Currently, there are many effective pharmacological treatments for generalized anxiety disorder (GAD), formulated herbal granule is also an alternative way. Our research intends to construct a pharmacological network on genetic targets and pathways between Jiu Wei Zhen Xin Formula (JWZXF) and GAD. Through the TCMSP database, we collected the active ingredients of JWZXF and potential targets of the active ingredients. The GAD-related proteins collected from GeneCards database and DisGeNET database were combined. Component-target protein networks were constructed and visualized using Cytoscape 3.8.2 software to comprehensively clarify the relationships between ingredients, components, and targets. The intersection targets were imported into the STRING database, and the protein-protein interaction (PPI) network was constructed. We constructed and analyzed the visualized “drug-target-disease” network. Gene Ontology (GO) enrichment together with Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis were conducted on the common target through R language. Forty-one effective components and 106 potential targets of JWZXF were found. There were top ten hub genes and multiple important signaling pathways involved in the treatment of GAD with the JWZXF. This study expounded the pharmacological actions and molecular mechanisms of the JWZXF in treating GAD from a holistic perspective. The potential pharmacological effects of the JWZXF are closely related to regulation because not only does it comprehensively analyze the possible mechanism of JWZXF treatment of GAD but it can also facilitate further in-depth research and provide a theoretical basis for the clinical expansion of its application. Generalized anxiety disorder (GAD) is a chronic mental disorder characterized by excessive tension, worry, and feelings of apprehension that last at least six months. The term GAD was first used in ICD-9; it got the attention of clinical researchers with the publication of DSM-III . Later, Panax ginseng C. A. Mey (P.G.), Ziziphi Spinosae Semen (Z.S.S.), Schisandrae chinensis Fructus (S.C.F.), Poria cocos Wolf (P.C.W.), Polygala tenuifolia Willd (P.T.W.), Corydalis Rhizoma (C.R.), Asparagi radix (A.R.), Rehmanniae radix Praeparata (R.R.P.), and Cinnanmomi cortex (C.C.). The total component list of each ingredient was identified from the TCMSP [1]  database and TCM-ID (TCM-information database https://bidd.group/TCMID/index.html); due to the lack of component information of P.T.W in TCMSP database, we inquired and listed P.T.W component information from TCM-ID. Subsequently, we identified the ADME  properties from the TCMSP database based on the candidate components' information. Currently, drug-likeness (DL) evaluation  is integrated into computational drug design/discovery pipelines. In this study, four main filtering criteria have been implemented in our research and used to screen components that could be involved in the central nervous regulation.The components of interest regarding the JWZXF were extracted based on their botanicals, which are https://www.uniprot.org) was used to convert and calibrate protein names into gene official symbols that are potential genetic targets of the JWZXF.With a DL value higher than 0.18, oral bioavailability value higher than 30%, half-life time longer than four hours, and blood-brain barrier (BBB) penetration rate >0.3 can be retained as candidate components for subsequent analysis . Finallyhttps://www.genecards.org/) filtered from its highest “relevance score” to the third quartile that retrieved 1338 items and DisGeNET  filtered by its “Score_gda” needed to be greater than and equal to 0.8 to help identify 1594 items. The GAD-related proteins collected from the GeneCards and the DisGeNET databases were combined to finally obtain 1977 related genetic targets (without duplication), after using the UniProt database to convert and calibrate gene names to official gene symbols, which are the potential genetic targets of GAD.With “generalized anxiety disorder” as the keyword, from the databases, GeneCards  database, with the “species” set as “Homo sapiens.” Under the condition that the lowest interaction score was equal to 0.400, the TSV result file of protein to protein interactions was obtained, and the PPI network was constructed using the Cytoscape 3.8.2 software. The topological attributes of the PPI network were analyzed, and the value (degree) was calculated, representing the number of connected nodes.The intersection targets were imported into STRING (version 11.5 K-core = 2 and node score cutoff = 0.2). After extracting the subclusters of PPI, we use the DAVID  database to analyze those subcluster interactions and reveal the biological processes of each cluster.Based on the PPI network, we can use the Cytoscape plug-in to extract subnetwork and hub genes. Cytoscape MCODE plug-in based on the K-core algorithm can be used to find clusters (highly interconnected regions) in a network  algorithm to screen hub genes. MCC algorithm integrates 11 topology analysis methods and six centrality analysis methods, which produced high accuracy.KEGG is a database that systematically analyzes the metabolic pathways and functions of gene products in cells. The KEGG database helps study genes and express information as a complete network by integrating data from genomes, chemical molecules, and biochemical systems, including metabolic pathways, drugs, diseases, gene sequences, and genomes. We selected the DAVID online database to implement the KEGG enrichment analysis.P-value ≤0.05 was set as the initial selection threshold. KEGG pathway enrichment analysis was realized, and the ascending order was sorted following the FDR to screen for the top 12 information pathways (ggplot2 package of R).The intersection genes were introduced into the DAVID database, the “select identifier” was set to “official gene symbol,” “list type” was set to “gene list,” with the species defined as Homo sapiens, P-value ≤0.05 were regarded significant and were studied further. Finally, we listed the top 15 GO terms sorted in the ascending order of FDR.To study meaningful functional annotation and biological characteristics of potential targets, GO enrichment analysis was conducted to extract key GO terms . In this case, we chose to use the DAVID database to conduct an online analysis that would allow researchers to use the KEGG enrichment together with the GO analysis. The targets, organized and condensed into several functional groups as denoted by their most significant leading term, were visualized in the network. The GO terms that had a The effective components were extracted based on the criteria we mentioned before, We used the data extracted above and Cytoscape 3.8.2 software to build a component-target protein network, which contains 151 nodes , and 528 edges with the size of node based on its degree value is clearly represented in K-core algorithm was used to find clusters (highly interconnected regions) in a network. After calculation, five subcluster interactions were obtained which have network feature scores from 3.333 to 4.625, in the meantime, the DAVID database was used to analyze those subcluster interactions and to reveal the biological process of each cluster represented in Proteins normally regulate their physiological functions through protein-protein interactions and other pathways. To better reveal the mechanism of the JWZXF in treating GAD, STRING, a database designed to collect and integrate all functional interactions between expressed proteins by integrating known and predicted protein-protein association data from a large number of organisms, was used. The intersection targets obtained above were entered into the STRING database, the PPI data obtained from the STRING database were imported into the software Cytoscape 3.8.2, to construct a PPI network related to GAD  that is represented in To macroscopically and comprehensively understand the biological function of the active ingredient target in the JWZXF, we implemented the GO functional enrichment analysis and KEGG pathway enrichment analysis on intersection targets. The results were mapped using the R software as bar plot and bubble plots for both KEGG and GO analysis. The top 12 pathways were screened based on the parameter of counts, as well as in combination with FDR-values in th BPs, CCs, and MFs are, respectively, presented in Figures The GO analysis showed that the result of BP was significantly enriched in response to drug (GO:0042493), response to cocaine (GO:0042220), response to estradiol GO:0032355), chemical synaptic transmission GO:0007268), gamma-aminobutyric acid signaling pathway (GO:0007214), and so on. CC was significantly enriched in the plasma membrane (GO:0005886), integral component of the plasma membrane (GO:0005887), postsynaptic membrane (GO:0045211), GABA-A receptor complex (GO:1902711), cell junction (GO:0030054), and so forth. MF was significantly enriched in extracellular ligand-gated ion channel activity (GO:0005230), drug binding (GO:0008144), GABA-A receptor activity (GO:0004890), dopamine binding (GO:0035240), serotonin binding (GO:0051378), and the like. Based on the FDR value, the top 155, chemic268, gammγ-aminobutyric acid (GABA) transmitter regulatory dysfunction in the animal and human brain contributes to anxiety disorders, especially in limbic systems. Brain circuits in the amygdala are thought to contain an inhibitory network of gamma-aminobutyric acid (GABAergic) interneurons, and thus this neurotransmitter plays a key role in regulating anxiety responses in both normal and pathological states [Establishing the PPI network of the JWZXF and GAD revealed different pathways, as l states . Reducinl states . Many rel states , 19  anxiolytics that have obvious side effects . We mustMAOA, MAOB, HTR3A, DRD2, HTR2A, CASP3, SLC6A4, SLC6A3, JUN, and ESR1, respectively. GAD is a heritable disorder with a risk of series genes [MAOA gene methylation may be related to the categories and severity of the neuropsychiatric disorder [MAOA gene also correlates with emotional stability, impulse control, and emotion control [HTR3A and HTR3B genes, which code subunits of serotonin receptors and their polymorphisms, may serve as predictors of 5-HT3 antagonists and SSRIs as well [HTR3A gene or HTR3A inactivation can induce anxiolytic-like features that have comparability of 5-HT3 antagonists [HTR3A gene to manage chronic pain [DRD2 is also a risk gene related to GAD and may predict mental disorders at an early age [SLC6A4 gene, which is a serotonin transporter gene, encodes a membrane protein through the transportation of serotonin to play a role in GAD [es genes . In the disorder , 29. The control . HTR3A a as well –33. Someagonists . More innic pain , 35, 36.arly age , 38. SLCe in GAD , 40. Adde in GAD .3 receptors. It is difficult to determine how the JWZXF affects the pharmacokinetics of monoamine oxidase; so, JWZXF may have a monoamine oxidase inhibitor-like component. Drug interactions between the JWZXF and other drugs should be looked into [The hub gene targets associated with the active components of the JWZXF mainly act on monoamine oxidase and serotonin receptors, especially selective 5-HTked into , 43. How+ combines with CaM-dependent protein kinases. The Ca+/CaM-dependent protein kinases is activated in a successive way. Additionally, cascade pathways of Ras/mitogen-activated protein kinase and cAMP-responsive element-binding protein (CREB) to make gene expression are related to neuronal plasticity and GAD [The KEGG pathway enrichment analysis showed multiple signaling pathways involved in the treatment of GAD with JWZXF. Particularly, the neuroactive ligand-receptor interaction, serotonergic synapse, calcium signaling pathway, and the cAMP signaling pathway, in which JWZXF-associated hub gene targets closely related to GAD were enriched. The neuroactive ligand-receptor interaction triggers intracellular signaling that modulates important gene expression dedicated to neural plasticity and stress processing that leads to emotional disorders , 45. A h and GAD , 49 (FigMAOA, MAOB, HTR3A, DRD2, HTR2A, CASP3, SLC6A4, and SLC6A3 are possible targets of treatment by the JWZXF. Additionally, JWZXF may primarily modulate the neuroactive ligand-receptor interaction, serotonergic synapse, calcium signaling pathway, and cAMP signaling pathway. The network pharmacology analysis of JWZXF treatment of GAD is of great significance. Not only does it holistically analyze the possible mechanism of JWZXF treatment of GAD, it also facilitates further in-depth research and provide a theoretical basis for the clinical expansion of its application.This study investigated the molecular mechanism of the JWZXF in the treatment of GAD by establishing multiple network models. Studies have shown that the most effective ingredient of the JWZXF in treating GAD is Corydalis, and the most involved compounds may be stigmasterol, sitosterol, and beta-sitosterol. The potential pharmacological effects of the abovementioned active compounds are closely related to regulated GABA receptors, dopamine receptors, chemical synaptic transmission, and membrane depolarization during action potential. Gene


