Vitamin D (VD) deficiency during pregnancy and early brain development is an important environmental risk factor for autism spectrum disorder (ASD). Its specific mechanism of action is still unclear. However, one study on the correlation between metabolomics and VD levels in children with ASD has found that the whole-blood arginine (Arg) levels of children with ASD are significantly negatively correlated with serum VD levels, suggesting that the effect of VD on ASD may be related to the signaling pathway involving Arg. Arg is a precursor of nitric oxide (NO), and changes in its levels most directly affect NO levels and signal transduction pathways. NO, a biologically active free radical, is both a neurotransmitter and a neuromodulator in the central nervous system and is related to the pathogeneses of various neurological diseases. The NO signaling pathway is not only affected by VD levels but also closely related to ASD through a series of mechanisms, such as neurotransmitter imbalance, immune disorders, and oxidative stress. Therefore, the effect of VD on ASD may be achieved via regulation of the NO signaling pathway. The current review discusses the relationship among VD, NO, and ASD as suggested by a large body of evidence in the literature in an effort to provide clues for researchers on the pathogenesis of ASD and the mechanism of VD's impact on ASD. Autism spectrum disorder (ASD) refers to a group of neurodevelopmental disorders that start in early childhood. These disorders, whose main symptoms are social communication impairment and repetitive stereotyped behaviors and interests, have a high prevalence and high disability rate. ASD is one of the fastest-growing severe diseases in the world . In 2016Nutrition is an environmental factor that interacts with genes from the beginning of the embryonic period and has major impacts on organisms. Vitamin D (VD) is an important nutrient associated with ASD. In 2008, Cannell's epidemiological survey found that the levels of VD in the American population decreased year over year, while the prevalence of ASD increased year over year during the same period; in addition, the prevalence was high in areas with heavy air pollution, cities, and high-altitude areas where VD absorption is reduced. This evidence, combined with evidence of the effects of VD on brain development, led to the hypothesis that VD deficiency may be the etiological factor leading to ASD . Since t2D. 1, 25(OH) 2D can also activate a variety of target genes through the VD receptor (VDR) to affect the activities of most organs in the body, including the brain. 25(OH)D is the main form of VD in the blood circulation and is used as an indicator of the nutritional VD status.VD is a fat-soluble vitamin and is the only chemical substance in our body that is obtained from sunlight. It is also known as the “sunshine vitamin.” VD is obtained in very small amounts from a normal diet, but it is primarily synthesized endogenously in the skin after irradiation by ultraviolet light. After being synthesized, VD binds to plasma VD-binding protein and is transported to the liver, where it is catalyzed by 25-hydroxylase to generate 25(OH)D; then, 25(OH)D is further hydroxylated by 1α-hydroxylase in the kidneys to generate the biologically active compound 1,25(OH)It was previously believed that the main role of VD was to regulate calcium and phosphorus metabolism and affect bone growth and development. In recent years, however, studies have shown that changes in VD levels can increase the risks of breast cancer, diabetes, dyslipidemia, cardiovascular disease, etc. –11. WithIn 2010, Meguid et al. reported that the serum 25(OH)D levels of Egyptian children with ASD were lower than those of normal control children . SubsequJia et al. administered VD to children with ASD and found that high-dose supplementation with VD could improve the core symptoms of the disorder , 42–44. Valproic acid (VPA), a deacetylase inhibitor, is an anticonvulsant and mood-stabilizing drug. Prenatal exposure to VPA can induce ASD behavior in male offspring, which is one of the methods for constructing ASD animal models . StudiesIn conclusion, VD deficiency is closely related to the pathogenesis of ASD, and supplementation with high-dose VD can improve the core symptoms of ASD. However, the mechanism of interaction between VD and ASD is still unclear.NO is a highly active intracellular and intercellular messenger molecule that is widely distributed in various tissues, especially nervous tissue. NO has physiological functions such as immune regulation, neurotransmission, blood pressure regulation and platelet aggregation inhibition, and changes in its concentrations are closely related to physiological functions . In the 2+ influx, activates nNOS, catalyzes Arg reactions, and generates citrulline and NO. NO acts on adjacent neurons through diffusion, thereby inducing long-term potentiation (LTP) related to learning and memory and exerting its biological activity . There are two types of NOS: native and inducible. Native NOS is inherent in cells and is divided into neuronal NOS (nNOS) and endothelial NOS (eNOS). Under normal physiological conditions, NO is produced in appropriate amounts through the action of native NOS, thereby exhibiting important physiological functions. nNOS is the major subtype of NOS in the central nervous system. The N-methyl-D-aspartate receptor (NMDAR) at the postsynaptic membrane is activated by glutamate (Glu), which in turn causes Caactivity . eNOS en2−) to generate peroxynitrite, which ultimately damages DNA, fats and proteins during oxidative stress  of many proteins and receptors, thereby changing the activity of receptors, the interactions between proteins and the localization of proteins, etc., resulting in changes in related signaling pathways –60. Dysre stress , 62  NO or nitrite a (metabolite of NO) levels in children with ASD are higher than those in normal children –68. AbnoT-3 gene .ASD patients have abnormal levels of neurotransmitters, and abnormal Glu metabolism is considered to be one of the causes of ASD . Glu can2D is closely related to NO production, and 1, 25(OH)2D and NO can modulate each other's concentrations. VD is a steroid hormone that is mainly derived from the conversion of 7-dehydrocholesterol in the skin under the action of ultraviolet rays. After two-step hydroxylation, the active product 1,25(OH)2D is formed, which binds to VDR. VDR forms a heterodimer with retinoic acid receptor (RXR), which specifically recognizes the VD response element (VDRE) and regulates the transcription of target genes. The VD signaling pathway can regulate NO and NOS levels. In a study on VDR-knockout mice, the loss of VDR resulted in decreased levels of native NOS and increased expression of arginase 2, which catalyzes the hydrolysis of Arg. Arginase 2 further competed with NOS for Arg to hydrolyze it into ornithine (Orn) and urea (UR), resulting in reduced Arg bioavailability and reduced NO levels 2D can inhibit LPS-stimulated changes in iNOS mRNA levels in macrophages and significantly reduce NO production 2D O levels . In vitroduction . 1,252D . TherefoVD deficiency during pregnancy and early brain development is an important environmental risk factor for ASD, and high-dose VD can improve the core symptoms of ASD. The serum VD levels in children with ASD are significantly negatively correlated with the levels of the raw material for NO synthesis, Arg. NO, one of the most important signaling molecules in the brain, is involved in the occurrence of various brain-related diseases and is an important pathological molecule in ASD, Its role in ASD is mediated by a series of mechanisms, such as neurotransmitter imbalance, immune disorders, and oxidative stress. VD can regulate the levels of molecules in the NO signaling pathway. This review hypothesizes a potential mechanism in which abnormal levels of NO during the critical period of brain development caused by various factors  are important factors in the pathogenesis of ASD, and VD affects the pathogenesis and severity of ASD by regulating NO levels. If this hypothesis is verified, monitoring NO levels during pregnancy and early brain development may become an important strategy for ASD risk prediction, and NO levels may guide the timing and dosing of VD supplementation. Researches on this pathway could contribute to detect more objective criteria for the diagnosis of ASD. The combination of VD and NO- related issues might indicate possible subtype of ASD. Eventually this may lead to more effective prevention and treatment of ASD.BW, LX, and HD contributed to conception of the review and wrote sections of the manuscript. BW, HL, and XY participated in the literature search. BW wrote the first draft of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.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.


