Ezetimibe treatment reduces oxidized low-density lipoprotein in biliary cirrhotic rats

BACKGROUND

In liver cirrhosis, chronic inflammation is associated with an increase in oxidative stress, and subsequently an increase in the concentration of oxidized low-density lipoprotein (ox-LDL). Ezetimibe is a lipid-lowering agent with anti-inflammation and anti-oxidative stress activities. This study aimed to investigate the effect of ezetimibe treatment on ox-LDL in cirrhotic rats.

METHODS

Biliary cirrhosis was induced in Sprague-Dawley rats with common bile duct ligation (BDL). Sham-operated rats served as surgical controls. Ezetimibe (10 mg/kg/d) or vehicle was administered in the sham-operated or BDL rats for 4 weeks, after which hemodynamic parameters, biochemistry data, and oxidative stress were evaluated. Plasma and intrahepatic ox-LDL levels were also examined, and hepatic proteins were analyzed to explore the mechanism of ezetimibe treatment.

RESULTS

The BDL rats had typical features of cirrhosis including jaundice, impaired liver function, hyperlipidemia, and elevated ox-LDL levels compared to the sham-operated rats. Ezetimibe treatment did not affect hemodynamics, liver biochemistry, or plasma lipid levels. However, it significantly reduced oxidative stress, plasma levels of ox-LDL, and tumor necrosis factor α. In addition, ezetimibe upregulated the hepatic protein expression of an ox-LDL scavenger (lectin-like ox-LDL rececptor-1), which resulted in reductions in intrahepatic ox-LDL and fat accumulation in the BDL rats. Nevertheless, ezetimibe treatment did not ameliorate hepatic inflammation or liver fibrosis.

CONCLUSION

Ezetimibe reduced plasma and intrahepatic ox-LDL levels in the cirrhotic rats. Furthermore, it ameliorated intrahepatic fat accumulation and oxidative stress. However, ezetimibe did not alleviate hepatic fibrosis or inflammation in the biliary cirrhotic rats.

Prediction in a driven-dissipative system displaying a continuous phase transition using machine learning

Prediction in complex systems at criticality is believed to be very difficult, if not impossible. Of particular interest is whether earthquakes, whose distribution follows a power-law (Gutenberg-Richter) distribution, are in principle unpredictable. We study the predictability of event sizes in the Olmai-Feder-Christensen model at different proximities to criticality using a convolutional neural network. The distribution of event sizes satisfies a power law with a cutoff for large events. We find that predictability decreases as criticality is approached and that prediction is possible only for large, nonscaling events. Our results suggest that earthquake faults that satisfy Gutenberg-Richter scaling are difficult to forecast.

Effective ergodicity breaking phase transition in a driven-dissipative system

We show that the Olami-Feder-Christensen model exhibits an effective ergodicity breaking transition as the noise is varied. Above the critical noise, the system is effectively ergodic because the time-averaged stress on each site converges to the global spatial average. In contrast, below the critical noise, the stress on individual sites becomes trapped in different limit cycles, and the system is not ergodic. To characterize this transition, we use ideas from the study of dynamical systems and compute recurrence plots and the recurrence rate. The order parameter is identified as the recurrence rate averaged over all sites and exhibits a jump at the critical noise. We also use ideas from percolation theory and analyze the clusters of failed sites to find numerical evidence that the transition, when approached from above, can be characterized by exponents that are consistent with hyperscaling.