Weibing Formula 1, a classic traditional formula, has been widely used clinically to treat gastritis in recent years. However, the potential pharmacological mechanism of Weibing Formula 1 is still unclear to date. A network pharmacology-based strategy was performed to uncover the underlying mechanisms of Weibing Formula 1 against gastritis. Furthermore, we structured the drug-active ingredients-genes–disease network and PPI network of shared targets, and function enrichment analysis of these targets was carried out. Ultimately, Gene Expression Omnibus (GEO) datasets and real-time quantitative PCR were used to verify the related genes. We found 251 potential targets corresponding to 135 bioactive components of Weibing Formula 1. Then, 327 gastritis-related targets were known gastritis-related targets. Among which, 60 common targets were shared between potential targets of Weibing Formula 1 and known gastritis-related targets. The results of pathway enrichment analysis displayed that 60 common targets mostly participated in various pathways related to Toll-like receptor signaling pathway, MAPK signaling pathway, cytokine-cytokine receptor interaction pathway, chemokine signaling pathway, and apoptosis. Based on the GSE60427 dataset, 15 common genes were shared between differentially expressed genes and 60 candidate targets. The verification results of the GSE5081 dataset showed that except for DUOX2 and VCAM1, the other 13 genes were significantly upregulated in gastritis, which was consistent with the results in the GSE60427 dataset. More importantly, real-time quantitative PCR results showed that the expressions of PTGS2, MMP9, CXCL2, and CXCL8 were significantly upregulated and NOS2, EGFR, and IL-10 were downregulated in gastritis patients, while the expressions of PTGS2, MMP9, CXCL2, and CXCL8 were significantly downregulated and NOS2, EGFR, and IL-10 were upregulated after the treatment of Weibing Formula 1. PTGS2, NOS2, EGFR, MMP9, CXCL2, CXCL8, and IL-10 may be the important direct targets of Weibing Formula 1 in gastritis treatment. Our study revealed the mechanism of Weibing Formula 1 in gastritis from an overall and systematic perspective, providing a theoretical basis for further knowing and application of this formula in the future. Gastritis is digestive diseases with high incidence, which is mainly manifested as variety of pathogenic factors invading the gastric mucosal epithelium, resulting in persistent chronic inflammatory changes . GastritTraditional Chinese medicine is a complementary therapy that plays a crucial role in healthcare of Asian population and is gradually recognized in western countries due to the feasible therapeutic efficacy and minimal side effects , 8. In tNetwork pharmacology is a novel approach to discover and develop traditional medicinal drug. Until now, this way has been successfully used to clarify the multitarget effects of traditional Chinese medicine in various diseases –13. Netwhttps://tcmspw.com/tcmsp.php)  OR “gastritis” [All Fields]) AND “Homo sapiens”[porgn] AND “gse”[Filter]. The inclusion criteria for the present study were as follows: (1) the type of dataset was described as expression profiling by array. (2) Dataset should be whole-genome mRNA expression profile by array. (3) Datasets were obtained by tissue samples of gastritis and normal control group. (4) The datasets should be normalized or original, and xx sets of mRNA data were selected. The exclusion criteria for this study were as follows: (1) cell line and animal level studies and (2) sample size less than 30. Eventually, two datasets (GSE60427 and) were included in this study. The GSE60427 was downloaded from the GEO database . First, the R software (v.3.6.3) was used for the identification of differentially expressed genes between the gastritis and normal samples. Then, the Benjamini-Hochberg method was used to improve the false discovery rate (FDR) in multiple comparisons. Furthermore, FDR value < 0.01 and ∣log fold change | >1 as the cut-off values were deemed statistically significant. The volcano map, Venn diagram, and heat map of the differentially expressed genes were produced using R package, Venny 2.1.0 and R package, respectively. GSE5081, previously researched using GPL570 platform of Affymetrix Human Genome U133 Plus 2.0 Array, included 16 control and 16 gastritis samples and was used to further validate the GSE60427 results.We searched datasets from the Gene Expression Omnibus (GEO) database (http://www.broadinstitute.org/gsea/index.jsp) was performed to understand the meaningful KEGG pathway in the two groups. The annotated gene sets of version 6.0 were downloaded from the Molecular Signatures Database . The inclusion criteria were normalized p < 0.05 and FDR < 25%.The samples in the GSE60427 dataset were divided into two groups including 24 gastritis patients and 8 normal controls. Gene set enrichment analysis  were measured by quantitative real-time PCR in blood samples of normal control, gastritis patients, and gastritis patients treated with Weibing Formula 1. Among which, MMP9, CXCL2, CXCL8, and IL-10 were members of 15 common genes, and PTGS2, NOS2, and EGFR were members of 60 candidate targets, but do not belong to 15 common genes. Compared with the control group, the expressions of PTGS2, MMP9, CXCL2, and CXCL8 were significantly upregulated and NOS2, EGFR, and IL-10 were downregulated in gastritis patients, while the expressions of PTGS2, MMP9, CXCL2, and CXCL8 were significantly downregulated and NOS2, EGFR, and IL-10 were upregulated after the treatment of Weibing Formula 1 . Of whicWestern drug is usually used for antibacterial therapy of gastritis and gastric-mucosa protection. However, western drug may cause bacterial resistance, with an increasing number of drugs failing to effectively control gastritis. Studies have shown that traditional Chinese medicine can achieve similar efficacy than western drug . In ChinNetwork pharmacology is a powerful way for defining a variety of components and studying the mechanisms of Chinese herbal medicine . In thisκB signaling pathway [KEGG pathway enrichment analysis displayed that 60 common targets were markedly enriched in Toll-like receptor signaling pathway, NOD-like receptor signaling pathway, apoptosis, T cell receptor signaling pathway, MAPK signaling pathway, cytokine-cytokine receptor interaction pathway, and chemokine signaling pathway. Toll-like receptor signaling pathway, MAPK signaling pathway, and chemokine signaling pathway are the main pathways associated with Agastache rugosa treatment of gastritis . The eth pathway . Quercet pathway . During  pathway . The aboEpidermal growth factor (EGF) is one of the fatal factors in gastric tissue repair and cell regeneration. It binds to its receptor epidermal growth factor receptor (EGFR) located in the basement membrane or bilateral membrane of gastric epithelium. EGFR is highly expressed in damaged gastric barrier and perfusion epithelial cells, but rarely in normal epithelial cells. Studies have shown that the expression of EGFR in the surface mucosa of the stomach is higher than that in the deep and muscle layers of the stomach, which indicates its role in promoting the growth and recovery of damaged gastric epithelial cells . It has GSE60427 was used to identify differentially expressed genes, and we obtained 895 differentially expressed genes between gastritis and normal control. A total of 15 common genes were shared between differentially expressed genes and 60 candidate targets. Among which, interleukin- (IL-) 6 (IL-6) and CC-chemokine ligand 2 (CCL2), as members of 15 common genes, have been reported to be associated with the progression of gastritis. IL-6 is a multipotent four-helix cytokine that performs a variety of functions in the body . IL-6 caIn conclusion, the potential mechanism of Weibing Formula 1 in treating gastritis has the multicomponent, multitarget, and multiway characteristics, and PTGS2, NOS2, EGFR, MMP9, CXCL2, CXCL8, and IL-10 may be the important direct targets of Weibing Formula 1 in gastritis treatment. Such data provide the basis for multi-ingredient synergies in future research. Our study revealed the mechanism of Weibing Formula 1 in gastritis from an overall and systematic perspective, providing a theoretical basis for further knowing and application of this formula in the future. However, whether Weibing Formula 1 is suitable for long-term maintenance treatment of gastritis still needs to be confirmed in future clinical trials. Thence, it is necessary to conduct further basic experimental verification of the potential active ingredients in order to clarify the theoretical prediction.


