Objective: To study the specificity of the determination method for catechin and epicatechin in Catechu. Method: Contents of catechin and epicatechin in 7 kinds of crude drugs and 3 kinds of preparations of Catechu were determined. Results: As sampling 100 times as much as Catechu, catechin and epicatechin could be both detected in Semen Arecae, and catechin could be detected in Radix et Rhizoma Rhei, Herba Ephedrae and Radix Sanguisorbae. Conclusion:This method can be used for the quality control of Catechu and its preparations and is of specificity.

Objective To observe the hindpaw withdrawal threshold (HWT), and the ultrastructure and expression of glia cell line-de-rived neurotrophic factor (GDNF) in sciatic nerve (SN) in chronic constriction injury (CCI) rats after pulsed radiofrequency (PRF). Meth-ods 30 Sprague-Dawley rats were divided into sham modeling-sham treating (SS) group, CCI-Sham treating (CS) group and CCI-PRF (CP) group. The right SNs of the rats in the CS and CP groups were ligated, and it was separated without ligation in the SS group. The CP group accepted PRF at the ligation 14 days after modeling, while the electrodes were placed without electricity in the SS and CS groups. Their HWT was measured before and 1, 7, 14 days after modeling, and 1, 7, 14 days after treatment. The right SN of ligation was observed under electron microscope 14 days after treatment, meanwhile, the GDNF expression was determined with enzyme-linked immunosorbent assay (ELISA). Results HWT was significantly shorter in the CS and CP groups than in the SS group after modeling, and it increased in the CP group 14 days after treatment compared with that of the CS group (P<0.01). The degeneration of SN significantly improved in the CP group compared with the CS group, while the expression of GDNF increased compared with that in the CS and SS groups (P<0.01). Conclusion PRF could relieve the CCI-induced neuropathic pain by upregulating the GDNF expression in the SN to prevent the SN from injury.

Objective To establish a reversed -phase HPLC m ethod for the determination of psora len and isopsoralen in Fukangbao capsule.Methods The contents of Psoralen and Isopsoralen were assayed on a ODS -C 18 column with a mobile phase of methanol -water(40∶60)at a column temperature of 35℃.The fl ow rate was 1.0mL /min.The detection wave-length was at 247nm.Results The linear ranges of Psoralen and Iso psoralen were 1.05～10.52?g /mL(r =0.9999)and1.02～10.20?g /mL(r =0.9999)respectively.Their recoveries were within 97.5%(Psoralen)and 100.8%(Isopsoralen),and both of their RSD were 0.6%.Conclusion This method is simple,rapid and accu rate and suitable for quantity -limiting control of Fukan gbao capsule.

Objective To observe the expression changes in microRNA (miR)-379 in the developmental process of the mouse heart and during the differentiation of P19 cells into cardiac myocytes,and to explore the possible relationship between miR-379 and the differentiation of cardiacmyocytes.Methods Heart tissues were collected from fetal mice in pregnant ones at their gestational age (8.5,11.5,14.5 and 18.5 days) respectively.Heart tissue sections of the fatal mice were obtained to observe the heart development process.Then total RNA was isolated from heart tissues by using the TRIzol method.Complementary DNA was synthesized from 1 μg total RNA by using a Reverse Transcriptase Kit.Finally,real-time PCR (RT-PCR) was employed to detect the expression of miR-379.At the same time,P19 cells were cultured with 10 mL/L Dimethyl sulfoxide in suspension for 4 days to form cell aggregation,and these aggregations were transferred into 6-wells plate for culturing by adherence.Beating cells were detected with microscopy on the 10th day after induction.Afterwards,total RNA was extracted from cultured P19 cells at different time points.Reverse transcription was executed to get DNA.At last,RT-PCR was used to explore the expression of miR-379 on 0,4,6,10 days after aggregation.Results The expression level of miR-379 was down-regulated gradually in the developing heart (at gestational age of 8.5,11.5,14.5,16.5 days,respectively),and there were significant differences on the different days (F =21.13,P ＜ 0.05).On the other hand,myocardial markers of troponin T represented an increasing trend during the process of P19 cells induction,which demonstrated that P19 cells were successfully induced into cardiomyocyte-like cells.Meanwhile,miR-379 showed a low expression on day 0 of P19 cells aggregation.On day 4,miR-379 demonstrated a higher level.Afterwards,miR-379 proved to be down-regulated gradually.Conclusions miR-379 plays a role in the process of the heart development,but the specific mechanisms need further research.

ObjectiveThis study aims to develop a prediction model for the compatibility of Chinese medicinal pairs based on Graph Convolutional Networks （GCN）， named HC-GCN. The model integrates the properties of herbs with modern pharmacological mechanisms to predict pairs with specific therapeutic effects. It serves as a demonstration by applying the model to predict and validate the efficacy of blood-activating herb pairs. MethodsThe training dataset for herb pair prediction was constructed by systematically collecting commonly used herb pairs along with their characteristic data， including Qi， flavor， meridian tropism， and target genes. Integrating traditional characteristics of herb with modern bioinformatics， we developed an efficacy-oriented herb pair compatibility prediction model （HC-GCN） using graph convolutional networks （GCN）. This model leverages machine learning to capture the complex relationships in herb pair compatibility， weighted by efficacy features. The performance of the HC-GCN model was evaluated using accuracy （ACC）， recall， precision， F1 score （F1）， and area under the ROC curve （AUC）. Its predictive effectiveness was then compared to five other machine learning models： eXtreme Gradient Boosting （XGBoost）， logistic regression （LR）， Naive Bayes， K-nearest neighbor （KNN）， and support vector machine （SVM）. ResultsUsing herb pairs with blood-activating effects as a demonstration， a prediction model was constructed based on a foundational dataset of 46 blood-activating herb pairs， incorporating their Qi， flavor， meridian tropism， and target gene characteristics. The HC-GCN model outperforms other commonly used machine learning models in key performance metrics， including ACC， recall， precision， F1 score， and AUC. Through the predictive analysis of the HC-GCN model， 60 herb pairs with blood-activating effects were successfully identified. Among of these potential herb pairs， 44 include at least one herb with blood-activating effects. ConclusionIn this study， we established an efficacy-oriented compatibility prediction model for herb pairs based on GCN by integrating the unique characteristics of traditional herbs with modern pharmacological mechanisms. This model demonstrated high predictive performance， offering a novel approach for the intelligent screening and optimization of traditional Chinese medicine prescriptions， as well as their clinical applications.