The outbreak of coronavirus disease 2019 (COVID-19) 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.

The prediction of daily stable warfarin dosage for a specific patient is difficult. To improve the predictive accuracy and build a highly accurate predictive model, we developed an ensemble learning method, called evolutionary fuzzy c-mean (EFCM) clustering algorithm with support vector regression (SVR). A dataset of 517 Han Chinese patients was collected from the data of The First Affiliated Hospital of Soochow University and dataset of International Warfarin Pharmacogenetics Consortium for training and testing. In EFCM+SVR, we adopted SVR to build a generalized base model (SVR model). To achieve an accurate prediction on patients with large dosage, we proposed an EFCM clustering algorithm that can be used to cluster the training set and designed a clustering model on clusters and centroids. The SVR and clustering models were integrated into an ensemble model by stepwise functions. In the experiment, three artificial neural networks, SVR, two ensemble models and three regression models were used as comparators to the EFCM+SVR model, which obtained the smallest mean absolute error (0.67 mg/day) in warfarin dose prediction and the largest R-squared (43.9%). The model achieved satisfactory prediction in terms of the percentage of patients whose predicted dose of warfarin was within 15% and 20% of the actual stable therapeutic dose (15%-p of 36% and 20%-p of 46.6%).

Previous research has established a correlation between PM2.5 exposure and aging-related cardiovascular diseases, primarily in blood vessels. However, the impact of PM2.5 on cardiomyocyte aging remains unclear. In this study, we observed that extractable organic matter (EOM) from PM2.5 exposure led to cellular senescence in H9c2 cardiomyoblast cells, as characterized by an increase in the percentage of β-galactosidase-positive cells, elevated expression levels of p16 and p21, and enhanced H3K9me3 foci. EOM also induced cell cycle arrest at the G1/S stage, accompanied by downregulation of CDK4 and Cyclin D1. Furthermore, EOM exposure led to a significant elevation in intracellular reactive oxygen species (ROS), mitochondrial ROS, and DNA damage. Supplementation with the antioxidant NAC effectively attenuated EOM-induced cardiac senescence. Our findings also revealed that exposure to EOM activated the aryl hydrocarbon receptor (AhR) signaling pathway, as evidenced by AhR translocation to the nucleus and upregulation of Cyp1a1 and Cyp1b1. Importantly, the AhR antagonist CH223191 effectively mitigated EOM-induced oxidative stress and cellular senescence. In conclusion, our results indicate that PM2.5-induced AhR activation leads to oxidative stress, DNA damage, and cell cycle arrest, leading to cardiac senescence. Targeting the AhR/ROS axis might be a promising therapeutic strategy for combating PM2.5-induced cardiac aging.

To interprete the importance of clinical features and genotypes for warfarin daily dose prediction, we developed a post-hoc interpretable framework based on an ensemble predictive model. This framework includes permutation importance for global interpretation and local interpretable model-agnostic explanation (LIME) and shapley additive explanations (SHAP) for local explanation. The permutation importance globally ranks the importance of features on the whole data set. This can guide us to build a predictive model with less variables and the complexity of final predictive model can be reduced. LIME and SHAP together explain how the predictive model give the predicted dosage for specific samples. This help clinicians prescribe accurate doses to patients using more effective clinical variables. Results showed that both the permutation importance and SHAP demonstrated that VKORC1, age, serum creatinine (SCr), left atrium (LA) size, CYP2C9 and weight were the most important features on the whole data set. In specific samples, both SHAP and LIME discovered that in Chinese patients, wild-type VKORC1-AA, mutant-type CYP2C9*3, age over 60, abnormal LA size, SCr within the normal range, and using amiodarone definitely required dosage reduction, whereas mutant-type VKORC1-AG/GG, small age, SCr out of normal range, normal LA size, diabetes and heavy weight required dosage enhancementt.