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Leontopodium leontopodioides (Willd.) Beauv. ( L. leontopodioides .) has been used to treat lung diseases in traditional Chinese medicine (TCM). However, a systematic analysis of its chemical components has not been reported so far. In this study, UPLC-Q-Orbitrap MS and GC-MS were applied to investigate the chemical composition of the water extracts and essential oils of L. leontopodioides. UPLC-Q-Orbitrap MS adopts a heating electrospray ionization source, collecting primary and secondary mass spectrometry data in positive and negative ions, respectively, and uses Compound Discoverer 3.2 software to analyze the collected raw data. As a result, a total of 39 compounds were identified from their high-resolution mass spectra in both positive and negative ionization modes, including 13 flavonoids and their glycosides, 15 phenolic acids, 4 oligosaccharides and glycosides, 4 pentacyclic triterpenoids, and 3 other compounds. Among them, 18 chemical components have not been reported in L. leontopodioides . In the GC-MS section, two common organic solvents (n-hexane and diethyl ether) were used to extract essential oils, and the mass spectra were recorded at 70 eV (electron impact) and scanned in the range of 35∼450 m/z. Compounds were identified using NIST (version 2017), and the peak area normalization method was used to calculate their relative amounts. Finally, 17 components were identified in the volatile oil extracted with n-hexane, accounting for 80.38% of the total volatile oil, including monoterpenoids, phenylpropene, fatty acids, and aliphatic hydrocarbons. In the volatile oil extracted with diethyl ether, 16 components were identified, accounting for 73.50% of the total volatile oil, including phenylpropene, aliphatic hydrocarbons, monoterpenoids, fatty acids, and esters. This study was the first to conduct a comprehensive analysis of the chemical composition of the L. leontopodioides water extract and its essential oil, and a comprehensive chemical composition spectrum was constructed, to lay a foundation for its further pharmacodynamic material basis and quality evaluation., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2024 Yuanyuan Chen et al.)

To investigate the mechanism of action of Cao Wu in the treatment of migraine from the perspective of network pharmacology. The Swiss Target Prediction Database and CTD database were used to predict the target information of Cao Wu. Human Genome Database gene cards were used to find migraine-related target genes. The target protein interaction network and the "active ingredient-target" network were obtained by combining Cytoscape 3.7.1 software and R language. Enrichment analysis of the Kyoto Encyclopedia of Genes and Genomes pathway and gene function analysis (GO) were performed using the R language to preliminarily explore the multiple pharmacological mechanisms of Radix Aconiti kusnezoffii. Forty-three indicators were identified. A total of 176 migraine targets were identified through the genecards database and OMIM database. Radix Aconiti kusnezoffii targets were compared with migraine targets and 12 overlapping targets were extracted. The protein interaction network of the overlapping targets was analyzed to identify the key targets for the drug to affect the disease. In addition, Kyoto Encyclopedia of Genes and Genomes pathway and go function enrichment analyses were performed on the overlapping targets to explore the therapeutic mechanism of migraine. The treatment of migraine with herbal woo is characterized by multi-component, multi-target, and multi-channel, which exerts complex network regulation through the interaction between different targets, providing a new idea and basis for further exploring the mechanism of action of herbal woo in the treatment of migraine., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc.)

Thirteen flavonol glycosides were isolated from the petals of Rosa species belonging to the section Gallicanae, and their structures were identified from their spectroscopic data. These flavonol glycosides, along with two flavonol glycosides isolated from Rosa rugosa, in the petals of 31 Rosa species belonging to sections Gallicanae, Cinnamomeae, Caninae, and Synstylae were quantitatively analyzed by UPLC. The results indicated that the species belonging to these sections could be classified into four types (Type A, B, C and D) based on the pattern of flavonol glycoside contents, whereas the R. rugosa flavonol glycosides were detected only in section Cinnamomeae. A principal components analysis (PCA) calculated from the 15 flavonol glycosides contained in these samples supported the presence of four types. The distribution of the species in Type D (a group of Cinnamomeae) was shown to reflect close interrelationships, but species in Type B (one group of Gallicanae) could be subdivided into two groups, one of which contained species in section Synstylae. Moreover, the flavonol glycosides were grouped by sugar moieties: a disaccharide composed of two hexoses (S1), a hexose (S2), including a hexose with galloyl group, a pentose (S3), and a disaccharide composed of a hexose and a pentose (S4). The ratios of the amounts of S1-S4 to total flavonol glycoside content indicated that differences among the four sections were more distinctive than the amounts of the 15 flavonol glycosides. The 31 samples were divided into Type B, composed of one type of Gallicanae and Synstylae, Type A+C, composed of another type of Gallicanae and Caninae, and Type D, composed of Cinnamomeae. The R. rugosa flavonol glycosides were shown to be important chemotaxonomic markers for the classification of species in Cinnamomeae, and this method of using flavonol glycosides as chemotaxonomic markers could be useful for the identification of Rosa species belonging to sections Gallicanae, Cinnamomeae, Caninae, and Synstylae., (Copyright © 2014 Elsevier Ltd. All rights reserved.)