Bioactive natural products (BNPs) are the marrow of medicinal plants, which are the secondary metabolites of organisms and have been the most famous drug discovery database. Bioactive natural products are famous for their enormous number and great safety in medical applications. However, BNPs are troubled by their poor druggability compared with synthesis drugs and are challenged as medicine (only a few BNPs are applied in clinical settings). In order to find a reasonable solution to improving the druggability of BNPs, this review summarizes their bioactive nature based on the enormous pharmacological research and tries to explain the reasons for the poor druggability of BNPs. And then focused on the boosting research on BNPs loaded drug delivery systems, this review further concludes the advantages of drug delivery systems on the druggability improvement of BNPs from the perspective of their bioactive nature, discusses why BNPs need drug delivery systems, and predicts the next direction.

Graph representation is a critical technology in the field of knowledge engineering and knowledge-based applications since most knowledge bases are represented in the graph structure. Nowadays, contrastive learning has become a prominent way for graph representation by contrasting positive-positive and positive-negative node pairs between two augmentation graphs. It has achieved new state-of-the-art in the field of self-supervised graph representation. However, existing contrastive graph representation methods mainly focus on modifying (normally removing some edges/nodes) the original graph structure to generate the augmentation graph for the contrastive. It inevitably changes the original graph structures, meaning the generated augmentation graph is no longer equivalent to the original graph. This harms the performance of the representation in many structure-sensitive graphs such as protein graphs, chemical graphs, molecular graphs, etc. Moreover, there is only one positive-positive node pair but relatively massive positive-negative node pairs in the self-supervised graph contrastive learning. This can lead to the same class, or very similar samples are considered negative samples. To this end, in this work, we propose a Virtual Masking Augmentation (VMA) to generate an augmentation graph without changing any structures from the original graph. Meanwhile, a node augmentation method is proposed to augment the positive node pairs by discovering the most similar nodes in the same graph. Then, two different augmentation graphs are generated and put into a contrastive learning model to learn the graph representation. Extensive experiments on massive datasets demonstrate that our method achieves new state-of-the-art results on self-supervised graph representation. The source code of the proposed method is available at https://github.com/DuanhaoranCC/CGRA.

Zero-shot learning (ZSL) aims to predict unseen classes without using samples of these classes in model training. The ZSL has been widely used in many knowledge-based models and applications to predict various parameters, including categories, subjects, and anomalies, in different domains. Nonetheless, most existing ZSL methods require the pre-defined semantics or attributes of particular data environments. Therefore, these methods are difficult to be applied to general data environments, such as ImageNet and other real-world datasets and applications. Recent research has tried to use open knowledge to enhance the ZSL methods to adapt it to an open data environment. However, the performance of these methods is relatively low, namely the accuracy is normally below 10%, which is due to the inadequate semantics that can be used from open knowledge. Moreover, the latest methods suffer from a significant "semantic gap" problem between the generated features of unseen classes and the real features of seen classes. To this end, this paper proposes a multi-view graph representation with a similarity diffusion model, applying the ZSL tasks to general data environments. This model applies a multi-view graph to enhance the semantics fully and proposes an innovative diffusion method to augment the graph representation. In addition, a feature diffusion method is proposed to augment the multi-view graph representation and bridge the semantic gap to realize zero-shot predicting. The results of numerous experiments in general data environments and on benchmark datasets show that the proposed method can achieve new state-of-the-art results in the field of general zero-shot learning. Furthermore, seven ablation studies analyze the effects of the settings and different modules of the proposed method on its performance in detail and prove the effectiveness of each module.

This study investigates the anti‐aging properties of Gastrodia elata ‐derived Parishin extracts (GEPE) by exploring its regulatory effects on oxidative stress, inflammatory responses, apoptosis, and metabolism. Network pharmacology identified 27 active components with parishins as core and 250 anti‐aging intersection targets of Gastrodia elata . Molecular docking confirmed strong binding activity of parishins to key targets such as EGFR, AKT1, and ALB. In vitro experiments showed that the GEPE concentration dependently reduced the activity of senescence marker SA‐β‐gal in D‐galactose‐induced senescent mouse microglial cell (BV2). In vivo experiments showed that GEPE improved cognitive and muscle functions, elevated organ indices of liver, thymus, and spleen, alleviated hepatic lipid accumulation and neuronal damage, reduced oxidative stress, inhibited inflammatory responses and cell apoptosis, and corrected metabolic disorders in D‐galactose‐induced aging mice. These findings demonstrate that GEPE is associated with attenuated aging, potentially through coordinated modulation of oxidative stress, inflammation, apoptosis, and metabolic homeostasis, highlighting its potential as a candidate for anti‐aging interventions.

Self-supervised graph representation learning has been widely used in many intelligent applications since labeled information can hardly be found in these data environments. Currently, masking and reconstruction-based (MR-based) methods lead the state-of-the-art records in the self-supervised graph representation field. However, existing MR-based methods did not fully consider both the deep-level node and structure information which might decrease the final performance of the graph representation. To this end, this paper proposes a node and edge dual-masked self-supervised graph representation model to consider both node and structure information. First, a dual masking model is proposed to perform node masking and edge masking on the original graph at the same time to generate two masking graphs. Second, a graph encoder is designed to encode the two generated masking graphs. Then, two reconstruction decoders are designed to reconstruct the nodes and edges according to the masking graphs. At last, the reconstructed nodes and edges are compared with the original nodes and edges to calculate the loss values without using the labeled information. The proposed method is validated on a total of 14 datasets for graph node classification tasks and graph classification tasks. The experimental results show that the method is effective in self-supervised graph representation. The code is available at: https://github.com/TangPeng0627/Node-and-Edge-Dual-Mask.