The outbreak of coronavirus disease 2019 which originated in Wuhan, China, constitutes a public health emergency of international concern with a very high risk of spread and impact at the global level.We developed data-driven susceptible-exposed-infectious-quarantine-recovered (SEIQR) models to simulate the epidemic with the interventions of social distancing and epicenter lockdown. Population migration data combined with officially reported data were used to estimate model parameters, and then calculated the daily exported infected individuals by estimating the daily infected ratio and daily susceptible population size. As of Jan 01, 2020, the estimated initial number of latently infected individuals was 380.1 (95%-CI: 379.8~381.0). With 30 days of substantial social distancing, the reproductive number in Wuhan and Hubei was reduced from 2.2 (95%-CI: 1.4~3.9) to 1.58 (95%-CI: 1.34~2.07), and in other provinces from 2.56 (95%-CI: 2.43~2.63) to 1.65 (95%-CI: 1.56~1.76). We found that earlier intervention of social distancing could significantly limit the epidemic in mainland China. The number of infections could be reduced up to 98.9%, and the number of deaths could be reduced by up to 99.3% as of Feb 23, 2020. However, earlier epicenter lockdown would partially neutralize this favorable effect.Because it would cause in situ deteriorating, which overwhelms the improvement out of the epicenter. To minimize the epidemic size and death, stepwise implementation of social distancing in the epicenter city first, then in the province, and later the whole nation without the epicenter lockdown would be practical and cost-effective.

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Objectives: Cellular senescence may play an important role in the pathology of heart aging. We aimed to explore whether induced pluripotent stem cells (iPSCs) could inhibit cardiac cellular senescence via a paracrine mechanism. Methods: We collected iPSC culture supernatant, with or without oxidative stress, as conditioned medium (CM) for the rat cardiomyocyte-derived cell line H9C2. Then we treated H9C2 cells, cultured with or without CM, with hypoxia/reoxygenation to induce cellular senescence and measured senescence-associated β-galactosidase (SA-β-gal) activity, G1 cell proportion and expression of the cell cycle regulators p16^INK4a, p21^Waf1/Cip1 and p53 at mRNA and protein levels in H9C2 cells. In addition, we used Luminex-based analysis to measure concentrations of trophic factors in iPSC-derived CM. Results: We found that iPSC-derived CM reduced SA-β-gal activity, attenuated G1 cell cycle arrest and reduced the expression of p16^INK4a, p21^Waf1/Cip1 and p53 in H9C2 cells. Furthermore, the CM contained more trophic factors, e.g. tissue inhibitor of metalloproteinase-1 and vascular endothelial growth factor, than H9C2-derived CM. Conclusions: Paracrine factors released from iPSCs prevent stress-induced senescence of H9C2 cells by inhibiting p53-p21 and p16-pRb pathways. This is the first report demonstrating that antisenescence effects of stem cell therapy may be a novel therapeutic strategy for age-related cardiovascular disease.

An evolutionary ensemble modeling (EEM) method is developed to improve the accuracy of warfarin dose prediction. In EEM, genetic programming (GP) evolves diverse base models, and genetic algorithm optimizes the parameters of the GP. The EEM model is assembled by using the prepared based models through a technique called "bagging." In the experiment, a dataset of 289 Chinese patients, which is provided by The First Affiliated Hospital of Soochow University, is used for training, validation, and testing. The EEM model with selected feature groups is benchmarked with four machine-learning methods and three conventional regression models. Results show that the EEM model with M2+G group, namely, age, height, weight, gender, CYP2C9, VKORC1, and amiodarone, presents the largest coefficients of determination (R2), highest percentage of predicted dose within 20% of the actual dose (20%-p), smallest mean absolute error (mae), mean squared error (mse), root-mse on the test set, and the least decrease in R2 from the training set to the test set. In conclusion, the EEM method with M2+G delivers superior performance and can therefore be a suitable prediction model of warfarin dose for clinical application.