Hyssopus cuspidatus Boriss. (Shen Xiang Cao (SXC)), a traditional medicine herb in Xinjiang, has a long history of being used by minorities to treat asthma. However, its active antiasthmatic compounds and underlying mechanism of action are still unknown. The aim of this study was to investigate the bioactive compounds and explore the molecular mechanism of SCX in the treatment of asthma using network pharmacology.  The compounds of SCX were collected by a literature search, and Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and SwissTargetPrediction were used to predict targets and screen active compounds. Moreover, asthma-related targets were obtained based on DisGeNET, Herb, and GeneCards databases, and a protein-protein interaction (PPI) network was built by the STRING database. Furthermore, the topological analysis of the PPI and SXC-compound-target networks were analyzed and established by Cytoscape software. Finally, the RStudio software package was used for carrying out Gene Ontology (GO) function enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. AutoDock tools and AutoDock Vina were used to molecularly dock the active compounds and key targets.  A total of 8 active compounds and 258 potential targets related to SXC were predicted, and PPI network screened out key targets, including IL-6, JUN, TNF, IL10, and CXCL8. GO enrichment analysis involved cell responses to reactive oxygen species, oxidative stress, chemical stress, etc. In addition, KEGG pathway analysis showed that SXC effectively treated asthma through regulation of mitogen-activated protein kinases (MAPK) signaling pathways, interleukin 17 (IL-17) signaling pathways, toll-like receptor (TLR) signaling pathways, and tumor necrosis factor (TNF) signaling pathways.  The preliminary study that was based on multiple compounds, multiple targets, and multiple pathways provides a scientific basis for further elucidating the molecules involved and the underlying antiasthma-related mechanisms of SXC. It is a regulatory factor in the immune response and in cell proliferation and differentiation [Asthma is a type of respiratory tract allergic inflammatory disease in which mast cells and airway epithelial cell dysfunction play an important role , 2. The y factor . Th17 practor [α . TNF-α intiation . IL-6 isntiation .Hyssopus cuspidatus Boriss. and is mainly distributed in Altay, Tianshan, and Kunlun Mountains [Traditional medicine in Xinjiang is an important medicinal system of Traditional Chinese Medicine (TCM), and it is one of the part of medicines in China. Shen Xiang Cao  comes from the dried ground aerial parts of ountains , 17.κB signaling pathway and MAPK signaling pathway [Previous studies performed by Jinhua He's team showed that different elution (water and ethanol) of SXC has a certain antagonistic effect on the acetylcholine-induced or histamine-induced smooth muscle contraction of isolated tracheal in guinea pigs, and 60% ethanol elution shows the strongest effect, which has similar effects with aminophylline at high mass concentration . Further pathway . Moreove pathway . As a tr pathway . Howeverhttps://www.cnki.net/), Wanfang database (http://www.wanfangdata.com.cn/index.html), Weipu database (http://www.cqvip.com/), and PubMed database (https://pubmed.ncbi.nlm.nih.gov/) to obtain chemical compounds of SXC. Subsequently, TCMSP (https://tcmspw.com/tcmsp.php) were adopted to screen compounds with conditions of oral bioavailability (OB) ≥ 30% [“Hyssopus cuspidatus Boriss.” was used as a key search term in the China National Knowledge Infrastructure (CNKI) database (B) ≥ 30%  and drugB) ≥ 30%  and collhttps://pubchem.ncbi.nlm.nih.gov/) database and we imported SMILES information into the SwissTargetPrediction platform (http://www.swisstargetprediction.ch/), and the attribution was set as “homo sapiens” and probability ≥0.4 to collect the target of the compound.In order to obtain more targets, simplified molecular input line entry specification (SMILES) information of bioactive compounds was obtained from the PubChem (https://www.genecards.org/), Herb database (http://herb.ac.cn/), and DisGeNET database (https://www.disgenet.org) and the attribute was set to “homo sapiens” to obtain targets in different databases. Subsequently, all targets were uploaded to the online Venn diagram (https://bioinfogp.cnb.csic.es/tools/venny/index.html) to obtain the common targets.The word “asthma” was used as a keyword in the GeneCards database (https://www.uniprot.org/) was applied to normalize the names of target genes and convert it into abbreviations, and targets without corresponding gene names were deleted. Finally, the obtained chemical compounds and their corresponding targets were imported into Cytoscape (version 3.8.0 for Windows) software to construct a network visual diagram of SXC-compounds-targets.The Uniprot database (https://string-db.org/). Species was set to “homo sapiens” and confidence level was set to ≥0.700, respectively. After deleting the free target, a target-protein interaction network was constructed. Finally, the data were imported into Cytoscape software for visual analysis and network topology analysis, and key target analysis was performed through plug-in CytoNCA to obtain the PPI network.The targets of compounds and disease were uploaded to the online Venn diagram, respectively, to obtain their intersection genes, which data were sequentially uploaded to the STRING 11.0 database  and RStudio (version 1.4.1103 for Windows) software which install the packages of org.Hs.eg.db, ggplot2, enrichplot, and clusterProfiler . To undehttp://www.rcsb.org/) and PubChem database, respectively. Then, budesonide was considered as a reference [http://openbabel.org/wiki/Main_Page). After dehydration, hydrogenation, and compound pretreatment of the downloaded proteins with AutoDock tools, AutoDock Vina (version 1.1.2 for Windows) software (http://vina.scripps. edu/) was used for molecular docking of the receptor and ligand. Finally, visualizing and analyzing the results of molecular docking conformation were completed by PyMOL (version 2.4.1 for Windows) software (https://pymol.org/2/) and ProteinsPlus (https://proteins.plus/).In preparation for molecular docking, the three-dimensional (3D) protein structures of the key proteins in the PPI network and four active chemical ingredients were downloaded from the Protein Data Bank (PDB) database , acacetin (degree = 29.0), salvigenin (degree = 15.0), and genkwanin (degree = 13.0) may be had relationship in the treatment of asthma.A total of 8 active chemical compounds of SXC and their corresponding targets were imported into Cytoscape software, and the SXC-compound-target network relationship diagram  was obtaAfter importing the common targets to the STRING database, the subnetworks that cannot be connected to the free target and the main network were deleted. The PPI network was imported into Cytoscape software for key target analysis, and 125 nodes and 983 edges were obtained . Then, CP < 0.05 were enriched  to regulate inflammation and immune responses in asthma [α in the serum of asthmatic rats in a dose-dependent trend [γ level was significantly increased.The GO enrichment data showed that SXC mainly plays a role in the responses of cells to ROS, oxidative stress, chemical stress, and LPS. Obviously, there is an obvious synergy between the different signaling pathways with the enrichment of the KEGG pathway, such as TLR signaling pathway, TNF signaling pathway, and MAPK signaling pathway. It is worth noting that the MAPK signaling pathway is an important pathway involved in the pathogenesis and progression of bronchial asthma . Relatedn asthma , 55. It nt trend . Hou et nt trend  observedIn this study, molecular docking was carried out to investigate the binding ability between the compounds and targets. The smaller the value of binding energy, the better binding between the compound and target; the results of molecular docking showed that the binding energy of receptor and ligand were both < −5.0 kcal/mol.Hyssopus cuspidatus Boriss. has multiple compounds, multiple targets, and multiple pathways to synergistically play a role in treating asthma. These findings point out the direction and provide scientific evidence for subsequent clinical and experimental research. However, considering the certain limitations in network pharmacology of the databases updates and different target prediction mechanisms, it is still necessary to verify the pathway and target through the experiments in the future to confirm the accuracy of the prediction results of this study.In summary, the results of network pharmacology showed that


