Lonicerae japonicae flos (LJF, Lonicera japonica Thunb.) is adopted as a core herb for preventing and treating influenza. However, the anti-influenza virus components of LJF and the impact of quality-affecting factors on the anti-influenza activity of LJF have not been systematically investigated. In this study, a strategy integrating anti-influenza virus activity, ultrahigh-performance liquid chromatography fingerprint and chemical pattern recognition was proposed for the efficacy and quality evaluation of LJF. As a result, six bioactive compounds were screened out and identified as neochlorogenic acid, chlorogenic acid, cryptochlorogenic acid, 4,5-Di-O-caffeoylquinic acid, sweroside and secoxyloganin. Based on the bioactive compounds, chemical pattern recognition models of LJF were established by a linear discriminant analysis (LDA). The results of the LDA models and anti-influenza virus activity demonstrated that cultivation pattern significantly affected the anti-influenza effect of LJF and that the neuraminidase inhibition rate of wild LJF was significantly higher than that of cultivated LJF. Moreover, the quality of LJF samples with different processing methods and geographical origins showed no obvious difference. Overall, the proposed strategy in the current study revealed the anti-influenza virus components of LJF and provided a feasible method for thequality evaluation of LJF, which has great importance for assuring the clinical effect against influenza of LJF.

BackgroundDeep venous thrombosis (DVT) highly occurs in patients with severe COVID-19 and probably accounted for their high mortality. DVT formation is a time-dependent inflammatory process in which NETosis plays an important role. However, whether ginsenoside Rg5 from species of Panax genus could alleviate DVT and its underlying mechanism has not been elucidated.MethodsThe interaction between Rg5 and P2RY12 was studied by molecular docking, molecular dynamics, surface plasmon resonance (SPR), and molecular biology assays. The preventive effect of Rg5 on DVT was evaluated in inferior vena cava stasis–induced mice, and immunocytochemistry, Western blot, and calcium flux assay were performed in neutrophils from bone marrow to explore the mechanism of Rg5 in NETosis via P2RY12.ResultsRg5 allosterically interacted with P2RY12, formed stable complex, and antagonized its activity via residue E188 and R265. Rg5 ameliorated the formation of thrombus in DVT mice; accompanied by decreased release of Interleukin (IL)-6, IL-1β, and tumor necrosis factor-α in plasma; and suppressed neutrophil infiltration and neutrophil extracellular trap (NET) release. In lipopolysaccharide- and platelet-activating factor–induced neutrophils, Rg5 reduced inflammatory responses via inhibiting the activation of ERK/NF-κB signaling pathway while decreasing cellular Ca2+ concentration, thus reducing the activity and expression of peptidyl arginine deiminase 4 to prevent NETosis. The inhibitory effect on neutrophil activity was dependent on P2RY12.ConclusionsRg5 could attenuate experimental DVT by counteracting NETosis and inflammatory response in neutrophils via P2RY12, which may pave the road for its clinical application in the prevention of DVT-related disorders.

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Rhodiola, especially Rhodiola crenulate and Rhodiola rosea, is an increasingly widely used traditional medicine or dietary supplement in Asian and western countries. Because of the phytochemical diversity and difference of therapeutic efficacy among Rhodiola species, it is crucial to accurately identify them. In this study, a simple and efficient method of the classification of Rhodiola crenulate, Rhodiola rosea, and their confusable species (Rhodiola serrata, Rhodiola yunnanensis, Rhodiola kirilowii and Rhodiola fastigiate) was established by UHPLC fingerprints combined with chemical pattern recognition analysis. The results showed that similarity analysis and principal component analysis (PCA) could not achieve accurate classification among the six Rhodiola species. Linear discriminant analysis (LDA) combined with stepwise feature selection exhibited effective discrimination. Seven characteristic peaks that are responsible for accurate classification were selected, and their distinguishing ability was successfully verified by partial least-squares discriminant analysis (PLS-DA) and orthogonal partial least-squares discriminant analysis (OPLS-DA), respectively. Finally, the components of these seven characteristic peaks were identified as 1-(2-Hydroxy-2-methylbutanoate) β-D-glucopyranose, 4-O-glucosyl-p-coumaric acid, salidroside, epigallocatechin, 1,2,3,4,6-pentagalloyglucose, epigallocatechin gallate, and (+)-isolarisiresinol-4′-O-β-D-glucopyranoside or (+)-isolarisiresinol-4-O-β-D-glucopyranoside, respectively. The results obtained in our study provided useful information for authenticity identification and classification of Rhodiola species.