[Identification of biomarkers in urine of rats with spleen Qi deficiency and biological significance].

To identify biomarkers for spleen Qi deficiency by analyzing small molecule metabolites in urine, in order to expound the relationship between biomarkers and metabolic pathways. The spleen Qi deficiency model was established through dietary restriction and overstrain. All of the rats received D-xylose absorption experiment and blood routine test. Urine samples were collected in the next day. The urine samples were analyzed using UPLC-Q-TOF-MS to obtain the dataset of urine metabolic group. Principal component analysis (PCA), orthogonal partialleast squares-discriminant analysis (OPLS-DA) and other multivariate statistical methods were employed to evaluate the quality of the dataset and screen out potential biomarkers of spleen Qi deficiency. The results of D-xylose absorption and blood routine demonstrated that the spleen Qi deficiency model was successfully established. In positive ion mode and negative ion mode, PCA and OPLS-DA score plots could clearly distinguish model group and blank group. According to S-plot of OPLS-DA, VIP value, t-test and area under receiver operating characteristic curve (ROC), 24 biomarkers, including phenylalanine, succinic acid, aconitic acid, isocitrate acid, betaine, kynurenine, indole, creatine, creatinine, orotic acid, xanthine, and xanthurenic acid, were identified as associated with the spleen Qi deficiency, mainly involving energy metabolism, amino acid metabolism, tryptophan metabolism, purine metabolism and pyrimidine metabolism. Urine metabolomics method combined with online software package for data processing and analysis metabolic pathway can provide new methods and ideas for studies for spleen Qi deficiency and other traditional Chinese medicine symptoms.

Multi-fingerprint profiling combined with chemometric methods for investigating the quality of Astragalus polysaccharides.

It is a challenge to ascertain the quality of polysaccharides due to their complex chemical structure; therefore, multi-fingerprint profiling was used to investigate the quality of Astragalus polysaccharides (APS) harvested from Inner Mongolia (NM) and Gansu (GS) with the help of chemometric analysis. Additionally, FT-IR and 1H NMR were applied to characterize the chemical structure of the harvested APS. The spectral fingerprinting results indicated that APS had reduced similarity when they were from different origins. Further, PCA showed that NM and GS could be distinguished and that the main differences from the loading plots were in the absorption intensity of carbonyls and H1 signals of Galp and ß-glucose. Moreover, UPLC/Q-TOF-MS fingerprints were established based on the monosaccharide composition of the APS. The concentration of monosaccharides and results of cluster analysis indicated that GlcA might be an indicator that can be used to distinguish NM and GS. Overall, this multiple fingerprint method was stable, comprehensive and valid for monitoring APS quality.