Background: Our previous studies have shown that Guanxin V (GXV) is safe and effective in the treatment of ventricular remodeling (VR), but its mechanism related to oxidative stress has not been studied deeply.Methods: We applied integrating virtual screening and network pharmacology strategy to obtain the GXV-, VR-, and oxidative stress-related targets at first, and then highlighted the shared targets. We built the networks and conducted enrichment analysis. Finally, the main results were validated by molecular docking and solid experiments.Results: We obtained 251, 11,425, and 9,727 GXV-, VR-, and oxidative stress-related targets, respectively. GXV-component-target-VR and protein–protein interaction networks showed the potential mechanism of GXV in the treatment of VR. The following enrichment analysis results gathered many biological processes and “two GXV pathways” of oxidative stress-related to VR. All our main results were validated by molecular docking and solid experiments.Conclusion: GXV could be prescribed for VR through the mechanism, including complex interactions between related components and targets, as predicted by virtual screening and network pharmacology and validated by molecular docking and solid experiments. Our study promotes the explanation of the biological mechanism of GXV for VR. Ventricular remodeling (VR) refers to the changes at the cellular and anatomical levels that occur based on gene expression changes, mainly cardiomyocytes, non-cardiomyocytes, and extracellular matrix . Cell stTraditional Chinese medicine, mainly from the East, has been used clinically for nearly 3,000 years and has gradually received acceptance and recognition abroad and overseas in the recent years . GuanxinOxidative stress is the result of the imbalance between reactive oxygen species formation and enzymatic and non-enzymatic antioxidants . ExcessiWe first obtained active compounds and corresponding targets for GXV from our previous study . In anotHomo sapiens (Human) and medium confidence was more than 0.400. Then, we used the MCODE plugin  , and theE plugin  in CytosE plugin  to clustHomo sapiens (Human) as the background. To avoid over counting duplicated genes, based on corresponding DAVID gene IDs, we calculated the Fisher Exact statistics to remove all redundancies in original IDs. The threshold of EASE score, a modified Fisher Exact P value, was applied for the evaluation of gene-enrichment analysis. Fisher Exact P value range from 0 to 1, ≤0.05 was considered strongly enriched, and equal to 0 were considered perfect enrichment. All results had to pass the thresholds  to ensure only significant enrichment terms were displayed. The top 20 results were visualized by the ggplot2 package in R  for 24 h to establish the oxidative stress model and then treated with GXV (1 g/L) for another 24 h , superoxide dismutase , lactate dehydrogenase , catalase , total antioxidant capacity , and glutathione peroxidase  according to the instructions of the manufacturers.H9c2(2-1) cells were incubated with Hher 24 h . SupernaWe obtained 119 active compounds and corresponding 196 targets for GXV from our previous study . MoreoveThe Venn diagram showed that there were 206 shared targets among 251 GXV-related targets, 11,425 VR-related targets, and 9,727 oxidative stress-related targets . These 2Moreover, in this study, we also constructed a PPI network of the 206 shared targets, which consisted of 206 nodes and 6,116 edges . This meTo clarify the oxidative stress mechanism of GXV in treating VR, we conducted GO and KEGG functional enrichment analysis for 206 shared targets. GO enrichment items were classified into three functional groups: molecular function , biologiThrough GO biological process enrichment of 4 cluster networks from the PPI network, we found that cluster 1 enriched in negative regulation of G1/S transition of the mitotic cell cycle, positive regulation of fibroblast proliferation, and histone phosphorylation; cluster 2 enriched in Notch receptor processing and peptidase activator activity; cluster 3 enriched in SMAD protein signal transduction, mesenchyme morphogenesis, striated muscle cell proliferation, positive regulation of pri-miRNA transcription by RNA polymerase II, and production of miRNAs involved in gene silencing by miRNA; and cluster 4 enriched in activation of cysteine-type endopeptidase activity involved in the apoptotic signaling pathway, positive regulation of pri-miRNA transcription by RNA polymerase II, nuclear receptor activity, and regulation of insulin receptor signaling pathway .The PDB entry codes for TGF-β1 and Caspase-3 are 6P7J  and 5I9B2O2 had increased LDH and MDA and decreased SOD, GSH-Px, CAT, and T-AOC (H9c2(2-1) cells incubated with 300 μM Hnd T-AOC  indicatind T-AOC . TogetheVR is an important factor leading to the poor prognosis of many cardiovascular diseases in the middle and late stages . Therefovia the inhibition of TLR4/MyD88/NF-κB signaling pathway   , and monpathways  and Akt/ignaling . Besidesignaling , 17. We There are some limitations that should be considered in our further study. First, traditional Chinese medicine generally has the characteristics of multitarget and multieffect, which means that it is not enough to determine only two pathways, although this is confirmed by our previous research results. Moreover, although we verified the results obtained from virtual screening and network pharmacology through molecular docking and solid experiments, more experiments are still required to further verify our reliable findings. Finally, some important targets of GXV, VR, and oxidative stress might be ignored and missed that is the inevitable bias of network pharmacology , so we nOur study predicts the targets of the synergistic pharmacological mechanism of GXV and explores the potential mechanism involved in alleviating and even reversing VR through integrating virtual screening and network pharmacology strategy and molecular docking and experimental validation, which provide a complementary and alternative medication for VR.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/in vitro experiments. All authors interpreted the results. BL drafted the manuscript. NG contributed to the critical revision of the manuscript. All authors read and approved the final manuscript.BL and NG conceived, designed, and planned the study. BL and YL acquired and analyzed the data. BL and RL completed the This work was partly funded by Research and Practice Innovation Plan for Postgraduates of Jiangsu, China [KYCX21_1641], National Natural Science Foundation of China [81774229], Jiangsu Leading Talent Project of Traditional Chinese Medicine [Jiangsu TCM 2018 No.4], and Jiangsu Universities Nursing Advantage Discipline Project [2019YSHL095].The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Type-2 diabetes and obesity are among the leading human diseases and highly complex in terms of diagnostic and therapeutic approaches and are among the most frequent and highly complex and heterogeneous in nature. Based on epidemiological evidence, it is known that the patients suffering from obesity are considered to be at a significantly higher risk of type-2 diabetes. There are several pieces of evidence that support the hypothesis that these diseases interlinked and obesity may aggravate the risk(s) of type-2 diabetes. Multi-level unwanted alterations such as (epi-) genetic alterations, changes at the transcriptional level, and altered signaling pathways  are the major sources that promote several complex diseases, and such a heterogeneous level of complexity is considered as a major barrier in the development of therapeutics. With so many known challenges, it is critical to understand the relationships and the shared causes between type-2 diabetes and obesity, and these are difficult to unravel and understand. For this purpose, we have selected publicly available datasets of gene expression for obesity and type-2 diabetes, have unraveled the genes and the pathways associated with the immune system, and have also focused on the T-cell signaling pathway and its components. We have applied a simplified computational approach to understanding differential gene expression and patterns and the enriched pathways for obesity and type-2 diabetes. Furthermore, we have also analyzed genes by using network-level understanding. In the analysis, we observe that there are fewer genes that are commonly differentially expressed while a comparatively higher number of pathways are shared between them. There are only 4 pathways that are associated with the immune system in case of obesity and 10 immune-associated pathways in case of type-2 diabetes, and, among them, only 2 pathways are commonly altered. Furthermore, we have presented SPNS1, PTPN6, CD247, FOS, and PIK3R5 as the overexpressed genes, which are the direct components of TCR signaling. Type 2 diabetes (T2D) is a global epidemic that is strongly correlated with obesity , 2. AdipSo far, there are several studies where a number of factors have been investigated to understand the biological mechanism of obesity and T2D and the relationship between the two –16. T2D via their components between obesity and T2D and with the major focus being on the roles of TCR signaling components.For this purpose, we have selected publicly available datasets of gene expression for obesity and T2D and have unraveled the genes and the pathways associated with the immune system; we have also focused on the T-cell signaling pathway and its components. We have applied a simplified computational approach to understanding differential gene expression and patterns and the enriched pathways for obesity and T2D. Furthermore, we have also analyzed the genes by using network-level understanding. In the analysis, we observe that there are fewer genes that are often differentially expressed while a comparatively higher number of pathways are shared between them. There are only 4 pathways that are associated with the immune system in the case of obesity and 10 immune-associated pathways in the case of T2D; among these, only two pathways are commonly altered. Here, we have studied the differentially expressed genes (DEGs), enriched pathways, and the connection between immune signaling pathways As mentioned above, we have compared immune signaling genes by using gene expression profiling of T2D and obesity for which the datasets have been collected from publicly available data. Here, the results have been presented from work from generalized analyses  to ensure precise analysis, i.e., TCR signaling.p-values, and these pathways are the Rap1, tight junction, leukocyte transendothelial migration, ECM-receptor interaction, and Ras signaling pathways. The complete details of the fold changes of the genes