Considering limited data exploring reimbursement trends at the subspecialty level within head and neck surgical oncology, we sought to characterize these trends for head and neck‐specific codes from 2000 to 2020.Using the Centers for Medicare and Medicaid Services' Physician Fee Schedule Look‐Up Tool, reimbursement data, adjusted to 2020 U.S. dollars, for 37 head and neck surgical oncologic procedure codes were collected from 2000 to 2020.From 2000 to 2020, despite gross reimbursement for all head and neck procedures increasing by 23.2%, when adjusted for inflation, physician reimbursement decreased by 19.4%. Only 4 of 37 examined codes increased in net reimbursement over the study period.Medicare reimbursement for the most common head and neck oncologic procedure codes decreased from 2000 to 2020. Further research is necessary to explore the implications of these trends on the delivery of patient care. For any given procedure or service, as defined by their designated Current Procedural Terminology (CPT) code, payment is dictated by three factors: (1) the Relative Value Units (RVU), (2) The Geographic Practice Cost Index, which adjusts for geographic variation in cost of care, and (3) a conversion factor to convert RVUs to dollars.Each code's allocated RVUs have three components: (1) work time and intensity, (2) practice expense and overhead, and (3) malpractice insurance.Given the complexity and the vague nature of RVU allocation to certain CPT codes, there is a great deal of uncertainty providers and hospitals face when it comes to reimbursement. Several studies have documented flaws in CMS reimbursement methodologies, especially given the tedious process of revaluation of CPT codes, often taking years of questioning and evaluation before going into effect.Reimbursement trend analysis is not new to otolaryngology. Specialty‐level analysis of 2017 Medicare Part B data suggested that otolaryngologic operative procedures were compensated significantly less than other surgical subspecialties.While these studies examine important questions on reimbursement for otolaryngologic procedures, there lacks an analysis of reimbursement trends for the most common head and neck surgical oncologic codes. A cross‐sectional analysis of 2018 CMS data by Kondamuri et al. suggest that ablative head and neck surgical codes are among the middle of the pack for inpatient payment rates and the lowest ambulatory payment rate, when compared to other otolaryngologic subspecialties.2This study was determined as non‐human research from the Institutional Review Board at University Hospitals Cleveland Medical Center. Two head and neck surgeons at our institution  identified the most commonly billed CPT codes within the institution's practice Table .Reimbursement data for these codes were obtained from CMS using the Physician Fee Schedule Look‐Up Tool.R‐squared values were calculated to confirm the accuracy of time as a predictor of adjusted annual reimbursement decrease. A subanalysis was performed by anatomic region per procedure to determine if there exist any trends in reimbursement based on surgical site. Analyses were made in Microsoft Excel.For each procedure, the unadjusted total and annual percent change in reimbursement was calculated and averaged. To standardize amounts over time, the reimbursement rate for each CPT code was adjusted for inflation to 2020 U.S. dollars over the study period using changes in the Consumer Price Index (CPI). The latest available data for the CPI was obtained from the U.S. Department of Labor's Bureau of Labor Statistics. This adjusted data was then used to analyze trends in reimbursement in 2020 dollars for the most frequently billed procedures in head and neck surgical oncology. Finally, 3Head and neck surgical oncology CPT codes have increased in nominal reimbursement by 23.2% since 2000. However, when adjusted for inflation, reimbursement decreased by 19.4% over the study period. Conversely, RVUs allocated per procedure have gone up steadily over time. Furthermore, our results revealed that over time, procedures are reimbursed less, but are valued at higher RVUs . These results showed that nearly all procedure types experienced a decrease in reimbursement when adjusted for inflation  at −47.0%. Only four procedures increased in reimbursement over the study period. The greatest mean increase was seen in CPT code 41130  at 14.1%.R‐squared values were obtained to assess the strength of the correlation between time and changes in reimbursement. The range for this set of R‐squared values was 0.00–0.92. The average R‐squared value for this analysis was 0.51 indicating a moderate correlation between time and a decrease in compensation .There are limitations to this study. Medicare reimbursement data alone was utilized in this analysis, and therefore it does not capture physician reimbursement for all head and neck surgical oncology procedures as some patients participate in private health insurance plans which are not accounted for. Future studies could evaluate differences in reimbursement between private practice compared with academic centers to achieve a more complete view of reimbursement for physicians within the field.5From 2000 to 2020, head and neck surgical oncological CPT codes have experienced a steady decline in inflation‐adjusted reimbursement, while RVU valuation of these codes have progressively risen. These trends parallel a greater trend across otolaryngology towards diminished reimbursement for providers; however, nuances within head and neck surgical oncology must be explored to further explain differences between head and neck and other subspecialties within otolaryngology. Considering political pressures to reduce reimbursement for providers, novel payment strategies must be implemented to improve physician reimbursement and sustain the growth of our specialty.


Over the past decades, pathway analysis has become one of the most commonly used approaches for the functional interpretation of metabolomics data. Although the approach is widely used, it is not well standardized and the impact of different methodologies on the functional outcome is not well understood. Using four publicly available datasets, we investigated two main aspects of topological pathway analysis, namely the consideration of non-human native enzymatic reactions  and the interconnectivity of individual pathways. The exclusion of non-human native reactions led to detached and poorly represented reaction networks and to loss of information. The consideration of connectivity between pathways led to better emphasis of certain central metabolites in the network; however, it occasionally overemphasized the hub compounds. We proposed and examined a penalization scheme to diminish the effect of such compounds in the pathway evaluation. In order to compare and assess the results between different methodologies, we also performed over-representation analysis of the same datasets. We believe that our findings will raise awareness on both the capabilities and shortcomings of the currently used pathway analysis practices in metabolomics. Additionally, it will provide insights on various methodologies and strategies that should be considered for the analysis and interpretation of metabolomics data. Over the last decades, the importance of robust computational pipelines to interpret experimental data has increased significantly. This is, at least in part, due to continuous advancements in analytical techniques that allow us to capture a large number of metabolites. Although various tools have emerged for the analysis of metabolomic data, proper guidelines and consensus in their correct use and interpretation is still lacking ,2.Pathway analysis aims to link changes in metabolic compounds to biological pathways . Over-reThe study of metabolism in the frame of graph theory has been around for years and can uncover several structural features of the metabolic network ,20,21,22Traditionally, ORA and TPA utilize publicly available pathway collections such as KEGG ,30, ReacTo investigate the effect of connectivity and inclusion of non-human native reactions, we performed TPA on four publicly available metabolomics datasets . We usedMetabolic networks can be translated to graphs, where each node represents a metabolite and each edge represents a reaction. In turn, a graph can be mathematically represented through an adjacency matrix, A. A is a square matrix, whose elements indicate whether pairs of nodes are adjacent or not in the graph. Since metabolic reactions do not always have the capability to operate in both directions, we considered a directed graph for our analysis, which means that A is not symmetric. For the case of non-single-substrate–single-product reactions, the reaction was split into pairwise singular ones. For example, reaction Vertex centrality measures express the importance of a node (or vertex) in the graph network and provides information about the latter’s layout. Various well-established measures of centrality have been defined based on degree, closeness, and betweenness criteria ,40. For We explored the effect of including non-human-specific reactions in the metabolic pathway definitions. In the KEGG database these pathways are labelled as “reference” (or “generic”), as opposed to the human-only pathway designations (“organism-specific”). Hence, in this manuscript we refer to them as “generic” and “human-only”, respectively. Additionally, we investigated the effect of pathway connectivity on TPA. The two approaches of disconnected and connected pathways are referred to as “disconnected” and “connected” in this manuscript, respectively. In the disconnected approach, each pathway was considered independent of the others, and the centrality scores were calculated accordingly using the above formulas. In the connected approach, all the connections between pathways were taken into account prior to the calculation of the centrality scores.For each pathway, the impact score was calculated as:k successes in m random draws without replacement, from a population of total size M that contains K objects with that attribute. We chose to use the hypergeometric test for our analysis.There are two tests that are most commonly used in ORA, namely the hypergeometric and Fisher’s exact tests. Both tests are based on the hypergeometric distribution, which describes the discreet probability of i, M is the total number of metabolites in all pathways of the collection, K is the number of metabolites in pathway i, m is the number of compounds measured in the experiment, and k is the subset of m that belongs to pathway i. The probability for the over-representation of each pathway was calculated as:In terms of metabolomics evaluation and for a single pathway One of the challenges in the connected approach is the appearance of hubs in the metabolic graph. Hubs are nodes that are very central in the metabolic network, which results in high centrality scores for these nodes compared to the vast majority of other nodes within the metabolic network. Thus, their presence might bias the scoring of pathways in a non-realistic and unbalanced manner ,42,43. TAs can be seen in the formula, outlier centrality scores that exceed two MADs are reduced to within one and two MADs, and ones that exceed one MAD are reduced to within the median and one MAD. This method of moderation was applied only in cases of connected approach scoring as stated in the results section, and the median and MAD values were calculated within each pathway. In the extreme case where both the median and MAD values of a single pathway where equal to zero, the MAD value was set to We developed an R-based in-house software. All calculations and figures were made using R version 4.0.2. Pathway definitions were obtained from KEGG KGMLs (September 2021). Betweenness centrality scores were calculated using the betweenness function of the igraph package.One of the challenges we face in human metabolic phenotyping is the consideration of metabolic reactions that are catalyzed by non-human native enzymes, usually associated with microbiota. Within this study, we investigated the impact of inclusion of non-human native enzymes on the outcome of TPA. We observed that certain human-only pathways cannot have an impact score larger than zero, independent of the dataset, i.e., they never appear as significantly impacted. This is due to multiple isolated and disconnected reactions within the pathway. In this case, all of the participating metabolites have a calculated betweenness centrality score of zero. However, this issue was resolved when non-human enzymes are taken into consideration in the network and all of the defined pathways are well connected within themselves. For example, “Valine, leucine and isoleucine biosynthesis” pathway consists of four individual reactions that are catalyzed by human native enzymes B. The coOn the other hand, the generic pathway offers another major advantage. In this case, multiple metabolites cannot be “accessed” by native human enzymes, thus getting lost in a human-only definition. For example, in Alternatively, the generic approach can lead to much lower pathway impact scores compared to the corresponding human-only one. This occurs if the human-only reactions are well connected within the pathway with limited number of metabolites. Such is the case of the “Phenylalanine, tyrosine