Background COVID-19, which is accompanied by acute respiratory distress, multiple organ failure, and death, has spread worldwide much faster than previously thought. However, at present, it has limited treatments. Objective To overcome this issue, we developed an artificial intelligence (AI) model of COVID-19, named EDRnet (ensemble learning model based on deep neural network and random forest models), to predict in-hospital mortality using a routine blood sample at the time of hospital admission. Methods We selected 28 blood biomarkers and used the age and gender information of patients as model inputs. To improve the mortality prediction, we adopted an ensemble approach combining deep neural network and random forest models. We trained our model with a database of blood samples from 361 COVID-19 patients in Wuhan, China, and applied it to 106 COVID-19 patients in three Korean medical institutions. Results In the testing data sets, EDRnet provided high sensitivity (100%), specificity (91%), and accuracy (92%). To extend the number of patient data points, we developed a web application (BeatCOVID19) where anyone can access the model to predict mortality and can register his or her own blood laboratory results. Conclusions Our new AI model, EDRnet, accurately predicts the mortality rate for COVID-19. It is publicly available and aims to help health care providers fight COVID-19 and improve patients’ outcomes.

Background The number of deaths from COVID-19 continues to surge worldwide. In particular, if a patient’s condition is sufficiently severe to require invasive ventilation, it is more likely to lead to death than to recovery. Objective The goal of our study was to analyze the factors related to COVID-19 severity in patients and to develop an artificial intelligence (AI) model to predict the severity of COVID-19 at an early stage. Methods We developed an AI model that predicts severity based on data from 5601 COVID-19 patients from all national and regional hospitals across South Korea as of April 2020. The clinical severity of COVID-19 was divided into two categories: low and high severity. The condition of patients in the low-severity group corresponded to no limit of activity, oxygen support with nasal prong or facial mask, and noninvasive ventilation. The condition of patients in the high-severity group corresponded to invasive ventilation, multi-organ failure with extracorporeal membrane oxygenation required, and death. For the AI model input, we used 37 variables from the medical records, including basic patient information, a physical index, initial examination findings, clinical findings, comorbid diseases, and general blood test results at an early stage. Feature importance analysis was performed with AdaBoost, random forest, and eXtreme Gradient Boosting (XGBoost); the AI model for predicting COVID-19 severity among patients was developed with a 5-layer deep neural network (DNN) with the 20 most important features, which were selected based on ranked feature importance analysis of 37 features from the comprehensive data set. The selection procedure was performed using sensitivity, specificity, accuracy, balanced accuracy, and area under the curve (AUC). Results We found that age was the most important factor for predicting disease severity, followed by lymphocyte level, platelet count, and shortness of breath or dyspnea. Our proposed 5-layer DNN with the 20 most important features provided high sensitivity (90.2%), specificity (90.4%), accuracy (90.4%), balanced accuracy (90.3%), and AUC (0.96). Conclusions Our proposed AI model with the selected features was able to predict the severity of COVID-19 accurately. We also made a web application so that anyone can access the model. We believe that sharing the AI model with the public will be helpful in validating and improving its performance.

Therapeutic, IV.

Two yellow-pigmented, Gram-reaction-negative strains, designated 01SU5-P T and 03SU3-P T , were isolated from the freshwater of Woopo wetland, Republic of Korea. Both strains were aerobic, non-motile and catalase-negative. Phylogenetic analysis based on 16S rRNA gene sequences indicated that the two isolates belong to the genus Sphingopyxis, showing the highest level of sequence similarity with respect to Sphingopyxis witflariensis W-50 T (95.4-95.7 %). The two novel isolates shared 99.4 % sequence similarity. DNA-DNA hybridization between the isolates and the type strain of S. witflariensis clearly suggested that strains 01SU5-P T and 03SU3-P T represent two separate novel species in the genus Sphingopyxis. The two strains displayed different fingerprints after PCR analysis using the repetitive primers BOX, ERIC and REP. Several phenotypic characteristics served to differentiate these two isolates from recognized members of the genus Sphingopyxis. The data from the polyphasic study presented here indicated that strains 01SU5-P T and 03SU3-P T should be classified as representing novel species in the genus Sphingopyxis, for which the names Sphingopyxis rigui sp. nov. and Sphingopyxis wooponensis sp. nov., respectively, are proposed. The type strain of Sphingopyxis rigui sp. nov. is 01SU5-P T (5KCTC 23326 T 5JCM 17509 T ) and the type strain of Sphingopyxis wooponensis sp. nov. is

Total ear reconstruction by the use of contralateral temporoparietal fascial free flap and autogenous costal cartilage was performed in 16 patients presenting with a devascularized temporoparietal region resulting from trauma or prior surgery. The microsurgical success rate was 87.5 percent (14 of 16 transplants). On evaluation of the final aesthetic result in 11 patients followed up for more than 3 years, nine patients were graded good-to-excellent and two patients exhibited fair-to-poor results. Despite the relatively long operating hours and the comparatively low microsurgical success rate, ear reconstruction by autogenous tissue transplantation has proved to be an encouraging and worthwhile experience. This article presents the clinical cases and discusses the technical details.