are presented in For diabetes and obesity, the data have been collected from the public database, and we have performed a comparative analysis in terms of altered gene expression patterns and their respective functions. Here, we have presented the DEGs for both the cases T2D and obesity Table 1,After analyzing the overall DEGs and all the enriched pathways, we have performed an immune-system-specific analysis for the genes and the pathways. Here, we observed that there are 10 pathways associated with the immune system in the case of T2D and four pathways in the case of obesity  that are components of the TCR signaling pathway and their association with the other TCR genes  and obesity are considered metabolic-disorder-related diseases, and DM is characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both , 18, 32.In this study, the obese samples totaled 36 samples where 18 were lean needle biopsy and 18 obese biopsy samples; these samples were age-matched to lean (n = 19) and obese (n = 18) female subjects, and the abdominal subcutaneous fat specimens have been collected. The average age (years) of the patients was 35.7 +/- 8.9 (obese-needle), 37.2 +/- 9.3 (obese-surgery), 37.2 +/- 9.9 (lean-needle), and 36.8 +/- 10.1 (lean-surgery), and the average body mass index (BMI) was 44.0 +/- 6.9 (obese-needle), 46.1 +/- 4.4 (obese-surgery), 21.9 +/- 1.9 (lean-needle), and 21.9 +/- 2.4 (lean-surgery)  are major parts of the immune system and are known to control major parts of the immune system. Furthermore, it is also known that altered TCR signaling is associated with T2D. Therefore, we have now mapped out the individual genes that are associated with the TCR signaling pathway and presented the networks for all the five genes , which are the components of the TCR signaling pathway, and their association with the other TCR genes Figure 3As we can see in the results, PIK3R5 and FOS, which are the components of the TCR signaling pathway, control a large set of biological functions, most of which are a part of the immune system, and CD247, PTPN6, and SPNS1 comparatively control fewer pathways; in terms of connectivity, however, PTPN6 has the highest connectivity followed by SPNS1, CD247, FOS, and PIK3R5. It means that PIK3R5 simultaneously controls a large number of biological functions but does not impact more genes; in other words, the higher connectivity of the gene will increase the likelihood of affecting the expression patterns of the gene or vice versa. In terms of future perspectives, this study will be helpful for the development of therapeutic approaches and drug targeting.For obese people, we used the GSE12050  dataset;These datasets were collected from GEO and have been processed for expression value calculation, fold change, and p-value calculation by the in-built code at GEO, i.e., GEO2R , 55. Themattest, we get the p-values, and we then go for p-value corrections, assign the p-value threshold, and use the volcano plot for fold change calculation and the selection of genes with p-values less than 0.05 and fold changes within the range >= +2.0 and <= -2.0. This range of fold change along with this p-value cutoff are commonly used, while, in our study, we selected the top-ranked genes to have the strict condition of selection based on fold change so the fold changes are either much higher or lower than the cutoff fold change. For pathway analysis, we used the KEGG , enriched pathways, and the connection between immune signaling pathways The original contributions presented in the study are included in the article/Conceptualization, BE, HS, HC, and MM. Methodology, BE, HS, HC, and MM. Software, MM. Validation, BE, HS, HC, and ZZ. Formal analysis, BE, HS, HC, and MM. Investigation: BE and MM. Resources, BE and MM. Data curation, BE and MM. Writing—original draft preparation, BE, HS, HC, and MM. Writing—review and editing, BE, HS, HC, and MM. Visualization, BE, HS, HC, and MM. Supervision, HC and MM. Project administration, BE and HC. Funding acquisition, BE. All authors contributed to the article and approved the submitted version.This research was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, grant number RG-5-140-39 and Swedish Cancer Society.The authors declare that the research was conducted in the absence of anycommercial or financial relationships that could be construed as a potentialconflict of interest.


There are still controversies about the curative effect of vitamin C in treating HIE, and its mechanism of action is not entirely clear. This study is designed to explore the potential molecular mechanism of vitamin C in treating neonatal hypoxic ischemic encephalopathy (HIE).  The effect targets of vitamin C and the pathogenic targets of neonatal HIE were obtained via retrieval of public databases to screen out the molecular targets of vitamin C acting on neonatal HIE. Gene Ontology (GO) functional annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on the main targets. Vitamin C and the optimum target structural components are subjected to molecular docking and molecular dynamics simulation analysis via computer software so as to verify their binding activity and stability.  Based on 16 overlapping targets of vitamin C and HIE, seven main targets were identified in this study. According to GO and KEGG analysis, molecular functions (top 25 items) and signal pathways (21 items) related to inflammatory reaction, immune response, and cell transcriptional control may be potential pathways for vitamin C to treat neonatal HIE. Molecular docking and molecular dynamics simulation were adopted to definitively determine the 4 optimum core target spots.  The efficacy of vitamin C on HIE is involved in the immunoregulation and inflammation-related functional processes and signal pathways. These molecular mechanisms, including core targets, will contribute to the clinical practice of neonatal HIE in the future. Hypoxic ischemic encephalopathy (HIE) is a brain damage caused by asphyxia at birth, as well as one of the most common causes of neonatal death and long-term disability, occurring in 2 to 3 per 1000 newborns . Many stMany anti-inflammatory and antioxidant potential drugs have been studied in preterm birth and term hypoxic ischemic brain damage, such as erythropoietin, melatonin, and histone deacetylase inhibitors, which have shown potential neuroprotective effects in animal models , 11, 12.In recent years, as a part of bioinformatics technology, network pharmacology has unprecedented advantages with the rapid development of bioinformatics technology, especially molecular docking, molecular dynamics, and other computer simulation technologies . On the Therefore, this study aimed to use this emerging approach of bioinformation technology to decipher the predicted targets and signaling pathways of vitamin C's role in HIE so as to provide a scientific basis for future clinical practice and experimental research.http://lsp.nwu.edu.cn/index, accessed July 18, 2021) [https://www.drugbank.com/, accessed July 18, 2021) [http://bindingdb.org/bind/index, accessed July 18, 2021) [https://www.swisstargetprediction.ch/, accessed July 18, 2021) [http://www.disgenet.org, accessed July 20, 2021) [https://www.drugbank.com/, accessed July 20, 2021), and GeneCards  [https://www.UniProt.org/, accessed July 23, 2021) [Traditional Chinese Medicine System Pharmacology  , Drugban8, 2021) , Bind DB8, 2021) , and Swi8, 2021)  database0, 2021) , DrugBan0, 2021)  database0, 2021) . Then, t3, 2021)  was applhttps://string-db.org/, accessed July 23, 2021) was used to obtain the protein-protein interaction (PPI) between target-to-target functions [Homo sapiens”; nodes with degree of confidence >0.4 and unconnected nodes were hidden; the remaining parameters are set to default values. Related data obtained were downloaded in TSV file format, and the Cytoscape v3.7.1 software  was used to set up the PPI network [The STRING database , cellular component (CC), molecular function (MF) of gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways , 40. Allhttps://pubchem.ncbi.nlm.nih.gov/, accessed July 26, 2021), and the Chem 3D software was used to convert the SDF format into a mol2 format file. The structure of the related protein in PDB format was downloaded from the RCSB database ; the PyMOL software  was applied to remove solvent molecules and ligands, and AutoDock Tools v1.5.6 was used for software hydrogenation, calculation of charge, allocation of atom types, and so on., and saved in pdbqt format. Finally, the AutoDock Vina v1.1.2 software was run to perform molecular docking, and conformations with binding free energy less than −5 kcal/mol were selected for docking and visual analysis via the Discovery Studio 2020 software .Molecular docking technology was used to study vitamin C and its related targets for the treatment of HIE, which can explain the mechanism and binding activity of vitamin C and target proteins to a certain extent . The str−1·nm−1. Then, the NVT and NPT equilibria were simulated at 300 K for 1 ns, respectively. Finally, a 100 ns MD simulation was performed on the system, and the conformation was saved every 10 ps. The simulation results were visualized using the Gromacs embedded program and VMD software .Gromacs is a powerful molecular dynamics simulation software, which has great advantages in simulating the Newtonian motion of a large number of molecular systems . The molhttps://www.ncbi.nlm.nih.gov/gds, accessed August 2, 2021) and converted into gene-IDs; then, the corresponding gene-ID was imported into the KEGG Mapper platform  that identified the relevant pathways [In order to further clarify their mechanism of action, the Cytoscape v3.7.1 software was applied to construct an intuitive relationship network via setting the optimal targets as the source node and the signaling pathway as the target node; next, the results of the finally determined stable binding targets with vitamin C and KEGG signal pathways were visualized . In addipathways .The HIE related genes of newborns were screened through the DisGeNET database; screening criteria: gene-disease association score >0.1; the HIE of newborns was screened through the GeneCards database; screening criteria: gene score >1.805; genes related to neonatal HIE were determined with the above criteria. Vitamin C drug targets were screened and identified via TCMSP, Bind DB, DrugBank, and Swiss Target Prediction databases, and 76 vitamin C targets were identified via the UniProt database. Then, 16 overlapping targets  were obtk-value screening (γ (PPARG), glycogen synthase kinase-3β (GSK3B), cysteine protease-1 (CASP1), and hypoxia-inducible factor 1-α (HIF1A).The PPI data obtained from the STRING database were imported into the Cytoscape software, and topological parameters