and tryptophan biosynthesis” pathway, as its score decreased from 0.5 to 0.04 for dataset 1b . The humWe had a similar observation for dataset 2, which focuses on microbiota metabolism . InclusiAlthough metabolic pathways are highly interconnected and affected by each other, many TPA tools consider each pathway as its own isolated network. This can result in major differences in pathway scoring. Even a single metabolite can have an extremely different centrality score when accounting for connectivity of pathways. For example, we observed this effect with dataset 3 for the “Linoleic acid metabolism” and “alpha-Linolenic acid metabolism” pathways. Their respective impact scores changed from zero for the disconnected approach to almost 1 for the connected approach . In thisSimilar to acetyl-CoA, amino acids are also very central to the metabolic network and can have similar effects on scoring. For dataset 4, the generic “Selenocompound metabolism” pathway exhibited a very high score of 0.95 when considering pathway connectivity A. When lWe thus decided to implement a penalty scheme for very highly scoring nodes, or hubs, which normalizes node scores across each pathway (see Methods 2.5). When applying this scheme in the calculation, we observed that while the majority of pathways retained an impact score similar to their original score, the ones suffering from a hub-induced misrepresentation exhibited significantly lowered impact scores A. ConcerIn addition, we performed an ORA calculation for all the datasets . AlthougAs the fields of metabolomics and lipidomics grow rapidly, accurate interpretation of such data has become essential for understanding the functional outcome of metabolomics and lipidomics data. ORA and TPA are two of the most commonly used approaches for the functional interpretation of metabolomics data, however, the connectivity between different metabolic pathways and the inclusion of non-human native reactions have not been fully investigated. In this work, we investigated these two aspects of TPA and their effects on the outcome, using four publicly available metabolomics datasets. We used betweenness centrality as the node scoring method and KEGG as our pathway database, since they are the most commonly used choices in the community ,34.Although there has been a huge effort in improving the available databases, definition and size of pathways across databases are not harmonized. Thus, the selection of pathway database can affect the results of ORA and TPA analyses ,49,50. EIn addition to pathway definitions, the decision regarding the treatment of individual pathways  is critical. Non-topology methods such as ORA that rely on statistical evaluation are not affected by network connectivity; however, this is not the case for TPA. Although the consideration of each individual metabolic pathway  is helpful in the deconstruction of the metabolic network, this assumption of metabolic independence does not reflect the reality and could lead to incorrect outcomes. Even though both approaches are useful in the functional interpretation of metabolomics datasets, we believe that the connected consideration should be utilized for most cases, since all metabolic processes are interconnected. If the organism-specific pathway definition is used, connectivity is crucial to ensure that the number of isolated reactions is minimized. However, if any specific part of the metabolic network is of interest, a disconnected approach could be beneficial. In this case, a disconnected approach would reduce the effect of other pathways in terms of centrality scores. The pitfall of the connected approach is that very central biomolecules such as acetyl-CoA or amino acids will have such big scores that they will overshadow the majority of other metabolites in any pathway. To provide a solution for this problem, we have proposed a penalty scheme that effectively dampens the large node centrality scores, which correspond to hub compounds. By using this penalty approach, we ensure that the centrality scores within a pathway have comparable magnitudes, thus the scoring calculation remains unbiased. We believe such a normalization scheme in a connected network definition provides more realistic outcomes. In addition, alternative normalization approaches could be applied and evaluated.Finally, it is important to note that ORA and TPA methods might lead to very different results in terms of pathway scoring. ORA does not account for network topology but the number of statistically significant compounds in each pathway will reflect on its output. Thus, both the connected and disconnected approaches would yield the same results. On the other hand, TPA puts emphasis on the topology, and the output might vary significantly depending on the pathway definitions and graph metrics. It is thus very important to have awareness of the advantages and limitations of each method, as well as being able to critically interpret their results. We believe that the combination of both methods would be advantageous. TPA should be used as the main tool to evaluate the magnitude of perturbation of each pathway, while ORA should be used as a statistical confirmation of the TPA outcome.This study aims to raise awareness to the inherent pitfalls of TPA and provide some insight on various methodologies and strategies that should be considered before proceeding to the analysis of metabolomics datasets. Even though there can be no conclusive verdicts in the absence of a ground-truth dataset, we hope that our findings will spark more discussions and investigations in the community on the proper usage and interpretation of pathway analysis methodologies.


With the aging of the population, the prevalence of IVDD increases preoperatively. How to better treat IVDD has become an important clinical issue. Deer antlers proved to have a great effect on the treatment of IVDD in many studies, but the molecular mechanism has not been clarified.  To investigate the molecular mechanism and target of deer antlers in the treatment of IVDD.  Compounds from deer antlers were collected and targets were predicted using HERB, TCMSP, TCMID, SwissADME, and SwissTargetPrediction. Collection of disease targets for IVDD was done using GeneCards, TTD, DrugBank, DisGeNET, and OMIM. Cytoscape 3.7.2, AutoDock Vina (v1.1.2), and R software were used for data analysis and the construction of network diagrams.  A total of 5 active compounds from deer antlers were screened and 104 therapeutic targets were predicted. A total of 1023 IVDD disease targets were collected. Subsequently, PPI network prediction analysis was performed for disease and treatment targets, and 112 core targets were collected after screening. After obtaining the core target, we used the clusterProfiler software package of R software to carry out GO and KEGG enrichment analyses for the core target and plot the bubble maps. According to the GO enrichment results, the main biological processes of IVDD treatment by deer antlers lie in the rhythmic process, mRNA catabolic process, and G1/S transition of the mitotic cell cycle. KEGG results were mainly related to the PI3K-Akt signaling pathway, thyroid hormone signaling pathway, and Notch signaling pathway. Molecular docking results showed that estrone had the best docking results on ESR1.  Deer antlers are rich in various compounds that can prevent the development of IVDD by upregulating the PI3K-Akt signaling pathway and Notch signaling pathway. Its key compounds estradiol and estrone can reduce the inflammatory response and oxidative stress in tissues and organs, thus slowing down the progression of IVDD. Estrone, the active compound in deer antlers, was found by molecular docking to have good results against ESR1, the target of the disease, which may be a potential site for drug therapy. Deer antlers (Cervi Cornu Pantotrichum) refer to the outgrowths on the forehead of the male of sika deer and red deer, which are covered with a layer of velvety fur . Deer anNetwork pharmacology, which is based on the concept of a multilevel and multiangle interaction network among diseases, genes, targets, and drugs, can observe the interventional mechanism and influence of drugs on the disease network systematically and comprehensively . It can https://herb.ac.cn/), TCMSP (https://tcmsp-e.com/), TCMID (https://47.100.169.139/tcmid/), and other Chinese medicine database platforms as well as the literature. Immediately afterward, the collected active ingredients were screened for ADME on the SwissADME (https://www.swissadme.ch/index.php) using the following criteria: “GI absorption” as “HIGH” and “YES” for any two of the “Drug-likeness” cells. After obtaining the screened active ingredients from the antlers, they were imported into SwissTargetPrediction to predict the drug targets, and those with P values >0 were selected, validated, and supplemented in the UniProt database.We searched for active ingredients in deer antlers using HERB , and the 3D structures of the components were obtained from the ZINC website (https://zinc.docking.org/). First, the intrinsic small ligand of the protein was docked to the protein structure, and the binding energy was calculated. Second, the target proteins and components were docked in AutoDock Vina (v1.1.2), and the binding energy was also calculated. The binding energy of the intrinsic small ligand to the protein was the standard for verifying the binding conditions of the new compounds. Ultimately, PyMOL (v2.5) was utilized to present a visual analysis of the molecular structure. Five core target proteins in the PPI network and significant components in the “component-target” network diagram were selected for molecular docking verification. The 3D structures of the target protein were downloaded from the PDB website , and a total of 2579 targets were obtained, that is, because the degree value can reflect the importance degree of a compound in deer antlers. Afterward, the new results were analyzed according to the three indicators “Betweenness,” “Degree,” and “Closeness,” and targets greater than or equal to the median were screened, resulting in 112 core targets , Table 3P value screening for each component. The main results are the rhythmic process, mRNA catabolic process, and G1/S transition of the mitotic cell cycle , cellular component (CC), and molecular function (MF), with a  Figures –6. Meanw pathway , among wAs can be found from the PPI interaction analysis diagram, NTRK1, ESR1, CDK2, CUL3, and TP53 were the proteins with the highest degree values, which were also selected as our core target proteins. We listed the basic information of the target protein and the binding energy of the intrinsic ligand and the protein given in First recorded in the Shen Nong Ben Cao Jing, deer antlers are true bone growth found in pairs on the heads of male deer and are commonly utilized as Chinese medicine to tonify “kidney-yang,” which is believed to have the effect of strengthening the muscles and bones. Modern pharmacology has found through various experimental studies that deer antlers have significant anti-inflammatory and antioxidant effects, which protect osteoblasts, and this may be the mechanism by which deer antlers treat degenerative disc degeneration. However, few studies explored the molecular mechanism of deer antlers in the treatment of IVDD. Through the network pharmacology and molecular docking analysis of deer antlers for the treatment of IVDD, we have made a preliminary exploration of its molecular mechanism.α-estradiol and 17-β-estradiol can increase the activity of anti-inflammatory markers such as IL-6 receptors through the action of estrogen receptors, thereby reducing the secretion of inflammatory factors TNF-α and IL-6 and inhibiting inflammatory stimulation [The mechanisms by which IVDD occurs are complex, and current studies mainly focus on oxidative stress , inflammmulation , 23. It mulation , 25. Themulation –28.All six pathways are crucial to the occurrence of IVDD. The PI3K-Akt signaling pathway plays a critical role in IVDD . After PMolecular docking was utilized to determine if the four components had an affinity for the five target proteins. With the results listed, it indicates that these components could bind tightly to the target proteins. Given that the binding affinity of alpha-estradiol on TP53 was slightly lower than the original ligand, we can conclude that estrone on ESR1 had the best docking result. We can speculate that estrone-ESR1 may have a potential antidenaturation effect.In summary, we initially explored the molecular mechanism of deer antlers for the treatment of IVDD through network pharmacology and molecular docking in this study, predicting that the key therapeutic mechanism is anti-inflammatory and antioxidant. We also found that estrone showed the best docking results against ESR1 and predicted that it could be used as a potential drug treatment site. Although the effective compounds from this study screen scored highly, it cannot be determined that the remaining compounds are not clinically effective, especially large-molecule compounds such as peptides. Furthermore, experimental validation of the results of this study is still required due to algorithmic limitations and database sources limited to published research reports.


