Detection of Intrinsically Resistant Candida in Mixed Samples by MALDI TOF-MS and a Modified Naïve Bayesian Classifier

Abstract

MALDI-TOF MS is one of the major methods for clinical fungal identification, but it is currently only suitable for pure cultures of isolated strains. However, multiple fungal coinfections might occur in clinical practice. Some fungi involved in coinfection, such as Candida krusei and Candida auris, are intrinsically resistant to certain drugs. Identifying intrinsically resistant fungi from coinfected mixed cultures is extremely important for clinical treatment because different treatment options would be pursued accordingly. In this study, we counted the peaks of various species generated by Bruker Daltonik MALDI Biotyper software and accordingly constructed a modified naïve Bayesian classifier to analyze the presence of C. krusei and C. auris in simulated mixed samples. When reasonable parameters were fixed, the modified naïve Bayesian classifier effectively identified C. krusei and C. auris in the mixed samples (sensitivity 93.52%, specificity 92.5%). Our method not only provides a viable solution for identifying the two highlighted intrinsically resistant Candida species but also provides a case for the use of MALDI-TOF MS for analyzing coinfections of other species.