Kang SJ, et al. Infect Chemother. 2020 Jun;52(2):154-164. https://doi.org/10.3947/ic.2020.52.2.154

Background: Coronavirus disease (COVID-19) has spread explosively worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) is a relevant screening tool due to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely occupied fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. Objective: We aimed to rapidly develop an AI technique to diagnose COVID-19 pneumonia in CT images and differentiate it from non–COVID-19 pneumonia and nonpneumonia diseases. Methods: A simple 2D deep learning framework, named the fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning using one of four state-of-the-art pretrained deep learning models (VGG16, ResNet-50, Inception-v3, or Xception) as a backbone. For training and testing of FCONet, we collected 3993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and nonpneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training set and a testing set at a ratio of 8:2. For the testing data set, the diagnostic performance of the four pretrained FCONet models to diagnose COVID-19 pneumonia was compared. In addition, we tested the FCONet models on an external testing data set extracted from embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. Results: Among the four pretrained models of FCONet, ResNet-50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100.00%, and accuracy 99.87%) and outperformed the other three pretrained models in the testing data set. In the additional external testing data set using low-quality CT images, the detection accuracy of the ResNet-50 model was the highest (96.97%), followed by Xception, Inception-v3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). Conclusions: FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing data set, the FCONet model based on ResNet-50 appears to be the best model, as it outperformed other FCONet models based on VGG16, Xception, and Inception-v3.

Kim SE, et al. Chonnam Med J. 2012 Aug;48(2):91-95. http://dx.doi.org/10.4068/cmj.2012.48.2.91

Few clinical data are available on the relationship between genospecies and outcome of Acinetobacter bacteremia, and the results are inconsistent. We performed this study to evaluate the relationship between genospecies and the outcome of Acinetobacter bacteremia. Clinical data from 180 patients who had Acinetobacter bacteremia from 2003 to 2010 were reviewed retrospectively. The genospecies were identified by rpoB gene sequence analysis. The clinical features and outcomes of 90 patients with A. baumannii bacteremia were compared to those of 90 patients with non-baumannii Acinetobacter bacteremia (60 with A. nosocomialis, 17 with Acinetobacter species “close to 13 TU”, 11 with A. pittii, and two with A. calcoaceticus). A. baumannii bacteremia was associated with intensive care unit-onset, mechanical ventilation, pneumonia, carbapenem resistance, and higher APACHE II scores, compared to non-baumannii Acinetobacter bacteremia (P<0.05). In univariate analyses, age, pneumonia, multidrug resistance, carbapenem resistance, inappropriate empirical antibiotics, higher APACHE II scores, and A. baumannii genospecies were risk factors for mortality (P<0.05). Multivariate analysis revealed A. baumannii genospecies (OR, 3.60; 95% CI, 1.56–8.33), age, pneumonia, and higher APACHE II scores to be independent risk factors for mortality (P<0.05). A. baumannii genospecies was an independent risk factor for mortality in patients with Acinetobacter bacteremia. Our results emphasize the importance of correct species identification of Acinetobacter blood isolates.

Since the first report of severe fever with thrombocytopenia syndrome (SFTS) in China, diagnostic methods have evolved. Despite definitive diagnoses, SFTS management has focused on prevention and supportive care. Although conservative treatment is effective for mild cases, there is an urgent need for an effective therapeutic modality to treat severe cases because of the high mortality associated with these. In this study, we report two cases of SFTS with neurological manifestations that fully recovered after a combination treatment consisting of intravenous immunoglobulin and corticosteroid.