This work aimed to study the compounds, targets, and pathways of turmeric and corn silk for gout and to explore the mechanism of “the same disease with different treatments” based on network pharmacology and molecular docking.  We used the TCMSP, PubChem, and SEA databases to screen the compounds and targets of turmeric and corn silk, gout-related proteins through TTD, Drugbank, DisGeNET, GeneCards, OMIM, and PharmGkb, and used Cytoscape to construct a “compound-target-disease” network. Then, we constructed a protein-protein interaction network (PPI) and used Metascape to perform GO and KEGG analysis. Finally, molecular docking (SYBYL) was used to verify the degree of binding between key targets and compounds.  We found bisacumol, campesterol, and stigmasterol to be the main turmeric compounds that exerted a marked effect on gout treatment by targeting protein processing in the endoplasmic reticulum through the HSPA1B, HSP90AB1, and STUB1 proteins. The main corn silk compound, Mandenol, treated gout by targeting the Hippo signaling pathway through the CTNNB1, YWHAG, and YWHAZ proteins.  Turmeric and corn silk can treat the same disease, gout, through different pathways and targets. The scientific connotation of “same disease with different treatments” can be preliminarily clarified by analyzing targets and pathways. With the global outbreak of COVID-19 in 2020, lifestyles changed significantly. Success in the treatment of the disease achieved by the combined use of traditional Chinese medicine (TCM) and modern medicine garnered the attention of society for TCM. As one of the four ancient civilizations, China has a lengthy history, with a traditional health-preserving and TCM culture. TCM is a treasure house of natural herbs. In recent years, its application worldwide has increased dramatically due to its excellent therapeutic effects and a paucity of side effects. The equilibrium state is one of TCM's main theories and includes the balance of the human body, the balance between man and nature, and the balance between man and the social environment . The appGout, the most common inflammatory arthritis, is caused by hyperuricemia, which causes sodium urate crystals to deposit in joints, tendons, and other tissues . Severaltong-feng)  is a common disease name used in TCM and modern medicine; in TCM, both gout and hyperuricemia are in the category of gout [Gout  parameters. The screening criterion was oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18, and this was combined with relevant literature to supplement the compounds. In addition, the PubChem database was used to confirm the molecular structure of the compounds, and TCMSP targets and the SEA database prediction model were used to predict the possible targets of the compounds .The keywords “hyperuricemia” and “gout” were used, and “Homo sapiens” was selected for the species. Next, the keyword search was conducted in the Drugbank, DisGeNET, GeneCards, OMIM, and PharmGkb databases. Lastly, duplicate genes were deleted.Cytoscape  was usedThe Cytoscape plug-in BisoGenet was used to construct a protein-protein interaction (PPI) network for compound potential and disease targets. Merge was used in the software to fuse the two network diagrams and extract the intersection. The direct and indirect intervention target regulation network graphs for turmeric and corn silk were obtained for gout. The plug-in CytoNCA  in CytosH. sapiens for analysis. The results of protein interaction and module analysis, GO enrichment analysis, and KEGG pathway analysis were retained, and the results were sorted according to the number of targets. Finally, the top results were retained and analyzed, and R package [Metascape  is a too package  visualizSYBYL-X  was usedThe three turmeric and twelve corn silk compounds were screened through the TCMSP database and supplemented by turmeronol A, turmeronol B, and bisacumol in turmeric . Their OR studio 4.0.5 to cross the compound targets of turmeric and corn silk with the disease targets of gout, we get 6 and 36 intersection targets, respectively. The above targets are presented as a Venn diagram , 49 nodes, and 62 edges (corn silk), respectively. In the nodes, purple represents disease, yellow represents turmeric (JH), corn silk (YMX), green represents compounds, and red represents the targets of turmeric and corn silk for gout   were usep values, and these were primarily involved in protein processing in the endoplasmic reticulum, the HIF-1 signaling pathway, and the MAPK signaling pathway.As shown in p values. These compounds are primarily involved in the Hippo signaling pathway, antigen processing and presentation, and the cell cycle.As shown in The results of the KEGG analysis showed that potential turmeric compounds might play a role in the treatment of gout by targeting protein processing in the endoplasmic reticulum signaling pathway, and potential corn silk compounds may play a therapeutic role by targeting the Hippo signaling pathway. We used SYBYL-X2.0 software  to verifThe scoring results showed that the turmeric, bisacumol, campesterol, and stigmasterol compounds had high activity when docking with the target HSPA1B and good activity when docking with the STUB1 and HSP90AB1 targets, suggesting that bisacumol, campesterol, and stigmasterol may be the key compounds of turmeric for gout. Mandenol, the corn silk compound, had high activity when docking with the target YWHAG and good activity when docking with the CTNNB1 and YWHAZ targets, suggesting that Mandenol may be an essential corn silk compound for gout . For exaIn this study, the potential mechanism of gout treatment using turmeric and corn silk was investigated using the network pharmacology method, including compound target network construction, PPI network analysis, GO enrichment analysis, and KEGG pathway analysis.α, IL-6, IL-1β, iNOS, and COX-2) and increase the expression of antiinflammatory cytokines (IL-10) in the joints of arthritic rats by down-regulating the expression of NF-kBp65 (inhibiting p-IKB-α activation) and p38MAPK [Network analysis of compound targets and molecular docking showed that bisacumol, campesterol, and stigmasterol might be the key turmeric compounds, and Mandenol might be the key corn silk compound. Bisacumol is a sesquiterpenoid . The ses p38MAPK . Mandeno p38MAPK . The aboβ [α, and IL1β induced by cisplatin [β-catenin), is a member of the cyclic catenin family, whose primary role is to regulate adhesion between cells and intercellular substances. In addition, some studies have indicated that activation of the canonical Wnt-1/β-catenin pathway regulates the immune response and induces appropriate T cell responses [Our PPI analysis showed that turmeric and corn silk influence gout through their impact on a complex biological network, including HSPA1B, HSP90AB1, STUB1, CTNNB1, YWHAG, and YWHAZ. Furthermore, the results of molecular docking indicate that the above compounds can also be combined with turmeric and corn silk. The essential turmeric targets HSPA1B and HSP90AB1 belong to the heat shock proteins (HSP) family, which is thought to play an essential role in the immune response. Studies have shown that HSPA1B inhibits viral proliferation in a viral infection's middle and late stages . HSP90ABβ . STUB1 bisplatin . The criesponses . YWHAG aesponses . When ceesponses . Studiesesponses . All of By referring to GO and KEGG pathway enrichment analyses for turmeric and corn silk targets for gout, module and overall analyses' results were found to be the same, indicating that protein processing in the endoplasmic reticulum, HIF-1 signaling pathway, and Hippo signaling pathway is important. The endoplasmic reticulum (ER) is a subcellular organelle in which proteins are folded with the help of luminal chaperones. Accumulating misfolded proteins in the ER causes ER stress and activates a signaling pathway called the unfolded protein response (UPR). Studies have shown that tophi can promote osteoclast differentiation and proliferation by a mechanism closely related to ER stress . This prCurcuma longa L., a member of the ginger family [p < 0.01), significantly down-regulating glutamic oxaloacetic transaminase (AST) and glutamic pyruvic transaminase (ALT) levels (p < 0.05), and improving liver and kidney tissue morphology (p < 0.05) [κB signaling pathway and expression levels of the NF-κB downstream inflammatory genes such as IL-1β, IL-6, TNFα, COX-2, and PGE2 (p < 0.05) in MSU-stimulated THP-1-derived macrophages [p < 0.05) in mouse models of acute gout [Stigma madis) is a style of maize (Zea mays L.) in the grass family [p < 0.05) and serum xanthine oxidase (XO) activity by 11.29% (p < 0.05) [p < 0.05) [Turmeric is the dried rhizome of r family  recordedr family . In addir family . Shen et < 0.05) . Chen etrophages . Furtherute gout . Corn sis family  and causs family . Modern s family , antibacs family , antitums family , blood ss family , and oth < 0.05) . In addi < 0.05) . In ChinTraditional culture has endured in China for thousands of years, and TCM has been integral to the country's traditions. Theories and thousands of years of practice, be it the same disease with different treatments, the theory of medicine and food homology, or other traditional Chinese medicine theories, have significantly protected the health of the country's people. Although modern medicine is mainstream in today's society, health problems persist. TCM has the potential to lead to breakthroughs in the health field, as evidenced by Youyou Tu, who invented artemisinin as a cure for malaria. We hope that TCM can better benefit people across the world.Through network pharmacology and molecular docking, bisacumol, campesterol, and stigmasterol have been found to be essential turmeric compounds for gout treatment. These active ingredients may target protein processing in the endoplasmic reticulum through HSPA1B, HSP90AB1, and STUB1 proteins and play a significant role in treating gout. The essential compound of corn silk is mandenol, which may target the hippo signaling pathway to treat gout through CTNNB1, YWHAG, and YWHAZ proteins. Turmeric is a blood-activating medicine, and corn silk is a diuretic medicine. These compounds have different effects and applications but can treat the same disease through different pathways and targets. Hence, the scientific definition of the TCM theory is “same disease with different treatments.”