A fundamental issue related to the understanding of the molecular mechanisms, is the way in which common pathways act across different biological experiments related to complex diseases. Using network-based approaches, this work aims to provide a numeric characterization of pathways across different biological experiments, in the prospect to create unique footprints that may characterise a specific disease under study at a pathway network level. In this line we propose PathExNET, a web service that allows the creation of pathway-to-pathway expression networks that hold the over- and under expression information obtained from differential gene expression analyses. The unique numeric characterization of pathway expression status related to a specific biological experiment (or disease), as well as the creation of diverse combination of pathway networks generated by PathExNET, is expected to provide a concrete contribution towards the individualization of disease, and further lead to a more precise personalised medicine and management of treatment.https://bioinformatics.cing.ac.cy/PathExNET and at https://pathexnet.cing-big.hpcf.cyi.ac.cy/PathExNET is available at:  PENs draw from the log-fold-change (2.1http://bioinformatics.cing.ac.cy). PathExNET is served by a Docker space at the CYTERA High Performance Computer Facility of the Cyprus Institute (https://hpcf.cyi.ac.cy). PathExNET further uses parallel processing scripts to handle and pre-process large file sizes that make the use of the “doParallel” R package PathExNET comes with a frontend web interface that consists of the mainframe and a help page, written in HTML, PHP and JavaScript language environments. The mainframe provides 2 individual steps designed to guide the user until the end of the workflow process. The backend of PathExNET has been written in R environment, where several functionalities have been parallelised to achieve fast performance. Evaluation, testing and understanding of PathExNET functionalities can be easily performed by means of several available example datasets provided through the web interface. The proposed tool is available online at the webpage of the Bioinformatics Department, at the Cyprus Institute of Neurology and Genetics (CING) . EA holds a large set of publicly available and controlled access datasets that at the time of writing derive from over 4,000 studies across 65 different species, including over 900 studies from plants. These datasets have been curated and re-analysed using standardized, open source pipelines and have been made available along with the analyses data for queries, download and visualization The Expression Atlas (EA) is a database repository that provides information about gene and protein expression in different species and contexts, namely: tissue, developmental stage, and disease or cell type. The EA web service is hosted at the European Bioinformatics Institute (EMBL-EBI)  DEA file accompanied with list of pathways of interest, (b) DEA file accompanied with list of genes of interest, and (c) DEA file accompanied with a list of pathways and a list of genes of interest. A significant differentiation in this approach is that PENs use all the genes included in the experiment, thus the DEA files should be used unfiltered without performing any specific threshold for reducing their size. The DEA file should at least include the gene-symbol and the log-fold-change value for each gene included in the file, while the p-value field is optional. These parameters should be strictly named as: “The sumFC value is obtained by calculating the sum of all the log-fold-change values included in the specific pathway, as follows:The rateFC value is the fraction of the number of over-expressed genes divided by the number of total genes included in the pathway, as follows:ForThe normMeanFC value is obtained by calculating the weighted mean of the normalised histogram of the log-fold-change values. Specifically, for a given vector of log-fold-change values Then the histogram of the normalised vectorEq. The combinedFC value is a combination of equations Typically forIn order to estimate a score for the overall pathway expression network we calculate the overall expression ratio which is defined as the fraction of:For values ofHerein, we clarify that there is not an optimal theory that clearly defines where is the transition line between over- and under-expression of gene sets. The most widely used methods that use the log-fold-change value, mainly examine how the 2.5In order to provide an indicative information that shows whether the selected by the user pathways are also significant in terms of enrichment analysis, we used the “gprofile2” R package which has been also suggested as a main analysis tool by the ELIXIR consortium 2.6Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST) SIGnaling Network Open Resource (SIGNOR) Homo Sapiens, Mus Musculus, and Rattus Novergicus, accordingly. Depending on the user’s input, PathExNET automatically examines the genes of interest and further provides the regulatory information to where is available, in a single table.Another significant issue in studying the expressional behaviour of pathways is the regulatory information in between the genes included in a specific pathway. Thus in order to provide such information through PathExNET framework, we further created a database repository that includes regulatory information in between genes, obtained from both the 2.7The mathematical content of complex networks in biological systems has become a benchmark approach towards identifying biomarkers, understanding their dynamics, their biological status and related biological mechanisms involved. In order to provide more statistics related to the complex nature of the proposed pathway expression networks, PathExNET further provides additional statistics, for network manipulation 3It should be stressed that the concept of PathExNET is not to serve as enrichment tool but as a post analysis tool that facilitates an estimation and the subsequent visualization of the collective over/under expression of the selected pathways’ gene members. As opposed to traditional gene enrichment tools and methodologies 3.1https://www.ebi.ac.uk/gxa/experiments/E-GEOD-147507. Herein, we used the PathExNET EA tool to download the analysis performed by EA, namely the unfiltered statistics required for the creation of the pathway expression networks proposed in this work. A common biological perspective for an effective treatment against COVID-19 and its causative virus, SARS-CoV-2, is the deciphering of the involved host pathways, as well as the related transmission and replication mechanisms Venn diagrams that represent the overlap between the two samples regarding (a) their over-expressed genes, (b) their under-expressed genes, and (c) their genes with zero Furthermore, It has been stated that for COVID-19, as well as for all infectious diseases, the host immune system, is a major component  see Eq.  describe see Eq.  was founExpanding on this type of analysis, the latter analysis was performed for all the proposed scores derived by the described in previous section, for each of the analysed datasets under study. Specifically, In analogous manner, “fc gamma r-mediated phagocytosis” and “fc epsilon ri signaling pathway” pathways which were found to be over-expressed in both A549 and NHBE expression networks, have been discussed in relevant studies that relate its involvement with antiviral immune responses to SARS-COV-2 infections chemokine signaling pathway” mentioned in natural killer cell mediated cytotoxicity” in COVID-19 patients leukocyte transendothelial migration” has been also associated with ACE2 expression platelet activation” mechanism has been found to be promoted by the TLR9 receptor, through the “Interleukin-1 receptor-associated kinase 1 (IRAK1)” and “protein kinase B (Akt/PKB)” pathways To further support the validity of the above results, we indicatively performed a literature search for the 9 over expressed immune system pathways depicted in 3.2https://www.ebi.ac.uk/gxa/experiments/E-GEOD-50760) while the experimental design involves two comparisons, as follows: (1) Primary Tumor vs Normal (PTN), and (2) CRC Metastatic in the Liver vs Normal (MLN). In order to provide a concrete PathExNET example that involves a set of significant pathways under study, we further examine how the pathways related to the primary tumor behave in the case of liver metastasis. Specifically, focusing on the pathways obtained from PTN comparison, we used PathExNET to obtain their expression status using as input the PTN and MLN expression datasets, accordingly. In this example we focus on a well-designed dataset that includes RNA-seq data of 54 samples, obtained from 18 patients with primary Colorectal Cancer and liver metastasis Herein a significant observation is that the first three pathways have been found to be higher expressed in the case of primary tumor (PTN dataset), while the rest of them have been found to be higher expressed in the case of liver metastasis (MLN dataset). On the contrary, the significant pathways obtained from MLN comparison seem to remain highly expressed in liver metastasis comparing to the primary tumor. However in both lists of pathways under study, the overall pathway-to-pathway network is higher expressed in the case of liver metastasis, where the percentage increment rate 4sphingolipid signaling pathway” and the “Sphingolipid metabolism” accordingly. PathExNet was used to calculate the combined fold change status for each of the two pathways, based on the expression change of the genes that participate in the respective pathways. The underlying methodology was executed for each dataset separately isolating the genes that participate in each of the selected pathways along with