Ongoing outbreak of human adenovirus-associated acute respiratory illness in the Republic of Korea military, 2013 to 2018.

BACKGROUND/AIMS: Human adenovirus type 55 (HAdV-55), an emerging epidemic strain, has caused several large outbreaks in the Korean military since 2014, and HAdV-associated acute respiratory illness (HAdV-ARI) has been continuously reported thereafter. METHODS: To evaluate the epidemiologic characteristics of HAdV-ARI in the Korean military, we analyzed respiratory virus polymerase chain reaction (RV-PCR) results, pneumonia surveillance results, and severe HAdV cases from all 14 Korean military hospitals from January 2013 to May 2018 and compared these data with nationwide RV surveillance data for the civilian population. RESULTS: A total of 14,630 RV-PCRs was performed at military hospitals. HAdV (45.4%) was the most frequently detected RV, followed by human rhinovirus (12.3%) and influenza virus (6.3%). The percentage of the military positive for HAdV was significantly greater than the percentage of civilians positive for HAdV throughout the study period, with a large outbreak occurring during the winter to spring of 2014 to 2015. The outbreak continued until the end of the study, and non-seasonal detections increased over time. The reported number of pneumonia patients also increased during the outbreak. Case fatality rate was 0.075% overall but 15.6% in patients with respiratory failure. The proportion of severe patients did not change significantly during the study period. CONCLUSION: A large HAdV outbreak is currently ongoing in the Korean military, with a trend away from seasonality, and HAdV-55 is likely the predominant strain. Persistent efforts to control the outbreak, HAdV type-specific surveillance, and vaccine development are required.

Automated detection of vulnerable plaque in intravascular ultrasound images.

Acute coronary syndrome (ACS) is a syndrome caused by a decrease in blood flow in the coronary arteries. The ACS is usually related to coronary thrombosis and is primarily caused by plaque rupture followed by plaque erosion and calcified nodule. Thin-cap fibroatheroma (TCFA) is known to be the most similar lesion morphologically to a plaque rupture. In this paper, we propose methods to classify TCFA using various machine learning classifiers including feed-forward neural network (FNN), K-nearest neighbor (KNN), random forest (RF), and convolutional neural network (CNN) to figure out a classifier that shows optimal TCFA classification accuracy. In addition, we suggest pixel range-based feature extraction method to extract the ratio of pixels in the different region of interests to reflect the physician's TCFA discrimination criteria. Our feature extraction method examines the pixel distribution of the intravascular ultrasound (IVUS) image at a given ROI, which allows us to extract general characteristics of the IVUS image while simultaneously reflecting the different properties of the vessel's substances such as necrotic core and calcified nodule depending on the brightness of the pixel. A total of 12,325 IVUS images were labeled with corresponding optical coherence tomography (OCT) images to train and evaluate the classifiers. We achieved 0.859, 0.848, 0.844, and 0.911 area under the ROC curve (AUC) in the order of using FNN, KNN, RF, and CNN classifiers. As a result, the CNN classifier performed best and the top 10 features of the feature-based classifiers (FNN, KNN, RF) were found to be similar to the physician's TCFA diagnostic criteria. Graphical Abstract AUC result of proposed classifiers.