Diabetic nephropathy (DN) is one of the most serious complications of diabetes and the main cause of end-stage renal failure. Rhubarb is a widely used traditional Chinese herb, and it has exhibited efficacy in reducing proteinuria, lowering blood sugar levels and improving kidney function in patients with DN. However, the exact pharmacological mechanism by rhubarb improves DN remain unclear due to the complexity of its ingredients. Hence, we systematically explored the underlying mechanisms of rhubarb in the treatment of DN. We adopted a network pharmacology approach, focusing on the identification of active ingredients, drug target prediction, gene collection, Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes enrichment. Molecular docking technology was used to verify the binding ability between the main active compounds and central therapeutic targets, and screen out the core active ingredients in rhubarb for the treatment of DN. Finally, molecular dynamics simulation was performed for the optimal core protein-ligand obtained by molecular docking using GROMACS software. The network analysis identified 16 active compounds in rhubarb that were linked to 37 possible therapeutic targets related to DN. Through protein–protein interaction analysis, TP53, CASP8, CASP3, MYC, JUN and PTGS2 were identified as the key therapeutic targets. By validation of molecular docking, finding that the central therapeutic targets have good affinities with the main active compounds of rhubarb, and rhein, beta-sitosterol and aloe-emodin were identified as the core active ingredients in rhubarb for the treatment of DN. Results from molecular dynamics simulations showed that TP53 and aloe-emodin bound very stably with a binding free energy of − 26.98 kcal/mol between the two. The results of the gene enrichment analysis revealed that the PI3K-Akt signalling pathway, p53 signalling pathway, AGE-RAGE signalling pathway and MAPK signalling pathway might be the key pathways for the treatment of DN, and these pathways were involved in podocyte apoptosis, glomerular mesangial cell proliferation, inflammation and renal fibrosis. Based on the network pharmacology approach and molecular docking technology, we successfully predicted the active compounds and their respective targets. In addition, we illustrated the molecular mechanisms that mediate the therapeutic effects of rhubarb against DN. These findings provided an important scientific basis for further research of the mechanism of rhubarb in the treatment of DN.The online version contains supplementary material available at 10.1186/s12906-022-03662-6. Because of the improvement of living conditions, increasingly sedentary lifestyles, increased rates of obesity and an ageing population, the global prevalence of diabetes is increasing at an alarming rate . DiabetiIn recent years, some hospitals have assessed the efficacy of traditional rhubarb (Radix Rhei Et Rhizome), a widely used traditional Chinese herb, as a long-term treatment for DN , 8. Clinhttp://tcmspw.com/tcmsp.php, Ver.2.3) and TCMID databases . Oral bioavailability (OB) refers to the percentage of an oral drug that reaches the systemic circulation. It reflects the degree of absorption and utilisation of drugs in the body [The Traditional Chinese Medicine Systems Pharmacology (TCMSP) database is an authoritative Chinese herbal medicine database that captures the relationships among drugs, targets and diseases , whereasthe body . Drug sithe body . Therefothe body .https://www.drugbank.ca/) is a comprehensive bioinformatics and cheminformatics database that provides detailed information on drug types, chemical structures, drug targets and other information [Homo sapiens. UniProt (http://www.uniprot.org/) is the most informative and extensive protein database, and it is used to standardise gene names [DrugBank (ormation . The relne names . We obtahttps://omim.org/) is an authoritative and comprehensive database of human genes and genetic phenotypes [https://www.genecards.org/) is an integrative database that provides information on all predicted and annotated human genes [https://www.pharmgkb.org/) is an integrated resource about how variation in human genetics leads to variation in responses to drugs [https://www.drugbank.ca/), as a comprehensive bioinformatics database, also provides relevant information on disease targets [The human genes associated with DN were gathered from four databases, namely Online Mendelian Inheritance in Man (OMIM), GeneCards, Pharmacogenomics Knowledge Base (PharmGkb) and DrugBank. OMIM , a database of known and predicted PPI that uses bioinformatic strategies to collect information [Homo sapiens’, collected the PPIs with confidence scores > 0.4 and hid the disconnected nodes in the network.The PPIs of the therapeutic targets of rhubarb in the treatment of DN were gathered using STRING , which can graphically display and analyse networks [The active compounds–therapeutic targets network was built by linking the active compounds with the therapeutic targets of rhubarb in the treatment of DN. The PPI network of therapeutic targets was established by connecting the therapeutic targets to their interacting targets. The two networks were visualised using Cytoscape , and their corresponding PDB IDs were as follows: 3DCY, 5IKR, 5I4Z, 6X8I, 5H31 and 5FV8 [https://pymol.org/2/) for various optimisation operations such as dehydration, hydrogenation and isolation of original ligands, and the optimised targets were entered into AutoDockTools − 1.5.6 to construct a docking grid box. The active site of molecular docking was determined using the ligand coordinate in the target protein complex, and Autodock vina 1.1.2 was used for molecular docking research [Molecular docking is the most widely used technique in drug design because it can predict the ability of ligands and proteins to bind, as well as the location of binding . In thisand 5FV8 –43. Thesresearch . Twenty To further investigate the dynamic interaction process and the stability of binding between proteins and small ligands, molecular dynamics simulation was also performed. Molecular dynamics simulation is a popular technique to study protein motion by tracking their conformational changes over time . The GROhttp://geneontology.org/) to clarify the possible biological mechanisms of therapeutic targets, including biological process (BP), cell component (CC) and molecular function (MF) terms deemed significant at P < 0.05 [https://www.kegg.jp/) to further explore the vital biological relevance of therapeutic targets and verify the reliability of the integrated results [We used the GO database , CC  and MF terms . Hence, rhubarb may function through the aforementioned pathways. For example, aldosterone is an endogenous steroid hormone that is involved in the formation and development of DN . AldosteCordyceps sinensis could mitigate epithelial–mesenchymal transition and the subsequent extracellular matrix deposition to exert a therapeutic effect on experimental diabetic renal fibrosis by inhibiting the p38 MAPK signalling pathway [According to the KEGG terms, the therapeutic targets of rhubarb against DN were mainly associated with the PI3K-Akt signalling pathway, p53 signalling pathway, AGE-RAGE signalling pathway in diabetic complications and MAPK signalling pathway. Podocytes maintain the glomerular filtration barrier composed of the basement membrane and slit diaphragm , and pod pathway . These fFinally, in order to explore the accurate therapeutic mechanism of rhubarb, molecular docking was performed. The results showed that the main active compounds of rhubarb have different affinities with the central therapeutic targets. These interactions are the main forces between proteins and compounds, which form stable complexes of proteins and compounds. Molecular docking studies provide an explanation of the protein-compound interactions, which laid the foundation for further research on the therapeutic mechanism of active compounds. Interestingly, Albersmeyer et al. reported a case of AKI induced by excessive ingestion of rhubarb in the treatment of DN . HoweverOur study still has some limitations. Our study relied on data mining and analysis, and further validation using experimental evidence is required. More databases on the composition and targets information of TCM should be included, which can make the results of our results more reliable. In summary, a comprehensive understanding of rhubarb for the treatment of DN still depends on the common development of multi-disciplines.We used network pharmacology analysis and molecular docking technology to explore the potential mechanism of the effects of rhubarb against DN, clarifying the core active ingredients in rhubarb were rhein, beta-sitosterol and aloe-emodin, and the key therapeutic targets were TP53, CASP8, CASP3, MYC, JUN and PTGS2. The therapeutic properties of rhubarb against DN arise from the regulation of biological pathways involved in podocyte apoptosis, inflammation and renal interstitial fibrosis. These findings demonstrate the importance of understanding traditional Chinese medicines and establish a base for further research on the pathogenesis of DN, as well as the development of new drugs.Additional file 1.Additional file 2.Additional file 3.Additional file 4.Additional file 5.Additional file 6.


Nuanxin capsule (NX), an in-hospital preparation of Guangdong Provincial Hospital of Chinese Medicine, has been used in heart failure (HF) treatment for 15 years, but its mechanism and protective effect have not been investigated. This study was aimed at exploring the mechanism and protective effect of NX on HF treatment via network pharmacology analysis and experimental validation. Network pharmacology analysis predicted that NX was involved in the regulation of response to apoptotic process and hypoxia via protecting cellular damage and mitochondrial dysfunction against chronic hypoxia. Its mechanism may be involved in the regulation of the PI3K-Akt signaling pathway, HIF-1 signaling pathway, AMPK signaling pathway, and MAPK signaling pathway. Experimental validation indicated that NX was capable of improving cellular viability, restoring cellular morphology, and suppressing cellular apoptosis cellular. NX also exerted cardioprotection by inhibiting mitochondrial membrane potential injury and protecting mitochondrial respiratory and energy metabolism in a chronic hypoxia cellular model, which was consistent with the results of network pharmacology prediction. In addition, the screened active compounds of NX did have a good binding with their key targets, indicating NX may exert protective effect through multicompounds and multitargets. In conclusion, NX had a protective effect on HF through cellular and mitochondrial protection against chronic hypoxia via multicompounds, multitargets, and multipathways, and its mechanism may be involved in modulating the PI3K-Akt signaling pathway, HIF-1 signaling pathway, AMPK signaling pathway, and MAPK signaling pathway. It affects approximately 23 million people and is accompanied by increasing mortality and hospitalization rates . Chronic hypoxia . Despite hypoxia .Red Ginseng, Radix Aconiti Lateralis Preparata, Poria, and Coix seed, which exert effects of warming heart qi by alleviating water retention, expelling blood stasis in the heart vessels, strengthening the spleen, and nourishing the heart in terms of Chinese medicine [Traditional Chinese medicine contains multicompounds and exerts therapeutic effects on HF by targeting multiple targets through multiple pathways; it has been proven to be effective and safe in preventing and treating cardiovascular diseases in China . The Nuamedicine . Clinicamedicine . A systemedicine . Our premedicine , but thein vitro was further validated using a chronic hypoxia cellular model.As NX has been identified as a multiple compound using ultraperformance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UPLC-Q-TOF-MS) method in our previous study, thus in this study, we further predicted the pharmacological effect and mechanism of the NX on HF based on the identified multicompounds using network pharmacology and molecule docking analyses. Then, the pharmacological effect of NX Red Ginseng, Radix Aconiti Lateralis Preparata, Poria, and Coix seed and was purchased from Guangdong Provincial Hospital of Chinese Medicine . Dulbecco's Modified Eagle Medium , fetal bovine serum , 0.25% Trypsin-EDTA Solution, 3--2,5-diphenyltetrazolium bromide , and phosphate-buffered saline  were purchased from Gibco , and Annexin V-FITC Apoptosis Detection Kit (No. C1062L) and mitochondrial