thesphingolipid signaling pathway” and the “sphingolipid metabolism” for each dataset of each tissue. The genes that were consistently differentially expressed (over- or under-expression) across two or more datasets per tissue were further investigated in that study.Offering web-based services for pathway based analyses, is an important forward technological step that aims to reduce the complexity and expertise required for searching, obtaining and combining information from large repositories with biological content. At the same time such web-services help users to avoid a significant amount of local resources required to perform such analyses at a single computer level, as well as compatibility issues and stringent installations in some cases. In this line, PathExNET provides a freely available, well-designed and easy to use framework of analysis, to perform post experimental pathway based analysis in the context of the proposed mathematical framework. To the authors’ knowledge, there is not any available web-tool able to perform such entire workflow. PathExNET has been successfully applied in a study that has been recently published by our group 5The powerful concept of the graph theory provides significant information towards understanding the organization of entities that sustain large and complex biological systems The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Individualized treatment of traditional Chinese medicine (TCM) provides a theoretical basis for the study of the personalized classification of complex diseases. Utilizing the TCM clinical electronic medical records (EMRs) of 7170 in patients with IS, a patient similarity network (PSN) with shared symptoms was constructed. Next, patient subgroups were identified using community detection methods and enrichment analyses were performed. Finally, genetic data of symptoms, herbs, and drugs were used for pathway and GO analysis to explore the characteristics of pathways of subgroups and to compare the similarities and differences in genetic pathways of herbs and drugs from the perspective of molecular pathways of symptoms.  We identified 34 patient modules from the PSN, of which 7 modules include 98.48% of the whole cases. The 7 patient subgroups have their own characteristics of risk factors, complications, and comorbidities and the underlying genetic pathways of symptoms, drugs, and herbs. Each subgroup has the largest number of herb pathways. For specific symptom pathways, the number of herb pathways is more than that of drugs.  The research of disease classification based on community detection of symptom-shared patient networks is practical; the common molecular pathway of symptoms and herbs reflects the rationality of TCM herbs on symptoms and the wide range of therapeutic targets. Ischemic stroke (IS) is not only a disease with high morbidity, high mortality, and high disability, but also has a great risk of recurrence , 2. An iThanks to plummeting costs of genetic testing, rapid advances in computational power, massive, linked databases, and new targeted therapies, making it increasingly possible to prevent or treat illnesses based on an individual patient's characteristics, the era of precision medicine is high –5. RecenNetwork medicine, which aims to gain an understanding of human disease from the perspectives of network science, has offered a new platform for identifying novel disease mechanisms and predicting drug efficacy, disease-phenotypic associations, and novel disease taxonomy –10. SympDiagnosis of “cerebral infarction” is referred to the “Guidelines for Diagnosis and Treatment of Acute Ischemic Stroke in China 2014”  issued bInclusion criteria were defined as follows: (1) the first Western medicine diagnosis is consistent with the “acute cerebral infarction”; (2) MRI report suggests a new responsible focus for ischemia; (3) both initial acute stroke and recurrent acute stroke are included; and (4) EMRs with more than or equal to 5 positive symptoms are obtained.Exclusion criteria were defined as follows: (1) the first Western medicine diagnosis included “cerebral hemorrhage”; (2) key content of the medical record is missing, such as chief complaint, symptoms, and history of present illness.EMR data extraction uses “the Clinical Research Information Sharing System of TCM” . Cases oThe extracted data of diagnosis, symptoms, drugs, and herbs were standardized. According to ICD-10, the disease diagnosis level is standardized to subcategories. After manual review by physicians with rich clinical experience in TCM, referring to the Unified Medical Language System (UMLS) , 362 staReferred to the Anatomical Therapeutic Chemical (ATC) developed by the WHO Collaborating Centre for Drug Statistics Methodology and “Catalogue of Chinese Listed Chemical Drugs” published by the China Food and Drug Administration (CFDA), 459 generic names were unified, and then, their genetic target information was obtained using the DrugBank database –22. 351 To better study the characteristics of the disease, the diagnosis has been divided into three categories based on the following principles: risk factors, including various metabolic syndrome and coronary heart disease, are the potential causes of ischemic stroke; the remaining diseases that have no exact causal relationship with ischemic stroke are called comorbidity , while cWith 362 normalized symptom terms as keywords, part of symptom-gene information was obtained in the SymMap database . SymptomA, B)=P(A∩B)/P(A ∪ B), and this means that if two patients have more intersected symptom phenotypes, they would have higher symptom similarity. In particular, the symptom similarity of two patients would be 1 if they have identical symptom phenotypes. Supposing that patient A has 5 symptoms, patient B has 8 symptoms, and 2 of them are common, then the Jaccard′sim=(P(A∩B)/P(A ∪ B))=(P(A∩B)/P(A)+P(B) − P(A∩B))=(2/5+8 − 2)=(2/11) Then, the edges were filtered by weight ≥0.5, which means that only the patient links with almost identical chief symptoms were kept while remaining most of the cases included at the same time. The Jaccard coefficient is a commonly used similarity measure, especially in network medicine research [The Jaccard coefficient  was usedresearch –31.Patients should be divided into some subgroups with more clear boundaries; thus, a nonoverlapping community detection method is more applicable for PSN . An effiCi is the number of patients in module (patient subgroup) i, Cj is the number of patients with disease j, Cij represents the number of patients in module i and with disease j, and N is the total number of patients in the study. If RR > 1.5, it indicates that the distribution of disease j in module i is higher than the distribution in the whole. A similar method is used to identify the significant symptoms, drugs, and herbs in each patient subgroup. In addition, the true significant disease symptom, drug, or herb is filtered by the chi-square test , which is a statistical hypothesis test whose result is evaluated by reference to the chi-square distribution [Relative risk (RR) is a classical statistical method, which is the ratio of the probability of an event occurring, in an exposed group that a certain condition is present versus a nonexposed group that lacks the condition . In thisribution .P values less than 0.05 as the specific medicines of the module, and then the medicines target as the genes of the module, and the significant pathway through Fisher's exact test [Pathway analysis of the gene data of symptoms, drugs, and herbs has been carried out. The KEGG pathway database is the main database in the Kyoto Encyclopedia of Genes and Genomes (KEGG), and it consists of manually drawn reference pathway maps together with organism-specific pathway maps . The enract test  was finaact test  was usedIn this study, 7170 patients  with IS were included, of which 54.22% were males and 45.78% were females. The proportion of patients with ischemic stroke in different age groups is different. It gradually increases with age to 40 years and decreases after 60 years. The proportion of the 60–79 years of age group was the highest, and it was irrelevant to gender .Analysis of the risk factors for IS shows that the incidence rates of hypertension, arteriosclerotic heart disease (AHD), type 2 diabetes (T2D), and hyperlipidemia are all greater than 10%, among which the incidence rate of hypertension was up to 77.56%; AHD, as the main risk factor for long-term recurrence of IS , has theIn IS complications, incidence rate of cardiac arrhythmia  is 19.35% higher than others; incidence rates of pneumonia and heart failure are greater than 5% . Among cn = 2046, 29.25%), M2 , M1 , M5 , M0 , M29 , and M4 .To identify the subtype of patients with ischemic stroke, by calculating the degree of the shared symptom phenotypes between patient pairs, a PSN with 6996 nodes  and 397775 edges was constructed. On this basis, a high-performance community detection method  was used to explore the subgroup of ischemic stroke disease population. Finally, 34 modules (modularity: 0.478) were obtained, in which the numbers of patients for each module ranged from 2 to 2046. These modules represent the subgroup of stroke patients, among which 7 