Traditional Chinese Medicine Bureau of Guangdong Province

This study aims to compare the effects and mechanisms of the standard decoction and formula granules of Paeoniae Radix Rubra in regulating the metabolism in the rat model of heat toxin and blood stasis. SD rats were randomized into control, model, standard decoction, and formula granules groups. After 14 days of administration, the rats in the latter three groups were subjected to subcutaneous injection with carrageenan and intraperitoneal injection with bacterial lipopolysaccharide for the modeling of heat toxin and blood stasis. The anal temperature, coagulation indexes, platelet aggregation(PAG), serum levels of thromboxane B_2(TXB_2), 6-ketone-prostaglondin F1α(6-keto-PGF1α), interleukin-1β(IL-1β), tumor necrosis factor-α(TNF-α), von Willebrand factor(vWF), endothelial nitric oxide synthase(eNOS), and endothelin-1(ET-1) were measured. The metabolites in rat serum were identified by ultra-high performance liquid chromatography-quadrupole-time of flight mass spectrometry(UPLC-Q-TOF-MS). Multivariate statistical analysis was performed to screen the differential metabolites, which were then subjected to the KEGG pathway enrichment analysis. The results showed that the standard decoction and formula granules of Paeoniae Radix Rubra improved the state of model rats by lowering the body temperature, inhibiting inflammation, reducing PAG, delaying coagulation, and regulating the vascular function. The metabolomics analysis screened out 15 and 65 differential metabolites in the standard decoction and formula granules groups, respectively, compared with the model group. The enrichment analysis of the differential metabolites showed that the standard decoction of Paeoniae Radix Rubra exerted effects by affecting four key metabolic pathways: α-linolenic acid metabolism, primary bile acid biosynthesis, linoleic acid metabolism, and phenylalanine metabolism. The formula granules of Paeoniae Radix Rubra exerted effects by affecting four key metabolic pathways: α-linolenic acid metabolism, citrate cycle(tricarboxylic acid cycle), purine metabolism, and glyoxylic acid and dicarboxylic acid metabolism. This study indicated that the standard decoction and formula granules of Paeoniae Radix Rubra had similar effects on the syndrome of heat toxin and blood stasis and both of them regulated α-linolenic acid catabolism, which provided a scientific basis for the clinical application of the formula granules of Paeoniae Radix Rubra.

Hyperlipidemia is a condition characterized by abnormal levels of lipids and lipoproteins in the plasma, posing significant health risks. Ferulic acid (FA) is an organic acid with therapeutic properties for diabetes and hyperlipidemia. To explore biomarkers for FA treatment of hyperlipidemia and elucidate the mechanisms of lipid-lowering-related changes in metabolic pathways by metabolomics and network pharmacology. Initially, a hyperlipidemic mouse model induced by triton WR-1339 was established to evaluate the therapeutic effects of FA. Subsequently, serum metabolomics was utilized to identify differential metabolites, and metabolic pathway analysis was performed using MetaboAnalyst 6.0. Thirdly, network pharmacology was employed to identify potential targets of FA for hyperlipidemia. Finally, the compound-target-metabolite (C-T-M) network obtained core targets and validated them with molecular docking. Biochemical analysis and histological examination showed that FA had lipid-lowering effects on hyperlipidemic mice. It identified 31 potential biomarkers for FA against hyperlipidemia by metabolomics involving lipid and amino acid metabolism. Lipid and atherosclerosis signaling pathways were identified as the key signaling pathways of FA against hyperlipidemia by KEGG analysis. Conjoint analysis showed that FA against hyperlipidemia was associated with 18 core targets and six biomarkers. Molecular docking results showed that FA has a high binding affinity to these core targets. Through the synergy of network pharmacology and metabolomics, this study provides insights into how FA regulates endogenous metabolites, underscoring its promise as a treatment for hyperlipidemia.

This paper discusses the practice and research progress of traditional Chinese medicine (TCM) auricular acupuncture massage therapy for insomnia in breast cancer patients. Firstly, it introduces the background and significance of insomnia in breast cancer patients, pointing out the limitations of traditional treatment methods. It then elucidates the theoretical basis of TCM auricular acupuncture massage for insomnia, including ear acupuncture theory, the association between massage and insomnia, and the treatment mechanism. Subsequently, it describes the clinical practice of TCM auricular acupuncture massage in the treatment of insomnia in breast cancer patients, including research methods, practical processes, and result analysis. Finally, it summarizes the current research progress and prospects future research directions and trends.

