Dual-channel hypergraph convolutional network for predicting herb-disease associations.

Herbs applicability in disease treatment has been verified through experiences over thousands of years. The understanding of herb-disease associations (HDAs) is yet far from complete due to the complicated mechanism inherent in multi-target and multi-component (MTMC) botanical therapeutics. Most of the existing prediction models fail to incorporate the MTMC mechanism. To overcome this problem, we propose a novel dual-channel hypergraph convolutional network, namely HGHDA, for HDA prediction. Technically, HGHDA first adopts an autoencoder to project components and target protein onto a low-dimensional latent space so as to obtain their embeddings by preserving similarity characteristics in their original feature spaces. To model the high-order relations between herbs and their components, we design a channel in HGHDA to encode a hypergraph that describes the high-order patterns of herb-component relations via hypergraph convolution. The other channel in HGHDA is also established in the same way to model the high-order relations between diseases and target proteins. The embeddings of drugs and diseases are then aggregated through our dual-channel network to obtain the prediction results with a scoring function. To evaluate the performance of HGHDA, a series of extensive experiments have been conducted on two benchmark datasets, and the results demonstrate the superiority of HGHDA over the state-of-the-art algorithms proposed for HDA prediction. Besides, our case study on Chuan Xiong and Astragalus membranaceus is a strong indicator to verify the effectiveness of HGHDA, as seven and eight out of the top 10 diseases predicted by HGHDA for Chuan-Xiong and Astragalus-membranaceus, respectively, have been reported in literature.

Multi-technical analysis on the antioxidative capacity and total phenol contents of 94 traditional Chinese dietary medicinal herbs.

Dietary medicinal herbs (DMHs) are superior reservoirs for natural antioxidants and safe for long-term consumption. Chinese government recently announced an official list of traditional Chinese DMHs to support civil health promotion. However, antioxidative capacity (AOC) and total phenol contents (TPC) of these listed herbs were not comprehensively and systematically evaluated. In this study, AOC of 94 listed DMHs in water extract were investigated by three techniques including radical scavenging and ferric reducing antioxidative power. The results showed that emblic leafflower, rose, and clove possessed the highest AOC, while TPC was significantly correlated with AOC. While traditional Chinese medicines are categorized by the nature of Yin-Yang, this study demonstrated that Yin DMHs are statistically correlated with high AOC. Practical application: In this study, AOC and TPC of 94 traditional Chinese DMHs were documented systematically. Emblic leafflower, rose and clove were shown to possess the highest AOC. TPC in traditional Chinese DMHs was significantly correlated with the AOC, while DMHs with Yin characteristic might be used as an indicator to identify potential antioxidative herbs.

Magnetic and pH dual-responsive mesoporous silica nanocomposites for effective and low-toxic photodynamic therapy.

Nonspecific targeting, large doses and phototoxicity severely hamper the clinical effect of photodynamic therapy (PDT). In this work, superparamagnetic Fe3O4 mesoporous silica nanoparticles grafted by pH-responsive block polymer polyethylene glycol-b-poly(aspartic acid) (PEG-b-PAsp) were fabricated to load the model photosensitizer rose bengal (RB) in the aim of enhancing the efficiency of PDT. Compared to free RB, the nanocomposites (polyethylene glycol-b-polyaspartate-modified rose bengal-loaded magnetic mesoporous silica [RB-MMSNs]) could greatly enhance the cellular uptake due to their effective endocytosis by mouse melanoma B16 cell and exhibited higher induced apoptosis although with little dark toxicity. RB-MMSNs had little dark toxicity and even much could be facilitated by magnetic field in vitro. RB-MMSNs demonstrated 10 times induced apoptosis efficiency than that of free RB at the same RB concentration, both by cell counting kit-8 (CCK-8) result and apoptosis detection. Furthermore, RB-MMSNs-mediated PDT in vivo on tumor-bearing mice showed steady physical targeting of RB-MMSNs to the tumor site; tumor volumes were significantly reduced in the magnetic field with green light irradiation. More importantly, the survival time of tumor-bearing mice treated with RB-MMSNs was much prolonged. Henceforth, polyethylene glycol-b-polyaspartate-modified magnetic mesoporous silica (MMSNs) probably have great potential in clinical cancer photodynamic treatment because of their effective and low-toxic performance as photosensitizers' vesicles.