scholarly journals Deciphering the Therapeutic Mechanisms of Wuzi Ershen Decoction in Treating Oligoasthenozoospermia through the Network Pharmacology Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Mingrui Hu ◽  
Yuanyuan Zhong ◽  
Wei Xiao ◽  
Yang Wang ◽  
Tao Tang ◽  
...  
Keyword(s):  
Bioactive Compounds ◽  
Protein Interaction ◽  
Cellular Response ◽  
Cyclic Compound ◽  
Docking Simulation ◽  
Network Pharmacology ◽  
Pubmed Database ◽  
Specific Proteins ◽  
Therapeutic Mechanisms ◽  
Disease Specific

Background. Infertility affects approximately 15% of couples around the world, and male factors are accounted for 40–50%. Oligoasthenozoospermia is the most common reason for male infertility. Unfortunately, effective drug therapy is still lacking except for assisted reproductive technology (ART). Previous researchers found that Wuzi Ershen decoction (WZESD) can increase sperm count, enhance sperm vitality, and improve semen quality. However, the pharmacological mechanisms remain unclear. Methods. In this study, we screened compounds and predicted the targets of WZESD based on the TCMSP and BATMAN-TCM database combined with literature searching in the PubMed database. We obtained proteins related to oligoasthenozoospermia through GeneCards and submitted them to STRING to obtain the protein-protein interaction (PPI) network. Potential targets of WZESD were mapped to the network, and the hub targets were screened by topology. We used online platform Metascape and Enrichr for GO and KEGG enrichment analyses. AutoDock Vina was utilized for further verification of the binding mode between compounds and targets. Results. Totally, 276 bioactive compounds were obtained and targeted 681 proteins. 446 oligoasthenozoospermia disease-specific proteins were acquired, and further bioinformatics analysis found that they were mainly involved in the formation of gametes, meiosis, and sperm differentiation. Protein interaction network analysis revealed that target proteins of WZESD were associated with oligoasthenozoospermia disease-specific proteins. The 79 targets of disease-specific proteins, which were anchored by WZESD, mainly participate in the cellular response to the organic cyclic compound, regulation of the apoptotic process, nitricoxide biosynthetic and metabolic process, oxidative stress, and protein phosphorylation regulation, which are the causes for oligoasthenozoospermia. Molecular docking simulation further validated that bioactive compounds originated from WZESD with targeted proteins showed high binding efficiency. Conclusions. This study uncovers the therapeutic mechanisms of WZESD for oligoasthenozoospermia treatment from the perspective of network pharmacology and may provide a valuable reference for further experimental research studies and clinical applications.

scholarly journals Identification of Active Compounds of Mahuang Fuzi Xixin Decoction and Their Mechanisms of Action by LC-MS/MS and Network Pharmacology

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xiao Liang ◽  
Chang-Shun Liu ◽  
Ting Xia ◽  
Qing-Fa Tang ◽  
Xiao-Mei Tan
Keyword(s):  
Signaling Pathways ◽  
Bioactive Compounds ◽  
Cell Receptor ◽  
Mechanisms Of Action ◽  
Network Pharmacology ◽  
Therapeutic Effects ◽  
Active Compounds ◽  
Beneficial Effects ◽  
T Cell Receptor Signaling ◽  
Therapeutic Mechanisms

The decoction is an important dosage form of traditional Chinese medicine (TCM) administration. The Mahuang Fuzi Xixin decoction (MFXD) is widely used to treat allergic rhinitis (AR) in China. However, its active compounds and therapeutic mechanisms are unclear. The aim of this study was to establish an integrative method to identify the bioactive compounds and reveal the mechanisms of action of MFXD. LC-MS/MS was used to identify the compounds in MFXD, followed by screening for oral bioavailability. TCMSP, BindingDB, STRING, DAVID, and KEGG databases and algorithms were used to gather information. Cytoscape was used to visualize the networks. Twenty-four bioactive compounds were identified, and thirty-seven predicted targets of these compounds were associated with AR. DAVID analysis suggested that these compounds exert their therapeutic effects by modulating the Fc epsilon RI, B-cell receptor, Toll-like receptor, TNF, NF-κB, and T-cell receptor signaling pathways. The PI3K/AKT and cAMP signaling pathways were also implicated. Ten of the identified compounds, quercetin, pseudoephedrine, ephedrine, β-asarone, methylephedrine, α-linolenic acid, cathine, ferulic acid, nardosinone, and higenamine, seemed to account for most of the beneficial effects of MFXD in AR. This study showed that LC-MS/MS followed by network pharmacology analysis is useful to elucidate the complex mechanisms of action of TCM formulas.

