Trends of obesity and overweight among children and adolescents in China.

BACKGROUND: Recent decades have shown a rapid increase in the prevalence of overweight and obesity among Chinese children based on several national surveys. Restrictions due to the coronavirus disease 2019 outbreak have worsened its epidemiology. This review updates the trends in the prevalence of overweight and obesity among Chinese children and adolescents and analyzes the underlying reasons to provide evidence for better policy making. METHODS: Studies published in English and Chinese were retrieved from PubMed, Google Scholar, China National Knowledge Infrastructure and Wanfang. RESULTS: The prevalence of overweight and obesity has been increasing for decades and varies with age, sex and geography but is more pronounced in primary school students. The increase in obesity in boys appeared to be slower, whereas that in girls showed a declining trend. The northern areas of China have persistently maintained the highest levels of obesity with a stable trend in recent years. Meanwhile, the prevalence in eastern regions has dramatically increased. Notably, the overall prevalence of obesity in children has shown a stabilizing trend in recent years. However, the occurrence of obesity-related metabolic diseases increased. The effect of migrants floating into east-coast cities should not be neglected. CONCLUSIONS: The high prevalence of overweight and obesity among Chinese children and adolescents persists but with varying patterns. Obesity-related metabolic diseases occur more frequently despite a stable trend of obesity. Multiple factors are responsible for the changing prevalence. Thus, comprehensive and flexible policies are needed to effectively manage and prevent the burden of obesity and its related complications.

DC-GAN-based synthetic X-ray images augmentation for increasing the performance of EfficientNet for COVID-19 detection.

Currently, many deep learning models are being used to classify COVID-19 and normal cases from chest X-rays. However, the available data (X-rays) for COVID-19 is limited to train a robust deep-learning model. Researchers have used data augmentation techniques to tackle this issue by increasing the numbers of samples through flipping, translation, and rotation. However, by adopting this strategy, the model compromises for the learning of high-dimensional features for a given problem. Hence, there are high chances of overfitting. In this paper, we used deep-convolutional generative adversarial networks algorithm to address this issue, which generates synthetic images for all the classes (Normal, Pneumonia, and COVID-19). To validate whether the generated images are accurate, we used the k-mean clustering technique with three clusters (Normal, Pneumonia, and COVID-19). We only selected the X-ray images classified in the correct clusters for training. In this way, we formed a synthetic dataset with three classes. The generated dataset was then fed to The EfficientNetB4 for training. The experiments achieved promising results of 95% in terms of area under the curve (AUC). To validate that our network has learned discriminated features associated with lung in the X-rays, we used the Grad-CAM technique to visualize the underlying pattern, which leads the network to its final decision.