Femoral neck fracture (FNF) is a common clinical trauma with high mortality and disability rates. Furthermore, its incidence increases exponentially with increasing age. Existing classifications have some disadvantages. Thus, this study aimed to establish a novel typing system for FNF. We retrospectively analyzed all adult patients with FNF admitted to our hospital between December 2015 and November 2017 for cannulated screw internal fixation. The study population was divided into the femoral varus offset group (VAR) and the valgus offset group (VAL). The data collected included sex, age, affected side, injury mode, body mass index, complications, pelvic incidence (PI), hip deflection angle (HDA), combined deflection angle (CDA), and neck shaft angle. Statistical analysis was conducted to determine the correlation between complications and deviation angles. A novel typing system was developed and compared with the Garden classification to detect its superiority. A total of 108 patients were recruited, with 59 patients in the VAR and 49 patients in the VAL groups. The incidence of complications in the VAR group was significantly higher than that in the VAL group (P < 0.05). Moreover, there were more male participants in the VAR group. Compared with the VAL group, the VAR group had significantly higher PI, HDA, and CDA (P < 0.05). The CDA classification (CDAC) was defined, with CDA as the main criterion and HDA as the supplementary criterion. Furthermore, there was a hierarchical correlation between the actual incidence of complications and the typing level, which was increased in CDAC but not in the Garden classification. This showed that CDAC was more accurate. A novel typing system, CDAC, for FNF was established, which was more accurate than the Garden classification. We suggest combining CDAC and Garden classifications for the preoperative diagnosis, treatment selection, and prognostic evaluation for patients with FNF.

Objective. Deep transfer learning has been widely used to address the nonstationarity of electroencephalogram (EEG) data during motor imagery (MI) classification. However, previous deep learning approaches suffer from limited classification accuracy because the temporal and spatial features cannot be effectively extracted. Approach. Here, we propose a novel end-to-end deep subject adaptation convolutional neural network (SACNN) to handle the problem of EEG-based MI classification. Our proposed model jointly optimizes three modules, i.e. a feature extractor, a classifier, and a subject adapter. Specifically, the feature extractor simultaneously extracts the temporal and spatial features from the raw EEG data using a parallel multiscale convolution network. In addition, we design a subject adapter to reduce the feature distribution shift between the source and target subjects by using the maximum mean discrepancy. By minimizing the classification loss and the distribution discrepancy, the model is able to extract the temporal-spatial features to the prediction of a new subject. Main results. Extensive experiments are carried out on three EEG-based MI datasets, i.e. brain–computer interface (BCI) competition IV dataset IIb, BCI competition III dataset IVa, and BCI competition IV dataset I, and the average accuracy reaches to 86.42%, 81.71% and 79.35% on the three datasets respectively. Furthermore, the statistical analysis also indicates the significant performance improvement of SACNN. Significance. This paper reveals the importance of the temporal-spatial features on EEG-based MI classification task. Our proposed SACNN model can make fully use of the temporal-spatial information to achieve the purpose.

Citri Reticulatae Pericarpium Viride (Qing Pi in Chinese) is a clinically effective Chinese herb, which contains biologically valuable flavonoids. Qing Pi is divided into two commodity specifications, Si Hua Qing Pi (SHQP) and Ge Qing Pi (GQP), based on the harvesting time. The flavonoid contents in Qing Pi from different origins and commodity specifications may vary significantly, which will affect their therapeutic functions. Thus, it is crucial to set up a reliable and comprehensive quality evaluation method for flavonoid analysis in Qing Pi. We aimed to establish a rapid and sensitive ultrahigh-performance liquid chromatography method coupled with diode-array detection and high-resolution mass spectrometry (UPLC-DAD-HRMS) for identification and quantification of ten flavonoids in Qing Pi. Chemometric methods were further applied to distinguish Qing Pi of different origins and specifications. An UPLC-DAD-HRMS method was developed for the simultaneous separation and quantification of ten flavonoids in 46 batches of Qing Pi samples from different sources in China. Chemometric approaches were applied to discriminate Qing Pi from different origins and commodity specifications. The chemometric procedures (i.e., hierarchical clustering analysis and principal component analysis) were employed to identify the differences of Qing Pi samples with different origins and commodity specifications. The results showed that the contents of ten flavonoids in Qing Pi samples of different origins were significantly different, and the same results were found out between SHQP and GQP. This study provides a chemical basis for quality control of Qing Pi.

To identify the different plant resources of Herba Rabdosiae Serrae by using Random Amplified Polymorphic DNA ( RAPD ) Analysis. The mini spin columns were used to extract the genomic DNA from five different plants of Herba Rabdosiae Serrae. With the DNA extracted from these plants as template,the 85 oligo nucleic acids (10 bp)as random primers,the polymer chain reaction (PCR) was done and the results were analysed by electro-pharoses. 12 primers were selected with polymorphism and 7 of them showed good polymorphism in RAPD map. RAPD method can be used to identify the plant resources of Herba Rabdosiae Serrae.

Plants in their natural environment are usually affected by mechanical stress because of their sessile growth habit. This work was aimed at determining the effects of mechanical vibration on the growth rate, physiological indexes, and secondary metabolite biosynthesis of Camptotheca acuminata calli. In this study, mechanical vibrations of 1–4 Hz in frequency were applied to stimulate the C. acuminata calli; we found that a mechanical vibration of moderate frequency (2 Hz) can clearly promote the growth rate and increase the soluble protein content and superoxide dismutase (SOD) activity. Under a mechanical vibration of a 2 Hz frequency, camptothecin (CPT), the main secondary metabolite produced by C. acuminata, was increased by about fourfold compared to the control group, In contrast, the increased accumulation disappears at higher frequencies. The optimal vibration time for obtaining the highest levels of biomass and CPT was 60 min. This study showed that there are neutral frequencies and optimal periods of mechanical vibration on C. acuminata calli.