membrane potential assay kit with JC-1  were purchased from Beyotime Biotechnology Co., Ltd. . Seahorse XF Cell Mitochondrial Stress Test Kit (103010-100) was purchased from Agilent (United States).Rat cardiomyocyte H9c2 cells were purchased from Cell Bioscience Inc. . Nuanxin capsule is composed of https://pubchem.ncbi.nlm.nih.gov/), and the 2D structures of these compounds were downloaded from the PubChem database.The compounds were identified by the UPLC-Q-TOF-MS method in our previous study ; thus, ahttps://old.tcmsp-e.com/tcmsp.php) and SwissTargetPrediction Platform (http://www.swisstargetprediction.ch/).The targets of compounds contained in NX were collected from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform  and the GeneCards database (http://www.genecards.org/) with a relevance score no less than 5.The key words “Heart failure”, “Cardiac Failure”, and “Heart Decompensation” were used to retrieve the targets related to HF in the OMIM database  and uploaded to the STRING database (https://cn.string-db.org/) to acquire protein-protein interaction (PPI) network, with a score no less than 0.75. Cytoscape_3.9.1 software was utilized to visualize the PPI network. Top 20 targets with the highest degree value were regarded as core targets.The UniProt IDs of candidate targets were matched from the UniProt database (https://david.ncifcrf.gov/), including cellular component (CC), biological process (BP), and molecular function (MF). KEGG pathway enrichment analysis on the intersection targets was also performed through DAVID platform. The item with p value < 0.05 was selected, and the top 20 items of GO enrichment and the top 40 items of KEGG pathway enrichment were visualized by enrichment bar or bubble plots.The GO enrichment analysis on the intersection targets was conducted through the DAVID platform , and the PyMOL software was used to embellish the protein structures via affixing hydrogen atoms and removing water molecules. Finally, the modificatory protein structures and 3D structures of the homologous small molecules were downloaded from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and were submitted to Schrödinger software to perform molecular docking. A binding energy explained that the ligand could spontaneously bind with the receptor, and the lower binding score indicated the stronger binding ability.Five key targets enriched in the potential pathways were selected, their structures were downloaded from the PDB database  at the time when modeling began; finally, the cells in the chronic hypoxia group and NX groups were transferred to a 37°C, 0.1% O2, 5% CO2, and 95% N2 for 24 h hypoxia.Rat cardiomyocyte H9c2 cells were cultured in DMEM supplemented with 10% FBS. The culture medium was replaced once every 2-3 days, and the cells were passaged at a ratio of 1 : 3. The cardiomyocytes were assigned into 5 groups: (a) control group, (b) chronic hypoxia group, (c) chronic hypoxia group+low-dose NX (NX-L) group, (d) chronic hypoxia group+median-dose NX (NX-M) group, and (e) chronic hypoxia group+high-dose NX (NX-H) group. The method of hypoxia model was as follows: when the cell confluency density reached about 80% in a 37°C, 5% COμL of DMSO solution was added to each well for 10 min at room temperature to dissolve the formazan crystals. The OD value of each well indicating cellular viability was recorded at 570 nm using a microplate reader ; the cytotoxicity of NX on cells was represented by cellular viability.Cytotoxicity of NX and cellular proliferation were detected by MTT assay. Cells were cultured in 96-well plates at a density of 6000 per well. When the cell confluency was 80%, the original medium was removed and the cells were washed twice with 1x PBS. After treatment and modeling, MTT solution (0.5 mg/mL) was added to each well after removing the drug solution, and cells were incubated in a 37°C incubator for 4 hours. Finally, the MTT solution was removed, 100 In cellular morphology observation, after cell culture and treatment, cellular morphology was imaged by a fully automatic inverted fluorescence microscopic analysis system .μL Annexin V-FITC binding solution (1x) and 10 μL Annexin V-FITC plus 5 μL propidium iodide (PI) solution for 15 min at room temperature. Finally, the cell apoptosis rate was immediately analyzed by flow cytometry .The cell apoptosis rate of H9c2 cells was measured by Annexin V-FITC Apoptosis Detection Kit according to the manufacturer's instructions. After receiving different treatments, the culture medium was aspirated and cells were collected; then, cells were stained with 195 μL JC-1 staining solution was added to each group of cell samples for 20 min incubation at 37°C, and then, it was washed twice with JC-1 buffer solution. Finally, the injury degree of ΔΨm was immediately detected by flow cytometry .The effects of different concentrations of NX working solution on the mitochondrial membrane potential of H9c2 cells were evaluated by flow cytometry using the mitochondrial membrane potential assay kit with JC-1 . The preprocessing method was performed according to the manufacturer's protocol. After treatment, 500 4; then, the cells were cultured and treated. After treatment, the medium was replaced with XFe assay medium dissolved with 10 mM glucose, 1 mM pyruvate, and 2 mM glutamine. Then, the cell plate was placed in a CO2-free incubator at 37°C for 1 h, and the stock solutions of oligomycin (complex V inhibitor), FCCP (respiratory uncoupler), and rotenone/antimycin A (inhibitor of complex I and complex III) were added to the A, B, and C ports, respectively. Basal respiratory, spare respiratory capacity, mitochondrial maximal respiration, and adenosine triphosphate (ATP) production of cells were measured through a Seahorse XFe24 analyzer . Finally, the OCR value was normalized by CCK8 detection.Mitochondrial respiration by oxygen consumption rate (OCR) in each group was measured by Seahorse XFe Cell Mitochondrial Stress Test Kit. Cells were plated overnight in 24-well plates at a density of 4 × 10p < 0.05 indicates significant statistical difference.The experimental data were expressed as the mean ± SEM, and one-way ANOVA for comparing the difference among groups was performed using GraphPad Prism Software . Postmultiple comparison was conducted by the Bonferroni test. The experimental data were repeated from 3 independent experiments; A total of 23 compounds were identified by the UPLC-Q-TOF-MS method. Apart from two compounds (Meringenin and 20(S)-NG-R2), which had no record about their information in the PubChem database, 21 compounds were collected for latter analysis see .401 protein targets of identified compounds of NX were harvested via SwissTargetPrediction website platform, and 120 targets were collected from the TCMSP database. After duplication, a total of 481 targets were selected. In addition, 12017 and 1279 HF targets were obtained from the GeneCards database and OMIM database, respectively, and 12063 HF targets were screened after duplication. Finally, Venn analysis screened 454 intersection targets of identified compounds of NX related to HF see .Tumor suppressor p53/oncoprotein Mdm2, Serine/threonine protein kinase AKT, and Axin1/beta-catenin . The PPI network consists of 438 nodes and 3963 interaction edges . 20 targenin see .GO functional annotation and KEGG pathway analysis were performed on 454 targets in the PPI network. In the cellular components (CC), targets were closely enriched on the cellular membrane, cell surface, and mitochondrion see . TargetsKEGG pathway analysis showed that the top pathways with higher degree included the PI3K-Akt signaling pathway, HIF-1 signaling pathway, AMPK signaling pathway, MAPK signaling pathway, and apoptosis signaling pathway see . When weMTT assay suggested that NX had no cytotoxicity on H9c2 cells as there was no statistical difference among the groups  compared with control group see . In addiΔΨm) was detected by JC-1 staining. ΔΨm in the chronic hypoxia group significantly increased compared with that in the control group, while in the 1, 2, and 4 mg/mL groups, it was decreased with statistical difference. All the above revealed that NX could inhibit mitochondrial membrane potential (ΔΨm) injury induced by chronic hypoxia.To determine whether NX protected mitochondria damage in response to chronic hypoxia, mitochondrial membrane potential , AKT1 (docking score = −5.765), GSK3B (docking score = −6.327), and SLC2A4 (docking score = −7.912). Naringenin had a strong binding ability with MAPK3 (docking score = −6.749) and GSK3B (docking score = −6.749). Acacetin had a strong binding ability with GSK3B (docking score = −6.788), Aurapten had a strong binding ability with GSK3B (docking score = −6.991), and Oxypeucedanin had a strong binding ability with GSK3B (docking score = −6.06) see . The docin vitro pharmacodynamic experiments to investigate the cardioprotective effects of NX on HF.Heart failure is one of the most common clinical diseases, accounting for abundant morbidity and mortality worldwide. At present, even though there is large progress in drug management, it is insufficient to halt HF progression , 15; thuvitro via inhibiting oxidative stress damage and enhancing mitochondrial biogenesis. Moreover, the cardiac function of Sprague-Dawley rats undergoing MI/R surgery improved after treatment with naringenin. Ginsenoside rb2 also has the potential to protect against HF. Wang et al. [vivo or vitro, but it still lacks verification on the pharmacological effect of NX as a mixture of multicompounds against HF.NX contains 4 kinds of herbs with multicompounds; a variety of chemical reactions can occur during the extraction and purification process. Therefore, in order to clarify the chemical composition of NX, we collected a composition identified by the UPLC-Q-TOF-MS method to predict the therapeutic mechanisms of NX on HF. 21 active components were obtained, including naringenin, and ginsenoside rb2. Naringenin, a flavonoid, has been demonstrated to exert cardioprotective effects . Yu et ag et al.  found thNetwork pharmacology analysis indicated that the NX was involved in the response to apoptotic process, response to hypoxia, and the mitochondrial response to hypoxia and was involved in cellular components including the cellular membrane, cell surface, and mitochondria. These indicate that NX may exert its antihypoxia effect and protection through inhibiting cellular injury and mitochondrial dysfunction. Hypoxia is a common feature in the pathophysiology of various cardiovascular diseases ; it can Mitochondrial dysfunction has been implicated in the development of HF due to myocardial hypoxia . The inaThe PPI network analysis predicted that TP53 might play an important role in NX against HF. TP53 is upregulated in the failing myocardium of humans . High coα protein to activate the HIF-1 pathway [α and a series of glycolysis-relevant enzymes in a HIF-1α-dependent manner by promoting adenosine triphosphate (ATP) production, glucose uptake, and glycolysis but also by activating the downstream vascular endothelial growth factor (VEGF) pathway to increase myocardial capillary density. The role of the AMPK pathway in the therapeutic mechanism of HF has been widely studied. As an “energy regulator,” AMPK plays an essential role in regulating autophagy, cell survival, and energy metabolism [The KEGG enrichment results showed that the potential targets were mostly involved in the PI3K-Akt, HIF-1, AMPK, and MAPK pathways. The PI3K-AKT pathway regulates cardiac metabolism and heart growth during pathological remodeling of HF , 45. The pathway . Wang et pathway  found thtabolism , 50. Pretabolism . The molvitro experiments. However, in terms of the bioactive forms of the components that may be different in vivo and vitro, our conclusions needed to be further verified with animal experiments or human trials in the future.It is worth mentioning that our study was a preliminary exploration of NX effect on HF. We collected compositional information of 21 compounds, but the quantitative determination of content required further experiments. In addition, we explored the mechanism of NX against HF via the network pharmacology and validated the cardioprotective efficacy of NX with In conclusion, NX had a protective effect on HF through cellular and mitochondrial protection against chronic hypoxia via multicompounds, multitargets, and multipathways; its mechanism may be involved in modulating the PI3K-Akt signaling pathway, HIF-1 signaling pathway, AMPK signaling pathway, and MAPK signaling pathway, which need to be verified in our future research.


