This study used bibliometric methods to conduct a comprehensive and systematic analysis of the literature on berberine and diabetes from 2004 to 2024. The analysis was conducted in terms of the number of papers per year, countries/institutions/author collaborations, literature/cited literature, and keywords. It can reflect the research hotspots and focus of a certain period in the field, and help researchers speculate on future research trends.

Objective To explore the target and mechanism of Qiwei Baizhu San in the treatment of type 2 diabetes based on network pharmacology integrating macromolecular docking. Methods The active ingredient and their corresponding action targets of Qiwei Baizhu San were obtained from TCMSP database, and the target genes of type 2 diabetes were obtained from Genecards and OMIM database. The intersection of the two was taken to obtain the action targets of the active ingredients of Qiwei Baizhu San in the treatment of T2DM. Cytoscape 3.9.1 was used to analyze the target PPI and draw a "drug-component-common target" network diagram to screen the core components; conduct Reactome, Wiki, KEGG Pathways analysis on the target using STRING database, and conduct GO analysis on Metascape; use Autodock and PyMol software for Macromolecular docking and visualization. Result Qiwei Baizhu San has a total of 57 active ingredients and 202 intersection targets with T2DM. Key targets mainly include AKT1, TP53, HSP90AA1, MAPK1, etc., which are mainly enriched in PI3K-Akt, MAPK, interleukin and other pathways. Macromolecular docking results show that key core components have good binding with core targets. Conclusion Qiwei Baizhu San can act on AKT1, MAPK1 and other targets to treat type 2 diabetes through active ingredients such as quercetin, kaempferol, luteolin, and formononetin, and play a multi-target and multi-pathway therapeutic role.

Background: A large number of studies have investigated the relationship between the polymorphism of potassium ion inwardly-rectifying channel, subfamily J, member 11 (KCNJ11) and the susceptibility to type 2 diabetes mellitus (T2DM), but the results are still controversial. Objective: We studied three gene polymorphisms:rs5219, rs5218, rs5215, and analyzed the allele genetic model, dominant genetic model and recessive genetic model respectively to research the association of the KCNJ11 gene polymorphism with T2D risk. Eligibility criteria: Relevant information of selected articles were independently extracted by 2 reviewers following a predetermined standardized data extraction form. Data sources: We researched databases through PubMed, Web of Science so as to identify all relevant articles published from January 2005 to October 2019. Review appraisl:We used a 95% confidence interval (CI) of the odds ratio (OR) to evaluate the strength of association between these three polymorphisms and T2DM. Z test was used to determine the significance of OR. Q test and I2test were used to evaluate inter-study heterogeneity. Eeggr’s test and Begg’s test were used to evaluate publication bias. Results:28 relevant studies which involved 30,373 patients, including 16,155 cases and 14,218 groups were included. The results showed that KCNJ11 rs5219 (OR: 1.15, 95%Cl: 1.07-1.23, P = 0.000)and rs5215 (OR: 1.48, 95%CI: 1.10-1.99, P=0.009)were strongly associated with T2DM, while there was no significant association between rs5218 (OR: 0.85, 95%CI: 0.75-0.96, P = 0.008)and T2DM. Dominant genetic model and recessive genetic model were consistent with these results. Besides, subgroup analysis (Caucasian, East Asian, North African and Others) and sensitivity analysis were consistent with the above results as well. Limitations: Due to the quantity and quality of literature, we only analyzed three polymorphisms (rs5219 rs5218 rs5215), while the others could not be explained because of insufficient literature. Conclusions: This meta-analysis confirmed that KCNJ11 rs5219 and rs5215 polymorphism (SNPs) were significantly associated with the risk of T2DM.

Objective: To investigate the mechanism of Qiwei Baizhu San in the treatment of T2DM by regulating liver miRNA. Methods: Establish the T2DM rat model by STZ injection combined with high sugar and high-fat diet induction. On the basis of previous research, three rats were randomly selected from model group, control group and treatment group respectively. Transcriptome sequencing was performed on the liver to screen its miRNA differential genes. Target genes were predicted through five databases such as miRDB, and miRNA-mRNA relationship pairs were constructed; After mRNA intersection of model group vs. control group and treatment group vs. model group, target genes were obtained, and PPI, GO and KEGG analyses were performed; The mRNA of Qiwei Baizhu San collected through TCMSP database and the target genes obtained by sequencing were again intersected. The active components corresponding to the target genes were obtained by reverse network pharmacology. The target points corresponding to the core components were screened according to the Degree value and verified by molecular docking. Results: There were 14 differential miRNAs that met the screening conditions in model group vs. control group; In treatment group vs. model group, miRNA differential genes were miR-204-5p and miR-224-5p. After the miRNA intersection in model group vs. control group and treatment group vs. model group, it was found that it regulated 194 mRNA. PPI analysis showed that the key genes were MAPK8, TFRC, SORT1, NTRK2, HNRNPC and AP1S2. The GO analysis results showed that the function was mainly enriched in BP mesenchymal cell development, and KEGG was mainly enriched in Pathways in cancer and PI3K-Akt signaling pathway. The active components obtained by reverse network pharmacology were lappadilactone, Cerevisterol and Genkwanin. The molecular docking results showed that the key active components had good binding with the core target. Conclusion: By regulating miR-204-5p and miR-2245p, Qiwei Baizhu San could target MAPK8, TFRC, SORT1 and other genes. Through mesenchymal cell development, PI3K-Akt signaling pathway and other pathways, it could affect glucose and lipid metabolism, improve IR, and play a role in treating T2DM1.

