No abstract

As an emerging infectious disease, the 2019 coronavirus disease (COVID-19) has developed into a global pandemic. During the initial spreading of the virus in China, we demonstrated the ensemble Kalman filter performed well as a short-term predictor of the daily cases reported in Wuhan City. Second, we used an individual-level network-based model to reconstruct the epidemic dynamics in Hubei Province and examine the effectiveness of non-pharmaceutical interventions on the epidemic spreading with various scenarios. Our simulation results show that without continued control measures, the epidemic in Hubei Province could have become persistent. Only by continuing to decrease the infection rate through 1) protective measures and 2) social distancing can the actual epidemic trajectory that happened in Hubei Province be reconstructed in simulation. Finally, we simulate the COVID-19 transmission with non-Markovian processes and show how these models produce different epidemic trajectories, compared to those obtained with Markov processes. Since recent studies show that COVID-19 epidemiological parameters do not follow exponential distributions leading to Markov processes, future works need to focus on non-Markovian models to better capture the COVID-19 spreading trajectories. In addition, shortening the infectious period via early case identification and isolation can slow the epidemic spreading significantly.

Fatty liver disease is one of the main hepatic complications associated with obesity. To date, there are no therapeutic drugs approved for this pathology. Insulin resistance (IR) is implicated both in pathogenesis of nonalcoholic fatty liver disease (NAFLD) and in disease progression from steatosis to nonalcoholic steatohepatitis. In this study, we have characterized effects of an α 2 ‐adrenoceptor agonist, dexmedetomidine (DEX), which can alleviate IR in hepatocytes in high‐fat diet (HFD)‐induced NAFLD mice. The NAFLD mice received a daily intraperitoneal administration of DEX (100 μg·kg ‐1 ) after 16 days exhibited lower body weight, fewer and smaller fat droplets in the liver, markedly reduced the plasma triglyceride levels, accompanied by improvement of liver damage. This inhibition of lipid accumulation activity in obese mice was associated with a robust reduction in the mRNA and protein expression of the lipogenic enzyme stearyl‐coenzyme A desaturase 1 (SCD1), which was probably mediated by the inhibition of C/EBP β, PPAR γ and C/EBP α through suppressing α 2A ‐adrenoceptor ( α 2A ‐AR ) via negative feedback. Additionally, DEX can also improve IR and inflammation by inhibiting the mitogen‐activated protein kinases (MAPK) and nuclear factor kappa beta (NFκB) signaling pathway in vivo. Our findings implicate that DEX may act as a potential anti‐steatotic drug which ameliorates obesity‐associated fatty liver and improves IR and inflammation, probably by suppressing the expression of SCD1 and the inhibition of MAPK/NFκB pathway and suggest the potential adjuvant use for the treatment of NAFLD.

No abstract

As an emerging infectious disease, the 2019 coronavirus disease has developed into a global pandemic. During the initial spreading of the virus in China, we demonstrated the ensemble Kalman filter performed well as a short-term predictor of the daily cases reported in Wuhan City. Second, we used an individual-level network-based model to reconstruct the epidemic dynamics in Hubei Province at its early stage and examine the effectiveness of non-pharmaceutical interventions on the epidemic spreading with various scenarios. Our simulation results show that without continued control measures, the epidemic in Hubei Province could have become persistent. Only by continuing to decrease the infection rate through 1) protective measures and 2) social distancing can the actual epidemic trajectory that happened in Hubei Province be reconstructed in simulation. Finally, we simulate the COVID-19 transmission with non-Markovian processes and show how these models produce different epidemic trajectories, compared to those obtained with Markov processes. Since recent studies show that COVID-19 epidemiological parameters do not follow exponential distributions leading to Markov processes, future works need to focus on non-Markovian models to better capture the COVID-19 spreading trajectories. In addition, shortening the infectious period via early case identification and isolation can slow the epidemic spreading significantly.