Dipsaci Radix, is verified to exert therapeutic effect on alleviating recurrent spontaneous abortion (RSA). However, due to the lack of relevant research, its molecular mechanism is still unclear. We retrieved targets for AS6 and RSA, and then used their overlapped targets for PPI analysis. In addition, we used GO and KEGG enrichment analyses, and molecular docking to investigate the anti-RSA mechanisms of AS6. Furthermore, we conducted in vitro experiments to validate the predictions of network pharmacology. Results showed that a total of 103 AS6-associated targets and 2084 RSA-associated targets, with 49 targets overlapped. GO enrichment analysis showed 845 significant biological processes like decidualization, while KEGG pathway enrichment analysis revealed 76 significant entries including 18 signaling pathways, which were closely linked to PI3K-Akt, HIF-1, TNF, IL-17, and VEGF signaling pathways, etc. Molecular docking findings verified that AS6 had tight link with the key targets including JUN, CASP3, STAT3, SRC, and PTGS2. Notably, in vitro experiments revealed that AS6 treatment could exert lower expressions of JUN, pro-CASP3, CASP3, STAT3, SRC, and PTGS2 in decidual cells compared with progesterone despite the expressions of STAT3, SRC, and PTGS2 with no significant difference, and mifepristone could interfere with the effects. In general, numerous targets and multiple pathways involve during the process of AS6 treatment against RSA. Moreover, our in vitro research first reported that AS6 may regulate the expressions of key targets  in decidual cells to promote decidualization, thus treating RSA.Asperosaponin VI (AS6), as the quality marker of  Recurrent spontaneous abortion (RSA) is a common reproductive endocrine disorder, which refers to consecutive spontaneous abortions happening twice or more before 20 weeks of gestation , 2. AccoDipsaci Radix, which is an important drug in Shoutai pill. Pharmacological studies in recent decades have shown that Dipsaci Radix has a variety of biological activities, including antiuterine contraction, antiinflammatory, antiaging, antiarthritic, antiosteoporosis, fracture healing, neuroprotection, and it has been verified to benefit Chinese women from miscarriages, serving as the preferred herb in clinical treatment  098.5 cells/ml. After 30 min, we removed nonadherent cells and replaced the medium after 48 h.Fresh decidua tissues were taken aseptically, washed in PBS to remove blood, cut into pieces, digested with trypsin-EDTA (0.25%) for 5–10 min, and digestion was stopped by adding DMEM/F12 medium containing 10% FBS. The cell clusters in the final digestion were extracted with a 23-gauge needle, filtered through a 200-mesh sieve, centrifuged at 2000 rpm for 5 min, resuspended in DMEM/F12 medium containing 10% FBS, and incubated in flask at 5 × 10We treated the decidual cells with a concentration gradient of progesterone (P), mifepristone (M), and AS6 for 12, 24, and 48 h. According to the manufacturer's protocol, we performed a cell counting kit 8 (CCK-8) assay to detect cell proliferation abilities using an optical density (OD) setting of 450 nm in the microplate reader .μL RIPA lysis buffer prepared with phosphatase inhibitor and protease inhibitor per dish (Beyotime). The protein bands were transferred to polyvinylidene fluoride membranes  by electrophoresis and wet transfer steps, closed with QuickBlock™ Western closing solution (Beyotime) at room temperature for 30 min, added primary antibody and incubated overnight at 4°C in a shaker, then added the corresponding secondary antibody and incubated in shaker at 24°C for 1.5 h. The antibody reactivity level was subsequently detected by gel imaging system (Bio-Rad). Finally, the grayscale values were quantitated using ImageJ software.We cultured decidual cells in 6-well plates and treated them with the specific concentration of progesterone, Asperosaponin VI with or without mifepristone according to CCK-8 screening results. After the treatment, the protein was extracted by adding 200 t-tests were used to compare two separate samples. One-way ANOVA was used for comparison of univariate samples between multiple groups. p-value <0.05 indicates statistical significance.All results were expressed as mean ± standard deviation. Student's We obtained a total of 103 AS6-associated targets. After data standardization by the UniProt database, we obtained AS6-related target proteins called as Gene symbols. AS6-associated structure and target results were shown in supplementary Tables A total of 2084 RSA-associated target proteins were retrieved. We took the overlap of AS6- and RSA-associated targets as OTPs, which included 49 overlapped targets, as shown in OTPs were imported into the STRING platform with the targets having no link to others hidden. We imported the PPI data into Cytoscape to draw PPI network in p-value set to 0.001. We got 845 items of biological process (BP). The top 20 items were shown in We finally got totally 76 items including 18 key signaling pathways listed in Among 21 core targets, the top five target proteins in terms of degree were chosen for molecular docking, including JUN, CASP3, STAT3, SRC, and PTGS2, respectively, which were considered as the key targets in the process of AS6 treating RSA. To verify how AS6 binds to the key targets, we adopted molecular docking using Autodock Vina to predict their docking interactions. Based on Based on Based on Based on Based on in vitro research. The concentrations of progesterone used in the study were 0 (control group), 5, 10, and 20 μmol/L. The results revealed that the progesterone concentration at 20 μmol/L exerted proliferative effect on the proliferation of decidual cells, which was selected for subsequent experiments  exerts essential effects on the maintenance of pregnancy, the declining level of which in blood in early pregnancy leads to necrosis of the decidua, thereby causing miscarriage . MifepriAccording to PPI network topology analysis of OTPs, we noticed that these targets were characteristics of decidualization, autophagy, angiogenesis, oxidative stress, inflammation, and endocrine-related proteins. We identified five key targets including JUN, CASP3, STAT3, SRC, and PTGS2, which are in close conjunction with AS6 according to molecular docking findings, indicating that they may be the key targets of AS6 in treating RSA.β, TNF-α, and IFN-γ in FTDCs, which can be mediated by the activation of JUN-related signaling [JUN (Transcription factor AP-1 subunit Jun), which is the mediator of trophoblast invasion, plays a critical role in decidualization , 31. It ignaling . NotablyCaspase-3 (CASP3) is an apoptosis-related gene, whose expression has close correlation with placental separation . Some stSignal transducer and activator of transcription 3 (STAT3) phosphorylation has a close relationship with embryo implantation and decidualization . RSA resα, IL-6, IL-1B, and TNF-α in a dose-dependent manner [Similar to PPI analysis, GO enrichment results show consistent results as demonstrated in t manner .in vitro by upregulating HIF-1α/VEGF pathway and can effectively promote the angiogenesis of regenerative tissues and promote wound healing in vivo [KEGG enrichment results revealed that PI3K-Akt, HIF-1, TNF, IL-17, and VEGF signaling pathways may exert regulatory functions on AS6 against RSA. Some studies have verified that the inhibition of PI3K-Akt signaling pathway can reduce trophoblast cell proliferation and migration . Moreove in vivo . So HIF- in vivo . Abnorma in vivo .in vivo and in vitro experiments are needed to confirm our findings.In summary, our results predict some potential therapeutic targets and pathways, providing reference for future studies on AS6 treatment against RSA. However, one limitation of this study is that further in vitro study first reported that AS6 may regulate the expressions of key targets in decidual cells including JUN, CASP3, STAT3, SRC, and PTGS2 to promote decidualization, thus treating RSA.Collectively, our results revealed that AS6 may treat RSA possibly by regulating numerous signaling pathways and targets related with decidualization, autophagy, blood vessel endothelial cell migration, angiogenesis, inflammatory response, oxidative stress, and immune response, etc. Moreover, our


San-Jiu-Wei-Tai granules (SJWTG) are a significant Chinese patent medicine for the treatment of chronic gastritis (CG), having outstanding advantages in long-term treatment; however, the chemical composition and potential mechanism have not been investigated until now. In this study, a rapid separation and identification method based on UPLC-QE-Orbitrap-MS was established, and 95 chemical components from SJWTGs were identified, including 6 chemical components of an unknown source that are not derived from the 8 herbs included in SJWTGs. The identified chemical components were subsequently analysed by network pharmacology, suggesting that the core targets for the treatment of CG with SJWTGs were EGFR, SRC, AKT1, HSP90AA1, MAPK1, and MAPK3 and thus indicating that SJWTGs could reduce the inflammatory response of gastric epithelial cells and prevent persistent chronic inflammation that induces cancerization by regulating the MAPK signalling pathway and the C-type lectin receptor signalling pathway as well as their upstream and downstream pathways in the treatment of CG. The key bioactive components in SJWTGs were identified as 2,6-bis(4-ethylphenyl)perhydro-1,3,5,7-tetraoxanaphth-4-ylethane-1,2-diol, a chemical component of an unknown source, murrangatin, meranzin hydrate, paeoniflorin, and albiflorin. The results of molecular docking showed the strong binding interaction between the key bioactive components and the core targets, demonstrating that the key bioactive components deserve to be further studied and considered as Q-markers. By acting on multiple targets, SJWTG is less susceptible to drug resistance during the long-term treatment of CG, indicating the advantage of Chinese patent medicines. Furthermore, the preventive effect of SJWTGs on gastric cancer also demonstrates the superiority of preventive treatment of disease with traditional Chinese medicine. Helicobacter pylori (H. pylori) infection ; and ultrapure water (ultra-purified by Milli-Q Advantage A10 Water system) were used.v/v) methanol-water, ultrasonically extracted for 30 min, and then cooled to room temperature. After centrifuging at 10000 rpm for 10 min, 20 µL supernatant was diluted 50 times with 50% (v/v) methanol-water, and the diluted sample was then centrifuged at 12000 rpm for 10 min to obtain the final sample solution.Accurately weighed granules (1.00 g) were suspended in 10 mL 50% ; CPA225D Electronic balance (SARTORIUS); KQ5200DA Ultrasonic Cleaners ; Pico 17 High-speed centrifuge (Thermo Scientific); and Nitrogen evaporator N-EVAP 116 (Organomation).The following instruments were used: Thermo Scientific Q Exactive (Thermo Scientific); Xcalibur (Thermo Scientific); Vanquish Duo UHPLC System for Dual LC Workflows (Thermo Scientific); Waters BEH UPLC C18 column , column temperature 40°C, injection volume 3 µL, flow rate 0.3 mL/min, mobile phase comprising 0.1% formic acid aqueous solution (A) and acetonitrile (B). The eluting program was 5–5%B for 0–1 min, 5–95%B for 1–18 min, and 95–95%B for 18–20 min.We followed the methods of Feng et al. and the methods' description partly matches their wording . Waters The ion source was electron spray ionization (ESI); MS was operated in negative/positive mode; the scan mode was full scan/ddMS2, with positive and negative ion alternating scanning; and the scanning mode was 100–1300 Da, with a capillary temperature of 350°C. The spray voltage in positive mode was 3800 V, the spray voltage in negative mode was 3200 V, the sheath gas was 35 arb, and the auxiliary gas was 15 arb. Three collision energies of low, medium, and high were used for MS2. The positive ion mode was 20 V, 40 V, and 60 V, and the negative ion mode was 30 V, 50 V, and 70 V. Resolutions of full scan and ddMS2 were 70000 FWHM  and 17500 FWHM, respectively.The chemical components in the SJWTG extract were identified according to the chromatographic retention time, relative molecular mass, fragment ion information, relevant literature data, and MS fragmentation ion.https://www.swisstargetprediction.ch/) with the conditions set as “Homo sapiens” and probability value >0. Afterwards, the SJWTG component-target network was constructed by Cytoscape 3.7.2 software.The 95 identified compounds were selected as target chemical components, the targets of which were obtained by Swiss Target Prediction (https://omim.org/) and GeneCards (https://www.genecards.org/) with “chronic gastritis” as the keywords.CG-related targets were retrieved from Online Mendelian Inheritance in Man .SJWTG-CG common targets were obtained by taking the intersection of SJWTG targets and CG-related targets via a Venn diagram plotted by EHBIO (https://string-db.org/cgi/input.pl) to obtain protein-protein interaction (PPI) information. The confidence score was set to 0.9 or higher, and the edge free proteins were excluded in order to ensure the reliability of the research data. Cytoscape 3.7.2 software was used to visualize the structure of the protein network and analyse its topological characteristics. The core targets for the treatment of CG with SJWTGs were obtained by taking the median of degree centrality (DC), betweenness centrality (BC), and closeness centrality (CC), as screening conditions, twice.The SJWTG-CG common targets were imported into the STRING database (https://metascape.org/) for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, and the enrichment diagram was plotted by bioinformatics.The core targets for the treatment of CG with SJWTGs were imported into Metascape (https://www.kegg.jp/kegg/mapper/) was then used to annotate the roles of important targets of the vital pathways.The effective components, core targets, and action pathways of SJWTGs were imported into Cytoscape 3.7.2 software to establish the core component-target-pathway network, with the purpose of mining the key bioactive components and vital pathways. KEGG Mapper (https://pubchem.ncbi.nlm.nih.gov/), processed to minimize energy, and transformed into the MOL format as ligands for docking through Chem3D. The X-ray crystal structures of the proteins were obtained from the Protein Data Bank . Afterwards, the structures of these proteins were optimized by Maestro 11.8 software, including the assignment of bonds and bond orders, the addition of hydrogens, filling in missing loops or side chains, capping uncapped termini, adjusting bonds and formal charges for metals, correcting mislabelled elements, and deleting unnecessary parts in the structure. Subsequently, each grid box was centred on the original ligand in each protein. The docking results were visualized and displayed as three-dimensional (3D) diagrams and 2D diagrams using Maestro 11.8.The two-dimensional (2D) structures of the compounds used as ligands were obtained from the PubChem database  is shown in (1) Identification of Characteristic Compounds in Melicope pteleifolia. Compound 60 was detected at the retention time of 8.40 min and exhibited the quasi-molecular ion at m/z 247.0963 [M+H]+. Attributed to the elimination of H2O, the fragment ion at m/z 229.0858 [M−H2O+H]+ further fragmented in two ways: the first produced fragment ions at m/z 213.0546 [M−H2O−CH4+H]+ via the neutral loss of CH4, while the second fragment ion at m/z 175.0390 [M−H2O−C4H6+H]+ was the result of the successive elimination of C4H6, which was the dominant peak in the MS2 spectrum, indicating that the elimination of C4H6 might be the major fragmentation pathway. The subsequent fragment ion at m/z 147.0435 [M−H2O−C4H6−CO+H]+ corresponded to the loss of CO, and the consecutive loss of CO and CO led to the formation of a product ion at m/z 213.0546 [M−H2O−C4H6−3CO+H]+. Using the data in MassBank of North America (https://mona.fiehnlab.ucdavis.edu/), compound 60 was identified as marmesin (marmesin .(2) Identification of Characteristic Compounds in Murraya exotica. Compound 72 was eluted at 9.15 min, with the protonated molecule ion at m/z 259.0962 [M+H]+. The fragment ion at m/z 231.1014 [M−CO+H]+ was formed by the successive elimination of CO. The fragment ion at m/z 203.0699 [M−2CO+H]+ was observed due to the characteristic coumarin loss of CO, and the subsequent loss of CH2 led to the fragment ions at m/z 189.0545 [M−2CO−CH2+H]+. The fragment ion at m/z 159.0440 [M−2CO−CH2−2CH3+H]+ was attributed to the consecutive loss of CH3 and CH3 and the high abundance ion at m/z 131.0492 [M−2CO−CH2−2CH3−CO+H]+ was caused by the loss of CO, producing the subsequent ion at m/z 103.0546 [M−2CO−CH2−2CH3−2CO+H]+ via the elimination of CO. Using MassBank of North America, the main fragment ions were found to be identical to those of murralongin  Identification of Characteristic Compounds in Zanthoxylum nitidum. Compound 61 yielded the molecule ion at m/z 348.1227 [M]+ at the retention time of 8.50 min. MS2 spectra of this compound exhibited the neutral loss of CH4, resulting in a product ion at m/z 332.0913 [M−CH4]+. The fragment ion at m/z 332.0931 further fragmented in two ways, both of which broke up into the ion at m/z 290.0811 [M−CH4−CH2−CO]+, corresponding to the consecutive loss of CH2 and CO: the first pathway exhibited a loss of CH2 first, leading to the fragment ion at m/z 318.0760 [M−CH4−CH2]+, while the second exhibited a loss of CO first, leading to the fragment ion at m/z 304.0964 [M−CH4−CO]+. Based on these results and the reference standard +. The product ion m/z 215.1428 [M−H2O+H]+ was observed due to the neutral loss of H2O, and the MS2 spectra showed a high abundance ion at m/z 187.1480 [M−H2O−CO+H]+, resulting from the successive elimination of CO; the removal of H2O followed by CO is a significant characteristic of the cleavage of the lactone ring. The fragment ions at m/z 145.1012 [M−H2O−CO−C3H6+H]+, m/z 145.1012 [M−H2O−CO−C3H6−CH2+H]+, and m/z 145.1012 [M−H2O−CO−C3H6−CH2−C2H2+H]+ were attributed to the consecutive loss of C3H6, CH2, and C2H2, indicating that compound 75 broke up into a chain compound of conjugated polyene after the consecutive loss of H2O and CO. Thus, compound 75 was identified as costunolide by consulting MassBank Europe (https://massbank.eu/MassBank/) (ssBank/) .(5) Identification of Characteristic Compounds in Scutellaria baicalensis. Compound 34 showed the quasi-molecular ion at m/z 463.0872 [M+H]+ at the retention time of 6.49 min. The neutral loss of glucuronic acid is an important characteristic of flavonoid glycoside since the O-glucosylic bond is easily cleaved to generate aglycone, corresponding to the fragment ion at m/z 287.0548 [M−C6H8O6+H]+. Besides, the fragment of glucuronic acid exhibited the ion at m/z 141.0185 [M−C15H14O8+H]+, suggesting that compound 34 is a glucuronoside. With regard to the fragmentation of aglycone, the fragment ions at m/z 169.0137 [M−C15H14O8−C8H6O+H]+ and 119.0494 [M−C15H14O8−C7H4O5+H]+ were attributed to retro-Diels–Alder (RDA) reaction, which is the characteristic of the flavone fragmentation pathway. Furthermore, the ion at m/z 169.0137 showed that the three substituent OH groups were located on the A-ring, while the ion at m/z 119.0494 showed that a substituent OH group was located on the B-ring. The fragment ion at m/z 123.0078 [M−C15H14O8−C8H6O−CO−H2O+H]+ was formed by the consecutive loss of CO and H2O from the ion at m/z 169.0137. Moreover, the fragment ion at m/z 91.0547 [M−C15H14O8−C7H4O5−CO+H]+ was observed due to the loss of CO from the ion at m/z 119.0494. Using the data in mzCloud (https://www.mzcloud.org/), compound 34 was identified as scutellarin (tellarin .(6) Identification of Characteristic Compounds in Poria cocos. Compound 8 exhibited the protonated molecule ion at m/z 152.0567 [M+H]+, which eluted at 1.22 min. The parent ion further fragmented via three competing fragmentation pathways: the first through the consecutive loss of CN, NH2, and NH3 to produce fragment ions at m/z 110.0351 [M−CN−NH2+H]+ and 93.0578 [M−CN−NH2−NH3+H]+; the second via the consecutive loss of CONH and CN to give fragment ions at m/z 109.517 [M−CONH+H]+ and 83.406 [M−CONH−CN+H]+; and the third by the cleavage of the pyrimidine ring, resulting in the fragment ion at m/z 135.0301 [M−NH3+H]+ by the neutral loss of NH3. As the MS2 spectra of this compound exhibited a high relative abundance of ions at m/z 110.0351 and 93.0578, the first fragmentation pathway was shown to be dominant, which is the characteristic of the cleavage of purine. Moreover, the fragment ions at m/z 110.0351, 109.517, and 135.0301 suggested that the substituent groups of CO and NH2 were located in the pyrimidine ring. Therefore, compound 8 was identified as guanine by consulting mzCloud ( mzCloud .(7) Identification of Characteristic Compounds in Rehmannia glutinosa. Compound 35 showed an [M−H]− peak at m/z 623.1966 at the retention time of 6.51 min. Due to the cleavage of the O-glucosylic bond linked with the carbonyl group, the parent ion produced fragment ions at m/z 461.1667 [M−C9H6O3−H]− and 161.0238 [M−C20H30O12−H]−. The ion at m/z 135.0443 [M−C9H6O3−C12H22O10−H]− was formed by the loss of C12H22O10 from the ion at m/z 461.1667, indicating that compound 35 is a diglucoside with two glucosylic bonds between glycoside and aglycone. The fragment ion at m/z 133.0287 [M−C20H30O12−CO−H]− was attributed to the elimination of CO from the ion at m/z 161.0238. The parent ion also exhibited an ion at m/z 179.0347 [M−C20H28O11−H]−, which was unstable and tended to lose H2O, leading to the fragment ion at m/z 161.0238 [M−C20H28O11−H2O−H]−. According to mzCloud, compound 35 was identified as acteoside (cteoside .(8) Identification of Characteristic Compounds in Paeonia lactiflora. Compound 26 was eluted at 5.78 min and displayed the protonated molecule ion at m/z 481.1707 [M+H]+. Cleavage of the O-glucosylic bond resulted in the parent ion breaking up into aglycone and glycose, subsequently exhibiting fragment ions at m/z 197.0808 [M−C13H16O7+H]+ and 105.0338 [M−C16H24O10+H]+, corresponding to the cleavage of the ester bond. The consecutive loss of H2O and CO led to the formation of product ions at m/z 179.0701 [M−C13H16O7−H2O+H]+ and 151.0754 [M−C13H16O7−H2O−CO+H]+, indicating the existence of a lactone ring. The fragment ion at m/z 133.0648 [M−C13H16O7−H2O−CO−H2O+H]+ was attributed to the successive elimination of H2O. The high relative abundance ion at m/z 105.0388 was observed due to the stabilization of the aromatic hydrocarbon compared with the bridged-ring compound with the lactone ring. Using the data in mzCloud, compound 26 was identified as albiflorin (biflorin .