MYD88) p.L265P mutation [Lymphoplasmacytic lymphoma (LPL) accounts for approximately 2% of hematologic malignancies , 2 and cmutation , 2, 3. Lmutation , 2, 4. FMYD88 p.L265P mutation. He underwent six cycles of bendamustine/rituximab and achieved complete remission. In 2019, he returned with a rising IgM level (859 mg/dl) in the setting of multiple new 3–4 cm subcutaneous nodules on his lower extremities (Figure We present a 78‐year‐old male with a history of WM diagnosed in 2014 when a bone marrow biopsy showed LPL Figure , which hs Figure . A biopss Figure . Moleculs Figure . At 1 mos Figure . The less Figure . At mostMYD88 p.L265P mutation is present. Wildtype MYD88 is activated by toll‐like receptors and interleukin‐1 pathways and subsequently activates downstream NF‐kB signaling [In nearly all LPL cases, the ignaling . The p.Lignaling . In receignaling , 10.MYD88 p.L265P mutation in the original bone marrow specimen; however, it was lacking in the lymph node [MYD88 mutation in the transformed sample, it is difficult to ascribe this initial response to effectiveness of that therapy in all stages of LPL as opposed to representing a case of the known phenomena of ibrutinib response in non‐GC DLBCL and the possibility that the LPL and DLBCL in that sample arose from the same ancestral clone through activation of different pathways [While ibrutinib is a common therapy for LPL and also shows some efficacy in certain subtypes of DLBCL, we know of only two papers addressing the use of ibrutinib in transformed LPL. The first is of a 63‐year‐old female originally presenting with LPL within the bone marrow who later developed CNS disease and a submandibular lymph node, the latter of which demonstrated DLBCL by histology . Interesmph node . Clonalimph node . Nonethemph node . Given tpathways . The secpathways . The ovepathways . HoweverIn conclusion, we report the first known case of a patient with molecularly confirmed high‐grade transformation of a previously diagnosed lymphoplasmacytic lymphoma who showed marked rapid and sustained control of his disease with initiation of targeted therapy. Historically, high‐intensity chemotherapy has been the most common therapy utilized following transformation. Small‐molecule inhibitors such as ibrutinib, though widely used in the setting of WM/LPL, have not been extensively studied in the setting of transformation. With their generally well‐tolerated side effect profile compared to high‐intensity chemotherapy, small‐molecule BTK inhibitors may be a reasonable treatment consideration in certain patients showing high‐grade transformation who are not considered to be ideal candidates for more intensive therapy due to age, frailty, or the presence of significant comorbidities or who have failed standard approaches.The authors declare that there is no conflict of interest.


Mingmu Dihuang Pill (MMDHP) is a traditional Chinese formula that has shown remarkable improvements of dry eyes, tearing, and blurry vision; however, the mechanisms underlying MMDHP treatment for diabetic retinopathy have not been fully understood. This study is aimed at identifying the molecular targets and active ingredients of MMDHP for the treatment of diabetic retinopathy based on network pharmacology.  All active ingredients of MMDHP were retrieved from TCMSP and BATMAN-TCM databases, and the targets of active ingredients of MMDHP were predicted on the SwissTargetPrediction website. Diabetic retinopathy-related target sets were retrieved from GeneCards and OMIM databases, and the intersecting targets between targets of active ingredients of MMDHP and potential therapeutic targets of diabetic retinopathy were collected to generate the traditional Chinese medicine-ingredient-target-diabetic retinopathy network and to create the protein-protein interaction network. In addition, GO terms and KEGG pathway enrichment analyses were performed to identify the potential pathways, and molecular docking was employed to verify the binding of active ingredients of MMDHP to key targets of diabetic retinopathy.  Network pharmacology predicted 183 active ingredients and 904 targets from MMDHP, and 203 targets were intersected with the therapeutic targets of diabetic retinopathy. The top 10 hub targets included PIK3RA, TP53, SRC, JUN, HRAS, AKT1, VEGFA, EGFR, ESR1, and PI3KCA. GO terms and KEGG pathway enrichment analyses identified AGE-RAGE, PI3K-AKT, and Rap1 signaling pathways as major pathways involved in MMDHP treatment for diabetic retinopathy. Molecular docking confirmed a good binding affinity of active ingredients of MMDHP, including luteolin, acacetin, naringenin, and alisol B, with AKT1, SRC, and VEGFA as the three key targets of diabetic retinopathy.  MMDHP may be effective for the treatment of diabetic retinopathy through active ingredients luteolin, acacetin, naringenin, and alisol B via AKT1, SRC, and VEGFA in AGE-RAGE, PI3K-AKT, and Rap1 signaling pathways. Diabetes mellitus, a group of metabolic diseases characterized by chronic hyperglycemia, is a major health problem worldwide, which greatly threatens human health and global economy . The gloDiabetic retinopathy, one of the most common and most severe complications of diabetes, is a chronic, progressive microvascular disorder that may lead to vision-threatening damages to the retina and even blindness . There aChrysanthemum morifolium, Lycium barbarum, Moutan cortex, Dioscoreae rhizoma, Poria cocos, Alisma orientalis, Cornus officinalis, Paeonia lactiflora, Tribulus terrestris, and Concha haliotidis [Mingmu Dihuang pill, a traditional Chinese formula that consists of multiple medicinal plants in optimal prescriptions, including liotidis , has sholiotidis . Previouliotidis –14. Howeliotidis .In the present study, the active ingredients and potential molecular targets of Mingmu Dihuang pill for the treatment of diabetic retinopathy were screened using network pharmacology, and the key targets and signaling pathways involved in Mingmu Dihuang pill treatment for diabetic retinopathy were identified and verified using molecular docking –20 Figu. Our finhttps://tcmsp-e.com/) and the Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine (BATMAN-TCM) database (http://bionet.ncpsb.org.cn/batman-tcm/), including Chrysanthemum morifolium, Lycium barbarum, Moutan cortex, Dioscoreae rhizoma, Poria cocos, Alisma orientalis, Cornus officinalis, Paeonia lactiflora, Radix rehmanniae preparata, Angelica sinensis, Tribulus terrestris, and Concha haliotidis. Active ingredients with oral bioavailability (OB) of 30% and higher and drug likeness (DL) of 0.18 and greater were screened.All active ingredients of Mingmu Dihuang pills were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) (https://pubchem.ncbi.nlm.nih.gov) or the mol2 files were downloaded and converted into smiles files using the software Open Babel. Then, the sdf or smiles files were loaded into the SwissTargetPrediction web server (http://swisstargetprediction.ch/). The targets of active ingredients acquired from the SwissTargetPrediction web server with a probability of 0 and higher were selected as potential targets.To predict the targets of active ingredients of Mingmu Dihuang pill, the sdf files regarding the active ingredients of Mingmu Dihuang pill were downloaded from the PubChem database (https://www.genecards.org) and Online Mendelian Inheritance in Man (OMIM) databases (http://www.omim.org) using the term “diabetic retinopathy”.Diabetic retinopathy-related genes were retrieved from the GeneCards (http://bioinformatics.cn), in order to screen the intersection between the diabetic retinopathy-related gene set and the drug target set, and the Venn diagram was plotted. The names of intersecting targets and names and ingredients of Mingmu Dihuang pill were loaded into the network visualization platform Cytoscape version 3.2.1, in order to generate the traditional Chinese medicine-ingredient-target-diabetic retinopathy network.Diabetic retinopathy-related genes and all targets of active ingredients of Mingmu Dihuang pill were input into the WeiShengxin database (https://string-db.org/cgi/), and the protein type was defined as “Homo sapiens” using the multiple protein tool and was saved as a TSV-format file. The PPI network was then visualized using the software Cytoscape version 3.2.1.To create a PPI network, the intersecting targets of Mingmu Dihuang pill and diabetic retinopathy were input into the STRING 11.5 platform (http://metascape.org/gp/index.html).GO terms and KEGG pathway enrichment analyses were performed to investigate the biological processes and metabolic pathways of all intersecting targets between Mingmu Dihuang pill and diabetic retinopathy, using the online Metascape platform  and three key targets AKT1 (PDB ID: 4EJN), SRC (PDB ID: 4U5J), and VEGFA (PDB ID: 3BDY) were selected for molecular docking. The AKT1 protein was fixed at the grid center set to 32, 42, and 13 for x, y, and z and at the grid box size set at 64, 64, and 64 Å for x, y, and z, respectively, and the SRC protein was fixed at the grid center set to −3.1, 51, and 25 for x, y, and z and at the grid box size set at 90, 77, and 104 Å for x, y, and z, respectively, while the VEGFA protein was fixed at the grid center set to −39, −57, and −3 for x, y, and z and at the grid box size set at 65, 138, and −365 Å for x, y, and z, respectively. The grid box size was set using the software AutoDock Vina version 1.5.6, and the docking results were visualized using the PyMOL software . A negatC. morifolium, 45 active ingredients of L. barbarum, 2 active ingredients of R. rehmanniae preparata, 11 active ingredients of M. cortex, 16 active ingredients of D. rhizoma, 15 active ingredients of P. cocos, 10 active ingredients of A. orientalis, 20 active ingredients of C. officinalis, 2 active ingredients of A. sinensis, 13 active ingredients of P. lactiflora, 12 active ingredients of T. terrestris, and 4 active ingredients of C. haliotidis. Following removal of the active ingredients