de Brito Eliana C A, Franca Thiago, Canassa Thalita, Weber Simone S, Paniago Anamaria M M, Cena Cicero
Department of Infectious and Parasitic Diseases, Faculty of Medicine - Federal University of Mato Grosso do Sul - UFMS, Brazil.
Optics and Photonics Group, Institute of Physics, Federal University of Mato Grosso do Sul - UFMS, Brazil.
Photodiagnosis Photodyn Ther. 2022 Sep;39:102921. doi: 10.1016/j.pdpdt.2022.102921. Epub 2022 May 19.
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.
副球孢子菌病（PCM）是一种在拉丁美洲发病率较高的系统性真菌病，由副球孢子菌属物种引起。巴西被认为是这种疾病的地方性流行中心，它被确定为该国慢性传染病死亡的第八大原因。PCM有多种特异性诊断方法，如微生物学、免疫学、组织病理学和分子诊断方法。然而，标准的实验室诊断主要依赖于真菌直接观察——这是PCM诊断的金标准。新技术如傅里叶变换红外光谱（FTIR）的应用有助于这种疾病的临床诊断试验。在此，我们通过使用20例PCM患者和20名健康个体的血清FTIR光谱评估了一种诊断PCM的新策略。机器学习算法在来自FTIR结果的主成分分析（PCA）数据中使用立方支持向量机（Cubic SVM）能够提供91.67%的总体准确率。