of the interaction network between vitamin C and HIE with common targets and function-related proteins were calculated  within 100 ns of the simulated trajectory. At the beginning of the simulation, there was a preliminary upward trend, which was mainly due to the interaction between the protein and the surrounding water solvent at the beginning of the simulation, causing certain fluctuations in the protein structure. As shown in Root mean square fluctuations can indicate the flexibility of amino acid residues in a protein. As shown in The Cytoscape software was used to import the optimal target and the signal pathway that intersected with the target, and the interaction diagram of vitamin C's core target-related pathways in HIE was generated , which fAt present, there is no unified standard and specialized treatment for HIE. In clinical practice, the challenge is that a gap remains between preclinical medical research and clinical application in the treatment of neonatal HIE . VitaminBased on bioinformatics analysis, this study efficiently obtained all potential biological targets of the effect of vitamin C on HIE and predicted 7 key pharmacological targets via topology analysis. Molecular docking technology confirmed that vitamin C has a good binding force with 4 core targets  among these 7 main targets. In addition, molecular dynamics simulation technology has further confirmed that the docked vitamin C molecules can well bind to the four core targets, and the stable complex structure is more conducive to the interaction between vitamin C and the targets to play the corresponding activity, which strongly supports the view that vitamin C may exert anti-inflammatory and antioxidant effects on HIE through these four targets.α1-dependent manner [β [κB-driven NADPH oxidase regulatory subunits [HIF1A is a major regulator of cell adaptation to hypoxia. The activation of HIF1A can upregulate broad-spectrum target genes involved in glucose metabolism, angiogenesis, and erythropoiesis to avoid or minimize hypoxic brain damage , 55. Howt manner . CASP1 it manner , 61. Acct manner . In addianner [β . Accordianner [β . PTGS2 ianner [β . Accordianner [β , 67. In anner [β . Relatedanner [β . As a stanner [β . The actsubunits . Accordiβ, thus aggravating hypoxic-ischemic brain injury [In network pharmacology analysis, the effect of vitamin C in treating HIE is related to inflammation, immune response, and cell transcription regulation according to GO function annotation and KEGG pathway analysis. From the overall bioinformatics data, the effect of vitamin C on HIE is achieved by regulating the IL-17 signaling pathway, C-type lectin receptor signaling pathway, Th17 cell differentiation, B cell receptor signaling pathway, Nod-like receptor signaling pathway, tumor necrosis factor signaling pathway, T cell receptor signaling pathway, HIF-1 signaling pathway, and other signaling pathways, most of which are related to immunity and inflammation mediated by cerebral ischemia and hypoxia. For example, the dendritic cell-related C-type lectin-1 receptor signaling pathway can mediate neuroinflammation after ischemic stroke . Nod-likn injury . In addin injury . Based oUnfortunately, there are still some limitations of this study that this study only uses public databases and related software to predict related mechanisms, which may lead to uncertain results. Therefore, the research team intends to further verify these potential targets and signal pathways by means of animal experiments and cell experiments to clarify the potential pharmacological mechanism of vitamin C on HIE in future studies.Overall, the emerging methods of network pharmacology, molecular docking, and molecular dynamics simulation were first used to systematically and intuitively reveal the mechanism and pharmacology of vitamin C in treating HIE through 4 targets  working together with the IL-17 signal pathway, C-type lectin receptor signal pathway, HIF-1 signal pathway, and other immune and inflammation-related multisignal pathways, indicating that vitamin C may be one of the effective drugs for the treatment of HIE. Moreover, these core targets and signal pathways will contribute to the clinical practice of neonatal HIE in the future. In addition, the timing, dosage, and couse of application of vitamin C are also worthy of future research because of the different inflammatory reactions caused by HIE of different severities.


Rhizoma Heterosmilacis and Radix Sophorae Flavescentis could synergistically target the PI3K-Akt signaling pathway and the cAMP signaling pathway to treat BC. Additionally, TRI analysis showed that the average affinity of HCGSC and targets involved in the key pathways reached -6.47 kcal/mmol, while in vitro experiments proved that two of the three high TRI-scored components in the HCGSC showed significant inhibitory effects on breast cancer cell proliferation and migration. These results demonstrate the accuracy and reliability of the proposed strategy.Breast cancer (BC) is one of the most common malignant tumors among women worldwide and can be treated using various methods; however, side effects of these treatments cannot be ignored. Increasing evidence indicates that compound kushen injection (CKI) can be used to treat BC. However, traditional Chinese medicine (TCM) is characterized by “multi-components” and “multi-targets”, which make it challenging to clarify the potential therapeutic mechanisms of CKI on BC. Herein, we designed a novel system pharmacology strategy using differentially expressed gene analysis, pharmacokinetics synthesis screening, target identification, network analysis, and docking validation to construct the synergy contribution degree (SCD) and therapeutic response index (TRI) model to capture the critical components responding to synergistic mechanisms of CKI in BC. Through our designed mathematical models, we defined 24 components as a high contribution group of synergistic components (HCGSC) from 113 potentially active components of CKI based on ADME parameters. Pathway enrichment analysis of HCGSC targets indicated that  Ganoderma lucidum from Ganoderma suppresses the proliferation and migration of breast cancer by inhibiting Wnt/β-catenin signaling  is the most common cancer in women worldwide and is responsible for the second highest death rate among female patients with cancer . Many opignaling , while ppathways . In addipathways , Shugan pathways , and Fanpathways . Based oRadix Sophorae flavescentis) and baituling (Rhizoma Heterosmilacis) at a ratio of 7:3, in which exist hundreds of components including alkaloids and flavonoids . TCMSP includes information from DrugBank, HIT, TTD, and PharmGKB. The pharmacokinetic properties of TCMSP include molecular weight (MW), AlogP, number of acceptor atoms for H-bonds (nHacc), number of donor atoms for H-bonds (nHdon), Caco-2 permeability (Caco-2), oral bioavailability (OB), drug-likeness (DL), blood–brain barrier (BBB), FASA-, and half-life (HL).All CKI components were collected from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database   is curreDrug-likeness (DL) may be defined as a complex balance of various molecular properties and structural features that determine whether a particular molecule is similar to known drugs. The drug similarity index of the new compound was calculated using the Tanimoto coefficient, which is defined asRadix Sophorae Flavescentis was 0.40, while that of Rhizoma Heterosmilacis was 0.25. Combined with other literature search criteria, we defined DL ≥ 0.18, as the screening criterion of DL.In this equation, A represents the molecular descriptor of herbal components, and B is the average molecular property of all components in DrugBank . Based oThree commonly used online tools were employed to identify the targets of active components in CKI, namely, similarity ensemble approach (SEA) , HitPickThe C-T network was used as a frame to uncover the relationship between active components and targets. Cytoscape 3.8.0 , an openhttps://bioconductor.org/) based on the R language was used for GO-BP enrichment analysis and KEGG pathway analysis. FDR-adjusted p values were set at 0.05, as the cut-off criterion.To analyze the main function of targets, clusterProfiler  in the BThe SCD represents the contribution of a potential active component in the C-T network and its effectiveness in the treatment of BC. To evaluate the effect of CKI on the treatment of BC, we built a mathematical model to calculate the SCD of each active component in CKI:i is the number of components, and j is the number of targets. C represents the betweenness centrality of each component. T represents the betweenness centrality count of all targets of each component. Ce represents the edge count of each component. Te represents the edge count of all targets of each component. CB represents the betweenness centrality of each component only in Rhizoma Heterosmilacis, and CK represents the betweenness centrality of each component only in Radix Sophorae Flavescentis. If a component consists only of Radix Sophorae Flavescentis or Rhizoma Heterosmilacis, d represents the dose of each component in CKI, which was extracted using HPLC (NORM (di) is the index of min-max normalization to the dose of each component. The betweenness centrality of each component and target was calculated using Cytoscape version 3.8.0. based on the C-T network of the potential active components.In this equation, ing HPLC . NORM  was used to explore the DEGs in BC. We used TCGAbiolinks  databases in the MOL2 format. Human proteins were collected from the Protein Data Bank (PDB) (http://www.rcsb.org). AutoDock Tools (ADT) (The components of HCGSC were collected from the ZINC  and PubCls (ADT)  was usedls (ADT)  was usedls (ADT) .We developed a mathematical model to calculate the therapeutic response index (TRI) of each component in a highly reliable docking relationship (HRDR) between HCGSC and its targets.i is the number of components, j is the number of topology parameters, n is the collection of components, and m is the collection of components and topology parameters. CDI represents the docking index of HRDR. GI represents the number of genes targeted by each component. AI represents the average affinity of all component–protein binding relationships of each component. NI represents the C-T network topology parameters of HCGSC and their targets, including degree, betweenness centrality, closeness centrality, average shortest path length, eccentricity, radiality, neighborhood connectivity, and topological coefficient. SCD is the calculation result for each component from method 2.6. TRI represents the mechanism of CKI in the treatment of BC, which is the sum of normalized CDI and normalized SCD. NORM is a normalized