with unidentified targets, the active ingredients of Mingmu Dihuang pill were finally predicted (Supplementary Table C. morifolium and T. terrestris (A); C. morifolium, M. cortex, P. lactiflora, and T. terrestris (B); C. morifolium, L. barbarum, and M. cortex (C1); C. morifolium, L. barbarum, R. rehmanniae preparata, C. officinalis, A. sinensis, and P. lactiflora (C2); C. morifolium and C. officinalis (D); L. barbarum and C. officinalis (E1); L. barbarum and C. officinalis (E2); L. barbarum, C. officinalis, R. rehmanniae preparata, D. rhizoma, and A. sinensis (E3); L. barbarum and D. rhizome (F1); and L. barbarum and D. rhizome (F2); the common ingredients of G1 include M. cortex and P. lactiflora (G1); M. cortex and P. lactiflora (G2); and M. cortex, A. orientalis, C. officinalis, P. lactiflora, and T. terrestris (G3). Then, the targets of active ingredients of Mingmu Dihuang pill were predicted in the SwissTargetPrediction platform and 402 targets of C. morifolium actions, 390 targets of L. barbarum actions, 44 targets of R. rehmanniae preparata actions, 221 targets of M. cortex actions, 345 targets of D. rhizoma actions, 284 targets of P. cocos actions, 301 targets of A. orientalis actions, 313 targets of C. officinalis actions, 43 targets of A. sinensis actions, 274 targets of P. lactiflora actions, 358 targets of T. terrestris actions, and 197 targets of C. haliotidis actions were identified. Following removal of repeated targets, a total of 904 targets of Mingmu Dihuang pill actions were finally identified.A total of 183 active ingredients of Mingmu Dihuang pill were retrieved in the TCMSP database, including 20 active ingredients of A total of 1,021 diabetic retinopathy-associated target genes were retrieved in the GeneCards database, and 59 target genes were retrieved in the OMIM database. Following target gene merging and removal of repeated targets, a total of 1,079 diabetic retinopathy-associated target genes were identified. Then, these targets were intersected with 904 potential targets of Mingmu Dihuang pill, and 203 potential targets were finally yielded . The traThe 203 predicted potential targets were input into the STRING platform to generate a PPI network, which contained 203 nodes and 1,169 lines. In this study, the targets with a confidence score of higher than the median were defined as the core targets of Mingmu Dihuang pill, and following screening of the median three times, the targets with a confidence score of > 0.979, 5 were identified as core targets of Mingmu Dihuang pill. The screened targets were visualized using the Cytoscape software and subjected to topology analysis, and a PPI network, which contained 111 nodes and 171 lines, was generated . The topGO annotations of the 203 predicted targets were classified into three categories, including biological processes, molecular functions, and cellular components . The tarA total of 378 pathways were identified . The topAKT1, SRC, and VEGFA were found to have the binding energy of < −5 kcal/mol with all key targets of diabetic retinopathy , suggestC. morifolium, L. barbarum, Catsia tora, P. lactiflora, A. sinensis, and T. terrestris in optimal prescriptions, has been used for the clinical treatment of eye dryness, photophobia, blurry eyes, and tears in wind [Mingmu Dihuang pill, a traditional Chinese formula based on Liuwei Dihuang pill with addition of  in wind , 10. In  in wind . In addi in wind . HoweverC. morifolium, and alisol B is only detected in A. orientalis.In this study, we identified 183 active ingredients, 904 targets of Mingmu Dihuang pill, and 203 targets that were intersected with the therapeutic targets of diabetic retinopathy using network pharmacology and molecular docking. PPI network analysis identified 111 hub targets, and the top 10 hub targets included PIK3RA, TP53, SRC, JUN, HRAS, AKT1, VEGFA, EGFR, ESR1, and PI3KCA. In addition, GO terms and KEGG pathway enrichment analyses revealed that Mingmu Dihuang pill may be effective for treatment of diabetic retinopathy through biological processes of positive regulation of cell migration, response to inorganic substance, transmembrane receptor protein tyrosine kinase signaling pathway via the AGE-RAGE signaling pathway in diabetic complications, PI3K-AKT signaling pathway, Rap1 signaling pathway, HIF-1 signaling pathway, VEGF signaling pathway, Erb B signaling pathway, and Foxo signaling pathway. It is therefore hypothesized that Mingmu Dihuang pill is active to mediate cell growth, participate in glucose metabolism, suppress cell apoptosis, and inhibit angiogenesis. Our data indicate that compound Chinese medicines function through multi-ingredient, multitarget, and multipathway actions. Molecular docking confirmed a good binding affinity of active ingredients of Mingmu Dihuang pill, including luteolin, acacetin, naringenin, and alisol B, with AKT1, SRC, and VEGFA, three hub targets of diabetic retinopathy. Of the four critical active ingredients of Mingmu Dihuang pill, luteolin, acacetin, and naringenin are only present in The hub target AKT1 is highly expressed in insulin-sensitive tissues and triggers the downstream glucose transport protein to increase glucose uptake, thereby mediating glucose metabolism , 26. TheκB signaling pathway and increase the VEGF release, thereby leading to intracellular oxidative stress and inflammation [Our findings showed that the three hub targets AKT1, VEGFA, and SRC were all significantly enriched in the Rap1 signaling pathway, and AKT1 and VEGFA were both significantly enriched in the AGE-RAGE signaling pathway in diabetic complications and PI3K-AKT signaling pathway. It is therefore hypothesized that Mingmu Dihuang pill may be active for treatment of diabetic retinopathy via these three signaling pathways. As a small GTPase, Rap1 is involved in multiple biological processes, including cell proliferation, migration, and adhesion . Angioteammation . Activatammation . PI3K/AKammation . Previouammation , 44. In ammation . Followiammation . It has ammation . These dC. morifolium for treatment of diabetic retinopathy deserves further investigations. Our data provide insights into development of novel targets for the treatment of diabetic retinopathy.In summary, the results of the present study demonstrate that Mingmu Dihuang pill may be effective for the clinical treatment of diabetic retinopathy through active ingredients luteolin, acacetin, naringenin, and alisol B via AKT1, SRC, and VEGFA in AGE-RAGE, PI3K-AKT, and Rap1 signaling pathways. Development of novel agents from


Using network pharmacology and gut microbiota sequencing to investigate the probable mechanism of Huangqin decoction in the treatment of Diabetic enteritis (DE).  The mechanism of Huangqin decoction on DE was studied by combining network pharmacology and gut microbiota sequencing analysis. The core components and possible targets of Huangqin decoction were analyzed by network pharmacology. The effect of Huangqin decoction on microorganisms was investigated by gut microbiota sequencing.  The results of gut microbiota sequencing analysis showed the abundance of TM7, Tenericutes, Chloroflexi, Cyanobacteria, Acidobacteria, WS6, [Prevotella], Helicobacter, Prevotella, Lactococcus, and Anaeroplasma in the Huangqin decoction group had a significant downward. Using a network pharmacology-related database, 141 main active components of Huangqin decoction were identified, as well as 256 corresponding component targets and 1777 corresponding disease targets; the disease targets and component targets were mapped, and topological analysis was used to determine the potential of Huangqin decoction in the treatment of DE. There were 156 targets, of which the top 20 genes were selected for GO and KEGG. The KEGG results showed that 134 pathways were enriched, which was partially consistent with the metabolic pathways of gut microbiota sequencing analysis.  The results show that Huangqin decoction can inhibit the expression of inflammatory factors and related inflammatory pathways in intestinal epithelial cells, thereby regulating the structure of intestinal flora. Using picurst2 for functional prediction and metabolic pathway statistics, seven metabolic pathways were obtained consistent with gut microbiota sequencing, and the NOD-like receptor signaling pathway may be its potential molecular mechanism. These results help to understand the mechanism of Huangqin decoction on DE and provide the theoretical basis for further study of Huangqin decoction. Diabetes mellitus is a widespread disease. According to the International Diabetes Federation, it affects 463 million people worldwide with an increasing prevalence . DiabeteHuangqin decoction is a traditional Chinese medicine formula in the classic Chinese medical book “Treatise on Febrile Diseases” written by Zhang Zhongjing of the Eastern Han Dynasty. It has a history of nearly 1800 years and is widely used in the clinical treatment of intestinal diseases such as ulcerative colitis –9; it isPharmacological studies have shown that Huangqin decoction has anti-inflammatory, antibacterial, analgesic, antipyretic, sedative, and other effects. In recent years, with extensive research on Huangqin decoction, it has been used to treat acute lung injury, colon cancer, gastric cancer, leukemia, and other diseases according to its anti-inflammatory, antiproliferation, and mucosal protective effects –14. It iTherefore, based on network pharmacology and gut microbiota, this study will investigate the mechanism of Huangqin decoction in the treatment of DE.Experimental materials are as follows: Scutellaria baicalensis; Baishao; Jujube; Licorice ; DNeasy PowerSoil Kit ; Quant-iT PicoGreen dsDNA Assay Kit ; Agcout AMPure Beads ; PicoGreen dsDNA Kit ; CD45 antibody ; PV9004 secondary antibody ; DAB chromogen ; Quantifluor-ST fluorometer (Promega); high-throughput sequencer ; low-temperature medical freezer ; gdsAUTO520 gel imaging system ; DYY-6C electrophoresis instrument ; paraffin embedding machine (LeicaEG1150H); paraffin microtome (Leica RM 2125 RTS); paraffin microtome (Leica HI1220); and fluorescence microscope (OLYMPUS DP80).Preparation of Huangqin decoction: weigh the decoction pieces of Radix Scutellariae, Radix Paeoniae Alba, jujube, and licorice in the ratio of 3 : 2 : 2 : 2, add 10 times the total weight of water, soak for 30 minutes, decoct for 1 hour, filter the filtrate, add 8 times the volume of water to decoct for 1 hour, filter the filtrate, combine the two filtrates, and concentrate to 1.5 g/mL, and the low-dose concentration of Huangqin decoction is 0.5 g/mL.The 48 male db/db mice and 8 db/m mice were purchased from Aiermaite Technology Co., Ltd. . The mice were housed in a single cage in a clean-grade barrier system with free food. Drinking water, temperature controlled at 20-26°C, humidity controlled at 40-70% kept 12/12 hours of animal lighting alternating in the light and dark cycle, changing cages, and bedding once a week. All animal experiment-related operations were followed relevant regulations of the Laboratory Animal Ethics Committee of Heilongjiang University of Traditional Chinese Medicine. The db transgenic mice were adaptively fed for one week after purchase, and 2 random blood glucose tests were performed. db/db mice with blood glucose values higher than 11.1 mol/L were selected for grouping and divided into a model group and a low-dose Huangqin decoction group (3.75 g/kg) and Huangqin decoction high-dose group (11.25 g/kg), and db/m mice were the blank group; after grouping, the mice were administered once a day, and the administration volume was 7.5 mg/kg for 8 weeks.Before the sacrifice, body weights of each mice in tested groups were recorded. After the mice were sacrificed, the cecum contents were taken and placed in a sterile tube in time. After sealing, they were quickly frozen with liquid nitrogen and transferred to a refrigerator at -80°C for storage for subsequent analysis. The epididymal adipose tissue was stored at −75°C. A small portion of the liver tissue was placed in formaldehyde solution of 4% concentration, and the remaining liver tissue was stored at −80°C for further tests. Similarly, intestinal tissues were collected and frozen for further experimental purposes.The cecal tissues of mice were dehydrated and waxed; slice and paste the cut film on the cover glass to dry; before staining, the paraffin in the section must be removed with xylene, then through high concentration to low concentration alcohol, and finally, into distilled water to dye; put the slices that have been put into distilled water into hematoxylin aqueous solution for dyeing for several minutes; color separation in acid water and ammonia water for several seconds, respectively; after washing with running water for 1 h, add distilled water for a moment; dehydrate in 70% and 90% alcohol for 10 minutes, respectively; add alcohol eosin staining solution for 2~3 minutes.+ cells are found within the parenchyma or in the islet-exocrine interface in ≥3 islets. The changes of expression of CD45 in the cecal tissue of diabetic cardiomyopathy mice were taken from the cecum tissues, fixed with wax blocks, and then, cut and sealed for 78 degrees in the oven. After the morning was taken out, the xylene was fixed for 10 min, dehydrated with alcohol, then dehydrated 5~10 min; distilled water was washed for 2 times, 3~5 min/times, 3% H2O2 blocked 10 min; distilled water was cleaned 2 times, 3~5 min/times. Citric acid repair , cool for about 50 minutes, wash the tissue with PBS for 3 times after cooling, 5 min/time, and add primary antibody (100 μ 50). Incubate overnight at 4°C, wash PBS for 3 times the next day, 5 min/time, and add secondary antibody (100 μ 50). Incubate at room temperature for 30 min, wash with PBS for 3 times, 5 min/time, DAB color development for 5-10 minutes (block the color development after observing the color development under the microscope), hematoxylin reappears for 1 min after washing with water for 3-5 minutes, wash with water for 10-15 minutes after observing the dyeing degree under the microscope , dehydrate and dealcoholic for 2-3 min/time after washing with distilled water, and finally, seal the neutral gum in the incubator at 87°C for 2-3 days. The film is taken under the microscope and stored in the computer.According to the consensus criteria developed for type 1 diabetes, an individual can be diagnosed with insulitis when ≥15 CD45The Dneasy powerOil kit of QIAGEN company was used for extraction and DNA detection. The absorbance values of the extracted DNA were measured at 260 nm and 280 nm, respectively, by fluorescence spectrophotometer, the concentration of DNA was calculated, and the quality of DNA was detected by agarose gel electrophoresis of 1%. Adjust the concentration of DNA solution according to the results, and then, store the adjusted DNA working solution in a 4°C refrigerator and the storage solution in a -20°C refrigerator.μL Q5 reaction buffer (5) ×, 5 μL Q5 high fidelity GC buffer (5) ×, zero point two five μL Q5 high fidelity DNA polymerase (5μ)/μL), 2 μL (2.5 mM) dNTPs, 1 μL (10 μM), DNA template solution 2 μL, and ddH2O 8.75 μL. The thermal cycle includes initial denaturation at 98°C for 2 min, then denaturation at 98°C for 15 s, annealing at 55°C for 30 s, extension at 72°C for 30 s, 25 cycles, and final extension at 72°C for 5 min. PCR amplicons were purified with Agcout AMPure Beads and quantified using PicoGreen dsDNA detection kit . After a single quantification step, equal amounts of amplified products were collected, and paired-end 2 × 300 bp sequencing was performed using the Illumina MiSeq platform and MiSeq Kit v3 at Shanghai Parsono Biotechnology  Co., Ltd.The v3-v4 region of the 16S rRNA gene of flora was amplified by PCR with forward primer (ACTCCTACGGGAGGCAGCA) and reverse primer (GGACTACHVGGGTWTCTAAT). The sample-specific 7 bp barcode was integrated into primers for multiple sequencing. The PCR component contains 5 The data obtained were dedried by DADA2 and Vsearch using the Parsono gene cloud platform. The R language script was used to calculate the high-quality sequences' length distribution in all samples. The QIIME2 analysis software was used to perform species taxonomic annotation, taxonomic composition analysis, and diversity analysis, and random forest analysis between multiple groups of samples was used to find the different flora between groups. Finally, the PICRUSt2 analysis software was used to predict functional potential .Statistical analysis was performed using the SPSS 20.0 software. Wilcoxon rank sum test was used to compare the two groups of samples, and Kruskal Wallis test was used to compare the three groups of samples.http://www.uniprot.org/). The target information of the interaction between Huangqin decoction and DE obtained above was imported into the STRING database (https://string-db.org/) to obtain the relationship between potential targets. The Cytoscape v3.8.2 software was used to visualize and analyze the protein interaction analysis results of the STRING database to construct a protein-protein interaction network. The clusterProfiler package in R language was used to perform GO functional annotation enrichment analysis and KEGG pathway analysis for common targets. The enriched pathways are the potential pathways for the drug to play a therapeutic role, and a histogram is drawn to visualize it.Using the TCMSP database, the active components of Huangqin decoction were screened under the conditions of oral absorption and utilization (OB) ≥30% and drug class (DL) ≥ 18%, and the screened components were supplemented in combination with literature reports. The obtained components were sequentially imported into the TCMSP database, the target was predicted, and the protein name of the relevant target was obtained, which was imported into the Uniprot database, and the gene name was transformed to obtain the relevant target of traditional Chinese medicine. The GeneCards database was used to search “DE,” and the intersection of the disease target and the drug target was used as the prediction target of the drug acting on the disease. Gene and protein names were standardized using the Uniprot database ). The results showed that the differences in the bacterial community structure diversity within the blank group, the model group, and the Huangqin decoction administration group were significantly smaller than the differences between the groups, suggesting that the blank group, the model group, and the Huangqin decoction administration group had significant effects on the bacterial community structure diversity. There were significant between-group differences, see Using the method of random forest analysis, we screened the different gut microbiota among the experimental groups. We found that the gut microbiota with differences at the phylum level included deferribacteris, Proteobacteria, tenericutes, Firmicutes, acidobacteria, cyanobacteria, Actinobacteria, Bacteroidetes, fusobacteria, tm7-3, Chloroflexi, and WS6. The gut microbiota with differences at the class level include Mollicutes, epsilon Proteobacteria, deferribacterias, deltaproteobacteria, Actinobacteria, tm7-3, gamma Proteobacteria, bacteroidia, coriobacteria, clostridia, erysipelotrichi, betaproteobacteria, Alphaproteobacteria, synechococcophycedeae, bacilli, [chloracidobacteria], Flavobacteria Chloroflexi,TM7-1, and Fusobacteriia. The gut microbiota with differences at the order level include deferribacteriales, desulfovibrionales, bacillales, actinomyceteales, campylobacterales, bifidobacteria, caulobacterales, enterobacteria, erysipelotrichales, neisseriales, cw040, Chlorophyta, clostridiales, burkholderiales, Rickettsiales, bacteroidales, mycoplastiales, coriobacteriales Anaeroplasmatales, and Pseudanabaenales. The gut microbiota with differences at the family level include [paraprevotellaceae], corynebactriaceae, Veillonella