. The obtained results are promising and present great potential to improve PCM definitive diagnosis and adequate pharmacological treatment, reducing the incidence of PCM sequelae and resulting in a better quality of life. IMPORTANCE Paracoccidioidomycosis (PCM) is a fungal infection typically found in Latin American countries, especially in Brazil. The identification of this disease is based on techniques that may fail sometimes. Intending to improve PCM detection in patient samples, this study used the combination of two of the newest technologies, artificial intelligence and metabolomics. This combination allowed PCM detection, independently of disease form, through identification of a set of molecules present in patients’ blood. The great difference in this research was the ability to detect disease with better confidence than the routine methods employed today. Another important point is that among the molecules, it was possible to identify some indicators of contamination and other infection that might worsen patients’ condition. Thus, the present work shows a great potential to improve PCM diagnosis and even disease management, considering the possibility to identify concomitant harmful factors.Estela de Oliveira LimaLuiz Claudio NavarroKaren Noda MorishitaCamila Mika KamikawaRafael Gustavo Martins RodriguesMohamed Ziad DabajaDiogo Noin de OliveiraJeany DelafioriFlávia Luísa Dias-AudibertMarta da Silva RibeiroAdriana Pardini VicentiniAnderson RochaRodrigo Ramos CatharinoAmerican Society for Microbiologyarticleartificial intelligencediagnosismetabolomicsparacoccidioidomycosisMicrobiologyQR1-502ENmSystems, Vol 5, Iss 3 (2020)
institution DOAJ
collection DOAJ
language EN
topic artificial intelligence
diagnosis
metabolomics
paracoccidioidomycosis
Microbiology
QR1-502
spellingShingle artificial intelligence
diagnosis
metabolomics
paracoccidioidomycosis
Microbiology
QR1-502
Estela de Oliveira Lima
Luiz Claudio Navarro
Karen Noda Morishita
Camila Mika Kamikawa
Rafael Gustavo Martins Rodrigues
Mohamed Ziad Dabaja
Diogo Noin de Oliveira
Jeany Delafiori
Flávia Luísa Dias-Audibert
Marta da Silva Ribeiro
Adriana Pardini Vicentini
Anderson Rocha
Rodrigo Ramos Catharino
Metabolomics and Machine Learning Approaches Combined in Pursuit for More Accurate Paracoccidioidomycosis Diagnoses
description ABSTRACT Brazil and many other Latin American countries are areas of endemicity for different neglected diseases, and the fungal infection paracoccidioidomycosis (PCM) is one of them. Among the clinical manifestations, pneumopathy associated with skin and mucosal lesions is the most frequent. PCM definitive diagnosis depends on yeast microscopic visualization and immunological tests, but both present ambiguous results and difficulty in differentiating PCM from other fungal infections. This research has employed metabolomics analysis through high-resolution mass spectrometry to identify PCM biomarkers in serum samples in order to improve diagnosis for this debilitating disease. To upgrade the biomarker selection, machine learning approaches, using Random Forest classifiers, were combined with metabolomics data analysis. The proposed combination of these two analytical methods resulted in the identification of a set of 19 PCM biomarkers that show accuracy of 97.1%, specificity of 100%, and sensitivity of 94.1%. The obtained results are promising and present great potential to improve PCM definitive diagnosis and adequate pharmacological treatment, reducing the incidence of PCM sequelae and resulting in a better quality of life. IMPORTANCE Paracoccidioidomycosis (PCM) is a fungal infection typically found in Latin American countries, especially in Brazil. The identification of this disease is based on techniques that may fail sometimes. Intending to improve PCM detection in patient samples, this study used the combination of two of the newest technologies, artificial intelligence and metabolomics. This combination allowed PCM detection, independently of disease form, through identification of a set of molecules present in patients’ blood. The great difference in this research was the ability to detect disease with better confidence than the routine methods employed today. Another important point is that among the molecules, it was possible to identify some indicators of contamination and other infection that might worsen patients’ condition. Thus, the present work shows a great potential to improve PCM diagnosis and even disease management, considering the possibility to identify concomitant harmful factors.
format article
author Estela de Oliveira Lima
Luiz Claudio Navarro
Karen Noda Morishita
Camila Mika Kamikawa
Rafael Gustavo Martins Rodrigues
Mohamed Ziad Dabaja
Diogo Noin de Oliveira
Jeany Delafiori
Flávia Luísa Dias-Audibert
Marta da Silva Ribeiro
Adriana Pardini Vicentini
Anderson Rocha
Rodrigo Ramos Catharino
author_facet Estela de Oliveira Lima
Luiz Claudio Navarro
Karen Noda Morishita
Camila Mika Kamikawa
Rafael Gustavo Martins Rodrigues
Mohamed Ziad Dabaja
Diogo Noin de Oliveira
Jeany Delafiori
Flávia Luísa Dias-Audibert
Marta da Silva Ribeiro
Adriana Pardini Vicentini
Anderson Rocha
Rodrigo Ramos Catharino
author_sort Estela de Oliveira Lima
title Metabolomics and Machine Learning Approaches Combined in Pursuit for More Accurate Paracoccidioidomycosis Diagnoses
title_short Metabolomics and Machine Learning Approaches Combined in Pursuit for More Accurate Paracoccidioidomycosis Diagnoses
title_full Metabolomics and Machine Learning Approaches Combined in Pursuit for More Accurate Paracoccidioidomycosis Diagnoses
title_fullStr Metabolomics and Machine Learning Approaches Combined in Pursuit for More Accurate Paracoccidioidomycosis Diagnoses
title_full_unstemmed Metabolomics and Machine Learning Approaches Combined in Pursuit for More Accurate Paracoccidioidomycosis Diagnoses
title_sort metabolomics and machine learning approaches combined in pursuit for more accurate paracoccidioidomycosis diagnoses
publisher American Society for Microbiology
publishDate 2020
url https://doaj.org/article/ac85687c0ff94853a2b955f83d53a6a6
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