Background: Clostridioides difficile is a major cause of hospital-acquired diarrhea and a driver of nosocomial outbreaks, yet rapid, accurate ribotype identification remains challenging. We sought to develop a MALDI-TOF MS-based workflow coupled with machine learning to distinguish epidemic toxigenic ribotypes (RT027 and RT181) from other strains in real time.
Results: We analyzed MALDI-TOF spectra from 379 clinical isolates collected across ten Spanish hospitals and identified seven discriminant biomarker peaks.
The identification of filamentous fungi by matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) represents a challenge due to their complex taxonomy and the lack of comprehensive databases. The aim of this study was to evaluate the current status of available MALDI-TOF MS databases for the identification of dermatophytes, including commercial, in-house, and web-based databases. We collected 289 dermatophyte strains from different centers and analyzed them using four databases and a combination of them.
View Article and Find Full Text PDFBackground: Fourier transform infrared (FT-IR) spectroscopy has emerged as a rapid and reliable method for bacterial typing. In this study, we evaluated FT-IR spectroscopy for characterizing a nosocomial outbreak caused by VIM-1-producing Klebsiella michiganensis (K. oxytoca complex).
View Article and Find Full Text PDFEur J Clin Microbiol Infect Dis
February 2025
Purpose: Clostridioides difficile is the main cause of antibiotic related diarrhea and some ribotypes (RT), such as RT027, RT181 or RT078, are considered high risk clones. A fast and reliable approach for C. difficile ribotyping is needed for a correct clinical approach.
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