equation.In the above model, High-glucose Dulbecco’s modified Eagle medium , fetal bovine serum (FBS), 0.25% trypsin, and 3--2,5-diphenyltetrazolium bromide (MTT) were purchased from Shanghai Sangon Biotechnology Co., Ltd. . The transwell invasion chamber was purchased from Corning costar company . Matrigel was purchased from BD . Luteolin and trifolirhizin (≥98% purity by HPLC) were obtained from Nanjing Jingzu Biotech Co., Ltd. .The human breast cancer cell line MCF-7 was purchased from Procell Life Science & Technology Co., Ltd. Cells were cultured in an incubator at 37°C in DMEM containing 10% FBS. When the cells reached 80% confluency, they were exposed to different concentrations of luteolin and trifolirhizin  for 24 h.4 cells/well) were seeded in 96-well plates and treated with 0, 5, 10, 20, 40, 80, and 100 μM luteolin and trifolirhizin for 24 h. MTT was added to a 96-well plate for 4 h, and the culture supernatant was removed. Finally, DMSO was used to dissolve the purple crystals. Absorbance at 570 nm was measured using a plate reader.MCF-7 cells  and Radix Sophorae Flavescentis (113). Detailed information on the components of Rhizoma Heterosmilacis and Radix Sophorae Flavescentis is provided in Rhizoma Heterosmilacis and Radix Sophorae Flavescentis, we compared nine properties of the two components, including drug likeness (DL), Caco-2, molecular weight (MW), AlogP, TPSA, FASA-, RBN, nHDon, and nHAcc, and drew boxplots using R packages ggplot2  and RBN (p = 0.015). These results indicate that Rhizoma Heterosmilacis and Radix Sophorae Flavescentis have similar chemical properties.From the TCMSP database, 187 compounds were retrieved from  ggplot2 , ggpubr  ggplot2 , and ggs ggplot2 . The res ggplot2  showed mRadix Sophorae Flavescentis 90 and Rhizoma Heterosmilacis 21) were selected from the 187 components of CKI. Additionally, some components, such as N-methylcytisine and trifolirhizin, did not pass the ADME screening, but were frequently reported in previous studies, so we added them to the list of active components (Radix Sophorae Flavescentis 92 and Rhizoma Heterosmilacis 21) were included for further analysis. Detailed information is provided in Traditional Chinese medicine uses a combination of herbs, each of which usually contains hundreds of components, and only a few of these components possess satisfactory pharmacodynamic and pharmacokinetic properties. In this study, two ADME-related properties, DL and Caco-2, were used to screen for active components. Through ADME screening, 111 active components  displays antitumor, anti-allergic, anti-inflammatory, anti-arrhythmia, and antiviral properties, and affects the central nervous system , a steroid compound, has been shown to promote apoptosis and anticancer effects  promoter may be associated with shorter survival time and increased risk of drug resistance to anti-endocrine therapy (Radix Sophorae Flavescentis and Stigmasterol (BTL5), quercetin (BTL1), and taxifolin (BTL13) of Rhizoma Heterosmilacis. Additionally, MAPT (degree = 65) is correlated with microtubule assembly and stabilization (Radix Sophorae Flavescentis and quercetin (BTL1) from Rhizoma Heterosmilacis could target MAPT. We noted that several components of Rhizoma Heterosmilacis and Radix Sophorae Flavescentis can both activate common targets to play their role in the treatment of BC. Thus, these results suggest that Rhizoma Heterosmilacis and Radix Sophorae Flavescentis may act synergistically to treat BC via the common targets based on the “multi-component” and “multi-target” features and provide therapeutic targets for the cooperative treatment of BC.Among all components, six components displayed a number of degrees above 120, namely palmitone , quercetin , quercetin , norartocarpetin , apigenin , and luteolin , all of which exhibit anticancer functions , and sup therapy  and is clization , which clization . Our datRadix Sophorae Flavescentis and Rhizoma Heterosmilacis, we investigated the change in common target proportion under the condition of different degrees as thresholds. The results showed that following the increase in degree, the proportion of common target retention maintained a high growth trend. The proportion of common targets increased from 32.31 to 100% when the degree threshold increased from 1 to 28. Additionally, when the degree threshold increased from 1 to 28, the proportion of unique targets of Rhizoma Heterosmilacis decreased from 26.76 to 0%; however, when the degree threshold increased from 1 to 8, the proportion of specific targets of Radix Sophorae Flavescentis decreased from 41.02 to 0%. The results indicated that the common targets of Rhizoma Heterosmilacis and Radix Sophorae Flavescentis have relatively high degrees. A higher degree indicates that these targets have a higher influence among all targets, and it also suggests that Rhizoma Heterosmilacis and Radix Sophorae Flavescentis exert major synergistic effects by targeting common targets, which have a greater influence on the intervention response network.To further decode the synergistic mechanism between Radix Sophorae Flavescentis and Rhizoma Heterosmilacis using degree as the threshold. As shown in Rhizoma Heterosmilacis and Radix Sophorae Flavescentis revealed a gradual downward trend. The ratio of unique targets of Radix Sophorae Flavescentis changed from 41.02 to 0% with a degree ranging from 1 to 28, while the ratio of unique targets of Rhizoma Heterosmilacis changed from 26.67 to 0% with a degree range from 1 to 7.To further decode the synergistic mechanism between them, we calculated the dynamic ratio change between common targets and unique targets of In the C-T network, nodes with a higher degree usually represent the importance of all nodes. The results indicated that too much background noise may obscure the synergistic mechanism. Therefore, a model should be used to extract the core components.Rhizoma Heterosmilacis and Radix Sophorae Flavescentis may play their roles through common targets based on the C-T network. However, the core component groups and mechanisms of synergy require further elucidation. To solve these problems, an SCD calculation method was designed that considers the synergy of both the topology of the components in the C-T network and the dose of the components. The SCD values of the active components of CKI are shown in It is worth noting that According to the calculation results, the top two components with an SCD sum of 49.05% were oxymatrine (KS80) and matrine (KS20). Twenty-four components contribute to the effects of CKI on BC, with a total of 95.03%. Surprisingly, 24 components accounted for only 21.23% of all active components in CKI and could cover 631 targets  . Thus, wRhizoma Heterosmilacis, while 43 were targeted by Radix Sophorae Flavescentis. These results confirm the reliability and accuracy of the HCGSC selection model. Two networks were constructed to analyze HCGSC. The first is a C-T network based on 24 components and 631 targets. The second is the target–pathway network based on 532 targets and 158 enriched pathways.To evaluate the reliability of HCGSC, we defined two references for further comparison: the first reference was 70 genes, which overlapped with DEGs and CKI targets, and the second reference was the 17 pathways, which are overlapped by enriched DEGs pathways and CKI targets. The targets of HCGSC contained 62 genes, which could cover 88.57%  of the fRhizoma Heterosmilacis decreased from 32.17 to 0%, while the proportion of specific targets of Radix Sophorae Flavescentis decreased from 40.57 to 0%. In the second network, we assigned the degree from the first network to the targets; targets with a degree higher than 3 (the third quartile of the targets in the C-T network) and their enriched pathways were selected to construct the network (Rhizoma Heterosmilacis (yellow triangle) and Radix Sophorae Flavescentis (yellow arrow) not only showed fewer connections with pathways, but were also smaller than the common targets. These two networks indicated that the common targets of Rhizoma Heterosmilacis and Radix Sophorae Flavescentis in HCGSC have relatively high degrees, which shows that Rhizoma Heterosmilacis and Radix Sophorae Flavescentis have a synergistic effect on the nodes with higher influence.In the first network, the degree of targets ranged from 1 to 16, with the third quartile equal to 3. The change in the proportion of common targets under different conditions showed that the common targets had a higher degree . The pro network . VisualiRhizoma Heterosmilacis and Radix Sophorae Flavescentis, we performed GO-BP enrichment analysis for the targets of Radix Sophorae Flavescentis and Rhizoma Heterosmilacis in HCGSC. Radix Sophorae Flavescentis targets in HCGSC were enriched in 1517 GO-BP terms, while Rhizoma Heterosmilacis targets in HCGSC were enriched in 1394 GO-BP terms. A total of 927 joint GO-BP terms were found between them  and steroid biosynthetic process . Regarding the genes of these GO terms, estrone (E1) can protect women from breast cancer, while estradiol (E2) and estriol (E3) may enhance the risk , lipid transport , and lipid localization . Including TRPCs, TPRVs, TRPMs, TRPA1, TRPPs, and TRPMLs .It is worth noting that most of the joint GO-BP terms were closely related to BC, such as the steroid metabolic process , and acetylcholine receptor signaling pathways  of Radix Sophorae Flavescentis in HCGSC. For the remaining GO-BP terms of Rhizoma Heterosmilacis in HCGSC, some steroid hormones, such as vitamin D, may have anticancer properties, while others may favor cancer progression, including estrogens and androgens .Additionally, the other GO-BP terms of targets for tivation , while αtivation . These gndrogens , which aRhizoma Heterosmilacis and Radix Sophorae Flavescentis, we performed the KEGG pathway enrichment analysis for the targets of Radix Sophorae Flavescentis and Rhizoma Heterosmilacis in HCGSC, respectively. Targets of Radix Sophorae Flavescentis in HCGSC were enriched in 55 pathways, while those of Rhizoma Heterosmilacis were enriched in 54 pathways. Thirty-four commonly enriched pathways were found between them , as a metabolic process, and the arachidonic acid (AA) pathway play key roles in carcinogenesis . The PI3K-Akt signaling pathway  plays an important role in the tumorigenesis of breast cancer . Regarding other pathways of Rhizoma Heterosmilacis in HCGSC, the higher alpha-linolenic acid content may reduce the risk of breast cancer, which is related to alpha-linolenic acid metabolism .Additionally, other pathways of targets for pathways , which iRadix Sophorae Flavescentis SC and Rhizoma Heterosmilacis in HCGSC and 25 DEG-enriched pathways. By analyzing these pathways, we found that five pathways overlapped between 34 and 