Resveratrol Alleviates Arsenic Exposure-Induced Liver Fibrosis in Rats by Inhibiting Hepatocyte Senescence.

Arsenic is an environmental pollutant that has garnered considerable attention from the World Health Organization. Liver fibrosis is an advanced pathological stage of liver injury that can be caused by chronic arsenic exposure and has the potential to be reversed to prevent cirrhosis and hepatic malignancies. However, effective treatment options are currently limited. Given the profibrogenic effect of hepatocyte senescence, we established a rat model of sub-chronic sodium arsenite exposure and investigated the ability of resveratrol (RSV), a potential anti-senescence agent, to ameliorate arsenic-induced liver fibrosis and elucidate the underlying mechanism from the perspective of hepatocyte senescence. The results demonstrated that RSV was capable of mitigating fibrosis phenotypes in rat livers, including the activation of hepatic stellate cell (HSC), the generation of extracellular matrix, and the deposition of collagen fibers in the liver vascular zone, which are all induced by arsenic exposure. Furthermore, as an activator of the longevity factor SIRT1, RSV antagonized the arsenic-induced inhibition of SIRT1 expression, thereby restoring the suppression of the senescence protein p16 by SIRT1. This prevented arsenic-induced hepatocyte senescence, manifesting as a decrease in telomere shortening and a reduction in the release of senescence-associated secretory phenotype (SASP)-related proteins. In conclusion, this study demonstrated that RSV counteracts arsenic-induced hepatocyte senescence and the release of SASP-related proteins by restoring the inhibitory effect of SIRT1 on p16, thereby suppressing the activation of fibrotic phenotypes and mitigating liver fibrosis. These findings provide new insights for understanding the mechanism of arsenic-induced liver fibrosis, and more importantly, they reveal novel potential interventional approaches.

Rapid assessment of cosmic radiation exposure in aviation based on BP neural network method.

Cosmic radiation exposure is one of the important health concerns for aircrews. In this work, we constructed a back propagation neural network model for the real-time and rapid assessment of cosmic radiation exposure to the public in aviation. The multi-dimensional dataset for this neural network was created from modeling the process of cosmic ray transportation in magnetic field by geomagnetic cutoff rigidity method and air shower simulation by a Monte Carlo based Geant4 code. The dataset was characterized by parameters including cosmic ray energy spectrum, Kp-index, coordinated universal time, altitude, latitude, and longitude. The effective dose and dose rate was finally converted from the particle fluxes at flight position by the neural network. This work shows a good agreement with other models from International Civil Aviation Organization. It is also illustrated that the effective dose rate by galactic cosmic ray is <10 µSv h-1 and the value during ground level enhancement (GLE) 42 is 4 ~ 10 times larger on the routes calculated in this work. In GLE 69, the effective dose rate reaches several mSv h-1 in the polar region. Based on this model, a real-time warning system is achieved.