Problematic online game use (POGU) has become a serious global public health concern among adolescents. However, its influencing factors and mediating mechanisms remain largely unknown. This study provides the first longitudinal design to test stage-environment fit theory empirically in POGU. A total of 356 Chinese students reported on teacher autonomy support, basic psychological needs satisfaction, school engagement, and POGU in the autumn of their 7th-9th grade years. Path analyses supported the proposed pathway: 7th grade teacher autonomy support increased 8th grade basic psychological needs satisfaction, which in turn increased 9th grade school engagement, which ultimately decreased 9th grade POGU. Furthermore, 7th grade teacher autonomy support directly increased 9th grade school engagement, which in turn decreased 9th grade POGU. These findings suggest that teacher autonomy support is an important protective predictor of adolescent POGU, and basic psychological needs satisfaction and school engagement are the primary mediators in this association.

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Background To date, the missed diagnosis rate of pulmonary hypertension (PH) was high, and there has been limited development of a rapid, simple, and effective way to screen the disease. The purpose of this study is to develop a deep learning approach to achieve rapid detection of possible abnormalities in chest radiographs suggesting PH for screening patients suspected of PH. Methods We retrospectively collected frontal chest radiographs and the pulmonary artery systolic pressure (PASP) value measured by Doppler transthoracic echocardiography from 762 patients (357 healthy controls and 405 with PH) from three institutes in China from January 2013 to May 2019. The wohle sample comprised 762 images (641 for training, 80 for internal test, and 41 for external test). We firstly performed a 8-fold cross-validation on the 641 images selected for training (561 for pre-training, 80 for validation), then decided to tune learning rate to 0.0008 according to the best score on validation data. Finally, we used all the pre-training and validation data (561+80 = 641) to train our models (Resnet50, Xception, and Inception V3), evaluated them on internal and external test dataset to classify the images as having manifestations of PH or healthy according to the area under the receiver operating characteristic curve (AUC/ROC). After that, the three deep learning models were further used for prediction of PASP using regression algorithm. Moreover, we invited an experienced chest radiologist to classify the images in the test dataset as having PH or not, and compared the prediction accuracy performed by deep learing models with that of manual classification.