(9) Identification of Compounds of an Unknown Source. Compound 88 was detected at the retention time of 13.16 min and exhibited the quasi-molecular ion at m/z 415.2113 [M+H]+. The parent ion further fragmented via three pathways: the first produced fragment ions at m/z 133.0649 [M−C15H22O5+H]+ by the loss of C15H22O5; the second produced the ion at m/z 119.0856 [M−C15H20O6+H]+ as a result of the successive elimination of C15H20O6, which was the dominant peak in the MS2 spectrum, indicating that the elimination of C15H20O6 might be the major fragmentation pathway; and the third exhibited the fragment ion at m/z 117.0700 [M−C15H22O6+H]+ corresponding to the elimination of C15H22O6 and subsequently produced the ion at m/z 103.0544 [M−C15H22O6−CH2+H]+ via the loss of CH2. Both of the ions at m/z 119.0856 and 117.0700 broke up into the fragment ion at m/z 91.0546 [M−C17H24O6+H]+ by the loss of C2H4 and C2H2, respectively. Based on these results, compound 88 was identified as 2,6-bis(4-ethylphenyl)perhydro-1,3,5,7-tetraoxanaphth-4-ylethane-1,2-diol by consulting mzCloud and is of an unknown source, not derived from the 8 herbs included in SJWTGs , molecular function (MF), and cellular components (CCs) were screened out. The drawing was made using bioinformatics . The topMetascape was employed to explore the KEGG pathway information that common targets may participate in. As a result, a total of 139 signal pathways were identified, and the top 10 pathways including distinctive genes were selected . The mecWith the purpose of gaining a holistic understanding of the underlying mechanism of SJWTGs, a component-target-pathway network was constructed by Cytoscape 3.7.2 software, including 94 nodes and 255 edges. Overall, 56 unique components of the 8 herbs, 13 common components, and 4 components of an unknown source participate in the regulation of 16 core targets and 10 pathways. The colour scales and the size of the nodes represent the degree level of the components, targets, and pathways. As shown in Based on the results of network pharmacology predictions, we believed that 2,6-bis(4-ethylphenyl)perhydro-1,3,5,7-tetraoxanaphth-4-ylethane-1,2-diol, murrangatin, meranzin hydrate, paeoniflorin, and albiflorin were the key bioactive components, while EGFR, SRC, AKT1, HSP90AA1, MAPK1, and MAPK3 were the core targets. These proteins not only play an important role in the KEGG signalling pathways but also serve as the key nodes of the PPI network and core component-target-pathway network. Therefore, the key bioactive components were used as ligands, and the core targets were used as proteins for the molecular docking, in order to further verify the network pharmacology results.As shown in H. pylori infection, and might also be a valuable antitumour-promoting agent, inhibiting the proliferation and migration of various cancer cells [https://www.ncbi.nlm.nih.gov/mesh), paeoniflorin is a nonsteroidal anti-inflammatory agent, which can modulate the activation of immune cells and decrease inflammatory medium production via regulating the MAPK/NF-κB pathway, JAK2/STAT3 pathway, and PI3K/Akt/mTOR pathway [κB signalling pathway by inhibiting the expression of inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), TNF-α, and IL-6, thereby acting as an anti-inflammatory [Here, we adopted a comprehensive method integrated UPLC-QE-Orbitrap-MS and network pharmacology to reveal the material basis and mechanism of action of the process by which SJWTG treats CG. For the first time, this study has shown that 2,6-bis(4-ethylphenyl)perhydro-1,3,5,7-tetraoxanaphth-4-ylethane-1,2-diol, murrangatin, meranzin hydrate, paeoniflorin, and albiflorin are the key bioactive components of SJWTGs treating CG. Specifically, 2,6-bis(4-ethylphenyl)perhydro-1,3,5,7-tetraoxanaphth-4-ylethane-1,2-diol is a chemical component of an unknown source that is not derived from the 8 herbs included in SJWTGs and has been rarely reported in pharmacological studies. Murrangatin has an antibacterial and anti-inflammatory effect on lipopolysaccharide (LPS)-stimulated inflammation , indicater cells –20. Meraer cells  as well er cells . Accordi pathway . Meanwhiammatory , 25.H. pylori-infected individuals [κB and MAPK1/3 pathways to induce cytokine production and macrophage activation, while EGFR-deficient macrophages display impaired Th1 and Th17 adaptive immune responses to H. pylori [H. pylori, in vitro and in vivo [H. pylori infection, CG, and gastric cancer [H. pylori-infected mice [H. pylori-infected tissues [We found that the core targets of SJWTGs in treating CG were EGFR, SRC, AKT1, HSP90AA1, MAPK1, and MAPK3. Gastric epithelial EGFR inhibition represents a potential strategy to prevent the development of gastric cancer in ividuals . Human g. pylori . SRC pla. pylori . Additio in vivo . Studies in vivo , and the in vivo , 32. HSP in vivo , and thec cancer , 27. Sevc cancer –37. Moreted mice ; HRAS, mted mice ; MAPK14 ted mice ; and the tissues .H. pylori infection to induce cytokine production and macrophage activation, resulting in the formation of CG [H. pylori-induced CG into early gastric cancer, Lewis antigens of H. pylori LPS could interact with Mincle (Macrophage inducible C-type lectin) and maintain a balance between pro- and anti-inflammatory cytokine production, suggesting that the C-type lectin receptor signalling pathway is closely related to the mechanism employed by H. pylori to escape the host innate immune receptors [H. pylori [α can increase the release of pro-inflammatory cytokines, augmenting apoptosis induced by H. pylori [α have been investigated for their potential to prevent inflammation-based gastric cancer [H. pylori-mediated CG, indicating that the HIF-1 signalling pathway has a potential therapeutical effect [H. pylori induces Th17 cell differentiation via infected macrophages, and the number of Th17 cells is positively correlated with the degree of CG [H. pylori protein HP1454, leading to an inflammatory response [H. pylori infection could initiate chemokine-mediated T lymphocyte trafficking into the inflamed epithelium and induce mucosal injury [H. pylori and sequentially contribute to the initiation and progression of gastric cancer cells [H. pylori infection perturbs the balance between T-regulatory and Th17 cells, which causes a spurt of IL-17 and gives rise to CG [Furthermore, we suggest that the MAPK signalling pathway and C-type lectin receptor signalling pathway are vital during the treatment of CG by SJWTGs. The MAPK signalling pathway could be activated by on of CG , which aon of CG . During eceptors . In addi. pylori , indicat. pylori , while mc cancer . As majol effect . H. pyloee of CG , 49. Theresponse . Moreovel injury . Toll-lier cells . As a haer cells . H. pylose to CG ; in partse to CG .The results of molecular docking showed the strong binding interaction between 5 key bioactive components and 6 core targets, which further verifies the network pharmacology results, to a certain degree. Due to the structural similarity of the 5 key bioactive components, that is, the existence of lactones, hydroxyl groups, and phenyl groups in the structure, they all have good conjugation with the core targets via polar interactions and hydrophobic interactions. Consequently, we suggest that the 5 key bioactive components could be considered as Q-markers. Nevertheless, molecular docking is still based on computer simulation, and the results need to be validated in vivo.H. pylori, indicating that SJWTG is also curative in the treatment of CG without H. pylori infection. Moreover, we conclude that the pathogenesis of CG may be related to the disorder of the MAPK signalling pathway and the C-type lectin receptor signalling pathway, and that H. pylori infection, duodenal-gastric reflux, drugs, and autoimmunity are only the key factors to induce the disorder of these pathways. Furthermore, the preventive effect of SJWTGs on gastric cancer also shows the superiority of preventive treatment of diseases with TCM. Due to the variety of targets, including EGFR, SRC, AKT1, HSP90AA1, MAPK1, and MAPK3, SJWTG is less susceptible to drug resistance during the long-term treatment of CG, suggesting the advantage of Chinese patent medicines. Additionally, 2,6-bis(4-ethylphenyl)perhydro-1,3,5,7-tetraoxanaphth-4-ylethane-1,2-diol, murrangatin, meranzin hydrate, paeoniflorin, and albiflorin have been shown to be the key bioactive components of SJWTGs in treating CG: the results of molecular docking demonstrate their potential therapeutic effects on CG and these components deserved to be further studied and considered as Q-markers, especially the chemical component of an unknown source that is not derived from the 8 herbs, 2,6-bis(4-ethylphenyl)perhydro-1,3,5,7-tetraoxanaphth-4-ylethane-1,2-diol.Therefore, we believe that SJWTGs could reduce the inflammatory response of gastric epithelial cells and prevent long-term chronic inflammation to induce cancerization by regulating the MAPK signalling pathway and the C-type lectin receptor signalling pathway as well as their upstream and downstream pathways. This is different to the triple and quadruple therapies that use antibiotics to eradicate In summary, a comprehensive approach based on UPLC-QE-Orbitrap-MS and network pharmacology has been used to explore the active compounds and potential mechanism of SJWTGs in treating CG. Overall, 95 chemical components were identified by UPLC-QE-Orbitrap-MS, including 6 chemical components of an unknown source that are not derived from the 8 herbs present in SJWTGs. Subsequently, the identified chemical components were further researched by network pharmacology, where it was found that SJWTGs could reduce the inflammatory response of gastric epithelial cells and prevent persistent chronic inflammation to induce cancerization by regulating the MAPK signalling pathway and the C-type lectin receptor signalling pathway, as well as their upstream and downstream pathways, in the treatment of CG, which could also be related to the pathogenesis of CG. The key bioactive components are 2,6-bis(4-ethylphenyl)perhydro-1,3,5,7-tetraoxanaphth-4-ylethane-1,2-diol, a chemical component of an unknown source, murrangatin, meranzin hydrate, paeoniflorin, and albiflorin. The results of molecular docking showed the strong binding interaction between the key bioactive components and the core targets, demonstrating that the key bioactive components deserved to be further studied and considered as Q-markers. Acting on multiple core targets, including EGFR, SRC, AKT1, HSP90AA1, MAPK1, and MAPK3, SJWTG is less susceptible to drug resistance during the long-term treatment of CG, suggesting the advantage of Chinese patent medicines. Furthermore, the preventive effect of SJWTGs on gastric cancer also demonstrates the superiority of the preventive treatment of disease with TCM.