25 pathways, which we considered highly reliable pathways. These include the PI3K-Akt signaling pathway (hsa04151), cAMP signaling pathway (hsa04024), neuroactive ligand–receptor interaction (hsa04080), dopaminergic synapse (hsa04728), and ABC transporters (hsa02010). The PI3K-Akt and cAMP signaling pathways are reportedly related to BC, with a greater number of published studies , and increased testicular AMH output . As the pathogenic factors of BC are related to metabolism, cell survival, proliferation, cell death, and cell cycle  from all the best docking relationships as a highly reliable docking relationship (HRDR). Twenty-two components and 82 proteins coded by 29 genes were identified in HRDR with 1,255 docking relationships. Among the 22 components, 15 components were from in vitro experiments to validate the reliability of our proposed strategy.To evaluate the therapeutic potential of components in HRDR, we designed a mathematical model to calculate the TRI of each component in the HRDR from the HCGSC. To further decode the mechanism of CKI in treating BC comprehensively, we integrated the CDI and SCD to calculate the TRI, which not only considered synergistic effects, but also considered the docking relationship. Detailed information on the TRI is listed in in vitro experiments. The in vitro experiments proved that two of the three core components of HCGSC showed significant inhibitory effects on breast cancer cell proliferation and migration. The effects of luteolin and trifolirhizin on the viability of MCF-7 cells were determined using the MTT assay. As shown in Based on the TRI results, trifolirhizin (KS91), beta-sitosterol (BTL3), and luteolin (KS1) were selected for Breast cancer is one of the most common malignant tumors among women worldwide, with the second highest death rate in female cancer patients . At presRhizoma Heterosmilacis and Radix Sophorae Flavescentis in CKI indicates that Rhizoma Heterosmilacis and Radix Sophorae Flavescentis may act synergistically to exert a therapeutic effect in treating BC. First, we constructed a C-T network and found that most targets with a higher degree were mapped on the joint targets between Rhizoma Heterosmilacis and Radix Sophorae Flavescentis. These results suggest that Rhizoma Heterosmilacis and Radix Sophorae Flavescentis may act synergistically to treat BC via common targets and provide therapeutic targets for the cooperative treatment of BC. However, in the complex C-T network, we know neither the key synergistic components in treating BC nor their synergistic mechanisms.In this study, we examined the synergistic effect of CKI on BC at three levels. At the first level, the target coincidence of Radix Sophorae Flavescentis and Rhizoma Heterosmilacis targets of HCGSC. We found that most of the overlapped GO-BP terms and pathways between them affect BC treatment, and most of them have a lower FDR adjusted p value.To solve this issue, at the second level, we designed an SCD model with the advantages of reflecting the topology character of the C-T network and the component dose, which implemented a semi-quantitative system pharmacology model. Through the analysis of the SCD model, 24 components compose HCGSC, which can contribute to the effects of CKI on BC with a sum of 95.03% and map 80.89% (631/780) of all targets of CKI. Two references were employed to confirm the reliability and accuracy of the HCGSC selection model, including genes and pathways. The targets of HCGSC contained 62 genes, which could cover 88.57% of the first reference, while the HCGSC targets enriched 81 pathways, which could cover 70.59% of the second reference. Third, we performed GO-BP and KEGG enrichment analysis for Rhizoma Heterosmilacis and Radix Sophorae Flavescentis in the treatment of BC using CKI. In the PI3K-Akt and cAMP signaling pathways, Rhizoma Heterosmilacis and Radix Sophorae Flavescentis can activate upstream and downstream targets to play their role in the treatment of BC. These results suggest that CKI can produce a combined effect on BC. Second, we designed a TRI model, which provides the results of SCD and docking, to comprehensively evaluate the synergy contribution rate of the components in HCGSC and the binding ability of the components and proteins. TRI calculation results showed that four components displayed a TRI score higher than 0.75, including matrine , trifolirhizin , beta-sitosterol , and luteolin , which may be the core components in the treatment of BC with synergistic mechanisms. Third, MTT and Transwell assays were employed to determine the function of luteolin and trifolirhizin. The results showed that they markedly inhibited the proliferation and migration of MCF-7 cells.At the third level, to further understand the synergistic mechanisms of CKI and infer the function of synergistic components and verify their effectiveness, three aspects were employed. First, a comprehensive pathway was constructed to explore the synergetic mechanism of In this study, we proposed two novel mathematical models for the speculation of synergistic components and mechanisms. The method provided a methodological reference for decoding the synergistic mechanism of TCM in treating complex diseases. However, several limitations must be mentioned. 1) The precise synergistic mechanisms based on the calculation predictions warrant further validation. 2) Because the interaction between herbs is extremely complex, it may be incomplete to explore the synergistic mechanisms based on our model. Multiple synergistic effects combining with other techniques should be further considered. 3) The TRI model is proposed based on network topology methodology, which is more suitable for the case that herbs have a complex network. This algorithm used undirected network, which ignores the activation or inhibition effects of the disease targets.


Although data mining methods are extensively used in the rule analysis of famous old traditional Chinese medicine (TCM) experts' prescriptions for the treatment of hypertension, most of them only mine the association between herbs and herbs, ignoring the importance of symptoms in the disease. This study collected 439 cases of hypertension treated by famous old TCM experts from the FangNet platform. Using the structure network algorithm, the symptom-herb network was constructed, which redefined the importance of herb in disease. Based on the network, 21 driver herbs, 76 herb pairs, and 41 symptom-herb associations were mined. Finally, the basic prescription composed of Gouteng (Uncariae Ramulus cum Uncis), Huanglian (Coptidis Rhizoma), Chuanxiong (Chuanxiong Rhizoma), Gegen (Puerariae Lobatae Radix), Danggui (Angelicae Sinensis Radix), and Huangqin (Scutellariae Radix) was found. These herbs are the most significant among all herbs, and they have a potential correlation with each other. To further verify the rationality of the data mining results, we adopted the network pharmacology method. Network pharmacological analysis shows that the five core targets in the basic prescription include IL6, VEGFA, TNF, TP53, and EGF, which link 10 significant active compounds and 7 important KEGG pathways. It was predicted that anti-inflammatory, antioxidant, vascular endothelial protection, emotion regulation, and ion channel intervention might be the main mechanisms of the basic prescription against hypertension. This study reveals the prescription rule of famous old TCM experts for treating hypertension from a new perspective, which provides a new approach to inherit the academic experience of famous old TCM experts and develop new drugs. As a worldwide public health problem, the incidence rate and mortality rate of hypertension are increasing year after year . It is eβ blockers, calcium channel blockers, angiotensin-converting enzyme inhibitors, and angiotensin II receptor blockers [At present, based on the different pathological mechanism of hypertension, its therapeutic drugs mainly include diuretics, blockers . Althougblockers –9. Thereblockers .TCM has been treating hypertension for more than 2000 years in China. TCM has a unique understanding of hypertension due to long-term clinical practice. In TCM theory, hypertension mainly falls into the category of “dizziness” and “headache.” Herb is one of the essential means of TCM against hypertension, which has the characteristics of multitarget, multicomponent, and multipathway for treating diseases. Herb treatment of hypertension has the advantages of synergistic lowering of blood pressure, improving symptoms, improving metabolism, and protecting target organs . It has In the clinical practice of TCM, the formula composed of at least two or more herbs is an important form in which herbs can play a therapeutic effect . These fCurrently, data mining methods such as association rules and cluster analysis are widely used in the field of TCM, including condition, diagnosis, and formula, which makes the hidden information in cases interpreted at multiple levels . Based oIn this study, we used data mining methods based on structure network algorithm with network pharmacology technology to mine and preliminarily verify the potential information in the treatment of hypertension cases by famous old TCM experts, which may be an efficient way to inherit the academic experience of famous old TCM experts and develop new drugs. The workflow of this paper is illustrated in https://fangnet.org), which is a platform for collecting the medication experience of famous old TCM experts. A new collection named “Hypertension” was established in the prescription collection module. In the prescription module, we can search for cases with a disease named hypertension and select them to copy to the new collection. A total of 439 relevant cases were finally retrieved.Related cases were collected from FangNet  represents the symptom node, V (hb) represents the herb node, W  represents the edge weights between herb and herb, W  represents the edge weights between symptom and herb, sa represents symptom a, hb represents herb b, hc represents herb c, N represents the number of total prescription, Px (hb) represents the prescriptions including the herb b, Px (hc) represents the prescriptions including the herb c, and Px (sa) represents the prescriptions including symptom a.Two types of nodes in the network, namely herb and symptom, are defined by formulas  and 2)..2). In oThe PageRank (PR) algorithm has been widely used to identify significant nodes in the network –22. The The herb-herb co-occurrence level and mutual exclusivity level were obtained by Fisher's test analysis, based on the herb-herb interaction network. A total of 9 levels were defined, including −1∼4, in which the positive value stood for the co-occurrence level and the higher the value, the