Objective: This study aimed to explore the mechanism of action of Wumei Pills (WMP) in treating gastric cancer (GC) based on network pharmacology and molecular docking. Methods: The Wumei Pills’ active ingredients were obtained from the traditional Chinese medicine system pharmacology database, and the target sites were obtained from the PharmMapper database. GC’ s target genes were identified through GeneCards, the Therapeutic Target Database, and other databases. The intersection of the two was used to determine the target of active ingredients of WMP that were related to GC. Cytoscape 3.7.0 was used to establish the network map of “ compound-traditional Chinese medicine-ingredient-target” to screen the core components. The Search Tool for the Retrieval of Interacting Genes/Proteins database and Cytoscape 3.7.0 were used to analyze and visualize potential genes of WMP in treating GC. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment were conducted through Metascape. The “ target-critical path” network diagram was created by screening relevant pathways with the enrichment score. KM plotter and Gene Expression Profiling Interactive Analysis database were used to draw GC related survival curve online for core genes. AutoDock Vina and PyMol software were used to conduct molecular docking and visualization. Results: There were 99 intersection targets of the active ingredients of WMP and the disease. Protein-protein interaction network topology analysis revealed ALB, EGFR, SRC, and other key targets. Molecular docking results showed that the key active components had good binding with the core target, and ALB and ESR1 genes were significant in survival analysis. Conclusion:WMP could treat GC via beta-sitosterol, stigmasterol, and other active ingredients that acted on ALB, EGFR, SRC, and other targets. The mechanism could be related to the epithelial cell signal transduction pathway in Helicobacter pylori infection, which played a multi-target and multi-pathway therapeutic role.

Objective: This study aims to explore the mechanism of Qiwei Baizhu San in treating T2DM by high-throughput transcriptome sequencing. Methods: T2DM rats were made by STZ + high fat and high sugar diet. The rats were divided into control group, model group and Qiwei Baizhu San treatment group. Three rats from each of the three groups were taken for transcriptome sequencing. The disease targets of T2DM were searched through Genecards and other databases, and the PPI network analysis and GO and KEGG enrichment analysis were carried out after crossing with the model-blank group differential genes of transcriptome sequencing data The intersection target was intersected with the gene of Qiwei Baizhu San reversing T2DM in transcriptome sequencing to obtain the target of Qiwei Baizhu San for treating T2DM. PPI network analysis and GO and KEGG enrichment analysis were carried out, and the mechanism of Qiwei Baizhu San for treating T2DM was explored through network pharmacology and molecular docking technology. Results: 13891 T2DM related targets were retrieved, and 295 overlapping targets were obtained after crossing with 507 differential genes sequenced by transcriptome. The PPI network was drawn to obtain LCk, CD8, CD28 and other key T2DM pathogenesis targets. A total of 56 key therapeutic targets were obtained by intersecting 295 intersection targets with the genes of Qiwei Baizhu San reversing T2DM in transcriptome sequencing, and key genes such as PTPRC, IRF4 and BTk were obtained by PPI network analysis. The results of molecular docking showed that the active ingredient of Qiwei Baizhu San had good binding with the therapeutic target of T2DM. Conclusion: Qiwei Baizhu San can obviously improve T2DM, and its treatment has the characteristics of multi-target and multi-channel, which provides a theoretical basis for the further study of Qiwei Baizhu San.

The theory of cold-heat property of drugs is the core theory of traditional Chinese medicine (TCM). Exploring its mechanism is of great significance to explore the essence of TCM. With the assistance of the network platform of “HERB herbal group evaluation ”, this paper studies the action targets of three cold drugs and three heat drugs, enriches their pharmic functions by using Gene Ontology, compares the targets and functional differences of cold-heat drugs, so as to explore the functional heterogeneity of cold-heat drugs, and then explain the action mechanism of cold-heat drugs, and accumulate experience for the scientific interpretation of the property theory of TCM.

Recently, several works have studied the problem of view selection in graph databases. However, existing methods cannot fully exploit the graph properties of views, e.g., supergraph views and common subgraph views, which leads to a low view utility and duplicate view content. To address the problem, we propose an extended graph view that persists all the edge-induced subgraphs to answer the subgraph and supergraph queries simultaneously. Furthermore, we present the graph gene algorithm (GGA), which relies on a set of view transformations to reduce the view space and optimize the view benefit. Extensive experiments on real-life and synthetic datasets demonstrated GGA outperformed other selection methods in both effectiveness and efficiency.