Background: Physical activity is known to prevent several diseases and positively affect mental health. Previous studies have shown that smartphone addiction negatively affects the physical activity of children and adolescents. This study aimed to investigate the relationship between problematic smartphone use and physical activity among adolescents and the related factors using path analysis. Methods: Using data from the 16th Youth Risk Behavior Web-based Survey from 2020, scores on the Smartphone Addiction Scale—Short Version for Adolescents, physical activity, sex, socioeconomic status (SES), academic performance, depression, smoking, drinking, and sitting time were assessed. Complex sampling and path analyses were performed. Results: Of the total 54,948 students, 25.5% were smartphone risk users, including potential and high-risk users. The direct path coefficients of each factor indicated that female sex (-0.14 for male), low SES (-0.062), high academic performance (0.056), low sitting time for studying purposes (-0.033), high sitting time for non-studying purposes (0.071), and depressive mood (0.130) were related to problematic smartphone use (all P<0.001). Each factor affected problematic smartphone use, and subsequently had a negative effect on the amount of physical activity, with a direct path coefficient of -0.115 (P<0.001). Conclusion In this study, we confirmed that problematic smartphone use among adolescents was negatively associated with performing an adequate amount of physical activity and that various factors, such as sex, SES, academic performance, and sitting time, directly and indirectly affected this relationship.

The severe acute respiratory coronavirus 2 (SARS-CoV-2), which emerged in December 2019 in Wuhan, China, has spread rapidly to over a dozen countries. Especially, the spike of case numbers in South Korea sparks pandemic worries. This virus is reported to spread mainly through personto- person contact via respiratory droplets generated by coughing and sneezing, or possibly through surface contaminated by people coughing or sneezing on them. More critically, there have been reports about the possibility of this virus to transmit even before a virus-carrying person to show symptoms. Therefore, a low-cost, easy-access protocol for early detection of this virus is desperately needed. Here, we have established a real-time reverse-transcription PCR (rtPCR)-based assay protocol composed of easy specimen self-collection from a subject via pharyngeal swab, Trizolbased RNA purification, and SYBR Green-based rtPCR. This protocol shows an accuracy and sensitivity limit of 1-10 virus particles as we tested with a known lentivirus. The cost for each sample is estimated to be less than 15 US dollars. Overall time it takes for an entire protocol is estimated to be less than 4 hours. We propose a cost-effective, quick-and-easy method for early detection of SARS-CoV-2 at any conventional Biosafety Level II laboratories that are equipped with a rtPCR machine. Our newly developed protocol should be helpful for a first-hand screening of the asymptomatic virus-carriers for further prevention of transmission and early intervention and treatment for the rapidly propagating virus.

Background: Surgical resection is the standard treatment for early-stage lung cancer. Since postoperative lung function is related to mortality, predicted postoperative lung function is used to determine the treatment modality. The aim of this study was to evaluate the predictive performance of linear regression and machine learning models. Methods: We extracted data from the Clinical Data Warehouse and developed three sets: set I, the linear regression model; set II, machine learning models omitting the missing data: and set III, machine learning models imputing the missing data. Six machine learning models, the least absolute shrinkage and selection operator (LASSO), Ridge regression, ElasticNet, Random Forest, eXtreme gradient boosting (XGBoost), and the light gradient boosting machine (LightGBM) were implemented. The forced expiratory volume in 1 second measured 6 months after surgery was defined as the outcome. Five-fold cross-validation was performed for hyperparameter tuning of the machine learning models. The dataset was split into training and test datasets at a 70:30 ratio. Implementation was done after dataset splitting in set III. Predictive performance was evaluated by R2 and mean squared error (MSE) in the three sets. Results: A total of 1,487 patients were included in sets I and III and 896 patients were included in set II. In set I, the R2 value was 0.27 and in set II, LightGBM was the best model with the highest R2 value of 0.5 and the lowest MSE of 154.95. In set III, LightGBM was the best model with the highest R2 value of 0.56 and the lowest MSE of 174.07. Conclusion The LightGBM model showed the best performance in predicting postoperative lung function.