New Data from University of Nebraska Medical Center Illuminate Findings in Staphylococcus aureus (Machine Learning Assisted Identification of Antibiotic-resistant Staphylococcus Aureus Strains Using a Paper-based Ratiometric...).

A recent study conducted at the University of Nebraska Medical Center focused on the identification of antibiotic-resistant Staphylococcus aureus strains using a paper-based ratiometric sensor array combined with machine learning algorithms. The research highlighted the sensor array's potential as a diagnostic tool to accurately and rapidly identify drug-resistant S. aureus strains, including methicillin-resistant S. aureus (MRSA) and vancomycin-intermediate S. aureus (VISA). The study demonstrated the sensor's ability to differentiate between MRSA, methicillin-susceptible S. aureus (MSSA), and VISA clinical isolates with high accuracy, showcasing its effectiveness in clinical settings. [Extracted from the article]