The current pandemic of coronavirus disease 2019 (COVID-19) has affected >160 million individuals to date, and has caused millions of deaths worldwide, at least in part due to the unclarified pathophysiology of this disease. Identifying the underlying molecular mechanisms of COVID-19 is critical to overcome this pandemic. Metabolites mirror the disease progression of an individual and can provide extensive insights into their pathophysiological significance at each stage of disease. We provide a comprehensive view of metabolic characterisation of sera from COVID-19 patients at all stages using untargeted and targeted metabolomic analysis. As compared with the healthy controls, we observed different alteration patterns of circulating metabolites from the mild, severe and recovery stages, in both the discovery cohort and the validation cohort, which suggests that metabolic reprogramming of glucose metabolism and the urea cycle are potential pathological mechanisms for COVID-19 progression. Our findings suggest that targeting glucose metabolism and the urea cycle may be a viable approach to fight COVID-19 at various stages along the disease course.

The prediction of relapse in childhood acute lymphoblastic leukemia (ALL) is a critical factor for successful treatment and follow-up planning. Our goal was to construct an ALL relapse prediction model based on machine learning algorithms. Monte Carlo cross-validation nested by 10-fold cross-validation was used to rank clinical variables on the randomly split training sets of 336 newly diagnosed ALL children, and a forward feature selection algorithm was employed to find the shortest list of most discriminatory variables. To enable an unbiased estimation of the prediction model to new patients, besides the split test sets of 150 patients, we introduced another independent data set of 84 patients to evaluate the model. The Random Forest model with 14 features achieved a cross-validation accuracy of 0.827 ± 0.031 on one set and an accuracy of 0.798 on the other, with the area under the curve of 0.902 ± 0.027 and 0.904, respectively. The model performed well across different risk-level groups, with the best accuracy of 0.829 in the standard-risk group. To our knowledge, this is the first study to use machine learning models to predict childhood ALL relapse based on medical data from Electronic Medical Record, which will further facilitate stratification treatments.

Early detection is critical to achieving improved treatment outcomes for child patients with congenital heart diseases (CHDs). Therefore, developing effective CHD detection techniques using low-cost and non-invasive pediatric electrocardiogram are highly desirable. We propose a deep learning approach for CHD detection, CHDdECG, which automatically extracts features from pediatric electrocardiogram and wavelet transformation characteristics, and integrates them with key human-concept features. Developed on 65,869 cases, CHDdECG achieved ROC-AUC of 0.915 and specificity of 0.881 on a real-world test set covering 12,000 cases. Additionally, on two external test sets with 7137 and 8121 cases, the overall ROC-AUC were 0.917 and 0.907 while specificities were 0.937 and 0.907. Notably, CHDdECG surpassed cardiologists in CHD detection performance comparison, and feature importance scores suggested greater influence of automatically extracted electrocardiogram features on CHD detection compared with human-concept features, implying that CHDdECG may grasp some knowledge beyond human cognition. Our study directly impacts CHD detection with pediatric electrocardiogram and demonstrates the potential of pediatric electrocardiogram for broader benefits.