Hematopoietic effect of small molecular fraction of Polygoni multiflori Radix Praeparata in cyclophosphamide-induced anemia mice.

The aim of this study is to investigate the protective effects of a small molecular fraction (SMF) of Polygoni multiflori Radix Praeparata (PMRP) in a cyclophosphamide (CTX) induced anemia mouse model. Small molecular fraction of PMRP was prepared and identified by high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (HPLC-Q-TOF-MS). In pharmacology, we examined the peripheral hemogram and thymus and spleen index. The content of granulocyte-macrophage colony-stimulating factor (GM-CSF) in serum was mensurated by enzyme-linked immunosorbent assay (ELISA); The level of superoxide dismutase (SOD), catalase (CAT), total antioxidant capacity (T-AOC), and malondialdehyde (MDA) in serum and spleen tissue homogenate were detected, and glutathione peroxidase (GSH-PX) was assayed in spleen. The results show that SMF can significantly accelerate the recovery of peripheral hemogram, increase the activity of antioxidant enzymes and GM-CSF in serum and spleen. SMF also increases the number of spleen cells, improves bone marrow pathology. In conclusion, the SMF of PMRP promoted the recovery of hematopoietic function in a CTX-induced anemia mouse, which can support SMF to be used as an adjunct to chemotherapy to counteract its side effects.

[Rapid identification of geographical origins and determination of polysaccharides contents in Ganoderma lucidum based on near infrared spectroscopy and chemometrics].

Near infrared spectroscopy combined with chemometrics methods was used to distinguish Ganoderma lucidum samples collected from different origins, and a prediction model was established for rapid determine polysaccharides contents in these samples. The classification accuracy for training dataset was 96.87%, while for independent dataset was 93.33%; as for the prediction model, 5-fold cross-validation was used to optimize the parameters, and different signal processing methods were also optimized to improve the prediction ability of the model. The best square of correlation coefficients for training dataset was 0.965 4, and 0.851 6 for validation dataset; while the root-mean-square deviation values for training dataset and validation dataset were 0.018 5 and 0.023 6, respectively. These results showed that combining near infrared spectroscopy with suitable chemometrics approaches could accuracy distinguish different origins of G. lucidum samples; the established prediction model could precious predict polysaccharides contents, the proposed method can help determine the activity compounds and quality evaluation of G. lucidum.