Ionic liquids (ILs), known as environmentally benign “green” solvents, were developed as an optimal solvent for the green extraction and separation field. In this paper, an ionic liquid-based ultrasonic-assisted extraction (IL-UAE) of flavonoids (FVs) from bamboo leaves of Phyllostachys heterocycla was developed for the first time. First, 1-butyl-3-methylimidazolium bromide ([Bmim] Br), with the best extraction efficiency, was selected from fifteen ionic liquids with diverse structure, like carbon chains or anions. Then, the influencing parameters of ionic liquid (IL) concentration, liquid-solid ratio, ultrasonic time, and ultrasonic power, were investigated by single factor tests, and further optimized using response surface methodology (RSM). In the optimization experiment, the best conditions were 1.5 mol/L [BMIM]Br aqueous solution, liquid-solid ratio 41 mL/g, ultrasonic time 90 min, and ultrasonic power 300 W. Furthermore, the microstructures of bamboo leaves and the recovery of FVs and [BMIM]Br were also studied. Therefore, this simple, green and effective IL-UAE method has potentiality for the extraction of FVs from bamboo leaves for the large-scale operations.

Accurate identification of potential drug–target interactions (DTIs) is a crucial task in drug development and repositioning. Despite the remarkable progress achieved in recent years, improving the performance of DTI prediction still presents significant challenges. In this study, we propose a novel end-to-end deep learning model called AMMVF-DTI (attention mechanism and multi-view fusion), which leverages a multi-head self-attention mechanism to explore varying degrees of interaction between drugs and target proteins. More importantly, AMMVF-DTI extracts interactive features between drugs and proteins from both node-level and graph-level embeddings, enabling a more effective modeling of DTIs. This advantage is generally lacking in existing DTI prediction models. Consequently, when compared to many of the start-of-the-art methods, AMMVF-DTI demonstrated excellent performance on the human, C. elegans, and DrugBank baseline datasets, which can be attributed to its ability to incorporate interactive information and mine features from both local and global structures. The results from additional ablation experiments also confirmed the importance of each module in our AMMVF-DTI model. Finally, a case study is presented utilizing our model for COVID-19-related DTI prediction. We believe the AMMVF-DTI model can not only achieve reasonable accuracy in DTI prediction, but also provide insights into the understanding of potential interactions between drugs and targets.

In this study, deep eutectic solvent oscillation-assisted extraction (DES-OS) combined with macroporous resin adsorption and desorption technology was used to achieve the rapid green extraction and separation of the characteristic component dihydromyricetin (DMY) from vine tea. Multivariate data analysis showed that the DES system composed of tetrabutylammonium bromide (N₄₄₄Br) and pyruvic acid (molar ratio 1 : 2) had good extraction performance for DMY. The influence parameters of DES-OS were studied, and optimized by the single-factor test and response surface methodology (RSM) with Box–Behnken design (BBD). The extraction model of DMY was established and verified. The results showed that the extraction yield of DMY could reach 40.1 mg g⁻¹ under the optimal conditions (DES water contents of 71.18%, extraction time of 2.80 h, extraction temperature of 46.40 °C), which is in good agreement with the predicted value. In addition, Fourier transform infrared spectroscopy (FT-IR) was used to characterize the solvent before and after extraction. Scanning electron microscopy (SEM) results further confirmed that tetrabutylammonium bromide:pyruvate enhanced the destruction of the cell wall structure, resulting in the release of more DMY. Furthermore, different macroporous resins were selected for the separation of DMY for the DES-OS extract, and it was found that the DM301 resin had the ideal recovery performance under optimized dynamic condition. Finally, the product was found to have an inhibitory effect against xanthine oxidase (XO) as a mixed-type competitive inhibitor with IC50 values of (5.79 ± 0.22) × 10⁻⁵ mol L⁻¹. The inhibitory mechanisms of DMY on XO were explored by enzyme kinetics, spectroscopy, molecular docking and molecular dynamics analysis approaches, which provided a theoretical basis for the above inhibition assays.