Attention-Guided Transfer Learning for Identification of Filamentous Fungi Encountered in the Clinical Laboratory.

This study addresses the challenge of accurately identifying filamentous fungi in medical laboratories using transfer learning with convolutional neural networks (CNNs). The study uses microscopic images from touch-tape slides with lactophenol cotton blue staining, the most common method in clinical settings, to classify fungal genera and identify Aspergillus species. The training and test data sets included 4,108 images with representative microscopic morphology for each genus, and a soft attention mechanism was incorporated to enhance classification accuracy.

Using a Knowledge-Based Clinical Decision Support System to Reduce the Time to Appropriate Antimicrobial Therapy in Hospitalized Patients With Bloodstream Infections: A Single-Center Observational Study.

Background: Inappropriate antimicrobial use is a crucial determinant of mortality in hospitalized patients with bloodstream infections. Current literature reporting on the impact of clinical decision support systems on optimizing antimicrobial prescription and reducing the time to appropriate antimicrobial therapy is limited.

Methods: Kaohsiung Veterans General Hospital implemented a hospital-wide, knowledge-based, active-delivery clinical decision support system, named RAPID (Real-time Alert for antimicrobial Prescription from virtual Infectious Diseases experts), to detect whether there was an antimicrobial agent-pathogen mismatch when a blood culture result was positive.

Synergy of β-Lactams with Vancomycin against Methicillin-Resistant Staphylococcus aureus: Correlation of Disk Diffusion and Checkerboard Methods.

Modified disk diffusion (MDD) and checkerboard tests were employed to assess the synergy of combinations of vancomycin and β-lactam antibiotics for 59 clinical isolates of methicillin-resistant Staphylococcus aureus (MRSA) and Mu50 (ATCC 700699). Bacterial inocula equivalent to 0.5 and 2.