Integration of bioinformatics analysis, molecular docking and animal experiments to study the therapeutic mechanisms of berberine against allergic rhinitis.

Allergic rhinitis is a prevalent inflammatory condition that impacts individuals of all age groups. Despite reports indicating the potential of berberine in alleviating allergic rhinitis symptoms, the specific molecular mechanisms and therapeutic targets of berberine remain unclear. This research aims to explore the pharmacological mechanism of berberine in the treatment of allergic rhinitis through bioinformatic analyses and experimental validation. The research utilized public databases to identify potential targets of berberine. Furthermore, differentially expressed genes (DEGs) related to allergic rhinitis were pinpointed from the GSE52804 dataset. Through bioinformatics techniques, the primary targets were discovered and key KEGG and GO-BP pathways were established. To confirm the therapeutic mechanisms of berberine on allergic rhinitis, an OVA-induced allergic rhinitis model was developed using guinea pigs. We identified 32 key genes responsible for the effectiveness of berberine in treating allergic rhinitis. In addition, five central genes (Alb, Il6, Tlr4, Ptas2, and Il1b) were pinpointed. Further examination using KEGG and GO-BP pathways revealed that the main targets were primarily involved in pathways such as NF-kappa B, IL-17, TNF, and inflammatory response. Molecular docking analysis demonstrated that berberine exhibited strong affinity towards these five key targets. Furthermore, the expression levels of IL-6, TLR4, PTGS2, and IL-1ß were significantly upregulated in the model group but downregulated following berberine treatment. This research has revealed the mechanism through which berberine combats allergic rhinitis and has identified its potential to regulate pathways linked to inflammation. These discoveries provide valuable insights for the development of novel medications for the treatment of allergic rhinitis.

Quality evaluation for Ficus hirta Vahl granules, using TLC and HPLC fingerprint combined with chemical pattern recognition.

Ficus hirta Vahl is a healthy food with both medicinal and culinary properties and with anti-inflammatory and anti-aging effects. There is currently no standardized or universally accepted research strategy for evaluating the quality of Ficus hirta Vahl granules (FHGs). Therefore, the development of a comprehensive quality evaluation method is crucial for the quality control of FHGs. In this study, we used n-hexane : trichloromethane : ethyl acetate : glacial acetic acid = 20 : 4 : 7 : 1 as the optimal developing agent for TLC to separate and identify 15 batches of FHGs from different origins. Using HPLC, a fingerprint with 7 common peaks was established, and peaks 6 and 7 were attributed to psoralen and bergapten, respectively. The content of the identified components was determined. Further quality evaluation of FHGs was performed using chemical pattern recognition, and the results showed that hierarchical cluster analysis (HCA) could cluster 15 batches of FHGs into 2 categories. Principal component analysis (PCA) showed that 2 principal components can show the similarities and differences between different batches of FHGs. Orthogonal partial least squares discrimination (OPLS-DA) showed that components 5, 6 (psoralen) and 7 (bergapten) are landmark components that cause differences in FHG quality from different regions. By integrating the analytical modes of TLC, HPLC fingerprint and chemical pattern recognition, a scientific basis is provided for the comprehensive control and evaluation of herbal medicine quality.