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The study showed that HCV infection, IFG, a family history of diabetes, male gender, tacrolimus and BMI are risk factors for NODM after liver transplantation.

Ischemia-reperfusion injury (IRI) is a major complication in liver transplantation (LT) and it is closely related to the recovery of grafts' function. Researches has verified that both innate and adaptive immune system are involved in the development of IRI and Kupffer cell (KC), the resident macrophages in the liver, play a pivotal role both in triggering and sustaining the sterile inflammation. Damage-associated molecular patterns (DAMPs), released by the initial dead cell because of the ischemia insult, firstly activate the KC through pattern recognition receptors (PRRs) such as toll-like receptors. Activated KCs is the dominant players in the IRI as it can secret various pro-inflammatory cytokines to exacerbate the injury and recruit other types of immune cells from the circulation. On the other hand, KCs can also serve in a contrary way to ameliorate IRI by upregulating the anti-inflammatory factors. Moreover, new standpoint has been put forward that KCs and macrophages from the circulation may function in different way to influence the inflammation. Managements towards KCs are expected to be the effective way to improve the IRI.

Background Although Rapamycin (RPM) have been studied extensively in ischemia models, its functional mechanisms remains to be defined. Methods We determined how RPM impacted the pathogenesis of ischemia reperfusion injury (IRI) in a murine liver partial warm ischemia model, with emphasis on its regulation of hepatocyte death. Results RPM protected livers from IRI in the presence of fully developed liver inflammatory immune response. RPM enhanced liver autophagy induction at the reperfusion stage. Dual mTOR1/2 inhibitor Torin 1, despite its ability to induced autophagy, failed to protect livers from IRI. The treatment with RPM, but not Torin 1, resulted in the enhanced activation of the mTORC2-Akt signaling pathway activation in livers post reperfusion. Inactivation of Akt by Triciribine abolished liver protective effect of RPM. The differential cytoprotective effect of RPM and Torin 1 was confirmed in vitro in hepatocyte cultures. RPM, but not Trin 1, protected hepatocytes from stress and TNF-α induced cell death; and inhibition of either autophagy by chloroquine or Akt by Triciribine abolished RPM-mediated cytoprotection. Conclusion RPM protected livers from IRI via both autophagy and mTORC2-Akt activation mechanisms.

Epidemiological, preclinical and cellular studies in the last 5 years have shown that metformin exerts anti-tumoral properties, but its mode of action in cancer remains unclear. Here, we investigated the effects of metformin on a mouse hepatocellular carcinoma (HCC) model and tumor-associated T cell immune responses. Oral metformin administration led to a significant reduction of tumor growth, which was accompanied by decreased interleukin-22 (IL-22). Meanwhile, IL-22-induced STAT3 phosphorylation and upregulation of downstream genes Bcl-2 and cyclin D1 were inhibited by metformin. At the cellular level, metformin attenuated Th1-and Th17-derived IL-22 production. Furthermore, metformin inhibited de novo generation of Th1 and Th17 cells from naive CD41 cells. These observations were further supported by the fact that metformin treatment inhibited CD3/CD28-induced IFN-c and IL-17A expression along with the transcription factors that drive their expression (T-bet [Th1] and ROR-ct [Th17], respectively). The effects of metformin on T cell differentiation were mediated by downregulated STAT3 and STAT4 phosphorylation via the AMP-activated kinase-mammalian target of rapamycin complex 1 pathway. Notably, metformin led to a reduction in glucose transporter Glut1 expression, resulting in less glucose uptake, which is critical to regulate CD4 1 T cell fate. Taken together, these findings provide evidence for the growth-inhibitory and immune-modulatory effects of metformin in HCC and thus, broaden our understanding about the action of metformin in liver cancer treatment.Hepatocellular carcinoma (HCC) is the third leading cause of cancer death worldwide, and the general prognosis is poor.

In a recent study, using the distribution of galaxies in the north galactic pole of SDSS DR7 region enclosed in a 500h −1 Mpc box, we carried out our ELUCID simulation (Wang et al. 2016, ELUCID III). Here we light the dark matter halos and subhalos in the reconstructed region in the simulation with galaxies in the SDSS observations using a novel neighborhood abundance matching method. Before we make use of thus established galaxy-subhalo connections in the ELUCID simulation to evaluate galaxy formation models, we set out to explore the reliability of such a link. For this purpose, we focus on the following a few aspects of galaxies: (1) the central-subhalo luminosity and mass relations;(2) the satellite fraction of galaxies; (3) the conditional luminosity function (CLF) and conditional stellar mass function (CSMF) of galaxies; and (4) the cross correlation functions between galaxies and the dark matter particles, most of which are measured separately for all, red and blue galaxy populations. We find that our neighborhood abundance matching method accurately reproduces the central-subhalo relations, satellite fraction, the CLFs and CSMFs and the biases of galaxies. These features ensure that thus established galaxy-subhalo connections will be very useful in constraining galaxy formation processes. And we provide some suggestions on the three levels of using the galaxysubhalo pairs for galaxy formation constraints. The galaxy-subhalo links and the subhalo merger trees in the SDSS DR7 region extracted from our ELUCID simulation are available upon request. Subject headings: dark matter -large-scale structure of the universe -galaxies: halos -methods: statistical

As the second paper of a series on studying galaxy-galaxy lensing signals using the Sloan Digital Sky Survey Data Release 7 (SDSS DR7), we present our measurement and modelling of the lensing signals around groups of galaxies. We divide the groups into four halo mass bins, and measure the signals around four different halo-center tracers: brightest central galaxy (BCG), luminosity-weighted center, number-weighted center and X-ray peak position. For X-ray and SDSS DR7 cross identified groups, we further split the groups into low and high X-ray emission subsamples, both of which are assigned with two halo-center tracers, BCGs and X-ray peak positions. The galaxy-galaxy lensing signals show that BCGs, among the four candidates, are the best halo-center tracers. We model the lensing signals using a combination of four contributions: off-centered NFW host halo profile, sub-halo contribution, stellar contribution, and projected 2-halo term. We sample the posterior of 5 parameters i.e., halo mass, concentration, off-centering distance, sub halo mass, and fraction of subhalos via a MCMC package using the galaxy-galaxy lensing signals. After taking into account the sampling effects (e.g. Eddington bias), we found the best fit halo masses obtained from lensing signals are quite consistent with those obtained in the group catalog based on an abundance matching method, except in the lowest mass bin.

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