Metabolomics and network pharmacology reveal the mechanism of antithrombotic effect of Asperosaponin VI.

Dipsaci Radix may possess antithrombotic properties, and one of its primary active ingredients is Asperosaponin VI. However, the antithrombotic effects and pharmacological mechanisms of Asperosaponin VI remain unclear. An in vivo experimental study has demonstrated the antithrombotic activity of Asperosaponin VI. Asperosaponin VI also exhibits anticoagulant properties. Asperosaponin VI significantly hindered collagen adrenergic-induced acute pulmonary thrombosis in mice and enhanced their survival rate. This hinders the formation of acute pulmonary embolisms induced by adenosine diphosphate (ADP) and decreases recovery time. A comprehensive strategy that combines metabolomics, network pharmacology, molecular docking, and experimental validation has the potential to reveal the antithrombotic mechanisms of Asperosaponin VI. Metabolomic evidence suggests that Asperosaponin VI may influence platelet aggregation and the production of anti-inflammatory metabolites through the regulation of pathways such as phenylalanine and arachidonic acid metabolism, thereby inhibiting thrombosis. Network pharmacology identified the pharmacological targets of Asperosaponin VI and indicated that it treats thrombi by partially regulating the signaling pathways related to inflammation and platelet aggregation. Asperosaponin VI showed strong binding affinity for F2, PTPRC, JUN, STAT3, SRC, AKT1. The antiplatelet aggregation activity of Asperosaponin VI was validated based on the metabolomic and network pharmacology results. Asperosaponin VI inhibits platelet aggregation induced by ADP, AA, and collagen. Therefore, Asperosaponin VI exerts antithrombotic effects through antiplatelet aggregation. Therefore, Asperosaponin VI is a promising antithrombotic agent.

Microbial Inoculation during the Short-Term Composting Process Enhances the Nutritional and Functional Properties of Oyster Mushrooms (Pleurotus ostreatus).

In recent years, short-term composting techniques have been widely applied in oyster mushroom cultivation, but there is still a lack of systematic research on their impact on the nutritional and functional properties of fruiting bodies. In this study, the microbial inoculant Streptomyces thermoviolaceus BUA-FM01 (ST) was applied in the short-term composting process for oyster mushroom cultivation. The agronomic traits, nutritional composition, flavor compounds, and antioxidant activity of fruiting bodies from the first three flushes were evaluated. The results show that microbial inoculation significantly (p < 0.05) reduced the total carbon content and C/N ratio of the composted substrates and, furthermore, increased the total yield of the fruiting bodies. Moreover, microbial inoculation significantly (p < 0.05) increased the crude protein, crude polysaccharide, total amino acid, and essential amino acid contents of the fruiting bodies. The fruiting bodies of the first flush of ST treatment possessed the highest umami amino acid content and equivalent umami concentration value. Furthermore, microbial inoculation significantly (p < 0.05) enhanced the scavenging ability of crude polysaccharides toward free radicals. The results indicate that microbial inoculation has many benefits for the composting cultivating process of oyster mushrooms and good application prospects.

MW-MADDPG: a meta-learning based decision-making method for collaborative UAV swarm.

Unmanned Aerial Vehicles (UAVs) have gained popularity due to their low lifecycle cost and minimal human risk, resulting in their widespread use in recent years. In the UAV swarm cooperative decision domain, multi-agent deep reinforcement learning has significant potential. However, current approaches are challenged by the multivariate mission environment and mission time constraints. In light of this, the present study proposes a meta-learning based multi-agent deep reinforcement learning approach that provides a viable solution to this problem. This paper presents an improved MAML-based multi-agent deep deterministic policy gradient (MADDPG) algorithm that achieves an unbiased initialization network by automatically assigning weights to meta-learning trajectories. In addition, a Reward-TD prioritized experience replay technique is introduced, which takes into account immediate reward and TD-error to improve the resilience and sample utilization of the algorithm. Experiment results show that the proposed approach effectively accomplishes the task in the new scenario, with significantly improved task success rate, average reward, and robustness compared to existing methods.

Analysis of the Composition of Deinagkistrodon acutus Snake Venom Based on Proteomics, and Its Antithrombotic Activity and Toxicity Studies.

There is a strong correlation between the composition of Deinagkistrodon acutus venom proteins and their potential pharmacological effects. The proteomic analysis revealed 103 proteins identified through label-free proteomics from 30 different snake venom families. Phospholipase A2 (30.0%), snaclec (21.0%), antithrombin (17.8%), thrombin (8.1%) and metalloproteinases (4.2%) were the most abundant proteins. The main toxicity of Deinagkistrodon acutus venom is hematotoxicity and neurotoxicity, and it acts on the lung. Deinagkistrodon acutus venom may have anticoagulant and antithrombotic effects. In summary, the protein profile and related toxicity and pharmacological activity of Deinagkistrodon acutus venom from southwest China were put forward for the first time. In addition, we revealed the relationship between the main toxicity, pharmacological effects, and the protein components of snake venom.