modules (such as M3 and M2) have 124 or more cases, accounting for 98.48% of all cases, and 27 modules have 20 or fewer cases, accounting for 1.52%. Considering a more comprehensive study of the diversity of ischemic stroke subgroups, we used four large modules  and three small modules , which accounted for 85.09% and 13.39% of all cases, respectively, to illustrate the characteristic phenotype and genotype of ischemic stroke subgroups . The casP < 0.05, RR > 1.5, The characteristic diseases in 7 subgroups . The number of symptom pathways in M2 (n = 47) and M4 (n = 59) is less than the average (n = 94); the number of drug pathways in M1 (n = 28), M0 (n = 8), and M4 (n = 31) is relatively small, which is less than the average of 34, while the number of herb pathways is large than that of symptom and drug pathways, which is not significantly different in each subgroup   .n = 16), and the top 20 enriched pathways of symptom genes and the corresponding P values of drug and herb genetic pathways are shown in Comparisons had been made between the pathways of symptom, drug, and herb, one and any of the others. It can be found that the number of symptom-herb pathways shared the most pathways in all subgroups . Most ofThrough the reliable symptom-shared PSN, we found significant quantitative differences between TCM herbs and Western medicine-enriched genetic pathways in acute IS subgroups.All the data were collected from EMRs of “the Clinical Research Information Sharing System of TCM.” From the overall data, the basic characteristics are consistent with the general IS population. For example, the distribution of age and sex is consistent with the report of China Stroke Statistics 2019 . From thThe most interesting subgroup is M3, conforming to the clinical practice in Western medicine and highly consistent with TCM theory as well. Through the enrichment analysis, the main symptoms in M3 are dizzy, headache, vertigo, tinnitus, etc., which are mainly differentiated as “liver Yang hyperactivity syndrome” (LYHS) in the theory of TCM . And theOn the other hand, we can infer the potential molecular pathway of LYHS from the result of pathways analysis. Using a systems biology approach with the combination of computational analysis and animal experiment, Li et al.  have fouCV≈6.18). The number of symptom-herb common pathways was larger than that of symptom-drug-shared pathways in all subgroups; in the same subgroup (such as M3), to the same symptom pathway (P < 0.01), the number of statistically significant pathways of herbs is obviously more than that of drugs. This may be due to the fact that Chinese herb is different from Western medicine with a single chemical composition, and the complex composition leads to a significantly richer number of herb pathways. It indicates that Chinese herbs play a more comprehensive role in the treatment of IS by acting on multiple targets. The key nodes of disease-herb-target network can be mined through network pharmacology methods to explain the mechanism of multicomponent and multitarget of herbs and provide ideas for solving the problems such as unclear active ingredients and mechanism of action of herbs [Moreover, through pathway enrichment analysis, we distinguished the feature of genetic pathway of TCM herbs in 7 subgroups (Supplementary Materials—of herbs . Networkof herbs  to invesThere are several potential limitations in our research. First of all, the patients in this study were all from one hospital, which might induce sample bias. Actually, “the Clinical Research Information Sharing System of TCM” used in the study has been deployed in 60 Chinese medicine hospitals across China and has been used in studying the mechanism of TCM diagnosis or treatment of many diseases, such as CHD , 50, AECTo our knowledge, this is the first study to demonstrate the utility of symptom-shared PSN for the classification of ischemic stroke and it can be used for other complex diseases as well. The distinct diseases of each subgroup and the underlying biological pathways of its diseases indicate that it is possible to further explore the molecular network mechanisms of complex diseases using this clinical disease subtyping approach. Meanwhile, the inherent consistency on symptom and TCM herb from both clinical and molecular features reflects the rationality of TCM on symptoms and the wide range of therapeutic targets.


As a secondary objective, we investigated sex differences in the MCAv response during HIIT. Twenty-four young healthy adults completed HIIT . HIIT included 10 min of 1-min high intensity  and active recovery  intervals on a recumbent stepper. MCAv, mean arterial pressure (MAP), heart rate (HR), and end-tidal carbon dioxide (minute 3 then decreased concomitantly with P < 0.001). Thirty minutes after HIIT, MCAv returned to BL (P = 0.47). Compared with men, women had a higher MCAv at BL (P = 0.001), during HIIT (P = 0.009), immediately following HIIT (P = 0.004), and 30 min after HIIT (P = 0.001). MCAv did not decrease below BL during low-volume HIIT. However, MCAv decreased below BL immediately following HIIT and returned to resting values 30 min after HIIT. MCAv also differed between sexes.High-intensity interval exercise (HIIT) is performed widely. However, there is a gap in knowledge regarding the acute cerebrovascular response to low-volume HIIT. Our objective was to characterize the middle cerebral artery blood velocity (MCAv) response during an acute bout of low-volume HIIT in young healthy adults. We hypothesized that MCAv would decrease below the baseline (BL), NEW & NOTEWORTHY We are the first, to our knowledge, to characterize the cerebrovascular and hemodynamic response to low-volume high-intensity interval exercise  in young healthy adults. Middle cerebral artery blood velocity (MCAv) decreased during the HIIT bout and rebounded during active recovery. Women demonstrated a significantly higher resting MCAv than men and the difference remained during HIIT. Here, we report a novel protocol and characterized the MCAv response during an acute bout of low-volume HIIT. Temporal changes in cerebral blood flow, measured by middle cerebral artery blood velocity (MCAv), have been well characterized during submaximal continuous exercise –4. DurinPaCO2) . To atteced MCAv .Although MCAv has been characterized during an acute bout of high-intensity continuous exercise , 3, our Previous studies have examined MCAv during sprint interval and high-volume HIIT in young healthy adults. During a single 30-s maximal sprint interval bout, MCAv initially increased during the sprint exercise followed by a decrease toward the baseline in young healthy women (∼ 27 yr of age) , and MCAbaseline .Interval exercise at moderate intensity has been proposed to be beneficial for cerebrovascular function in older men due to the active recovery intervals sustaining an elevated MCAv with recovery of expired end-tidal carbon dioxide (PETCO2) . However  during passive recovery immediately following HIIT and 2) at rest, 30 min after HIIT.Our primary hypothesis was that MCAv would significantly decrease below BL MCAv during an acute 10-min bout of low-volume HIIT and would be related to decreases in 1) a higher absolute resting BL MCAv and 2) a subsequently higher absolute MCAv response to HIIT compared with men.Our previous work identified unique sex-specific trajectories in resting MCAv over the lifespan  and the 1) 18–30 yr old and 2) low cardiac risk defined by the American College of Sports Medicine (ACSM)  . The stuWe collected demographic information including age, sex, race/ethnicity, body mass index (BMI), current medications, medical history, and self-reported physical activity within the past month via an exercise questionnaire , 31. Parstudy visit 1 for ease of signal acquisition on study visit 2.Following the questionnaires, participants were screened to ensure an MCAv signal on the right and/or left temporal window. Participants were fitted with an adjustable headband that secured bilateral transcranial Doppler ultrasound (TCD) probes  with ultrasonic gel onto the temporal region of the participant’s head. MCAv signal was acquired using standard probe positioning, orientation, depth, and direction . The TCD1) completion of all four stages of the exercise test, 2) participant requested to stop the test, or 3) participant reached 85% of their age-predicted maximal HR [(220 − age) × 0.85] (o2max) (max)  . Based ox) (max) , and themax for high intensity and 10% estimatedWattmax for active recovery  images as well as systolic and diastolic velocity measures were sent to a collaborating physician (S.X.B.) for carotid stenosis assessment .1) TCD headset and bilateral ultrasound probes to measure MCAv, 2) a 5-lead electrocardiogram  for HR, 3) a nasal cannula attached to a capnograph  for 4) a beat-to-beat blood pressure cuff on the left middle finger  for mean arterial pressure (MAP), and 5) a brachial automated sphygmomanometer with a microphone on the right arm  to ensure an accurate calibration of the Finometer. The participant’s arms were placed on stable platforms at heart level. To examine the conductance of peripheral blood pressure to the brain via MCAv, we calculated the beat-to-beat cerebrovascular conductance