more significant the level; while the negative value stood for the mutual exclusivity level and the smaller the value, the more significant the level; and 0 stood for insignificance. The higher co-occurrence level indicates more likely potential interactions between herbs. The analysis results can be visualized through triangular heat maps.Highly correlated relationships of specific symptoms to herbs were mined based on the edge weights of the symptom-herb network as well as co-occurrence events. We obtained the correlation between herbs and specific symptoms by setting the herb support degree to 0.1∼0.6 and the interaction weight to ≥0.6.https://tcmspw.com/tcmsp.php). The candidate compounds were further screened under the conditions of oral bioavailability (OB) ≥30% and drug-like characteristics (DL) ≥0.18 and then transformed into corresponding gene names with the help of UniProt database (https://www.unitprot.org/).Based on the importance ranking of herbs and co-occurrence level of herbs, we finally obtained a basic prescription consisting of 6 herbs. We searched all the active compounds of these herbs in the TCM systematic pharmacology analysis platform . Obtained targets with disease were normalized to gene names in the UniProt database.Hypertension was searched as a keyword to obtain protein targets related to hypertension in the GeneCards database . All the node information obtained was then imported into Cytoscape 3.6.1 software to determine the core targets based on the degree value. Meanwhile, the main protein functional modules of the PPI network were analyzed through the built-in molecular complex detection (MCODE) plug-in.According to the common targets between herbs and diseases, the protein-protein interaction (PPI) network was constructed with the assistance of STRING 11.0 database (https://david.ncifcrf.gov/), Gene Ontology (GO) function enrichment analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on target proteins.With the help of the Database for Annotation Visualization and Integrated Discovery . The PDB format of core target proteins was obtained by searching the RCSB PDB database (https://www.rcsb.org/). To optimize the structures of the core target proteins and avoid the water molecules in them from interfering with the molecular docking results, water molecules of the core target proteins were removed and the original ligands were isolated from the core target proteins with the aid of PyMOL 2.4 software. The targets after hydrogenation, charge calculations, and atomic type setting were saved in the pdbqt format using AutoDock Tools 1.5.6 software. Finally, we ran AutoDock Vina 1.1.2 for molecular docking simulation and visualized the results.The significant active compounds in the basic prescription were determined by the degree value in the herb-compound-target-disease network, and the top ten compounds whose degree value exceeded the median were selected as the significant ingredients. For initial validation, we performed molecular docking of core target proteins with important active ingredients and control drugs . MOL2 files of the important active ingredients and control drugs were searched and downloaded from the ZINC database (Scutellariae Radix). Furthermore, the Latin names, medicinal parts, and efficacy classifications of driver herbs are shown in 158 herbs were contained in 439 prescriptions, of which 21 were driver herbs and 137 were passenger herbs. The weight of interactions between herbs was set to ≥0.15, and the interaction relationships were represented in the network in the form of edges, resulting in 344 edges for 158 herbs, as shown in The triangular heat maps of the co-occurrence and mutual exclusion levels of 47 herbs with frequencies over 0.05 were obtained and are shown in Based on interaction weight ≥0.6 and herbal support degree between 0.1 and 0.6, 41 significant herbs-to-specific-symptoms associations were obtained and visualized in a circular layout, as shown in Based on the herb importance ranking , we founA total of 86 candidate active compounds were collected from the TCMSP database after removing duplicate compounds according to the set screening conditions, as detailed in Supplementary 8904 disease targets were obtained from the GeneCards database. Using the UniProt database, herb ingredients and disease targets were assigned standardized human gene names. Then, 135 intersection targets of herbs and diseases were found out with the help of the Venn diagram, as shown in The herb-compound-target-disease network was visualized with Cytoscape 3.6.1 software, as shown in The intersection target genes were analyzed using the STRING database to construct a PPI network to explore the mechanism of the basic prescription in treating hypertension. After removing free targets, the PPI network with 134 targets and 1409 edges was obtained and visualized in Cytoscape 3.6.1 software, as shown in P < 0.05 and FDR ＜0.05. The top 10 ranked results according to P values are shown in In order to understand the functional role of effective compound targets in the basic prescription more macroscopically and comprehensively, we enriched and analyzed all intersection targets in the DAVID database. The results of GO enrichment analysis included 151 biological processes (BPs), 28 cell components (CCs), and 42 molecular functions (MFs) with a threshold value of P < 0.05 and FDR ＜0.05, including neuroactive ligand-receptor interaction, calcium signaling pathway, HIF-1 signaling pathway, TNF signaling pathway, cGMP-PKG signaling pathway, cAMP signaling pathway, and PI3K-Akt signaling pathway. The first 15 results of KEGG signal enrichment analysis are shown in KEGG pathway analysis obtained 101 pathways with a threshold value of With the in-depth study of systems biology, we found that proteins mostly participate in a common process in the form of protein clusters. Therefore, it was still necessary to further cluster analyze all targets. We used the MCODE plug-in to analyze the PPI network. 7 meaningful protein clusters are shown in In order to better understand the functions of significant modules and the physiological processes involved, we carried out GO function enrichment analysis and KEGG pathway enrichment analysis for modules with score >4 and carried out visual analysis, as shown in We selected the top five target proteins  in Diseases, symptoms, and herbs are obviously related to each other, rather than independent of each other . PreviouBased on the symptom-herb network, three categories of information were obtained. (1) 27 driver herbs: in fact, these herbs have been found to reduce blood pressure . The claβ2-adrenoceptor, which reduces blood pressure by mediating peripheral vasodilation [Network pharmacology preliminarily verified the possible pharmacological mechanism of the basic prescription in the treatment of hypertension. Based on the herb-compound-target-disease network, the important active compounds were extracted, which were mainly divided into three categories: flavonoids , phytosterols , and alkaloids -canadine, hirsutine, hirsuteine). Multiple studies have confirmed their beneficial effects on cardiovascular diseases –36. Querdilation . Yang etdilation . Huang edilation .α levels can reduce systolic blood pressure and relieve left ventricular hypertrophy and activate the Akt/eNOS pathway to improve vascular endothelial function [+ absorption. It is considered to be a key molecular substrate leading to salt-sensitive hypertension [Through PPI network analysis, we found five core targets: IL6, VEGFA, TNF, TP53, and EGF, which may play a more important role in the disease. Chronic inflammation, as well as vascular endothelial dysfunction, is closely associated with the occurrence and progression of hypertension and has also increasingly become a research focus of the mechanism against hypertension in recent years. The study has demonstrated that IL6 and TNF, as inflammatory factors, are independent risk factors for hypertension . IL6 canfunction . VEGFA ifunction . Clinicafunction . EGF is rtension . Moreovertension . Feng etrtension .α may mediate vascular damage in hypertension by inhibiting endothelial cells [α is regulated by the PI3K/Akt signaling pathway [α expression is able to protect cardiomyocytes and promote angiogenesis [In KEGG pathway analysis, 7 significant pathways closely related to the pathogenesis or treatment of hypertension were found: neuroactive ligand-receptor interaction, calcium signaling pathway, HIF-1 signaling pathway, TNF signaling pathway, cGMP-PKG signaling pathway, cAMP signaling pathway, and PI3K-Akt signaling pathway. Neuroactive ligand-receptor interaction pathways are involved in the regulation of physiological rhythms, endocrine system, mood, learning, and memory . Endocrial cells . Insulinal cells , 59. Preal cells , but thial cells . Further pathway . Upregulogenesis . As ubiqogenesis . The cGMogenesis . It has ogenesis . cAMP alogenesis .Treatise on Cold Damage Disease. In recent years, there are more and more studies on its treatment of hypertension on China Knowledge Network (https://www.cnki.net/), which further confirms the credibility of the results. However, this study also has obvious limitations: (1) large sample data are the basis of mining potential information. Due to the limitation of the amount of data, the results may be biased; (2) dose is the key to the curative effect of TCM. Although the dose standard deviation of herbs is involved in In addition, Gegen, Huanglian, and Huangqin in the basic prescription are the components of Gegen Qinlian decoction, a classic prescription from Based on the symptom-herb network, we redefined the important role of herbs in diseases, mined 21 driver herbs, 76 pairs of highly co-occurrence herbs, and 41 herb-symptom associations. Among the driver herbs, the top six are Gouteng, Huanglian, Chuanxiong, Huangqin, and Danggui. In the co-occurrence level of herb pairs, the top 10 significant herb pairs are Huanglian-Gouteng, Sanqi-Yanhusuo, Huanglian-Huangqin, Chuanxiong-Gegen, Gouteng-Gegen, Wuweizi-Maidong, Gouteng-Danggui, Danggui-Chuanxiong, Huangqi-Maidong, and Gouteng-Huangqin. Based on the above results, we summarized the important herb combination composed of Gouteng, Huanglian, Chuanxiong, Gegen, Huangqin, and Danggui as the basic prescription. The basic prescription is involved in anti-inflammation, antioxidation, and protection of vascular endothelium to exert therapeutic effects on hypertension through 10 significant active compounds, 5 core targets, and 7 important pathways. The results indicate that the method adopted in this study can provide an accurate analysis of the prescribing rules of clinicians treating hypertension and effectively mine important herb combinations.


