Single-cell sequencing of immune cells after marathon and symptom-limited cardiopulmonary exercise.

Vigorous-intensity leisure-time physical activity, such as marathon, has become increasingly popular, but its effect on immune functions and health is poorly understood. Here, we performed scRNA-seq analysis of peripheral blood mononuclear cells (PBMCs) after a bout of symptom-limited cardiopulmonary exercise (CPX) test or marathon. Time-series single-cell analysis revealed the detailed series of landscapes of immune cells in response to short and long vigorous-intensity activities. Reduction of effective T cells was observed with the cell migration and motility pathways enriched in circulation following marathon. Baseline values of PBMCs abundance were reached around 1 h after CPX and 24 h following marathon, but longer time was required for expression recovery of cytotoxicity genes. The ratio of effector/naive T cells was found to change uniformly among the participants and could serve as a better indicator for exercise intensity than the CD4+/CD8+ T cell ratio. Moreover, we identified time-dependent monocyte state transitions after marathon.

Decision support analysis for risk identification and control of patients affected by COVID-19 based on Bayesian Networks.

In the context of the outbreak of coronavirus disease (COVID-19), this paper proposes an innovative and systematic decision support model based on Bayesian networks (BNs) to identify and control the risk of COVID-19 patients spreading the virus, which requires the following three steps. First, by consulting the related literature and combining this with expert knowledge, we identify and classify the characteristics (risk factors) of COVID-19 and obtain a conceptual framework for COVID-19 Risk Assessment Bayesian Networks (CRABNs). Second, data on COVID-19 patients with expert scoring results on patient risk levels were collected from hospitals in Hubei Province of China and are used as the training set, and the structure and parameters of the CRABNs model are obtained through machine learning. Finally, we propose two indicators, namely, Model Bias and Model Accuracy, and use the remaining data to verify the feasibility and effectiveness of the CRABNs model to ensure that there are no significant differences between the predicted results of the model and the actual results provided by experts who have relevant experience in treating COVID-19. At the same time, we compared the CRABNs model with the support vector machine (SVM), random forest (RF), and k-nearest neighbour (KNN) models through four indicators: accuracy, sensitivity, specificity, and F-score. The results suggest the reliability of the model and show that it has promising application potential. The proposed model can be used globally by doctors in hospitals as a decision support tool to improve the accuracy of assessing the severity of COVID-19 symptoms in patients. Furthermore, with the further improvement of the model in the future, it can be used for risk assessments in the field of epidemics.