index (CVCi = MCAv/MAP)  .max) separated by 1-min active recovery (10% estimatedWattmax)  , 39–42. Wattmax) . After 1Raw data were acquired through an analog-to-digital unit  and custom-written software within MATLAB . Sampling rate was set at 500 Hz. Raw data were then time aligned and interpolated beat-to-beat using the R spike on ECG as a trigger. The seated rest recordings at BL, immediately following HIIT, and 30 min after HIIT, were calculated as a 5-min average. The 10-min HIIT recording was separated into 1-min averages of each high intensity and active recovery bout. Consistent with previously published work, the left MCAv signal was used within the analysis to compare time points but if the left MCAv signal was not obtainable, then right MCAv was used  completed the study consisting of two visits to the laboratory. The second visit occurred 48 h to 2 wk after the initial visit. No adverse events occurred. Due to the loss of TCD signal during HIIT, we excluded one male individual from the data analysis. Due to loss of capnograph signal during HIIT, one female individual did not have a valid for 24 h  and fromfor 24 h –48 and afor 24 h . All paral cycle . Study val cycle . Particial cycle . Seven oP > 0.99). In fact, MCAv was higher than BL during minutes 1 through 10 of HIIT (P ≤ 0.003). MCAv peaked at minute 3 and began to decrease during high intensity throughout the remainder of the acute exercise bout. We observed MCAv reach its peak at minute 3 followed by a significantly lower response at minutes 4, 6, 8, 9, and 10 (P ≤ 0.001). Following high intensity, we observed a “rebounding” response whereby MCAv increased significantly during active recovery minute 3 (P < 0.001), minute 7 (P = 0.001), and minute 9 (P < 0.001). Compared with the BL, MCAv was also lower immediately following HIIT (P < 0.001) and returned to BL values at 30 min after HIIT (P = 0.47).The average MCAv for each minute of HIIT is presented in P < 0.001), shown in P < 0.001). The change in MAP was significantly related to the change in MCAv between: 1) BL and minute 1 , 2) minutes 1 and 2 , 3) minutes 2 and 3 , 4) minutes 6 and 7 , and 5) minutes 8 and 9 . MAP returned to resting values and was not significantly different from BL, immediately following HIIT (P = 0.25) or 30 min after HIIT (P = 0.12).During HIIT, MAP increased significantly from BL values , specifically during active recovery minutes 3, 5, 7, and 9 (P ≤ 0.002) and high-intensity minute 4 (P < 0.001). Compared with BL, RR increased during HIIT  and was increased during high-intensity minutes 2, 4, 6, and 8 (P ≤ 0.003) compared with active recovery. Compared with minute 3 (peak MCAv), RR increased during high-intensity minutes 4, 6, 8, and 10 and active recovery minute 9 (P < 0.001), which is consistent with the pattern of hyperventilation- and hypocapnia-induced decreases in MCAv, shown in minutes 6, 8, and 10. The change in minute 1 , minutes 1 and 2 , minutes 4 and 5 , minutes 5 and 6 , minutes 6 and 7 , and minutes 7 and 8 . During recovery, P = 0.001) and 30 min after HIIT (P < 0.001). RR was lower than BL immediately following HIIT (P < 0.001) but returned to BL levels at 30 min after HIIT (P = 0.27).Compared with the BL, minutes 8 and 10 (P ≤ 0.004). CVCi returned to BL resting values immediately following HIIT (P = 0.71) and 30 min after HIIT (P = 0.28). Following minute 3 (peak MCAv), CVCi decreased during high intensity and increased with active recovery (P < 0.001), shown in minutes 1, 2, 3, 5, 7, and 9. With CVCi decreasing during minutes 4, 6, 8, and 10, the slope from onset to nadir CVCi value was calculated. The slopes of the CVCi response were different between high intensity and active recovery following minute 3 (P < 0.001). The first high-intensity bout, or minute 2, was faster to peak CVCi [19.1 (SD = 13.8) s] compared with other high-intensity intervals (P < 0.001). Following minute 3, the time to peak or nadir CVCi was also significantly faster during active recovery compared with high intensity such as minutes 4 and 5 [P = 0.006], and minutes 8 and 9 [P = 0.002].CVCi significantly decreased below BL during HIIT, specifically high-intensity P = 0.009), shown in P = 0.001) and during HIIT (P ≤ 0.03) except for high-intensity minute 4 (P = 0.07). Although not significant, the percentage change in MCAv was visually higher in men compared with women during HIIT minutes 2 through 10, shown in P = 0.002), the percent age-predicted HRmax response was significantly higher in women compared with men during HIIT (P = 0.009). MAP (P = 0.22) and P = 0.18) were not significantly different between sexes during HIIT. Immediately following HIIT and 30 min after HIIT, MCAv remained significantly higher in women compared with men (P ≤ 0.004).The MCAv response to HIIT was significantly different between men and women (1) MCAv did not decrease below resting values during HIIT; 2) MCAv peaked within 3 min of HIIT and then began to decrease from the peak and was significantly lower at minutes 4, 6, 8, 9, and 10; 3) MCAv rebounded during active recovery; 4) MCAv decreased below resting BL values immediately following low-volume HIIT; and 5) MCAv was significantly higher in women compared with men at BL, during HIIT, and up to 30 min after HIIT.Low-volume HIIT is widely used to improve aerobic fitness and peripheral vascular health , 52–54 dminutes 6 to 7 and minutes 8 to 9. The change in MCAv was also moderately correlated to the change in minutes 4 through 8 of HIIT, MCAv began decreasing with hyperventilation and the change in MCAv was moderately correlated with the change in The primary objective of this study was to characterize the MCAv response during an acute bout of 1-min interval low-volume HIIT in young healthy adults. Based on previously published work , we hypoactivity . We acknPrior research has demonstrated a decrease in MCAv below BL resting MCAv during 1-min interval low-volume HIIT in young children . In contAlthough MCAv started to decrease during high intensity after 3 min of HIIT, MCAv increased or “rebounded” during active recovery. Previous studies measuring MCAv during HIIT and sprint interval exercise have also reported this “rebounding” effect , 18, 20. effects . However1) immediately following HIIT and 2) 30 min after HIIT. Our hypotheses were partially supported by our findings. MCAv was lower during the 5-min rest immediately following HIIT but not at 30 min after HIIT. Our findings are supported by previous research also reporting a reduction in MCAv below BL immediately following low-volume HIIT  diameter during high-intensity exercise, it was not measured during this study. There is much controversy as to how much the MCA diameter may change with a specific exercise stimulus –68. Howete 3 see  during texercise .Prior literature reviews suggest HIIT results in rapid changes (oscillations) in blood pressure that could challenge the cerebrovascular system, particularly those with cerebrovascular impairment . DespiteDuring an acute 10-min bout of low-volume HIIT, MCAv does not decrease below BL resting MCAv in young healthy adults. However, MCAv decreased below BL resting MCAv immediately following HIIT but returned to BL at 30 min after HIIT. Further exploration into the regulatory response and physiological mechanisms contributing to a reduction in MCAv during passive recovery after HIIT is needed. Changes to the cerebrovascular response after a long-term exercise intervention of low-volume HIIT is also needed to address the gaps in knowledge on chronic cerebrovascular adaptations. This study contributes novel characterizations of the MCAv response to low-volume HIIT in young healthy adult controls, including changes in CVCi and sex differences. Although previous studies have provided groundwork on the MCAv response to sprint interval exercise and high-volume HIIT , 18, we A.W. was supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health Grant T32HD057850. S.E.A. was supported by a CTSA Grant from NCATS awarded to the University of Kansas for Frontiers: University of Kansas Clinical and Translational Science Institute Grant TL1TR002368. S.A.B. and E.D.V. were supported in part by the KU Alzheimer's Disease Research Center (P30 AG072973) from the National Institute on Aging. REDCap at University of Kansas Medical Center is supported by Clinical and Translational Science Awards (CTSA) Award No. UL1TR000001 from National Center for Research Resources (NCRR). The Georgia Holland Research in Exercise and Cardiovascular Health (REACH) laboratory space was supported by the Georgia Holland Endowment Fund.The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.No conflicts of interest, financial or otherwise, are declared by the authors.A.A.W., E.D.V., and S.A.B. conceived and designed research; A.A.W. and B.K.K. performed experiments; A.A.W., S.E.A., C.S.K., B.K.K., S.X.B., R.N.M., and S.A.B. analyzed data; A.A.W., S.E.A., C.S.K., and S.A.B. interpreted results of experiments; A.A.W. prepared figures; A.A.W., S.E.A., C.S.K., and S.A.B. drafted manuscript; A.A.W., S.E.A., C.S.K., B.K.K., S.X.B., E.D.V., R.N.M., and S.A.B. edited and revised manuscript; A.A.W., S.E.A., C.S.K., B.K.K., S.X.B., E.D.V., R.N.M., and S.A.B. approved final version of manuscript.


