The graphical user interface of the classifier software.

<p>A software window is displayed in a web-browser. <b>(A) Use of the software.</b> The software interface can be easily used by specifying the path to a directory containing the MALDI-TOF MS data for each sample to be classified and by specifying a database file containing spectral data with a .RData extension (for example, the “example_data” directory and “spectraDB.RData” file are included in the software package) and by clicking on the “Analyze” button. <b>(B) Output.</b> After the computation is finished, an output CSV file is created. Its name and file path are automatically displayed in the browser window. Clicking on the “Display CSV” button yields predictions for each sample, which are displayed in the browser window.</p

Rapid and easy detection of low-level resistance to vancomycin in methicillin-resistant <i>Staphylococcus aureus</i> by matrix-assisted laser desorption ionization time-of-flight mass spectrometry

<div><p>Vancomycin-intermediately resistant <i>Staphylococcus aureus</i> (VISA) and heterogeneous VISA (hVISA) are associated with treatment failure. hVISA contains only a subpopulation of cells with increased minimal inhibitory concentrations, and its detection is problematic because it is classified as vancomycin-susceptible by standard susceptibility testing and the gold-standard method for its detection is impractical in clinical microbiology laboratories. Recently, a research group developed a machine-learning classifier to distinguish VISA and hVISA from vancomycin-susceptible <i>S</i>. <i>aureus</i> (VSSA) according to matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) data. Nonetheless, the sensitivity of hVISA classification was found to be 76%, and the program was not completely automated with a graphical user interface. Here, we developed a more accurate machine-learning classifier for discrimination of hVISA from VSSA and VISA among MRSA isolates in Japanese hospitals by means of MALDI-TOF MS data. The classifier showed 99% sensitivity of hVISA classification. Furthermore, we clarified the procedures for preparing samples and obtaining MALDI-TOF MS data and developed all-in-one software, hVISA Classifier, with a graphical user interface that automates the classification and is easy for medical workers to use; it is publicly available at <a href="https://github.com/bioprojects/hVISAclassifier" target="_blank">https://github.com/bioprojects/hVISAclassifier</a>. This system is useful and practical for screening MRSA isolates for the hVISA phenotype in clinical microbiology laboratories and thus should improve treatment of MRSA infections.</p></div

Performance of the software for classification into VISA, hVISA and VSSA.

<p>Performance of the software for classification into VISA, hVISA and VSSA.</p

Additional file 6 of Impact of respiratory bacterial infections on mortality in Japanese patients with COVID-19: a retrospective cohort study

Additional file 6. Admission to intensive care unitand use of invasive mechanical ventilationof bacterial respiratory infection with coronavirus disease 2019

Additional file 1 of Impact of respiratory bacterial infections on mortality in Japanese patients with COVID-19: a retrospective cohort study

Additional file 1. Identification of organisms in ventilator-associated pneumoniacase

Additional file 4 of Impact of respiratory bacterial infections on mortality in Japanese patients with COVID-19: a retrospective cohort study

Additional file 4. Details of respiratory secondary infection

Additional file 8 of Impact of respiratory bacterial infections on mortality in Japanese patients with COVID-19: a retrospective cohort study

Additional file 8. Proportion of thrombosis and myocardial injury in bacterial respiratory co-infection and secondary infection with coronavirus disease 2019

Additional file 5 of Impact of respiratory bacterial infections on mortality in Japanese patients with COVID-19: a retrospective cohort study

Additional file 5. Association of anti-IL-6 receptor antibody use with incidence of secondary infection and death

Additional file 3 of Impact of respiratory bacterial infections on mortality in Japanese patients with COVID-19: a retrospective cohort study

Additional file 3. Neutrophil-lymphocyte ratioas a predictor of co-infection in steroid and non-steroid user

Additional file 2 of Impact of respiratory bacterial infections on mortality in Japanese patients with COVID-19: a retrospective cohort study

Additional file 2. Evaluation of white blood cells, neutrophils, lymphocytes, neutrophil-lymphocyte ratio, C-reactive protein, and procalcitonin on admission as predictors of respiratory bacterial co-infection based on the area under the curv