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Paeonia lactiflora Pall. is a perennial herb of the buttercup family with a long history of medicinal use in China. With population migration and geographical changes, it gradually differentiates into different species, namely Paeoniae Radix Alba (PRA) and Paeoniae Radix Rubra (PRR). This study established an integrated strategy to investigate the different species of Paeonia lactiflora Pall. through ultra-high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS), chemometric analysis combined with network pharmacology. Firstly, a total of 36 compounds were identified using UPLC-Q-TOF/MS, mainly including monoterpene glycosides, polyphenols, and phenolic acids. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) indicated that the studied samples were classified into PRA and PRR based on their species, while PRA or PRR could be classified as corresponding to geographical origins and morphological characteristics. Moreover, the orthogonal partial least squares discriminant analysis (OPLS-DA) and counter propagation artificial neural networks (CP-ANN) were applied to confirm classification results and obtain the chemical markers on different samples. Network pharmacology and molecular docking techniques demonstrated that active molecules have a higher affinity for ACTB and AKT1. The results of the content of representative components demonstrated differences between the two species. In conclusion, the methodology and results of this study provided a reliable basis for the identification and rational utilization of PRA and PRR, which were important for the appropriate clinical use and quality assurance of pharmaceutical materials. [ABSTRACT FROM AUTHOR]

Schisandra chinensis, as a traditional Chinese herbal medicine, has clear pharmacological effects such as treating asthma, protecting nerves and blood vessels, and having anti-inflammatory properties. Although the Schisandra chinensis fruit contain multiple active components, the lignans have been widely studied as the primary pharmacologically active compound. The volatile chemical components of Schisandra chinensis include a large amount of terpenes, which have been proven to have broad pharmacological activities. However, when to harvest to ensure the highest accumulation of pharmacologically active components in Schisandra chinensis fruits is a critical issue. The Schisandra chinensis fruit trees in the resource nursery were all planted in 2019 and began bearing fruit in 2021. Their nutritional status and tree vigor remain consistently good. The content of lignans and organic acids in the fruits of Schisandra chinensis over seven different harvest periods was tested, and the results of high-performance liquid chromatography (HPLC) indicated that the lignan content was higher, at 35 mg/g, in late July, and the organic acid content was higher, at 72.34 mg/g, in early September. If lignans and organic acids are to be selected as raw materials for pharmacological development, the harvest can be carried out at this stage. Using HS-GC-IMS technology, a total of 67 volatile flavor substances were detected, and the fingerprint of the volatile flavor substances in the different picking periods was established. It was shown by the results that the content of volatile flavor substances was the highest in early August, and 16 flavor substances were selected by odor activity value (OAV). The variable importance in projection (VIP) values of 16 substances were further screened, and terpinolene was identified as the key volatile flavor substance that caused the aroma characteristics of Schisandra chinensis fruit at different harvesting periods. If the aroma component content of Schisandra chinensis fruit is planned to be used as raw material for development and utilization, then early August, when the aroma component content is higher, should be chosen as the time for harvest. This study provides a theoretical basis for the suitable harvesting time of Schisandra chinensis for different uses, and promotes the high-quality development of the Schisandra chinensis industry. [ABSTRACT FROM AUTHOR]

BACKGROUND: In todays' society, the rapid development of the wine industry and the improvement of peoples' living standards make people pay more and more attention to wine grape quality. OBJECTIVE: To evaluate the wine grape varieties in Northeast Chinas' grape growing regions for better wine grape quality, we evaluated the quality of different varieties of wine grapes. METHODS: The grape varieties 'Hassan' 'Zuoshaner' 'Beibinghong' 'Zuoyouhong' 'Beta' 'Shuanghong' 'Zijingganlu' 'Cabernet Sauvignon' and 'Syrah' were planted in the grape growing area of Jilin, Northeast China, were used as the subjects of this study. The grape berries were analyzed and tested for morphological indicators, basic physicochemical indicators, color, and phenolic and aromatic composition. RESULTS: According to lab results, 'Hassan' contained the highest amount of total phenolics; 'Zuoyouhong' had the highest solids and total sugar content; 'Shuanghong' had the most elevated total acid and anthocyanin content; 'Zijngganlu' had the highest tannin content and acid fixation ratio; Seventy-one volatile compounds were detected in nine grape varieties. CONCLUSIONS: Each of the nine grape varieties has a distinctive flavor, and because of this, grape processing products with regional flavors can be created. The same offer valuable data for future scientific grape resource collection, conservation, and exploitation. [ABSTRACT FROM AUTHOR]

