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Influenza A (H1N1) viruses are prone to antigenic mutations and are more variable than other influenza viruses. Therefore, they have caused continuous harm to human public health since the pandemic in 2009 and in recent times. Influenza A (H1N1) can be prevented and treated in various ways, such as direct inhibition of the virus and regulation of human immunity. Among antiviral drugs, the use of natural products in treating influenza has a long history, and natural medicine has been widely considered the focus of development programs for new, safe anti-influenza drugs. In this paper, we focus on influenza A (H1N1) and summarize the natural product-derived phytochemicals for influenza A virus (H1N1) prevention and treatment, including marine natural products, flavonoids, alkaloids, terpenoids and their derivatives, phenols and their derivatives, polysaccharides, and derivatives of natural products for prevention and treatment of influenza A (H1N1) virus. We further discuss the toxicity and antiviral mechanism against influenza A (H1N1) as well as the druggability of natural products. We hope that this review will facilitate the study of the role of natural products against influenza A (H1N1) activity and provide a promising alternative for further anti-influenza A drug development. [ABSTRACT FROM AUTHOR]

Knowledge graph (KG) reasoning improves the perception ability of graph structure features, improving model accuracy and enhancing model learning and reasoning capabilities. This paper proposes a new GraphDIVA model based on the variational reasoning divergent autoencoder (DIVA) model. The network structures and calculation processes of the models are analyzed. The GraphSAGE algorithm is introduced into the path reasoning module to solve the inability of the original model to perceive the features of the graph structure, which leads to a decline in the accuracy rate. Hence, GraphDIVA can achieve a higher accuracy rate with fewer learning iterations. The experiments show the efficiency and effectiveness of our model and proves that our method has a better effect on the accuracy rate and training difficulty than the baseline model on the FB15k-237 and NELL-995 benchmark datasets. [ABSTRACT FROM AUTHOR]

To explore hemodynamic interaction between the human respiratory system (RS) and cardiovascular system (CVS), here we propose an integrated computational model to predict the CVS hemodynamics with consideration of the respiratory fluctuation (RF). A submodule of the intrathoracic pressure (ITP) adjustment is developed and incorporated in a 0-1D multiscale hemodynamic model of the CVS specified for infant, adolescent, and adult individuals. The model is verified to enable reasonable estimation of the blood pressure waveforms accounting for the RF-induced pressure fluctuations in comparison with clinical data. The results show that the negative ITP caused by respiration increases the blood flow rates in superior and inferior vena cavae; the deep breathing improves the venous return in adolescents but has less influence on infants. It is found that a marked reduction in ITP under pathological conditions can excessively increase the flow rates in cavae independent of the individual ages, which may cause the hemodynamic instability and hence increase the risk of heart failure. Our results indicate that the present 0-1D multiscale CVS model incorporated with the RF effect is capable of providing a useful and effective tool to explore the physiological and pathological mechanisms in association with cardiopulmonary interactions and their clinical applications. [ABSTRACT FROM AUTHOR]