Mario Blázquez‐Sánchez

This paper develops automated tools using MALDI-TOF MS and machine learning to accurately classify C. difficile ribotypes from clinical isolates.

The paper introduces a large MALDI-TOF MS dataset from Spain to support AI-driven clinical microbiology research and improve antimicrobial resistance detection.

This study uses FT-IR spectroscopy to rapidly characterize a nosocomial outbreak of VIM-1-producing K. oxytoca, showing high concordance with whole-genome sequencing.

This study uses MALDI-TOF MS and machine learning to classify C. difficile ribotypes, achieving high accuracy for some ribotypes but not all.

This study improves ML-based C. difficile typing from MALDI-TOF MS spectra by using data augmentation to reduce performance variability across different experimental conditions.