Despite their anatomical and functional distinctions, there is growing evidence that the dorsal and ventral visual pathways interact to support object recognition. However, the exact nature of these interactions remains poorly understood. Is the presence of identity-relevant object information in the dorsal pathway simply a byproduct of ventral input? Or, might the dorsal pathway be a source of input to the ventral pathway for object recognition? In the current study, we used high-density EEG—a technique with high temporal precision and spatial resolution sufficient to distinguish parietal and temporal lobes—to characterise the dynamics of dorsal and ventral pathways during object viewing. Using multivariate analyses, we found that category decoding in the dorsal pathway preceded that in the ventral pathway. Importantly, the dorsal pathway predicted the multivariate responses of the ventral pathway in a time-dependent manner, rather than the other way around. Together, these findings suggest that the dorsal pathway is a critical source of input to the ventral pathway for object recognition. The primate visual system has historically been segregated into at least two independent visual pathways. The ventral visual pathway, which projects along the inferior temporal cortex (IT) and is involved primarily in visual object recognition, and the dorsal visual pathway, which projects along the parietal cortex and is involved primarily in visuospatial processing in the service of visually guided action. Despite their anatomical and functional differences, accumulating evidence suggests that the dorsal pathway may contribute to object recognition processes through its interactions with the ventral pathway. Yet, few studies have examined the precise temporal dynamics of object processing in dorsal and ventral pathways, leaving the directionality of these interactions unclear.The dorsal pathway exhibits many functional properties that are crucial for object recognition. For instance, fMRI studies have shown that, like the ventral pathway, the dorsal pathway exhibited greater univariate activation to intact object images compared with scrambled object images, even when the objects did not afford action . MoreoveThere is also a high degree of anatomical and functional connectivity between dorsal and ventral pathways , suggestAn alternative proposal, however, is that the dorsal pathway computes identity-relevant properties independently of the ventral pathway and may even propagate object information to the ventral pathway to support recognition . ConsistIn a recent paper, In the current study, we measure the temporal dynamics of the dorsal and ventral pathways using high-density electroencephalography (HD-EEG)—a neuroimaging method with millisecond precision and sufficient spatial resolution to localise dorsal and ventral pathways. Specifically, we selected channels of interest anatomically, corresponding to PPC and LO, and examined the directionality of their interactions. Although a limitation of EEG is its low spatial resolution, many studies have shown that the spatial resolution of HD-EEG is sufficient to dissociate lobes of the brain . Indeed,We used an existing HD-EEG dataset in which participants were shown four categories of objects: tools, non-tools, insects and birds see . In theiFor a full description of the methods and procedures, see Stimuli were displayed on a Mitsubishi Diamond Pro270 CRT monitor  with a 120-Hz refresh rate, running via a 2.6-Mhz Mac Mini and presented using the PsychToolbox  for MATLThe EEG signal was continuously recorded using a 256-channel HydroCel Geodesic Sensor Net via an EGI Net Amps Bio 300 amplifier  sampling at 1000 Hz. The digital data were recorded using Netstation 5.0(1) software. Impedance values were kept at or below 100 Ω. A photodiode was used to validate frame-accurate timing of stimulus presentation.EEG data were preprocessed using the EEG Lab Toolbox  and custThe filtered time series was then segmented into 550-ms epochs . Event segmentation was performed using trigger markers that were sent to the acquisition computer at the onset of each trial. The temporal offset that existed between the physical presentation of the stimulus and the registration of the stimulus marker in the acquisition computer was measured using a photodiode and corrected for during trial segmentation. Following event segmentation, artifacts were detected using EEG Lab functions (pop_eegthresh), which rejects artifacts using thresholding. The EEG epochs described above for each participant were grouped into four conditions: tools, non-tools, insects and birds.Anatomically defined channels of interest were selected a priori in regions corresponding to PPC and LO using well-established cortical projections of EEG sensors . SpecifiTo further validate our channel selection decisions, we tested whether our anatomically defined occipital, dorsal, ventral and frontal channels are functionally distinct. Given the diffuse nature of the EEG signal, the goal of this analysis is to test whether the functional profile of each region was unduly affected by signals from elsewhere in the brain. Principal component analysis (PCA) was conducted on all channels for each participant, and we retained components that explained 95% of the variance for each participant—resulting in a unique number of components for each participant. Then, we tested whether occipital, dorsal, ventral and frontal channels load onto distinct components. Specifically, we calculated the Euclidean distance between the component loadings for each channel with every other channel in a pairwise fashion, providing a metric of functional similarity between all channels. Hierarchical cluster analyses of the mean participant similarity matrix revealed that our anatomically selected occipital, dorsal, ventral and frontal channels are more similar to one another than to the other regions . Thus, aPs > 0.090; occipital: 6.26, dorsal: 4.85, ventral: 5.59, frontal: 6.19). In the second approach, we computed the SNR by dividing the mean signal during the stimulus period by the standard deviation of the pre-stimulus baseline period. As was true of the first approach, this analysis revealed no significant differences between regions . Thus, the equivalence of the SNR between regions indicates that any differences we obtain are not a function of signal strength in any lobe or of differential noise.Finally, to ensure that any differences between regions could not be accounted for by different levels of noise, we also computed the signal-to-noise ratio (SNR) for each region. SNR was computed using two methods. We first computed the overall SNR for the entire time course by dividing the mean signal by the standard deviation of the time course. This analysis method revealed no differences in SNR between regions  for 300 ms followed by an interstimulus interval (ISI) lasting between 800 and 1200 ms. Each of the 20 exemplars that made up the four categories was presented 84 times resulting in 420 trials per category and 1680 trials in total. The order of presentation was randomised. To maintain their attention, participants performed an orthogonal task wherein they pressed the space bar if an image appeared at a reduced luminance (50% reduction), and this occurred in 5% of the trials. Reduced luminance trials were excluded from all subsequent analysis.t-test comparisons to chance).For each participant, region and timepoint, a support vector machine classifier was trained on the multivariate pattern for three exemplars from each category, and then required to predict the category of the two left out exemplars . Above chance (0.25) decoding accuracy for each timepoint was determined using a bootstrap procedure, wherein participants’ decoding data was resampled 10 000 times (with replacement) and 95% bootstrap confidence intervals were constructed around the classification accuracy for each timepoint. Qualitatively similar results were found when using different classifiers (e.g. Naïve Bayes) or statistical tests  response for each object was concatenated with one another to form one continuous timeseries. A single null value was inserted between every object’s timeseries to prevent the prediction of temporally discontinuous timepoints. Multivariate Granger causality analyses  were they-axis) and 120 ms in the ventral pathway (x-axis) was subtracted from the time point with coordinates 120 ms in the ventral pathway and 100 ms in the dorsal pathway. A significant positive difference from zero would indicate that the dorsal pathway better predicts future times points of the ventral pathway, rather than the other way around. The mean difference across all correlation subtractions was taken for each participant, and submitted to a one-sample t-test comparison to chance (0).For each participant and region, a 20 × 20 representational dissimilarity matrix (RDM) was created for every time point by computing the Cosine similarity between the multivariate channel responses for one object with every other object in a pairwise fashion. RDMs were then averaged across participants separately for PPC and LO. Then, for every timepoint, the upper triangle of the resulting matrix  for the dorsal pathway was correlated with the RDM of every timepoint of ventral pathway. To test whether the dorsal pathway predicted the ventral pathway, or the other way around, we subtracted the correlations below the diagonal  from the correlations above the diagonal . Specifically, for each participant, we subtracted each timepoint from the timepoint with the equivalent coordinates mirrored along the diagonal. For example, the timepoint with coordinates 100 ms in the dorsal pathway .We next tested whether these timing differences between dorsal and ventral pathways were statistically consistent. Participant data from dorsal and ventral pathways were resampled 10 000 times (with replacement). On each resample, a one-sided, one-sample P < 0.001; Binomial test; see Across resamples of the data, the decoding in the dorsal pathway occurred at a median time of 66 ms, whereas decoding in the ventral pathway occurred at a median time of 94 ms, a difference of 28 ms. A Binomial comparison to chance (0.50) revealed that the dorsal pathway preceded the ventral pathway in 70.1% of resamples, significantly more often than would be predicted by chance  and frontal cortex . In contrast, decoding in the ventral pathway was preceded by occipital cortex , and the ventral pathway preceded frontal cortex .These analyses revealed above chance decoding at 66 ms in occipital cortex  and 110 ms in frontal cortex . Resamples of the data revealed a median decoding time of 70 ms in occipital cortex and 106 ms in frontal cortex. Binomial comparisons to the dorsal pathway of these distributions revealed that decoding in the dorsal pathway preceded both occipital cortex , it is possible that these differences are spurious and reflect the idiosyncrasies of the stimulus categories, the channels selected, or the influence of signals outside our regions of interest. We conducted several control analyses to address these concerns and investigate the consistency of identity-relevant information in dorsal and ventral pathways.We first tested whether differences between dorsal and ventral pathways were driven by the inclusion of the tool category. It is well known that the dorsal pathway is particularly sensitive to objects that afford action, such as tools  and, indP < 0.001), with dorsal preceding ventral in 66.2% of resamples. Together, these analyses suggest that our results are not driven by tool category specifically, nor variations in which stimulus categories are included in the decoding.We next