Cryptotanshinone induces apoptosis of activated hepatic stellate cells via modulating endoplasmic reticulum stress

BACKGROUND Cryptotanshinone(CPT)has wide biological functions,including anti-oxidative,antifibrosis,and anti-inflammatory properties.However,the effect of CPT on hepatic fibrosis is unknown.AIM To investigate the effects of CPT treatment on hepatic fibrosis and its underlying mechanism of action.METHODS Hepatic stellate cells(HSCs)and normal hepatocytes were treated with different concentrations of CPT and salubrinal.The CCK-8 assay was used to determine cell viability.Flow cytometry was used to measure apoptosis and cell cycle arrest.Reverse transcription polymerase chain reaction(RT-PCR)and Western blot analyses were used to measure mRNA levels and protein expression of endoplasmic reticulum stress(ERS)signaling pathway related molecules,respectively.Carbon tetrachloride(CCL4)was used to induce in vivo hepatic fibrosis in mice.Mice were treated with CPT and salubrinal,and blood and liver samples were collected for histopathological examination.RESULTS We found that CPT treatment significantly reduced fibrogenesis by modulating the synthesis and degradation of the extracellular matrix in vitro.CPT inhibited cell proliferation and induced cell cycle arrest at the G2/M phase in cultured HSCs.Furthermore,we found that CPT promoted apoptosis of activated HSCs by upregulating expression of ERS markers(CHOP and GRP78)and activating ERS pathway molecules(PERK,IRE1α,and ATF4),which were inhibited by salubrinal.Inhibition of ERS by salubrinal partially eliminated the therapeutic effect of CPT in our CCL4-induced hepatic fibrosis mouse model.CONCLUSION CPT can promote apoptosis of HSCs and alleviate hepatic fibrosis through modulating the ERS pathway,which represents a promising strategy for treating hepatic fibrosis.

Goodness-of-fit tests for multi-dimensional copulas:Expanding application to historical drought data

The question of how to choose a copula model that best fits a given dataset is a predominant limitation of the copula approach, and the present study aims to investigate the techniques of goodness-of-fit tests for multi-dimensional copulas. A goodness-of-fit test based on Rosenblatt＇s transformation was mathematically expanded from two dimensions to three dimensions and procedures of a bootstrap version of the test were provided. Through stochastic copula simulation, an empirical application of historical drought data at the Lintong Gauge Station shows that the goodness-of-fit tests perform well, revealing that both trivariate Gaussian and Student t copulas are acceptable for modeling the dependence structures of the observed drought duration, severity, and peak. The goodness-of-fit tests for multi-dimensional copulas can provide further support and help a lot in the potential applications of a wider range of copulas to describe the associations of correlated hydrological variables. However, for the application of copulas with the number of dimensions larger than three, more complicated computational efforts as well as exploration and parameterization of corresponding copulas are required.

On the intrinsic shape of the gamma-ray spectrum for Fermi blazars

The curvature of the γ-ray spectrum in blazars may reflect the intrinsic distribution of emitting electrons, which will further give some information on the possible acceleration and cooling processes in the emitting region. The γ-ray spectra of Fermi blazars are normally fitted either by a single power-law（PL） or a log-normal（call Logarithmic Parabola, LP） form. The possible reason for this difference is not clear. We statistically explore this issue based on the different observational properties of 1419 Fermi blazars in the 3 LAC Clean Sample. We find that the γ-ray flux（100 Me V–100 Ge V） and variability index follow bimodal distributions for PL and LP blazars, where the γ-ray flux and variability index show a positive correlation. However, the distributions of γ-ray luminosity and redshift follow a unimodal distribution. Our results suggest that the bimodal distribution of γ-ray fluxes for LP and PL blazars may not be intrinsic and all blazars may have an intrinsically curved γ-ray spectrum, and the PL spectrum is just caused by the fitting effect due to less photons.

Association between clustering of cardiovascular risk factors and resting heart rate in Chinese population:a cross-sectional study

BACKGROUND Epidemiologic studies have explored the association between a single cardiovascular risk factor(CVRF)and resting heart rate(RHR),but the research on the relation of multiple risk factors with RHR remains scarce.This study aimed to explore the associations between CVRFs clustering and the risk of elevated RHR.METHODS In this cross-sectional study,adults aged 35-75 years from 31 provinces were recruited by the China PEACE Million Persons Projects from September 2015 to August 2020.We focused on seven risk factors:hypertension,diabetes mellitus,dyslipidemia,obesity,smoking,alcohol use,and low physical activity.Multivariate logistic regression was used to calculate odds ratios(OR)for elevated RHR(>80 beats/min).RESULTS Among 1,045,405 participants,the mean age was 55.67±9.86 years,and 60.4%of participants were women.The OR(95%CI)for elevated RHR for the groups with 1,2,3,4 and≥5 risk factor were 1.11(1.08-1.13),1.36(1.33-1.39),1.68(1.64-1.72),2.01(1.96-2.07)and 2.58(2.50-2.67),respectively(Ptrend<0.001).The association between the CVRFs clustering number and elevated RHR was much more pronounced in young males than in other age-sex subgroups.Clusters comprising more metabolic risk factors were associated with a higher risk of elevated RHR than those comprising more behavioral risk factors.CONCLUSIONS There was a significant positive association between the CVRFs clustering number and the risk of elevated RHR,particularly in young males.Compared clusters comprising more behavioral risk factors,clusters comprising more metabolic risk factors were associated with a higher risk of elevated RHR.RHR may serve as an indicator of the cumulative effect of multiple risk factors.