In addition, these herbs could facilitate the deceleration in the decline of renal function and relieve the symptoms of hypertensive nephropathy. In this study, the traditional Chinese medicine approach for treating hypertensive renal damage is summarized and effective treatment prescriptions were identified and analyzed. Data mining technology provided a feasible method for the collation and extraction of traditional Chinese medicine prescription data and provided an objective and reliable tool for use in determining the TCM treatments of hypertensive nephropathy.Hypertensive nephropathy is a common complication of hypertension. Traditional Chinese medicine has been used in the clinical treatment of hypertensive nephropathy for a long time, but the commonly used prescriptions have not been summarized, and the basic therapeutic approaches have not been discussed. Based on data from 3 years of electronic medical records of traditional Chinese medicine used at the Affiliated Hospital of Shandong University of Traditional Chinese Medicine, a complex network and machine learning algorithm was used to explore the prescribed herbs of traditional Chinese medicine in the treatment of hypertensive nephropathy (HN). In this study, complex network algorithms were used to describe traditional Chinese medicine prescriptions for HN treatment. The Apriori algorithm was used to analyze the compatibility of these treatments with modern medicine. Data on the targets and regulatory genes related to hypertensive nephropathy and the herbs that affect their expression were obtained from public databases, and then, the signaling pathways enriched with these genes were identified on the basis of their participation in biological processes. A clustering algorithm was used to analyze the therapeutic pathways at multiple levels. A total of 1499 prescriptions of traditional Chinese medicines used for the treatment of hypertensive renal damage were identified. Fourteen herbs used to treat hypertensive nephropathy act through different biological pathways: huangqi, danshen, dangshen, fuling, baizhu, danggui, chenpi, banxia, gancao, qumai, cheqianzi, ezhu, qianshi, and niuxi. We found the formulae of these herbs and observed that they could downregulate the expression of inflammatory cytokines such as TNF, IL1B, and IL6 and the NF- HN caused by hypertension commonly damages the kidneys . The Intμg/min is defined as microalbuminuria (MA), reflecting glomerular endothelial dysfunction and declining nephron filtration function, which is the main marker of HN [Glomerulosclerosis is the most common pathological change associated with hypertension. Long-term hypertension leads to thickening of the renal artery intima, hyaline changes in afferent glomerular arterioles and interlobar arterioles, increased renal vascular resistance, and induced inflammatory reactions and renal tubulointerstitial fibrosis at the same time . First, er of HN , 7, 8. Rer of HN . Data frer of HN . An increr of HN .In HN cases, complementary therapies can be good choices for timely intervention and protection against renal damage in the early stage of HN. As a supplementary means of modern medicine, traditional Chinese medicine (TCM) has received increasing attention, and it is one of the most widely used complementary therapies in the world. TCM has clear advantages in the treatment of HN –14; TCM In this study, complex networks were used to collect relevant information about TCM prescriptions and disease symptoms from electronic medical records (EMRs) and biological databases, which were used to analyze the prescription network as generated by doctors using herbs in the real world, discuss the topological characteristics of the network, and find the core nodes and connection characteristics of the network, which can be used to mitigate the difficulty posed by the diverse clinical manifestations of this disease. This approach allows the rapid and accurate identification of the common herb combinations used for HN and other diseases and specific symptoms by making efficient use of real-world data –18. WhenCurrently, specific prescriptions for the treatment of HN are lacking. Under real-world conditions, the advantages and characteristics of syndrome differentiation for use of TCM treatment can be fully implemented  to obtaiWe collected the EMRs of 30695 anonymous patients diagnosed with hypertension at the Affiliated Hospital of Shandong University of Traditional Chinese Medicine between July 1, 2014, and May 31, 2017, including the diagnosis, demographic characteristics (such as age and sex), chief complaint recorded, and formulae in the TCM prescription. We extracted data on 2055 patients diagnosed with HN and used common terms to identify the signs and symptoms of the HN patients on the basis of the chief recorded complaint and manually referred to the NLM Medical Subject Heading (MeSH) database. Combined with its medical history, other related diseases that can lead to hypertension and CKD were deleted, and the quality was controlled by 2 doctors with over 10 years' experience in the treatment of cardiovascular and renal diseases.α = 2.4. The compatibility relationship of the main herb shell was satisfied with the formulaN is the number of all herbs in the data, SN is the frequency set of all herb pairs, and Sk represents the total frequency of the first K herb pairs. The main herb combination structure was extracted iteratively for the analysis of multilayer main herb compatibility. The algorithm was run with LiquoRice software [We extracted a list of herbs from the formulae of HN patients and set up the herb network with herbs as nodes and the occurrence frequency of the use of two herbs in different formulae as weights. Then, we ran the hierarchical network extraction algorithm to extract herb pairs from the weighted herb network on the basis of the degree coefficient software .ni is the sum of the frequency of the herbs in HN patient formulae, nj is the total frequency of herbj, and nij is the frequency of herbj in the HN patient formulae. N is the frequency of all herbs. We retained the herbs with RR > 1. Then, we established a 2 × 2 joint table based on these data, calculated the chi-square value, and selected the significant herbs (P < 0.05) for further study.We used the relative risk (RR) to find highly sensitive herbs between HN patient formulae and non-HN patient formulae. We used the HN patient formulae as the exposure group and the non-HN patient formulae as the nonexposure group, and single herbs used in each group were the outcomes.arules package (version 1.6-6) in R 3.6.1, where the support degree (Support), confidence (Confidence), and promotion degree (Lift) retain Support > 0.2 and Confidence > 0.5.We analyzed the compatibility of the herbs by using the HN prescription of association rules, which are often used to find the conditional dependencies between tuples. We used the classical Apriori algorithm  to calcuObserving the symptoms of the disease is an important link between disease diagnosis and treatment with TCM. Under the guidance of TCM theory, different herbs can be aimed at different TCM symptoms. The SymMap database contains the TCM symptoms corresponding to herbal medicine that have been agreed upon by experts. The TCM symptoms for which these herbs were used to treat HN were retrieved one by one in the SymMap database.Homo sapiens protein-protein interaction network (PPIN) data with a confidence ≥ 0.9 to establish the PPIN, and the MCODE plug-in of Cytoscape was used to determine the core targets of each herbal medicine.We identified compounds and targets of the herbs for the treatment of HN from online databases: Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) , SymMap Q < 0.05 and adj. P < 0.05 were regarded as differentially expressed genes in HN.The HN gene expression data were retrieved from the NCBI GEO database and analyzed by the GEO2R online analysis tool. The GSE99325 data set contained 20 HN samples and 4 control samples. The genes with In addition, we used disease terms in several established databases of human disease-related genes, including Online Mendelian Inheritance in Man (OMIM) , DisGeNECRWR of diffusion of the compounds to measure their importance, which was performed with the pyrwr package (version 1.0.0) in Python 3.7.5.The human biological functions cannot be realized by individual genes but require ubiquitous interactions between different genes. In this study, we combined the compounds and targets of various herbs with the PPIN we had established to create a biological network. The key compounds in the biological network were calculated by restarting the random walk (RWR) algorithm to measure the closeness between each node and seed node. We used HN-related targets as seed nodes, and the restart probability was 0.75 . The RWRclusterProfiler package [R 3.6.1 and org.Hs.eg.db (version 3.10.0) for gene annotation, we carried out a KEGG enrichment analysis of HN and related genes affected by various herbal treatments. Based on the hypergeometric distribution, Q < 0.05 was considered an indicator of a significantly enriched pathway, and the same method was used for GO enrichment analysis, with Q < 0.05 and adj. P < 0.05 as the levels indicating significant enrichment. In addition, we introduced each group of genes into Metascape [Using the  package   