Oleanolic acid promotes liver regeneration after partial hepatectomy via regulating pregnane X receptor signaling pathway in mice.

Liver regeneration after liver tumor resection or liver transplantation is crucial, the remaining liver frequently fails to regenerate in some patients. Oleanolic acid (OA), a pentacyclic triterpenoid compound which has been shown to protect against various liver diseases. However, the effect of OA on liver regeneration after partial hepatectomy (PHx) is still unclear. In this study, the results showed that OA (50 mg/kg, twice daily) treatment induced liver mass restoration and increased the liver-to-body weight ratio of mice following PHx. Meanwhile, OA promoted hepatocyte proliferation and increased the number of BrdU-, Ki67-and PCNA-positive cells. Furthermore, OA increased the nuclear accumulation of PXR and induced the expression of PXR downstream proteins such as CYP3A11, UGT1A1 and GSTM2 in mice, as well as in AML12 and HepRG cells. Luciferase reporter assay and nuclear localization of PXR further demonstrated the effect of OA on PXR activation in vitro. Molecular docking simulation showed that OA could interact with the PXR active sites. Moreover, OA inhibited the expression of FOXO1, RBL2 and CDKN1B, and increased the expression of PCNA, CCND1 and CCNE1 in vivo and in vitro. Silencing of Pxr further confirmed that OA-mediated upregulation of proliferation-related proteins depended on PXR. The current study illustrated that OA exhibited a significant promoting effect on liver regeneration following PHx, potentially through regulation of the PXR signaling pathway to accelerate liver recovery.

Pregnane X Receptor Activation in Liver Macrophages Protects against Endotoxin-Induced Liver Injury.

Endotoxemia-related acute liver injury has a poor prognosis and high mortality, and macrophage polarization plays a central role in the pathological process. Pregnane X receptor (PXR) serves as a nuclear receptor and xenosensor, safeguarding the liver from toxic stimuli. However, the effect and underlying mechanism of PXR activation on endotoxemic liver injury remain largely unknown. Here, the expression of PXR is reported in human and murine macrophages, and PXR activation modified immunotypes of macrophages. Moreover, PXR activation significantly attenuated endotoxemic liver injury and promoted macrophage M2 polarization. Macrophage depletion by GdCl3 confirmed the essential of macrophages in the beneficial effects observed with PXR activation. The role of PXR in macrophages is further validated using AAV8-F4/80-Pxr shRNA-treated mice; the PXR-mediated hepatoprotection is impaired, and M2 polarization enhancement is blunted. Additionally, treatment with PXR agonists inhibited lipopolysaccharide (LPS)-induced M1 polarization and favored M2 polarization in BMDM, Raw264.7, and THP-1 cells. Further analyses revealed an interaction between PXR and p-STAT6 in vivo and in vitro. Moreover, blocking Pxr or Stat6 abolished the PXR-induced polarization shift. Collectively, macrophage PXR activation attenuated endotoxin-induced liver injury and regulated macrophage polarization through the STAT6 signaling pathway, which provided a potential therapeutic target for managing endotoxemic liver injury.

A New Feature Extraction Method for Ship-Radiated Noise Based on Improved CEEMDAN, Normalized Mutual Information and Multiscale Improved Permutation Entropy.

Extracting useful features from ship-radiated noise can improve the performance of passive sonar. The entropy feature is an important supplement to existing technologies for ship classification. However, the existing entropy feature extraction methods for ship-radiated noise are less reliable under noisy conditions because they lack noise reduction procedures or are single-scale based. In order to simultaneously solve these problems, a new feature extraction method is proposed based on improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), normalized mutual information (norMI), and multiscale improved permutation entropy (MIPE). Firstly, the ICEEMDAN is utilized to obtain a group of intrinsic mode functions (IMFs) from ship-radiated noise. The noise reduction process is then conducted by identifying and eliminating the noise IMFs. Next, the norMI and MIPE of the signal-dominant IMFs are calculated, respectively; and the norMI is used to weigh the corresponding MIPE result. The multi-scale entropy feature is finally defined as the sum of the weighted MIPE results. Experimental results show that the recognition rate of the proposed method achieves 90.67% and 83%, respectively, under noise free and 5 dB conditions, which is much higher than existing entropy feature extraction algorithms. Hence, the proposed method is more reliable and suitable for feature extraction of ship-radiated noise in practice.