ABSTRACT
This study used metabolomics to explore the improvement effect of raw and honey-processed Glycyrrhizae Radix et Rhizoma on acute kidney injury (AKI) in rats. All animal experiments were approved by the Animal Ethics Committee of Shandong Academy of Chinese Medicine (approval No.: SDZYY20200101001). SD rats were randomly divided into normal group, model group, raw Glycyrrhizae Radix et Rhizoma group (0.9 g·kg-1) and honey-processed Glycyrrhizae Radix et Rhizoma group (0.9 g·kg-1), 6 rats in each group. The rats model of acute kidney injury was established by single intraperitoneal injection of cisplatin (CP) and treated with raw and honey-processed Glycyrrhizae Radix et Rhizoma. The pathological changes of renal tissue were evaluated by hematoxylin and eosin (HE) and PAS staining, the contents creatinine (Cr), blood urea nitrogen (BUN) and superoxide dismutase (SOD) in serum were detected. UPLC-Q-TOF/MS was used to study tissue metabolomics to screen the biomarkers affected by raw and honey-processed Glycyrrhizae Radix et Rhizoma and analyz the metabolic pathways. The results showed that compared with the model group, raw and honey-processed Glycyrrhizae Radix et Rhizoma can significantly improve the pathological changes of renal tissue and decrease the content of Cr, BUN and increase the activity of SOD. In addition, honey-processed Glycyrrhizae Radix et Rhizoma can also significantly reduce the kidney index. In tissue samples, 45 biomarkers were measured in AKI rats. Raw Glycyrrhizae Radix et Rhizoma and honey-processed Glycyrrhizae Radix et Rhizoma could simultaneously call back 11 differential metabolites, which were involved in the regulation of glycerophospholipid metabolism, tryptophan metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, and glutathione metabolism. In addition, raw Glycyrrhizae Radix et Rhizoma is also involved in the regulation of glycine, serine and threonine metabolism and pyrimidine metabolism. In summary, raw and honey-processed Glycyrrhizae Radix et Rhizoma can participate in the regulation of different metabolic pathways, and play an improvement role in AKI rats by regulating amino acid, lipid metabolism, energy metabolism and oxidative stress.
ABSTRACT
Aiming at the hysteresis and destructiveness of off-line static detection of critical quality attribute of the moisture content of the raw material unit of the traditional Chinese medicine manufacturing process, honey-processed Tussilago farfara, honey-processed Astragalus and honey-processed Glycyrrhiza uralensis were used as the research carriers, and the drying method was used to measure the moisture content as a reference value. The moving stage was used to simulate the movement process of samples on the conveyor belt in the actual on-site production process, and near-infrared (NIR) spectra were collected, combined with machine learning, to establish NIR on-site dynamic detection model of moisture content in multi-variety honey-processed Chinese herbal slice. The results show that the second derivative method is used to preprocess the spectrum. The number of decision trees (ntree), the number of random features (max feature), and the minimum number of samples for generating leaf nodes (node size) are selected: 46, 76, and 8, respectively. The quantitative analysis model of moisture content has the best effect. The prediction coefficient of determination (the prediction coefficient of determination, R2pre) and the root mean square error of prediction (root mean square error of prediction, RMSEP) of the model were 0.903 2 and 0.330 2, respectively. The NIR quantitative model for the moisture content of multi-variety honey-processed Chinese herbal slice established in this study has good predictive performance, and can achieve rapid, accurate and non-destructive quantitative analysis of the moisture content of honey-processed Tussilago farfara, honey-processed Astragalus and honey-processed Glycyrrhiza uralensis at the same time, and provides a method for determining the moisture content of honey-processed Chinese herbal slice of the raw material unit of the traditional Chinese medicine manufacturing process.
ABSTRACT
OBJECTIVE：To provide reference for the identification and proces sing end-point determination of raw Morus alba and its processed products （honey-processed M. alba ）. METHODS ：UPLC method was adopted. The determination was performed on Waters BEH Shield RP C 18 column with mobile phase consisted of acetonitrile- 0.1％ phosphoric acid solution （gradient elution ） at the flow rate of 0.30 mL/min. The column temperature was set at 30 ℃. The program wavelengths were set at 280 nm（0-4 min） and 320 nm（4-35 min）. Similarity Evaluation System for Chromatogram Fingerprint of TCM （2012 edition）was used to establish UPLC fingerprint and carry out similarity evaluation of 13 batches of M. alba and honey-processed M. alba . The chromatographic peaks were identified with reference substance fingerprint. The colorimetric value （L，a，b） of 13 batches of M. alba and honey-processed M. alba powder were determined ，and average total colorimetric value （E）was calculated. OPLS-DA and cluster analysis were adopted to analyze the differences in fingerprints and colorimetric values of M. alba before and after processing. At the same time ，the dynamic change rule of fingerprint and colorimetric value of honey-processed M. alba at different processing time points were analyzed to determine the processing end-point. RESULTS ：There were obvious differences in fingerprints before and after processing ，and the similarity of 13 batches of M. alba and honey-processed M. alba were all higher than 0.9. Totally 21 common peaks were calibrated for M. alba ，and 23 common peaks for honey-processed M. alba ；peak 1 and peak 2 were newly produced compounds of honey-processed M. alba . Peak 2，peak 7，peak 14 and peak 19 were identified as 5-hydroxymethylfurfural， mulberry glucoside A ，oxidized resveratrol ，mulberry flavonoids G. Results of OPLS-DA showed that the peak area-sample quantity ratio of peak 1，peak 2，peak 18，peak 20 and the chromaticity values （L，a，b）were the most important factors affecting the difference of raw and processed products of M. alba . When the E ranged 75.84-80.88 as the processing end-point of honey-processed M. alba ，the processing time was determined as 22-34 min. CONCLUSIONS ： The established UPLC fingerprint and colorimetric value determination method can be used to identify the raw and processed products of M. alba as well as determine the processing end-point of honey-processed M. alba .
ABSTRACT
A metabolomics method was used to search for chemical markers in prepared slices of Glycyrrhiza uralensis with different degrees of honey processing. Coupled with these metabolomics analytical methods, ultra-performance liquid chromatography with quadrupole time-of-flight tandem mass spectrometry (UPLC-Q-TOF/MS) was used to generate global chemical profiles of the raw material of Glycyrrhiza uralensis and the prepared slices. The samples were collected in Shanxi, Hebei Zhangjiakou and Inner Mongolia. A total of 57 chemical components were identified in Glycyrrhiza uralensis by using the UNIFI theoretical database combined with the library of reference samples. Among them, 37 compounds were identified in positive ion mode and 56 compounds were identified in negative ion mode. Unsupervised principal component analysis (PCA) showed that the chemical ingredients differed considerably depending on the extent of processing. Supervised orthogonal partial least squares discriminant analysis (OPLS-DA) was used to differentiate the moderate processing group and the raw group, and partial least squares discriminant analysis (PLS-DA) was used to differentiate the less, the moderate, and the excessive processing groups. The results showed that the contents of glycyrrhizic acid, licoricesaponin G2, and licoricesaponin E2 varied with the extent of processing. The content of these components increased after processing, and reached the highest level when the extent of processing was moderate (P < 0.05). Glycyrrhizic acid, licoricesaponin G2 and licoricesaponin E2 can be regarded as the chemical markers to differentiate the samples with different degrees of processing. These three compounds can be used to monitor the processing of Glycyrrhiza uralensis.