Siraitia grosvenorii (Swingle) C. Jeffrey, a member of the family Cucurbitaceae, is a unique economic and medicinal plant grown in China. For more than 300 years, S. grosvenorii has been used as a natural sweetener and as a traditional medicine for the treatment of pharyngitis, pharyngeal pain, as well as an anti-tussive remedy in China. It is one of the first approved medicine food homology species in China. It has been widely studied as a natural product with high development potential. Therefore, the present paper provides a review of the botanical characterization, traditional uses and ethnopharmacology, food and nutritional values, chemical constituents, pharmacological effects, toxicology, and development direction for the future of S. grosvenorii. Phytochemical studies have revealed that the chemical composition of this plant mainly includes iridoid and phenylpropanoid glycosides. Several compounds such as triterpenoids, flavonoids, and amino acids have been isolated from the plant. S. grosvenorii and its active constituents possess broad pharmacological properties, such as antioxidant, hypoglycemic, immunologic, anti-tussive and sputum-reducing, hepatoprotective, and antimicrobial activities, etc. By documenting the comprehensive information of S. grosvenorii, we hope to establishes the groundwork for further research on the mechanism of action of S. grosvenorii and its development as a new health food in the future.

No abstract

Machine learning (ML) has emerged as one of the most powerful tools transforming all areas of science and engineering. The nature of molecular dynamics (MD) simulations, complex and time-consuming calculations, makes them particularly suitable for ML research. This review article focuses on recent advancements in developing efficient and accurate coarse-grained (CG) models using various ML methods, in terms of regulating the coarse-graining process, constructing adequate descriptors/features, generating representative training data sets, and optimization of the loss function. Two classes of the CG models are introduced: bottom-up and top-down CG methods. To illustrate these methods and demonstrate the open methodological questions, we survey several important principles in constructing CG models and how these are incorporated into ML methods and improved with specific learning techniques. Finally, we discuss some key aspects of developing machine-learned CG models with high accuracy and efficiency. Besides, we describe how these aspects are tackled in state-of-the-art methods and which remain to be addressed in the near future. We expect that these machine-learned CG models can address thermodynamic consistent, transferable, and representative issues in classical CG models.

No abstract

Resveratrol is a promising multi-biofunctional phytochemical, which is abundant in Polygonum cuspidatum. Several methods for resveratrol extraction have been reported, while they often take a long extraction time accompanying with poor extraction yield. In this study, a novel enzyme-assisted ultrasonic approach for highly efficient extraction of resveratrol from P. cuspidatum was developed. According to results, the resveratrol yield significantly increased after glycosidases (Pectinex® or Viscozyme®) were applied in the process of extraction, and better extraction efficacy was found in the Pectinex®-assisted extraction compared to Viscozyme®-assisted extraction. Following, a 5-level-4-factor central composite rotatable design with response surface methodology (RSM) and artificial neural network (ANN) was selected to model and optimize the Pectinex®-assisted ultrasonic extraction. Based on the coefficient of determination (R(2)) calculated from the design data, ANN model displayed much more accurate in data fitting as compared to RSM model. The optimum conditions for the extraction determined by ANN model were substrate concentration of 5%, acoustic power of 150W, pH of 5.4, temperature of 55°C, the ratio of enzyme to substrate of 3950 polygalacturonase units (PGNU)/g of P. cuspidatum, and reaction time of 5h, which can lead to a significantly high resveratrol yield of 11.88mg/g.

No abstract

No abstract

No abstract