Metabolomics and machine learning approaches combined in pursuit for more accurate paracoccidioidomycosis diagnoses - de Oliveira Lima, Estela [UNESP]; Navarro, Luiz Claudio; Morishita, Karen Noda; Kamikawa, Camila Mika; Rodrigues, Rafael Gustavo Martins; Dabaja, Mohamed Ziad; de Oliveira, Diogo Noin; Delaﬁori, Jeany; Dias-Audibert, Flávia Luísa; da Silva Ribeiro, Marta; Vicentini, Adriana Pardini; Rocha, Anderson; et al. (American Society for Microbiology, 2020)
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.
Keywords: Artiﬁcial intelligence; Diagnosis; Metabolomics; Paracoccidioidomycosis