A review of Phyllanthus urinaria L. in the treatment of liver disease: viral hepatitis, liver fibrosis/cirrhosis and hepatocellular carcinoma

Due to the pathological production of liver disease in utility particularly complexity, the morbidity and mortality of liver disease including viral hepatitis, liver fibrosis/cirrhosis and hepatocellular carcinoma (HCC) are rapidly increasing worldwide. Considering its insidious onset, rapid progression and drug resistance, finding an effective therapy is particularly worthwhile. Phyllanthus urinaria L. (P. urinaria), an ethnic medicine, can be applied at the stages of viral hepatitis, liver fibrosis/cirrhosis and HCC, which demonstrates great potential in the treatment of liver disease. Currently, there are numerous reports on the application of P. urinaria in treating liver diseases, but a detailed analysis of its metabolites and a complete summary of its pharmacological mechanism are still scarce. In this review, the phytochemical metabolites and ethnopharmacological applications of P. urinaria are summarized. Briefly, P. urinaria mainly contains flavonoids, lignans, tannins, phenolic acids, terpenoids and other metabolites. The mechanisms of P. urinaria are mainly reflected in reducing surface antigen secretion and interfering with DNA polymerase synthesis for anti-viral hepatitis activity, reducing hepatic stellate cells activity, inflammation and oxidative stress for anti-liver fibrosis/cirrhosis activity, as well as preventing tumor proliferation, invasion and angiogenesis for anti-HCC activity via relevant signaling pathways. Accordingly, this review provides insights into the future application of natural products in the trilogy of liver diseases and will provide a scientific basis for further research and rational utilization of P. urinaria

Deep Deformable Artistic Font Style Transfer

The essence of font style transfer is to move the style features of an image into a font while maintaining the font’s glyph structure. At present, generative adversarial networks based on convolutional neural networks play an important role in font style generation. However, traditional convolutional neural networks that recognize font images suffer from poor adaptability to unknown image changes, weak generalization abilities, and poor texture feature extractions. When the glyph structure is very complex, stylized font images cannot be effectively recognized. In this paper, a deep deformable style transfer network is proposed for artistic font style transfer, which can adjust the degree of font deformation according to the style and realize the multiscale artistic style transfer of text. The new model consists of a sketch module for learning glyph mapping, a glyph module for learning style features, and a transfer module for a fusion of style textures. In the glyph module, the Deform-Resblock encoder is designed to extract glyph features, in which a deformable convolution is introduced and the size of the residual module is changed to achieve a fusion of feature information at different scales, preserve the font structure better, and enhance the controllability of text deformation. Therefore, our network has greater control over text, processes image feature information better, and can produce more exquisite artistic fonts

Graph Autoencoder with Preserving Node Attribute Similarity

The graph autoencoder (GAE) is a powerful graph representation learning tool in an unsupervised learning manner for graph data. However, most existing GAE-based methods typically focus on preserving the graph topological structure by reconstructing the adjacency matrix while ignoring the preservation of the attribute information of nodes. Thus, the node attributes cannot be fully learned and the ability of the GAE to learn higher-quality representations is weakened. To address the issue, this paper proposes a novel GAE model that preserves node attribute similarity. The structural graph and the attribute neighbor graph, which is constructed based on the attribute similarity between nodes, are integrated as the encoder input using an effective fusion strategy. In the encoder, the attributes of the nodes can be aggregated both in their structural neighborhood and by their attribute similarity in their attribute neighborhood. This allows performing the fusion of the structural and node attribute information in the node representation by sharing the same encoder. In the decoder module, the adjacency matrix and the attribute similarity matrix of the nodes are reconstructed using dual decoders. The cross-entropy loss of the reconstructed adjacency matrix and the mean-squared error loss of the reconstructed node attribute similarity matrix are used to update the model parameters and ensure that the node representation preserves the original structural and node attribute similarity information. Extensive experiments on three citation networks show that the proposed method outperforms state-of-the-art algorithms in link prediction and node clustering tasks

Self-Assembled Matrine-PROTAC Encapsulating Zinc(II) Phthalocyanine with GSH-Depletion-Enhanced ROS Generation for Cancer Therapy

The integration of a multidimensional treatment dominated by active ingredients of traditional Chinese medicine (TCM), including enhanced chemotherapy and synergistically amplification of oxidative damage, into a nanoplatform would be of great significance for furthering accurate and effective cancer treatment with the active ingredients of TCM. Herein, in this study, we designed and synthesized four matrine-proteolysis-targeting chimeras (PROTACs) (depending on different lengths of the chains named LST-1, LST-2, LST-3, and LST-4) based on PROTAC technology to overcome the limitations of matrine. LST-4, with better anti-tumor activity than matrine, still degrades p-Erk and p-Akt proteins. Moreover, LST-4 NPs formed via LST-4 self-assembly with stronger anti-tumor activity and glutathione (GSH) depletion ability could be enriched in lysosomes through their outstanding enhanced permeability and retention (EPR) effect. Then, we synthesized LST-4@ZnPc NPs with a low-pH-triggered drug release property that could release zinc(II) phthalocyanine (ZnPc) in tumor sites. LST-4@ZnPc NPs combine the application of chemotherapy and phototherapy, including both enhanced chemotherapy from LST-4 NPs and the synergistic amplification of oxidative damage, through increasing the reactive oxygen species (ROS) by photodynamic therapy (PDT), causing an GSH decrease via LST-4 mediation to effectively kill tumor cells. Therefore, multifunctional LST-4@ZnPc NPs are a promising method for killing cancer cells, which also provides a new paradigm for using natural products to kill tumors

N²‑Selective β‑Thioalkylation of Benzotriazoles with Alkenes

Herein, N2-selective β-thioalkylation of benzotriazoles with unactivated alkenes and styrenes is reported. The N2-selective β-thioalkylation of benzotriazoles is highly stereospecific and works under simple and mild conditions, exhibiting excellent functional group tolerance. The high N2-selectivity is a consequence of the combination of hydrogen bonding and Lewis acid/base activation, which reverses the N2-position to be favored for alkylation

Noninvasive right ventricular work in patients with atrial septal defects: a proof-of-concept study

Abstract Background Noninvasive right ventricular (RV) myocardial work (RVMW) determined by echocardiography is a novel indicator used to estimate RV systolic function. To date, the feasibility of using RVMW has not been verified in assessing RV function in patients with atrial septal defect (ASD). Methods Noninvasive RVMW was analysed in 29 ASD patients (median age, 49 years; 21% male) and 29 age- and sex-matched individuals without cardiovascular disease. The ASD patients underwent echocardiography and right heart catheterization (RHC) within 24 h. Results The RV global work index (RVGWI), RV global constructive work (RVGCW), and RV global wasted work (RVGWW) were significantly higher in the ASD patients than in the controls, while there was no significant difference in RV global work efficiency (RVGWE). RV global longitudinal strain (RV GLS), RVGWI, RVGCW, and RVGWW demonstrated significant correlations with RHC-derived stroke volume (SV) and SV index. The RVGWI (area under receiver operating characteristic curve [AUC] = 0.895), RVGCW (AUC = 0.922), and RVGWW (AUC = 0.870) could be considered good predictors of ASD and were superior to RV GLS (AUC = 0.656). Conclusion The RVGWI, RVGCW, and RVGWW could be used to assess RV systolic function and are correlated with RHC-derived SV and SV index in patients with ASD. Graphical Abstrac