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. As a result, the study achieved an overall classification accuracy of 94.9% for four frequently encountered genera and 84.5% for Aspergillus species. One of the distinct features is the involvement of medical technologists in developing a model that seamlessly integrates into routine workflows. In addition, the study highlights the potential of merging advanced technology with medical laboratory practices to diagnose filamentous fungi accurately and efficiently. IMPORTANCE This study utilizes transfer learning with CNNs to classify fungal genera and identify Aspergillus species using microscopic images from touch-tape preparation and lactophenol cotton blue staining. 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. As a result, the study achieved an overall classification accuracy of 94.9% for four frequently encountered genera and 84.5% for Aspergillus species. One of the distinct features is the involvement of medical technologists in developing a model that seamlessly integrates into routine workflows. In addition, the study highlights the potential of merging advanced technology with medical laboratory practices to diagnose filamentous fungi accurately and efficiently.

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. Once RAPID determines the current antimicrobials as inappropriate, an alert text message is immediately sent to the clinicians in charge. This study evaluated how RAPID impacted the time to appropriate antimicrobial therapy among patients with bloodstream infections. Results: During the study period, 633 of 11 297 recorded observations (5.6%) were determined as inappropriate antimicrobial prescriptions. The time to appropriate antimicrobial therapy was significantly shortened after the implementation of RAPID (1.65 vs 2.45 hours, P < .001), especially outside working hours (1.24 vs 6.43 hours, P < .001), in the medical wards (1.40 vs 2.14 hours, P < .001), in participants with candidemia (0.74 vs 5.36 hours, P < .001), and for bacteremia due to non-multidrug-resistant organisms (1.66 vs 2.49 hours, P < .001). Conclusions: Using a knowledge-based clinical decision support system to reduce the time to appropriate antimicrobial therapy in a real-world scenario is feasible and effective. Our results support the continued use of RAPID.

Synergy of ß-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 ß-lactam antibiotics for 59 clinical isolates of methicillin-resistant Staphylococcus aureus (MRSA) and Mu50 (ATCC 700699). Bacterial inocula equivalent to 0.5 and 2.0 McFarland standard were inoculated on agar plates containing 0, 0.5, 1, and 2 µg/ml of vancomycin. Oxacillin-, cefazolin-, and cefoxitin-impregnated disks were applied to the surface, and the zones of inhibition were measured at 24 h. The CLSI-recommended checkerboard method was used as a reference to detect synergy. The MICs for vancomycin were determined using the Etest method, broth microdilution, and the Vitek 2 automated system. Synergy was observed with the checkerboard method in 51% to 60% of the isolates when vancomycin was combined with any ß-lactam. The fractional inhibitory concentration indices were significantly lower in MRSA isolates with higher vancomycin MIC combinations (P < 0.05). The overall agreement between the MDD and checkerboard methods to detect synergy in MRSA isolates with bacterial inocula equivalent to McFarland standard 0.5 were 33.0% and 62.5% for oxacillin, 45.1% and 52.4% for cefazolin, and 43.1% and 52.4% for cefoxitin when combined with 0.5 and 2 µg/ml of vancomycin, respectively. Based on our study, the simple MDD method is not recommended as a replacement for the checkerboard method to detect synergy. However, it may serve as an initial screening method for the detection of potential synergy when it is not feasible to perform other labor-intensive synergy tests.