tested whether any of the other categories could explain the timing differences between dorsal and ventral pathways. To this end, we conducted decoding analyses on dorsal and ventral pathways in each participant after iteratively removing each category one at a time . To provide an overall measure of decoding consistency across all object iterations, we resampled these data 1000 (with replacement) and calculated the onset of first decoding on every resample. Across the aggregate of all resamples of the data (i.e. every iteration of stimulus categories for participants), we found that median category decoding in the dorsal pathway (58 ms) preceded the ventral pathway (86 ms) by 28 ms. A Binomial comparison of the distributions (0.50) revealed that this difference was statistically significant (P < 0.001), with dorsal preceding ventral in 89.3% of resamples. Thus, our decoding results are consistent across variations in the specific channels selected.The next possibility we tested is whether differences between dorsal and ventral pathways are driven by the specific channels-of-interest we selected for each region. If this possibility is true, then slight differences to the channel selection criteria may alter the results. To test this possibility, we randomly resampled (with replacement) the channels-of-interest for each participant within each region 1000 times and repeated our primary decoding analysis on every resample. This analysis revealed that, across resamples of the channels, decoding in the dorsal pathway preceded the ventral pathway (medians = 66 ms vs. 98 ms), by a difference of 32 ms. A Binomial comparison of the distributions (0.50) revealed that this difference was statistically significant , the signals we measured over dorsal and ventral pathways are likely influenced by channels elsewhere in the brain. To test this possibility, we repeated our decoding analyses after controlling for the response of all channels outside of our two key regions of interest.To this end, we first conducted PCA on the timeseries response for all the channels of the brain for every participant, while excluding dorsal and ventral pathways, and retained components that explain 95% of the variance. These components provide a multivariate summary of the functional responses of all channels outside of the dorsal and ventral electrodes. Next, for each participant, we regressed each channel corresponding to the dorsal and ventral pathway by the principal components from the rest of the brain. Finally, we repeated our decoding analyses using the residual responses of the dorsal and ventral channels. This analysis revealed significant decoding in the dorsal pathway at 50 ms, and significant decoding in the ventral pathway at 78 ms, a 28-ms difference. Thus, although our channels-of-interest were likely influenced by sources outside dorsal and ventral pathways, these external sources do not explain the differences in timing between dorsal and ventral pathways. Altogether, these findings suggest that our decoding results cannot be explained by variations in the stimulus categories, selected channels nor sources outside our regions of interest. Instead, our findings are robust across all these variations.The results above revealed that object classification in the dorsal pathway preceded that of the ventral pathway. However, it is unclear whether the dorsal pathway is also a source of input to the ventral pathway. Indeed, it is possible that object processing simply occurs in parallel in dorsal and ventral pathways with a temporal advantage for dorsal over ventral but with little, if any, interaction.t − 1) improves the prediction of current multivariate responses of the ventral pathway over above ventral’s own past responses.To test whether the dorsal pathway is a potential source of input to the ventral pathway, we conducted hypothesis-driven multivariate Granger causality analyses . The preF statistics subtracted from one another. A Wilcoxon signed-rank one-sample t-tests comparisons with 0 revealed significant effective connectivity from the dorsal to the ventral pathway, W(19) = 160, P = 0.020, d = 0.52. This finding suggests that the multivariate time course of the dorsal pathway predicts the ventral pathway, rather than the other way around.Granger causality analyses were conducted in both directions , with the resulting W(19) = 181, P = 0.002, d = 0.72, but not the dorsal pathway (P = 0.378). We also found that the dorsal pathway was a significant predictor of frontal cortex, W(19) = 160, P = 0.005, d = 0.65, whereas the ventral pathway was not (P = 0.249).For comparison, we also examined the interactions between occipital and frontal cortices with dorsal and ventral pathways. These analyses revealed that occipital cortex was a significant predictor of the ventral pathway, If the dorsal pathway precedes and predicts future responses of the ventral pathway, then the strongest correlation between dorsal and ventral pathways should be between the present timepoint of the dorsal pathway with future timepoints of the ventral pathway. To test this prediction, we conducted time generalised representational similarity analyses .t(19) = 2.83, P = 0.011, d = 0.63.See To further visualise the patterns of interactions between dorsal and ventral pathways, we rank ordered the correlations from strongest to weakest and labeled each according to whether they reflected dorsal → ventral correlations, ventral → dorsal correlations or correlations along the diagonal of the matrix see . This viP = 0.006). At its peak, dorsal → ventral correlations comprised 66% of the top 300 correlations (P < 0.001), with a mean prediction delay of 34.3 ms. Altogether, these results suggest that the dorsal pathway predicts the future response of the ventral pathway, rather than other way around.To characterise this pattern quantitatively, we conducted a rolling binomial analysis in increments of 50, where we tested whether dorsal → ventral correlations comprise a larger proportion of the correlations than would be predicted by chance (0.49). This analysis revealed that dorsal → ventral correlations comprised the majority of the top correlations up to the first 500 indexed correlations  = 2.85, P = 0.010, d = 0.64.To test this possibility, we conducted partial correlation analyses controlling for the influence of frontal channels. As in the previous analysis, each timepoint of the dorsal pathway was correlated with each timepoint of the ventral pathway; however, here, the multivariate response of frontal cortex for each dorsal timepoint was included as a covariate. This analysis revealed that, across participants, partial correlations were strongest when the dorsal pathway was correlated with future timepoints of the ventral pathway, even when controlling for the influence of frontal cortex, t(19) = 4.14, P < 0.001, d = 0.93. Altogether, these findings are indicative of direct interactions between dorsal and ventral pathways, with dorsal propagating information to the ventral pathway, rather than the other way around.For completeness, we also examined whether the dorsal pathway predicted the ventral pathway, when, instead, we used the multivariate response of occipital cortex as a covariate. This analysis again revealed that the dorsal pathway was correlated with future timepoints of the ventral pathway, even when controlling the influence of occipital cortex, t(19) = 3.03, P = 0.007, d = 0.68, but not the dorsal pathway (P = 0.78). We did not find any significant relations between frontal cortex with either dorsal or ventral pathways (Ps > 0.226).Finally, we examined whether there are direct functional connections between occipital and frontal cortices with dorsal and ventral pathways. We found that occipital cortex predicted the response of the ventral pathway, In the current study, we examined the temporal dynamics and interactions of the dorsal and ventral visual pathways. Although an increasing number of studies have proposed that dorsal and ventral pathways interact during the process of object recognition, few studies have examined the timing and directionality of object processing in the two visual pathways. Using HD-EEG, we found that the dorsal pathway represented identity-relevant object information prior to the ventral pathway. Moreover, we found that the dorsal pathway predicted the response of the ventral pathway in a time-dependent manner. Altogether, these findings suggest that identity-relevant object information in the dorsal pathway arises independently of the ventral pathway and may be a source of input to the ventral pathway for object recognition.However, a limitation of the current study is that EEG and the study design affords only coarse anatomical specificity. Although previous work has shown that HD-EEG can localise responses to within 8 mm of their anatomical location , the preAlthough there are relatively few studies that compare the timing of object processing in dorsal and ventral pathways, our timing results are remarkably consistent with existing data. Specifically, in our primary analyses we found decoding in the dorsal pathway at 66 ms and in the ventral pathway at 94 ms. Similarly, electrocorticographic data from patients showed visual onsets in IPS at approximately 60 ms and at approximately 100 ms in LOC . Other wWhy might category information arise earlier in the dorsal pathway? One possibility is that earlier onsets in the dorsal pathway reflect attentional processing. Attention is the process of selecting a target stimulus over competing distractors. It is well known that the dorsal pathway plays an important role in visual attention , and thuA more probable explanation for the observed dorsal and ventral timing results is that they reflect the differential input of magnocellular and parvocellular pathways to dorsal and ventral cortices, respectively . The magAlthough our results are most consistent with the dorsal pathway as a source of input to the ventral pathway, it is almost certainly the case that there are bidirectional interactions between the two pathways . For insThere is also evidence that dorsal and ventral pathways are situated within a broader network of regions that are involved in object recognition . For insThe primary goal of our study was to explore the temporal dynamics and interactions of dorsal and ventral pathways. Yet, one might nevertheless wonder, what kind of information supports decoding of object category in the dorsal pathway? One possibility is that, in the current study, decoding was accomplished by relying on low-level visual properties. This possibility is unlikely because the stimuli were controlled for low-level properties like luminance and spatial frequency (see Methods). Another possibility is that very coarse object properties, such as elongation, were sufficient to decode the objects’ categories. Indeed, anterior regions of the parietal cortex are particularly sensitive to object elongation because it facilitates grasping . This poAnother possibility is that that the dorsal pathway accomplishes categorisation by computing an object’s structural description and then transmitting this information to the ventral pathway for recognition . StructuIn summary, our results expand our understanding of the interactions between dorsal and ventral visual pathways. Although it is increasingly accepted that the two visual pathways interact, the precise nature of these interactions remain poorly understood. Here, we showed that identity-relevant object processing in the dorsal pathway precedes and predicts processing in the ventral pathway. Together, these findings provide an expanded understanding of the biological network that support visual object recognition.