The aim of this study was to systematically evaluate the effect of the cultivation year on the quality of different ginseng tissues. Qualitative and quantitative analyses of ginsenosides were conducted using a UPLC-UV-MS method. Eight main ginsenosides in three tissues (leaf, rhizome and main root) and four parts (periderm, phloem, cambium and xylem) of ginseng aged from 1 to 13 years were determined using a UPLC-PDA method. Additionally, the antioxidant capacities of ginseng leaves were analyzed by the DPPH, ABTS and HRSA methods. It was found that the contents of ginsenosides increased with cultivation years, causing a sequential content change of ginsenosides in an organ-specific manner: leaf > rhizome > main root. The ratio between protopanaxatriol (PPT, Rg₁, Re and RF) and protopanaxadiol (PPD, Rb₁, Rb₂, RC and Rd) in the main root remained stable (about 1.0), while it increased in leaf from 1.37 to 3.14 and decreased in the rhizome from 0.99 to 0.72. The amount of ginsenosides accumulated in the periderm was 45.48 mg/g, which was more than twice as high compared with the other three parts. Furthermore, the antioxidant activities of ginseng leaves were measured as Trolox equivalents, showing that antioxidant activity increased along with time of cultivation. The results show that the best harvest time for shizhu ginseng is the fifth year of cultivation, and the root and rhizome could be used together within seven planting years for their similar PPT/PPD level. Besides, the quality of the ginseng products would be enhanced with the periderm. The ginseng leaf is rich in ginsenosides and has potential application for its antioxidant capacity.

Background: Yixinshu Capsules (YXSC) are widely used in Chinese medicine for the treatment of cardiovascular diseases. However, the therapeutic mechanisms of action are not well understood. Method: In this study, a metabonomic approach based on integrated UPLC-Q/TOF-MS technique and MALDI-MS was utilized to explore potential metabolic biomarkers that may help increase the understanding of heart failure (HF) and in order to assess the potential mechanisms of YXSC against HF. Plasma metabolic profiles were analyzed by UPLC-Q/TOF-MS with complementary hydrophilic interaction chromatography and reversed-phase liquid chromatography. Moreover, time-course analysis at the 2nd, 4th, and 10th week after permanent occlusion was conducted. In an effort to identify a more reliable potential metabolic marker, common metabolic markers of the 2nd, 4th, and 10th week were selected through multivariate data analysis. Furthermore, MALDI-MS was applied to identify metabolic biomarkers in the blood at apoptotic positions of heart tissues. Results: The results showed that HF appeared at the fourth week after permanent occlusion based on echocardiographic assessment. Clear separations were observed between the sham and model group by loading plots of orthogonal projection to latent structure discrimination analysis (OPLS-DA) at different time points after permanent occlusion. Potential markers of interest were extracted from the combining S-plots, variable importance for the projections values (VIP > 1), and t-test (p < 0.05). Twenty-one common metabolic markers over the course of the development and progression of HF after permanent occlusion were identified. These were determined to be mainly related to disturbances in fatty acids, phosphatidylcholine, bile acids, amino acid metabolism, and pyruvate metabolism. Of the metabolic markers, 16 metabolites such as palmitoleic acid, arachidonic acid, and lactic acid showed obvious changes (p < 0.05) and a tendency for returning to baseline values in YXSC-treated HF rats at the 10th week. Moreover, four biomarkers, including palmitoleic acid, palmitic acid, arachidonic, acid and lactic acid, were further validated at the apoptotic position of heart tissue using MALDI-MS, consistent to the variation trends in the plasma. Conclusions: Taken in concert, our proposed strategy may contribute to the understanding of the complex pathogenesis of ischemia-induced HF and the potential mechanism of YXSC.

In this study, a method using ultrahigh-performance liquid chromatography with photodiode array (UHPLC-PDA) was established and validated for the simultaneous quantification of 10 active components, including eight macamides and two glucosinolates, in Lepidium meyenii (maca). A gas chromatographic mass spectroscopy (GC-MS) method was used to determine the levels of three benzyl isothiocyanates and two sterols in maca. Liquid chromatographic separation was achieved on a Waters Acquity UHPLC HSS T3 column (2.1mm×100mm, 1.8μm) with gradient elution over 15min. The mobile phase was (B) acetonitrile-(A) 10mM aqueous ammonium phosphate, and the detection wavelength was 210nm. The gas chromatographic separation was performed on an SH-Rxi-1 MS column, and the ionization mode was electron ionization (EI). Two methods were confirmed to have desirable precision (RSD<1.58), repeatability (RSD<1.97), stability (RSD<1.76), and good linearity (R²0.999) within the test range. The recoveries were in the range of 96.79109.99, with an RSD below 2.39. We applied the established methods and successfully analyzed 15 compounds in maca processed under different drying conditions, providing a comprehensive reference for maca processing method of development. In summary, this study provided two rapid and effective methods for the quantification of 15 active components, which contributed to the in-depth maca quality control and provided a reference for the development of maca products.

To address the commingling of different varieties of Ligusticum decoction pieces in the market and the difficulty of achieving rapid, efficient, and accurate on-site identification, this study aimed to develop a lightweight deep learning model that combines high identification accuracy with fast inference speed. Methods: Images of six categories of Ligusticum decoction pieces and their adulterants were acquired in an enclosed photographic light box to construct a standardized image dataset. Using ImageNet-pretrained weights, several high-accuracy models were selected as teacher models through comparative model evaluation, and knowledge distillation was subsequently employed to optimize the lightweight MobileNetV2 model as the student model. Finally, an interactive identification application was developed using PyQt5. Results: After knowledge distillation, the MobileNetV2 model achieved an identification accuracy of 99.73%, approaching the accuracy of the teacher models. Meanwhile, the distilled model contained only 2.23 million parameters, substantially fewer than the teacher models, thereby achieving both high identification accuracy and ease of deployment. Conclusion: The lightweight identification model developed in this study efficiently extracted high-level discriminative features from images of Ligusticum decoction pieces and their adulterants, enabling rapid and accurate identification and providing a feasible technical approach for the rapid on-site authentication of traditional Chinese medicine decoction pieces.