Background: Chronic kidney disease (CKD) is the end point of a number of renal and systemic diseases. The metabolomics with a highly multiplexed and efficient manner is a challenging goal in nephrology. Methods: A 1H-NMR based metabolomics approach was applied to establish a human CKD serum metabolic profile. Serum samples were obtained from CKD patients with four stages ( N= 80) and healthy controls ( N= 28). The data acquired by CMPG spectrum were further processed by pattern recognition (PR) analysis. Principal components analysis (PCA) and partial least-squares-discriminant analysis (PLS-DA) was capable of clustering the disease groups and establishing disease-specific metabolites profile. Results: The classification models could grade CKD patients with considerably high value of Q2 and R2. The significant endogenous metabolites that contributed to distinguish CKD in different stages included the products of glycolysis (glucose, lactate), amino acids (valine, alanine, glutamate, glycine), organic osmolytes (betaine, myo-inositol, taurine, glycerophosphcholine), and so on. Based on these metabolites, the model for diagnosing patients with CKD achieved the sensitivity and specificity of 100%. Conclusion: The study illustrated that serum metabolic profile was altered in response to renal dysfunction and the progression of CKD. The identified metabolic biomarkers may provide useful information for the diagnosis of CKD, especially in early stages. Clin Trans Sci 2012; Volume 5: 379-385 [ABSTRACT FROM AUTHOR]

Introduction: Scutellariae Radix (SR), derived from the root of Scutellaria baicalensis Georgi, is a traditional Chinese medicine (TCM) for clearing heat and cooling blood. It has been used as a traditional herbal medicine and is popular as a functional food in Asian countries today., Methods: In this study, UPLC-Q-TOF-MS was first employed to identify the chemical components in the ethanol extract of SR. Then, the extraction process was optimized using star point design-response surface methodology. Fingerprints of different batches and processed products were established, and chemical markers were screened through a combination of various artificial neural network models. Finally, network pharmacology and molecular simulation techniques were utilized for verification to determine the quality markers., Results: A total of 35 chemical components in SR were identified, and the optimal extraction process was determined as follows: ultrasonic extraction with 80% methanol at a ratio of 120:1 for 70 minutes, with a soaking time of 30 minutes. Through discriminant analysis using various artificial neural network models, the samples of SR could be classified into two categories based on their growth years: Kuqin (dried roots of older plants) and Ziqin (roots of younger plants). Moreover, the samples within each category could be further clustered according to their origins. The four different processed products of SR could also be distinguished separately. Finally, through the integration of network pharmacology and molecular simulation techniques, it was determined that baicalin, baicalein, wogonin, norwogonin, norwogonin-8-O-glucuronide, skullcapflavone II, hispidulin, 8, 8"-bibaicalein, and oroxylin A-7-O-beta-D-glucuronide could serve as quality markers for SR., Discussion: The primary factors affecting the quality of SR were its growth years. The geographic origin of SR was identified as a secondary factor affecting its quality. Processing also had a significant impact on its quality. The selected quality markers have laid the foundation for the quality control of SR, and this research strategy also provides a research paradigm for improving the quality of TCM., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Liu, Zhao, Wen, Sun, Huang, Zhang, Liu and Ren.)

Background: Chronic kidney disease (CKD) is the end point of a number of renal and systemic diseases. The metabolomics with a highly multiplexed and efficient manner is a challenging goal in nephrology., Methods: A (1) H-NMR based metabolomics approach was applied to establish a human CKD serum metabolic profile. Serum samples were obtained from CKD patients with four stages (N= 80) and healthy controls (N= 28). The data acquired by CMPG spectrum were further processed by pattern recognition (PR) analysis. Principal components analysis (PCA) and partial least-squares-discriminant analysis (PLS-DA) was capable of clustering the disease groups and establishing disease-specific metabolites profile., Results: The classification models could grade CKD patients with considerably high value of Q(2) and R(2) . The significant endogenous metabolites that contributed to distinguish CKD in different stages included the products of glycolysis (glucose, lactate), amino acids (valine, alanine, glutamate, glycine), organic osmolytes (betaine, myo-inositol, taurine, glycerophosphcholine), and so on. Based on these metabolites, the model for diagnosing patients with CKD achieved the sensitivity and specificity of 100%., Conclusion: The study illustrated that serum metabolic profile was altered in response to renal dysfunction and the progression of CKD. The identified metabolic biomarkers may provide useful information for the diagnosis of CKD, especially in early stages., (© 2012 Wiley Periodicals, Inc.)