• Antifungal Susceptibility Testing
• C.parapsilosis
• MALDI-TOF

Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) offers rapid microbial identification at the genus and species level, with growing applications in detecting antimicrobial resistance. This study explores the use of MALDI-TOF MS to identify C. parapsilosis strains with varying antifungal susceptibility profiles, particularly fluconazole resistance. To achieve 90% accuracy in classifying resistant and susceptible strains, advanced data analysis techniques such as spectral alignment, variance stabilization, and normalization were employed. These methods, paired with machine learning algorithms like Random Forest and Support Vector Machines (SVM), enabled fast, precise strain classification, enhancing the efficiency of antifungal susceptibility testing.
The study also uncovers key resistance mechanisms, such as mutations in the ERG11 gene, which confer fluconazole resistance by modifying the lanosterol 14α-demethylase enzyme. Moreover, MALDI-TOF MS provides valuable insights into the epidemiology and persistence of resistant strains in healthcare settings, despite potential fitness costs. This research underscores MALDI-TOF MS’s transformative potential in antifungal resistance detection, supporting antifungal stewardship programs, and improving patient outcomes. Future studies should focus on expanding spectral databases, refining machine learning models, and integrating MALDI-TOF MS into routine clinical diagnostics to address the growing threat of antifungal resistance.