Michael Krauthammer

Abstract Antimicrobial resistance (AMR) is a significant global health threat. Recent studies have shown that combining MALDI-TOF mass spectrometry with machine learning algorithms can accelerate AMR determination. However, these efforts have predominantly focused on bacterial pathogens. The significant morbidity, mortality, and healthcare costs associated with fungal infections highlight the need for accurate and early detection of antifungal resistance.

Abstract Recognition of protospacer adjacent motifs (PAMs) is crucial for target site recognition by CRISPR–Cas systems. In genome editing applications, the requirement for specific PAM sequences at the target locus imposes substantial constraints, driving efforts to search for novel Cas9 orthologs with extended or alternative PAM compatibilities.