Vascular dementia (VD), a cerebrovascular disease which causes cognitive impairment, is one of the significant factors that affects the quality of senectitude. Atherosclerosis (AS) is a chronic inflammatory syndrome and closely associated with VD. Analyzing the role of AS in VD contribute greatly to its early detection and prevention, but their relationship has not been integrated into a complete network. This review summarizes AS biomarkers as VD predictors for the first time and describes the direct mechanisms of AS causing VD from five aspects: vascular morphogenesis, hemodynamic change, neurovascular unit damage (NVU), oxidative stress, and microRNA (miRNA). Finally, it discriminates the relationship between AS and VD in common risk factors which can be disease or some molecules. In particular, these data imply that the role of AS in VD is not only a pathogenic factor but also a comorbidity in VD. This review aims to bring new ideas for the prediction and treatment of VD. Atherosclerosis (AS) is a chronic inflammation, and its lesions are often located in large or medium-sized arteries. It is characterized by the accumulation of low-density lipoproteins (LDLs), together with oxidized lipoproteins and cholesterols. The LDLs are engulfed by phagocytes to form foam cells. Subsequent recruited inflammatory cells exacerbate the AS progress . In the Vascular dementia (VD) is a type of cognitive impairment contributed by cerebrovascular disease, whose clinical manifestations are memory decline, early gait disorder, incontinence with mood , and personality changes . The WHOAccording to an experiment by Chen et al., vegetarians with a lower risk of AS also had a lower incidence of cognitive impairment , which rIn this review, we address the predictive role of AS in VD, followed by summarizing two existing hypotheses about the role of AS in VD, before ending with perspectives for future research.Midlife vascular disease plays a crucial part in the subsequent development of dementia . Hence, Intima media thickness (IMT) is a pivotal AS biomarker which is measured by ultrasonography. Higher midlife IMT has prominent relevance to VD and a strong relationship with increased risk of dementia . So IMT When using AS biomarkers for prediction, clinicians should consider multiple parameters, and combine more than one biomarker to improve the accuracy of VD prediction. According to AS progress, different AS biomarkers can be selected for detection. For example, FDM can be used to predict in the early stage of AS, while microalbuminuria can be used in the later stage . HoweverMiddle-aged vascular risk factors affect cognitive function and dementia in later life through different mechanisms. Ample evidence suggests that AS may play a direct role in the progress of VD through a complex network . AS can The changes in vascular morphology included stenosis, occlusion, and intima-media thickening, which are all characteristics of AS . OcclusiAS alters stable blood flow pattern, which triggers chronic hypoperfusion . When th2) is also damaged. CO2 controls the increase and decrease of CBF by controlling the contraction and relaxation of cerebral blood vessels under normal conditions  and extracranial atherosclerosis (ECAS). The lesions of ICAS occur in the arteries surrounded by the skull and dura mater or located in the subarachnoid space . This meIn addition to the fact that AS is directly associated with VD through its mechanisms, the two can be linked by stroke as a mediator. Because many reports only investigate the relationship between AS and stroke, and stroke can directly lead to VD, this paper describes this situation separately . Stroke AS and stroke have a genetic correlation . For exaMCP-1 is a pro-inflammatory factor that plays a significant role in AS and stroke. Its obligation in AS is to recruit monocytes into the nidus. Therefore, the increased level of this factor will aggravate the condition of AS. Research shows MCP-1 level increase for genetic factors compounds the risk of stroke. It should be emphasized that MCP-1 levels are associated with the risk of ischemic stroke, not hemorrhagic stroke. This is consistent with the atherogenic characteristics of stroke . This meThe occurrence of disease is not caused by a single factor, but by the combination of multiple factors in most cases, which is determined by the diversity of pathogenesis. Therefore, as the common risk factors of AS and VD, it may play a predominant or auxiliary role in the relationship between them. In parallel, the role of AS in VD is double status, which means it can promote VD, directly or indirectly, and can be a co-victim of VD. The common risk factors can be a disease or hereditary factor. Since the pathogenesis of AS and VD overlaps to a certain extent, it is undeniable that among these common risk factors, AS and VD may have a potential causality correlation. In the future, more studies are needed to prove whether these common factors independently lead to AS and VD. By summarizing common risk factors, more associations between AS and VD can be found, which can provide more evidence for clinicians’ diagnosis.There is vascular comparability between kidney and brain, so the pathogenesis of kidney disease and VD is similar . VD is tThe gut microbiota (GM) communicates with the brain via hormones, nerves, and the immune system. It is an important component of the gut-brain axis, and studies have shown that GM can ameliorate the disease development of VD, which provides theoretical foundations for the correlation between GM and VD . There iApolipoprotein E (ApoE) ɜ4 is an allele and produces ApoE. It is a plasma protein involved in lipid metabolism and cholesterol transport . CarrierThis review attempts to identify the role of AS in VD, confirms its irreplaceable role in VD, and provides theoretical support for the prediction of VD in the future. The biomarkers of AS were summarized and discussed for the first time, which demonstrates that it makes a significant contribution to the prediction of VD and provides a reference for clinicians to select the best VD predictor at various stages of AS development. Then, according to the direct-action mechanisms of AS on VD, we analyze them from five pathways: vascular morphogenesis, hemodynamic changes, NVU damage, oxidative stress, and miRNA. These deleterious mechanisms synergistically cause the onset of VD pathogenesis. The last topic, which explores the common risk factors of AS and VD, is intricate and attempts to find out other roles of AS in VD. It is concluded that, in addition to being a pathogenic factor, AS can be a comorbidity of VD. To determine the causality between AS and VD, large-scale, fully equipped clinical trials and repeated measurements over longer follow-up periods will be necessary in the future.Although momentous progress has been made in the relationship between AS and VD so far, there are many deficiencies. For example, the starting point about chronic diseases is still misty, with the coincidence degree of pathogenic mechanism too high between AS and other diseases among the common risk factors, which increases difficulties in the research of the relationship, and the fuzziness of causal relationship may cause biased results. Research about genes may be the emphasis in the future owing to the development of precision medicine and the enhancement of personalization.In conclusion, identifying the role of AS in VD deeply may not only reveal new therapeutic targets but also improve risk stratification in VD prevention. The accurate prediction of high-risk groups can also greatly save social resources and reduce panic among the population.Y-TH designed and conceptualized the study and drafted the manuscript for intellectual content. S-LY played a major role in the acquisition of date, provided funds, and revised the manuscript for intellectual content. F-FH revised the manuscript for intellectual content and provided funds.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The collection of expression quantitative trait loci (eQTLs) is an important resource to study complex traits through understanding where and how transcriptional regulations are controlled by genetic variations in the non-coding regions of the genome. Previous studies have focused on associating eQTLs with traits to identify the roles of trait-related eQTLs and their corresponding target genes involved in trait determination. Since most genes function as a part of pathways in a systematic manner, it is crucial to explore the pathways’ involvements in complex traits to test potentially novel hypotheses and to reveal underlying mechanisms of disease pathogenesis. In this study, we expanded and applied loci2path software to perform large-scale eQTLs enrichment  analysis at pathway level to identify the tissue-specific enriched pathways within trait-related genomic intervals. By utilizing 13,791,909 eQTLs cataloged in the Genotype-Tissue Expression (GTEx) V8 data for 49 tissue types, 2,893 pathway sets reported from MSigDB, and query regions derived from the Phenotype-Genotype Integrator (PheGenI) catalog, we identified intriguing biological pathways that are likely to be involved in ten traits . Furthermore, we extracted the most significant pathways for AD, such as BioCarta D4-GDI pathway and WikiPathways sulfation biotransformation reaction and viral acute myocarditis pathways, to study specific genes within pathways. Our data presented new hypotheses in AD pathogenesis supported by previous studies, like the increased level of caspase-3 in the amygdala that cleaves GDP dissociation inhibitor and binds to beta-amyloid, leading to increased apoptosis and neuronal loss. Our findings also revealed potential pathogenesis mechanisms for PD, schizophrenia, NSCLC, blood pressure, autism spectrum disorder, and myocardial infarction, which were consistent with past studies. Our results indicated that loci2path′s eQTLs enrichment test was valuable in unveiling novel biological mechanisms of complex traits. The discovered mechanisms of disease pathogenesis and traits require further in-depth analysis and experimental validation. Expression quantitative trait loci (eQTLs) have been one of the major focuses in determining the genetic variants that affect gene expressions locating in non-coding regions of the genome. eQTLs’ nature of influencing expression levels of their target genes (eGenes) makes them powerful at studying transcription regulation . The traResearchers often use eQTLs associations to link expression traits to genotypes of genetic variants located in genomic intervals. Multiple studies have been conducted on connecting eQTLs and various traits including Alzheimer’s disease (AD) to determine the roles trait-related eQTLs and their corresponding eGenes play in pathogenesis . Though The objective of this study was to perform large-scale eQTLs enrichment tests at the pathway level and determine the tissue-specific enriched pathways for trait-related genomic intervals based on the Bioconductor package loci2path . There aIn this study, we used the computer program loci2path to perform eQTLs enrichment analysis for genomic regions of ten traits . We have updated the loci2path to utilize the most current data sets of query regions, eQTLs sets, and pathway sets. We used the entire multi-tissue eQTLs data from the GTEx V8 data release that contains 13,791,909 eQTLs with 32,958 unique eGenes for 49 tissue types. In addition to BioCarta and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway sets that were included in the original loci2path , we havep-values calculated using Fisher’s exact test for an eQTLs set could be computed for each pathway to evaluate the enrichment significance, and those pathways with greater enrichments were indicated by smaller p-values. The results were filtered with a p-value of 10−4, which was chosen after multiple trials to balance the number of most significant tissue-pathway combinations and specificity, and used to construct heatmaps for further analysis. We have tried other p-values and obtained similar outcomes.In this study, we extended the Bioconductor package loci2path  that runhttps://storage.googleapis.com/gtex_analysis_v8/multi_tissue_qtl_data/GTEx_Analysis_v8.metasoft.txt.gz. eQTLs