Paracoccidioidomycosis screening diagnosis by FTIR spectroscopy and multivariate analysis

Paracoccidioidomycosis (PCM) is a systemic mycosis with high incidence in Latin America, caused by species of the genus Paracoccidioides spp. Brazil is considered to be the endemic center of this disease, which is identified as the eighth cause of mortality from chronic infectious disease in the country. There are several specific diagnostic methods in PCM, such as microbiological, immunological, histopathological, and molecular. However, the standard laboratory diagnosis depends mostly on fungus direct observation -the gold standard of PCM diagnosis. The implementation of new technologies, such as Fourier Transform Infrared (FTIR), can contribute to the clinical diagnosis trial of this disease. Here, we evaluated a new strategy for the diagnosis of PCM by using blood serum FTIR spectra from 20 patients with PCM and 20 healthy individuals. Machine learning algorithms were able to provide an overall accuracy of 91.67% by using Cubic SVM in the PCA data from FTIR results.

Automated summary

PCM is a systemic mycosis with high incidence in Latin America, and Brazil is considered the endemic center of this disease. Various diagnostic methods, such as microbiological, immunological, histopathological, and molecular, are used for PCM. This study evaluated a new strategy for PCM diagnosis using blood serum FTIR spectra and achieved a high accuracy rate through machine learning algorithms.