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Three undescribed sesquiterpenoids, phaeocaulisguatriol (1) and phaeocaulistriol A-B (19, 20), along with twenty known sesquiterpenoids were isolated from a chloroform-soluble extract of Curcuma phaeocaulis Valeton rhizome. Their structures were determined using comprehensive spectroscopic methods. All isolated compounds were evaluated for their cytotoxicity against five human tumor cell lines Hela, HepG2, MCF-7, BGC823, and A549. Among them, phaeocaulisguatriol (1) and phaeocaulistriol B (20) showed potent cytotoxic activity against MCF-7 (IC50 of 40.7 and 58.8 μM, respectively), by inducing apoptosis. The effectiveness of compounds 1 and 20 was supported by predictive intermolecular interactions in molecular docking. Molecular dynamics simulation studies further probed the interaction mechanism under simulated physiological conditions between the two most active compounds and the TP53 protein, an apoptotic effector protein. Moreover, Western blot experiments confirmed that compound 1 induces apoptosis by activating the expression of TP53 and caspase 3 proteins. This work provides a deep insight into new sesquiterpenoids isolated from C. phaeocaulis Valeton acting as potential antitumor activity inhibitors. [ABSTRACT FROM AUTHOR]

Currently, extraction process optimization is generally based on a few features, regardless of their different changing trends and the panoramic view of the extraction process. Comprehensive evaluation and understanding is hard to establish due to the small number of experiments. Here, machine learning-assisted optimization is demonstrated for better understanding the complex extraction process based on data from an orthogonal experimental design (OED). From two perspectives of panoramic characteristics and specific characteristics, several observations are adopted to evaluate the performance of the extraction process, including quantitative 1H NMR, HPLC fingerprint, molecular weight, yield of dry extract and content of components. The close relationship between influencing factors and the extraction performance is described by grey relation analysis. With the help of radial basis function neural network (RBFNN), a nonlinear fitting regression equation is developed for every observation and influencing factor. A genetic algorithm is then introduced for multi-objective optimization and Pareto fronts are obtained. To select the best combination of water extraction process and ethanol extraction process, a list of the combinations of Pareto front points from those extraction processes is formed and ranked using CRITIC-TOPSIS. Finally, the ideal extraction is characterized by molecular weight, monosaccharide composition and UHPLC-MS/MS. With the verification between OED experiments and machine learning, the changing rates of all observations range from 1.33% to 30.11%, which confirms that machine learning-assisted optimization gives better performance than conventional OED. Molecular weight could range from 61.5~594.9 kDa with some are over measuring range, furthermore mannose and glucose are the most abuntant monosaccharides of the polysaccharide from ideal extraction. 160 components are identified via UHPLC-MS/MS as well. In conclusion, ML is a powerful tool for predicting and understanding extraction processes, thus accelerating the development of eco-friendly extraction processes. [ABSTRACT FROM AUTHOR]

Esculentoside A (EsA) is a kind of triterpenoid saponins from the root tuber of Phytolacca acinosa Roxb. It has extensive medicinal activity, such as antibacterial, anti‐inflammatory, immune regulation, and cell proliferation inhibition. However, some researches suggested that EsA can cause hepatotoxicity, whose mechanism is not precise. To ensure the safety and reliability in the clinical use of Phytolacca acinosa Roxb., it is necessary to establish a rapid and accurate method to evaluate the toxicity, analyze and verify the toxicity mechanism of EsA. Therefore, this research explored the mechanism of hepatotoxicity induced by EsA in rats and analyzed endogenous metabolites' changes in rat plasma by combining network toxicology with non‐targeted metabolomics. We obtained 58 critical targets of EsA induced hepatotoxicity in rats based on the strategy of network toxicology, including albumin, mitogen‐activated protein kinase 1, Caspase‐3, etc. Many important pathways were obtained by Kyoto Encyclopedia of Genes and Genomes enrichment analysis, such as HIF‐1 signaling pathway, TNF signaling pathway, IL‐17 signaling pathway, and other concerning pathways. Sixteen biomarkers, including 5‐hydroxykynurenamine, N‐acetylserotonin, palmitic acid, etc., were screened from rat plasma using Ultra‐performance liquid chromatography coupled with quadrupole time of flight mass spectrometry (UPLC‐Q‐TOF/MS), mainly involve Glycerophospholipid metabolism, Tryptophan metabolism, and other metabolic pathways. Further analysis showed that EsA may induce liver injury by activating oxidative stress and energy metabolism disorders, triggering inflammation and apoptosis. [ABSTRACT FROM AUTHOR]

Nanoparticles modified with aminopropyl group were prepared by a single-step electrospray process and used as a pseudo-stationary phase for the separation of anions, (e.g., organic acids in capillary electrophoresis). The nanoparticles form a uniform suspension which is stable in a buffer without adding other stabilizer. The amino containing nanoparticles have significant impact on the electro-osmotic flow (EOF) of the system, which, in turn, affects the overall separation of other analytes in this system. For example, five organic acids, which were not all separable in zone electrophoresis, were fully resolved in the presence of the nanoparticles in a wide range of pH. Various experimental parameters that are related to the electrophoresis including EOF, pH, organic modifier, voltage, concentration of the running buffer and concentration of the nanoparticles have been investigated. The repeatability of the migration time and peak area in this system are better than 5% for both inter- and intra-day evaluation. [ABSTRACT FROM AUTHOR]

A chiral complex, SalenZn(II) (S), was synthesized and characterized. Its coordination with imidazole derivatives and amino acid ester derivatives was studied by UV-vis spectrophotometric titrations and CD spectroscopy. The binding constants decreased in the order K (Im)> K (2-MeIm)> K (2-Et-4-MeIm)> K (N-MeIm) for imidazole derivatives, and K (AlaOMe)> K (PheOMe)> K (ValOMe) for amino acid ester derivatives with the same configuration and K D > K L for amino acid esters with different configuration. CD spectra can quantify the strength of SalenZn(II)-ligand interactions, giving results consistent with the magnitudes of the binding constants. Moreover the minimum energy conformations of the adducts were obtained by simulated annealing, and quantum chemical calculations were performed based on those conformations to explain experimental results at the molecular level. [ABSTRACT FROM AUTHOR]