sets for each tissue were filtered with a p-value threshold of 10−4. Each gene’s entrez ID and gene name were obtained by using the given gene’s ensemble gene ID and the Bioconductor package biomaRt.For this study, we used the full set of multi-tissue QTL data from the GTEx V8 data release as the input data of eQTLs sets, consisting of 49 tissue types . The dathttp://www.gsea-msigdb.org/gsea/msigdb/collections.jsp.A total of 2,893 pathways from BioCarta, KEGG, PID, Reactome, and WikiPathways gene sets were used in this study as the input data of gene sets. The data were downloaded from the MSigDB website: https://www.ncbi.nlm.nih.gov/gap/phegeni. For a given genetic variant, the genomic region is defined as a flanking 50 kb on each of left and right sides of that variant, which spans 100 kb. Overlapped regions were merged. A total of 9,894 genomic intervals were used in this study, and the numbers of genomic regions for each trait are demonstrated in The list of known trait-associated variants was obtained from National Center for Biotechnology Information (NCBI) via PheGenI website: p-values. We used loci2path to conduct eQTLs enrichment analyses by computing the p-values of Fisher’s exact test adjusted by Benjamini & Hochberg correction method  gene  gene . The D4-Similarly, One key sign of PD is the accumulation of α-synuclein and the formation of Lewy bodies in brainstem, limbic system, and cortical areas . PatholoAs demonstrated in the HLA-C) gene  gene, the amino acid methionine at the 99th position of HLA-C may contribute to individuals’ susceptibility to schizophrenia, in which the glutamic acid in HLA-DRB1 has a protective function against the disease  gene was also shared by most tissues with such a pathway, a molecule that presents peptides derived from extracellular proteins and is expressed in antigen expression cells  gene ; Table 3ophrenia . One stu disease . Intereson cells . No studFOXO1 and FOXO3A were significantly lower in patients with acute schizophrenia  holoenzyme, which plays an essential role in regulating a diverse array of myocyte functions through dephosphorylation of target molecules. Functioning as an important phosphatase, the PP2A holoenzyme is critical for serving as a regulatory module within the heart, such that dysregulation of PP2A function may contribute to cardiac diseases. Alterations in PP2A activity are associated with heart failure and arrhythmia (Furthermore, the AT1R pathway is responsible for promoting hypertension, G protein-dependent signaling, transactivation of growth factor receptors, NADPH oxidase, and ROS signaling explaining why the  pathway . In addirhythmia . The varrhythmia . While tHLA-C, HLA-DRB1, and HLA-DQB1 is likely to take part in schizophrenia’s pathogenesis as well.We have extended the loci2path  by usingOur study has extensively evaluated multiple gene pathways’ involvements in the ten traits and further investigated significant genes in each pathway that were hit in the given genomic query regions. The proposed hypotheses have opened new avenues to explore the underlying molecular mechanisms and thus could illuminate further investigations on these traits. We have also found many interesting associations between eQTLs and gene pathways at trait-associated variants of NSCLC, blood pressure, autism spectrum disorder, and myocardial infarction which provided valuable insights into our comprehensive understandings of them. Furthermore, our study has confirmed the advantages of using tissue-specific eQTLs enrichment analysis at pathway level, because our findings based on loci2path software were strongly supported by multiple previous studies . This haThere were several limitations in our study. Due to the nature of the statistical analysis, our findings from loci2path could not be considered as providing direct understandings of biological mechanisms underpinning these traits, and we were only able to generate hypotheses for trait determination. These hypotheses should be experimentally verified by conducting further in-depth functional studies by molecular biology laboratories. In addition, loci2path′s reliance on current eQTLs sets data from GTEx could also lead to biased results since the eQTLs sets data from brain tissues were significantly smaller than other tissues like tibial nerves, leg skin without sun exposure, and thyroid. This was caused by the limited sample sizes of brain tissues from GTEx, which may result in missing important biological pathways in brain tissues for neurodegenerative diseases due to inadequate statistical power. The imbalance of eQTLs sizes of various tissues could also bring false-positive results in tissues with more samples and generate coincidental enrichment of certain pathways at tissues not related to the traits. Therefore, results from loci2path need to be treated with extra care, and only the most significant tissue-pathway associations should be extracted for analysis with sufficient past evidence. The software itself also has rooms for improvement, like including new gene pathway sets and adding annotations on pathways uniquely enriched in a tissue.Future studies on neurodegenerative diseases specifically should implement more data on brain tissues to increase the accuracy of loci2path. Other neurodegenerative diseases like bipolar disorder and attention deficit disorder could be added for a systematic analysis on their patterns to find potential patterns for commonality among this type of disease.


Food frequency questionnaire (FFQ) is an important method for the estimation of dietary intake in epidemiologic studies. The aim of the study is to develop a FFQ and evaluate its relative validity for adolescents 12 to 18 years old.FFQ was developed from a previously validated youth/adolescent diet questionnaire (YAQ) by modifying it in order to include Croatian national foods. The final version of the FFQ (FFQ-m) comprised 87 food items. The reference method was a set of two 3-day food records (3DFR) administered twice during the 3 non-consecutive days, one month apart. The FFQ-m was administered approximately on the last day of the second applied dietary food record. Adolescents were recruited from randomly selected elementary and high schools in urban and rural areas of Croatia. FFQ-m was validated on a sample of 84 adolescents . Nutritional intake from FFQ-m and 3DFR were analysed for each participant. Spearman correlation coefficients (r) and Bland-Altman method were used to assess the validity of the FFQ-m compared to 3DFR. Anthropometric parameters were assessed in 78 adolescents.The mean nutrient intake estimated by the FFQ-m was higher than that of the 3DFR. The average correlation coefficient for energy and nutrients in our validation study was 0.40. On average, 76.5% of adolescents were classified in the same or adjacent quartile of the nutrient intake. Bland-Altman analysis showed good agreement with all macronutrients and some micronutrients . A simple self-administered questionnaire completed by adolescents is a valid tool for measuring energy and nutrient intake among adolescent population.This is the first FFQ developed and validated for population of adolescents in Croatia. It will contribute to further research of nutritional intake in the population of adolescents, especially those from the region. A set of two 3-day food records (3DFR) was applied twice during 3 non-consecutive days (including one day of the weekend). The time interval between each dietary food record was approximately one month. The FFQ-m was administered in elementary or high school approximately on the last day of the second applied 3DFR (approximately one month after the first 3DFR).FFQ-m and 3DFR were distributed and anthropometric parameters were assessed in randomly selected elementary and high schools in urban and rural areas in Croatia. Each participant was required to fill out an FFQ-m and two 3DFRs. Both 3DFRs were distributed in schools at the same time. Participants were instructed to fill out one 3DFR shorty after its distribution, and the second after approximately one month. A month after the distribution of the two 3DFRs, FFQ-m was administered on site (at school), so that the interviewer (a physician) was available to provide assistance and clarification of the questionable responses. The 3DFRs were self-administered by adolescents who were 13 years old or older. For children who were 12 years old, data were collected with the help of their parents. Participants or their parents were instructed to enter all consumed foods and drinks in the 3DFR during the 3 non-consecutive days (including one day of the weekend). They were instructed to use kitchen scales to measure the amounts, but when that was not possible, they were allowed to use household measures such as cups, tablespoons and teaspoons. All household measures were converted into grams by the dietitian.The study included healthy adolescents aged 12 to 18, recruited from randomly selected elementary and high schools in urban and rural areas in Croatia who responded positively to the invitation to participate in the study. Permission for the study was obtained from appropriate authorities, parents were informed about the survey by the school principals, and their written consent was obtained. Study was approved by Ethics Committee of the Children’s Hospital Zagreb (IRB number: 21102014).Recruitment was carried out until a total of 100 included participants (71 girls and 29 boys) had not been reached. Exclusion criteria were chronic illness or family history positive for chronic intestinal diseases  and the use of elimination diets. Furthermore, participants who did not fill out correctly and provided all the questionnaires, or whose energy intake estimated by the FFQ-m was lower than 500 kcal (2092 kJ) or higher than 4000 kcal (16736 kJ) were excluded from the analysis. As proposed by Willet (i) milk and milk products, (ii) cereals and grains, (iii) juices and sodas, (iv) fruits, (v) vegetables, (vi) snacks, (vii) meat, poultry, fish, eggs and fat, and (viii) fast food. Available frequencies of food consumption were: never, 1-3 times a month, once a week, 2-4 times a week, 5-6 times a week, once a day, 2-3 times a day, 4-5 times a day or 6+ times a day. Available portion sizes were small, medium and large, and participants were able to distinguish their usual portion size using three portion size photos   v. 3.01 (BLS 3.01) database , body height (BH), body mass index (BMI) and BM-for-age, BH-for-age and BMI-for-age. BM was measured on an electronic scale , with subjects wearing light-weight gym clothes. BH was measured with a portable stadiometer . Nutritional status of participants was determined using World Health Organization (WHO) growth reference data for children and adolescents (5-19 years)  were used to evaluate the validity of the FFQ-m against the two 3DFR. Comparisons of nutrient intakes estimated by the FFQ-m and 3DFR records were made using Wilcoxon signed-rank test. The energy and nutrient intakes estimated by the FFQ-m and 3DFR were categorized into quartiles . The proportion of subjects categorized into the same, adjacent or opposite quartile was estimated. The average correlation coefficient for all nutrients, as well as macronutrients and micronutrients separately, was calculated as a sum of the correlation coefficient divided by the number of items (nutrients). Additionally, Bland-Altman analysis . To our knowledge, this is the first FFQ developed and validated for Croatian adolescent population. FFQs are low-cost, fast and easy to administer tools, and therefore commonly used in epidemiologic studies  and its validation study showed that the modified FFQ (FFQ-m) is a reliable tool to estimate the relative intake of energy, macronutrients and some micronutrients by adolescents in Croatia. It is affordable, quick and, most importantly, has a small respondent burden, which is particularly important for adolescent population. We recommend FFQ-m to be interview-administered, since adolescents are prone to overestimating the frequency and portion sizes of the consumed foods. Nevertheless, FFQ-m could further be adapted and validated for younger children or adults.