in R. (2) Hierarchical Clustering. We used the clustering method of Ward, which is based on analysis of variance. If a classification is reasonable, the error sum of squares (ESS) among the same kinds of herbs should be relatively small, and the ESS between classes should be relatively large [ly large . (4)ESS=flexclust package (version 1.4-0) in R 3.6.1.Initially, we regarded a single TCM symptom, target, or biological process as a class, calculated the ESS of each pair of classes after merging, determined the combination with the least increase in total ESS after merging, and then iterated the process until all objects formed a large class with the We selected 1449 prescriptions of traditional Chinese medicine from the EMR data of 2055 patients with HN, and these prescriptions included 425 herbs used 35734 times, and each prescription contained, on average, 23.84 ± 3.88 herbs. The core traditional Chinese medicines used in the treatment prescriptions for HN were obtained by hierarchical extraction algorithm: Radix Astragali (huangqi), Radix Salviae liguliobae (danshen), Radix Codonopsis pilosulae (dangshen), Poria (fuling), Rhizoma Atractylodis macrocephalae (baizhu), Radix Angelicae sinensis (danggui), Pericarpium Citri Reticulatae (chenpi), Rhizoma Pinelliae (banxia), and Radix Glycyrrhizae (gancao). After comparing the RR values, herba Dianthi (qumai), Semen Plantaginis (cheqianzi), Rhizoma Curcumae (ezhu), Semen Euryales (qianshi), and Radix Achyranthis bidentatae (niuxi) were determined to be specific traditional Chinese medicines used for the treatment of HN.We obtained 179 differentially expressed genes related to HN from the GSE99325 data set. As shown in We found 41 important association rules, as shown in Fourteen kinds of traditional Chinese herbs are used in the clinical treatment of HN, according to their compatibility characteristics, targets, signal pathways, and other information, as determined using MCODE, hierarchical clustering, and other methods for comparative analysis to determine the potential biological mechanism of herbal medicines in the treatment of HN.Using the SymMap database, we found a total of 68 effective TCM symptoms treated by the 14 herbs, and we established a 14 × 68-dimensional symptom profile in which an herbal medicine that was directed toward a symptom was given a value of 1; otherwise, it was given a value of 0. Then, we used the Ward clustering algorithm for hierarchical clustering in which the similarity between herbs was measured by Euclidean distance. After introduction into the STRING database, a total of 58 targets for the 14 herbs were retained, and we established a 14 × 58-dimensional target profile for hierarchical clustering. We analyzed the pathway enrichment of various herbs and HN-related genes and retained the pathways in which the number of enriched genes for each herb treatment in a significantly enriched HN pathway was greater than the quartile of enriched genes expressed upon herbal treatment, and these retained pathways were considered the core pathways for hierarchical clustering. Figures The table shows that the 14 herbs could be divided into 3 or 4 groups in terms of TCM symptoms, targets, GO enrichment terms, and KEGG enrichment terms . The cluIn terms of TCM symptoms and according to TCM theory, the 14 herbs can be aimed at HN or symptoms related to essential kidney deficiency, such as edema, diarrhea, fatigue, sore waist and knees, fatigue, and diarrhea. Symptoms such as turbid urination, oliguria, and obvious decline of renal function can be specifically treated by fuling.CRWR in each group are shown in According to the RWR algorithm, compounds with values in the upper quartile of the RWR were retained as the core effective compounds. Ultimately, 241 effective core compounds were retained, of which the ten compounds with the highest Studies have shown that a variety of traditional Chinese medicine prescriptions exhibit clear efficacy in the treatment of HN , 12. TraκB signaling pathway. These herbs can regulate fibrotic pathways such as the MAPK signaling pathway and PI3K-Akt signaling pathway and vascular endothelial hyperplasia pathways such as the VEGF signaling pathway. Among these herbs, the enrichment of danshen is the most obvious. In addition, danshen can regulate the cAMP signaling pathway, cGMP-PKG signaling pathway, mTOR signaling pathway, Ras signaling pathway, and Rap1 signaling pathway to mediate cell proliferation and differentiation. On the other hand, baizhu and qianshi can participate in the metabolism of amino acids such as glycine, serine, threonine, alanine, aspartate, and glutamate to regulate the cellular processes. Huangqi shows the most extensive regulation cellular life processes, including the regulation of DNA transcription factors and a variety of amino acid metabolic pathways, and it shows a variety of regulatory effects on the function of the kidney and heart. It is suggested that these herbs can produce synergistic and complementary effects in the treatment of HN.From the perspective of target and pathway enrichment, 14 herbs were involved with 25 core targets, and CHRM2, ADRB2, CXCL8, GCG, TNF, and CXCL10 were the most frequently identified targets. These herbs, particularly danshen, niuxi, cheqianzi, huangqi, and gancao, could act on inflammatory factors, which can be significantly enriched in inflammatory-related pathways such as the HIF-1 signaling pathway, TNF signaling pathway, NOD-like receptor signaling pathway, and NF-HN is one of the main causes of chronic nephropathy. Arteriosclerosis and hyaline degeneration caused by hypertension are the main pathological changes in HN and are closely related to inflammation and tubulointerstitial fibrosis . Under pκB signaling pathway, PI3K-Akt signaling pathway, and other inflammatory-related pathways, degrading secretion of inflammatory factors such as TNF and IL-6. At the same times, it can combat the early renal injury and the inflammatory response caused by Ang II [The herbs obtained by data mining in this study can inhibit kidney injury with Ang II. The targets of dangfui, niuxi, chenqianzi, huangqi, gancao, dangshen, chenpi, and banxia are significantly related to cAMP signaling pathway, mTOR signaling pathway, and cGMP-PKG signaling pathway. These herbs can inhibit the activity of CHRM1 receptor and GCG on the cell membrane and reduce the activation of cAMP signaling pathway and mTOR signaling pathway, thus inhibiting NF-y Ang II , alleviay Ang II , 35. Pody Ang II , and vasy Ang II . Chenpi,y Ang II .γ, IL-6, and TNF overactivated STAT1, STAT3, and NF-κB transcription factors to promote glomerulosclerosis and renal interstitial fibrosis [κB/p65 has been identified as the key factor mediating renal damage induced by Ang II [κB transcription factors and reduce the renal inflammation and injury induced by Ang II, downregulate the expression levels of TNF, IL-6, and IL-8 in the kidney, and reduce the mRNA and protein levels of type I and IV collagen [HN is also a long-term cause of nephron damage. A previous study showed that the pathological renal changes caused by HN were progressive, with glomerular damage appearing first and renal tubular atrophy and interstitial fibrosis appearing as late manifestations in an SHR model . Inflammfibrosis –42. NF-κy Ang II . Qianshicollagen . AccordiκB signaling pathway, cAMP signaling pathway, mTOR signaling pathway, and other pathways need to be further studied.The therapeutic effects of these herbs on HN are mainly the deceleration of the renal damage caused by hypertension, protection of nephrons and podocytes, reduction in blood pressure and the amount of protein in urine, and slowing renal interstitial fibrosis and maintaining nephron function, but the effect of a single herbal medicine is often limited. To achieve the expected therapeutic effects, these herbs need to be combined. These herbs are often used in the clinic. This finding is consistent with the results in this study, showing that these herbs fall under the important compatibility rules identified by the Apriori algorithm. To some extent, the results of the multilevel analysis indicated the possible effective pathway of these traditional Chinese medicine combinations, which is consistent with the mechanism of renal injury caused by HN, but this study also has many limitations. First, the compound information, targets, and disease-related genes were all identified in open databases, and there is still a lack of experimental studies on proteomics and metabonomics. Second, the effects of these herbs on the NF-Based on the traditional Chinese medicine prescription data of HN treated in the Affiliated Hospital of Shandong University of Traditional Chinese Medicine from 2014 to 2017, traditional Chinese medicine for HN treatment was found by using a complex network algorithm and calculating the relative risk. Fourteen herbs were compared and analyzed at multiple levels with a clustering algorithm. Through the clustering results obtained for different levels of analysis, these herbs were found to play multilevel roles in the biological regulation of HN. This study also showed that the use of a data mining algorithm can be used to summarize the TCM treatment prescription of HN accurately and with high reliability and can provide a direction for clinical practice and future experimental research.