CEAE, [odoribacterae], prevotellaceae, deferribacterae, staphylococcaceae, Rhodospirillaceae, anaeroplasmataceae, caulobacteraceae, streptococcaceae, dehalobactriaceae, christensenellaceae, desulfovibrionaceae, ruminococcaceae, F16 Bifidobacteriaceae, Helicobacteraceae, S24-7, and Pianococcacese; gut microbiota groups that differ at the genus level include [Prevotella], blautia, bilophila, Corynebacterium, Prevotella, Helicobacter, Lactococcus, rikenella, parabacteroides, anaeroplasma, Megasphaera, dehalobacterium, oscillospira, Clostridium, Streptococcus, Shigella, AF12, Desulfovibrio, and Candidatus_Arthromitus; the gut microbiota that produce differences at the species level Parabacteroides_distasonis, Mucispirillum_schaedleri, Alistipes_indistinctus, Bacteroides_uniformis, Corynebacterium_stationis, Lactobacillus_helveticus, Desulfovibrio_C21_c20, [Ruminococcus]_gnavus, Alistipes_finegoldii, Alistipes_onderdonkii, Sulfuricurvum_kujiense, Cellulomonas_uda, Butyricicoccus_pullicaecorum, Lactobacillus_vaginalis, Silene_vulgaris, Staphylococcus_sciuri, Helicobacter_hepaticus, Cetobacterium_somerae, Alistipes_massiliensis, Clostridium_cocleatum; by observing the gut microbiota with different levels of phyla and genus, it is found that there are 6 species of gut microbiota with callback effect at phyla level, namely, TM7, tenericutes, Chloroflex, cyanobacteria, acidobacteria, and WS6. There are 5 gut microbiota species with callback effect at the genus level, namely, [Prevotella], Helicobacter, Prevotella, Lactococcus, and anaeroplasma, as shown in In order to identify the signaling pathway of Huangqin decoction in the treatment of DE, this study used picurst2 to perform functional analysis on the treatment group and identified 170 signaling pathways through enrichment, as shown in https://pubchem.ncbi.nlm.nih.gov/), obtain the SMILE standard structural formula of active ingredients and import it into the swisstargetprediction database (http://swisstargetprediction.ch/). Target prediction was carried out, duplicate targets were deleted, and a total of 265 targets related to active components were obtained.After entering the keyword, “Scutellaria baicalensis, Paeonia alba, jujube and licorice” in the TCMSP database, 34 effective components of Scutellaria baicalensis, 8 effective components of Paeonia alba, 20 effective components of jujube, and 90 effective components of licorice were screened according to the screening conditions of OB ≥ 30% and DL ≥ 0.18. After merging and deleting multiple items, 141 components were obtained, as shown in To identify the main genes of Huangqin decoction's anti-DE , CytoscaIn order to identify the signaling pathway of Huangqin decoction in the treatment of DE, this study used picurst2 to analyze the function of the treatment group and identified 170 signaling pathways through enrichment. Similar signaling pathways are followed by apoptosis (apoptosis), calcium signaling pathway , cell cycle-Caulobacter (cell cycle-Caulobacter), insulin signaling pathway , neurotrophin signaling pathway , NOD-like receptor signaling pathway, and RIG-I-like receptor signaling pathway. Based on the core genes obtained from these seven signaling pathways and network pharmacology, an integrated network map of Huangqin decoction for the treatment of DE was drawn. As shown in This study combined network pharmacology and 16S rRNA sequencing. This study screened 156 active ingredients of Huangqin decoction in treating DE. Among them, quercetin as the core active ingredient, also known as quercetin, is a flavonoid compound with various biological activities. Quercetin and its derivatives are widely distributed in the plant kingdom, mostly in flowers, leaves, and fruits. Exist in the form of glycosides. It has expectorant, antitussive, antiasthmatic, anti-inflammatory, antiallergic, antispasmodic, cardiotonic, blood pressure lowering, coronary artery dilation, blood lipid lowering, antiarrhythmic, antiplatelet aggregation, antioxidant, antitumor, antioxidant, antidiabetic complications, and other pharmacological effects . Ling anβ function of cells was maintained and played a role in improving insulin resistance. MAPK is the key kinase downstream of FGF21. This enzyme accelerates the absorption and utilization of sugars by increasing the expression of GLUT4, and the increase of insulin receptor sensitivity and insulin resistance is achieved through MAPK phosphorylation to achieve the effect of treating type 2 diabetes [In contrast, the high level of FGF21 could significantly reduce the bodyweight of type 2 diabetic rats and accelerate the reabsorption of blood glucose, thereby lowering the level of blood sugar. diabetes . Mao Xiadiabetes ; in the α), among others [Tumor necrosis factor (TNF) has typical cytokine properties and is a major inflammatory factor and pleiotropic cellular regulatory protein. The excessive local release can trigger an inflammatory response and the body's immune response process. This factor is closely related to insulin and promotes insulin resistance by interfering with the insulin signal transduction pathway, resulting in the clinical manifestations of insulin resistance . The KEGg others . This isSome human diseases, including inflammatory bowel disease (IBD), diabetes, obesity, metabolic syndrome, fatty liver, and some neurological diseases, have been confirmed to be related to intestinal flora imbalance. The continuous exposure of intestinal tissue to microorganisms puts the intestinal mucosa in a state of physiological inflammation, where proinflammatory and anti-inflammatory responses are in balance to maintain body homeostasis . If thisThrough network pharmacology and 16S rRNA sequencing analysis, it was found that the mechanism of Huangqin decoction in the treatment of DE may be the prevention and treatment of the disease by inhibiting the expression of inflammatory factors in intestinal epithelial cells, thereby regulating the intestinal flora, but the exact molecular mechanism remains to need further verified.


The purpose of this study was to investigate the mechanisms of action of Tong Xie Yao Fang (TXYF) against ulcerative colitis (UC) by employing a network pharmacology approach.  The network pharmacology approach, including screening of the active ingredients and targets, construction of the active ingredient-drug target network, the active ingredient-diseasetarget network, the protein–protein interaction (PPI) network, enrichment analyses, molecular docking, and targets validation, was used to explore the mechanisms of TXYF against UC. κB) signaling pathway, interleukin-17 (IL-17) signaling pathway, and tumor necrosis factor (TNF) signaling pathway were important pathways. Results of molecular docking indicated that the core ingredients and the target molecule had strong binding affinities. We have validated the high levels of expression of PTGS2 in UC by analyzing three additional datasets from the Gene Expression Omnibus (GEO) database.  34 active ingredients and 129 and 772 targets of TXYF and UC, respectively, were identified. The intersection of the active ingredient-drug target network, the active ingredient-disease target network, and the PPI network suggested that kaempferol, beta-sitosterol, wogonin, and naringenin were the core ingredients and prostaglandin-endoperoxide synthase 2 (PTGS2) was the core target. Enrichment analyses showed that regulation of exogenous protein binding and other functions were of great significance. Nuclear factor-kappa B (NF- There are multiple ingredients, targets, and pathways involved in TXYF's effectiveness against UC, and these findings will promote further research and clinical applications. Ulcerative colitis (UC) is a chronic nonspecific inflammatory bowel disease with diffuse inflammation of both the colon and rectum mucosa . The disTong Xie Yao Fang (TXYF) is a classic and famous Chinese medicine prescription . Four heIn treating diseases, herbs contain many ingredients and target multiple targets, making it difficult to identify their pharmacodynamic ingredients and molecular mechanisms. With the advancement of network technology, the combination of network pharmacology, molecular docking, and bioinformation provides a powerful method for uncovering the complex molecular mechanisms of TCM . They inhttps://tcmspw.com/tcmsp.php)  benzodioxin-9-one, nobiletin, and divaricatol, and so on, according to our findings. PTGS2 was found to be the core target of the TXYF in treating UC and it reflects a strong affinity with the core ingredients, such as aempferol, and so on.PTGS2, also known as cyclooxygenase-2 (COX-2), encodes a prostaglandin synthase that catalyzes the synthesis of arachidonic acid derivatives (prostaglandins) . In coloammation . Reducinammation  revealedammation  showed we et al.  found thdulating . Severaldulating . On the dulating . In UC mdulating  found thdulating  showed kdulating  that condulating . In our Nonetheless, some limitations were present in our study. In the evaluations, the ingredients and targets were derived mostly from databases. However, some ingredients and targets may have been omitted. Moreover, dose of herbs was not taken into consideration. Further experimentation is therefore needed to verify TXYF's multiple mechanisms of action for treating UC, both in vivo and in vitro.κB, IL-17, TNF, and other signaling pathways. Finally, molecular docking is vital to testing and exploring the binding between active ingredients and targets.We obtained the core active ingredients of TXYF in the treatment of UC, namely, kaempferol, -3-(4-hydroxy-3-methoxy-phenyl)-5-methoxy-2-methylol-2,3-dihydropyranobenzodioxin-9-one, nobiletin, divaricatol, beta-sitosterol, wogonin, and naringenin. By constructing the active ingredient-drug target network and PPI network, 1 core gene was screened, namely, PTGS2. GO functional enrichment analysis showed that the cross gene mainly performed exogenous protein binding and other functions, and the results of KEGG pathway enrichment analysis showed that the cross gene was mainly involved in NF-