scholarly journals Elucidation of the mechanism of action of the anticholecystitis effect of the Tibetan medicine “Dida” using network pharmacology

2020 ◽  
Vol 19 (7) ◽  
pp. 1449-1457
Author(s):  
Chuan Liu ◽  
Fang-Fang Fan ◽  
Xuan-Hao Li ◽  
Wen-Xiang Wang ◽  
Ya Tu ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Bioactive Compounds ◽  
Kegg Pathway ◽  
Docking Simulation ◽  
Cell Receptor ◽  
Mapk Signaling ◽  
Tibetan Medicine ◽  
Network Pharmacology ◽  
Molecular Docking Simulation ◽  
Pathway Network

Purpose: To study the mechanism involved in the anti-cholecystitis effect the Tibetan medicine “Dida”, using network pharmacology-integrated molecular docking simulationsMethods: In this investigation, the bioactive compounds of Dida were collected, network pharmacology methods to predict their targets, and networks were constructed through GO and KEGG pathway analyses. The potential binding between the bioactive compounds and the targets were demonstrated using molecular docking simulations.Results: A total of 12 bioactive compounds and 50 key targets of Dida were identified. Two networks, namely, protein–protein interaction (PPI) network of cholecystitis targets, and compound–target– pathway network, were established. Network analysis showed that 10 targets (GAPDH, AKT1, CASP3, EGFR, TNF, MAPK3, MAPK1, HSP90AA1, STAT3, and BCL2L1) may be the therapeutic targets of Dida in cholecystitis. Analysis of the KEGG pathway indicated that the anti-cholecystitis effect of Dida may its regulation of a few crucial pathways, such as apoptosis, as well as toll-like  receptor, T cell receptor, NOD-like receptor, and MAPK signaling pathways. Furthermore, molecular docking simulation revealed that CASP3, CAPDH, HSP90AA1, MAPK3, MAPK1, and STAT3 had well-characterized interactions with the corresponding compounds.Conclusion: The mechanism underlying the anti-cholecystitis effect of Dida has been successfully predicted and verified using a combination of network pharmacology and molecular docking simulation. This provides a firm basis for the experimental verification of the use of Dida in the treatment of cholecystitis, and enhances its rational application in clinical practice. Keywords: Tibetan medicine, Dida, Cholecystitis, Mechanism, Network pharmacology, Molecular docking simulation

scholarly journals Elucidation of the mechanisms underlying the anticholecystitis effect of the Tibetan medicine “Dida” using Network pharmacology

2020 ◽  
Vol 19 (9) ◽  
pp. 1953-1961
Author(s):  
Chuan Liu ◽  
Fang-Fang Fan ◽  
Xuan-Hao Li ◽  
Wen-Xiang Wang ◽  
Ya Tu ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Bioactive Compounds ◽  
Kegg Pathway ◽  
Docking Simulation ◽  
Cell Receptor ◽  
Mapk Signaling ◽  
Tibetan Medicine ◽  
Network Pharmacology ◽  
Molecular Docking Simulation ◽  
Pathway Network

Purpose: To study the mechanism involved in the anti-cholecystitis effect the Tibetan medicine “Dida”, using network pharmacology-integrated molecular docking simulationsMethods: In this investigation, the bioactive compounds of Dida were collected, network pharmacology methods to predict their targets, and networks were constructed through GO and KEGG pathway analyses. The potential binding between the bioactive compounds and the targets were demonstrated using molecular docking simulations.Results: A total of 12 bioactive compounds and 50 key targets of Dida were identified. Two networks, namely, protein-protein interaction (PPI) network of cholecystitis targets, and compound-target-pathway network, were established. Network analysis showed that 10 targets (GAPDH, AKT1, CASP3, EGFR, TNF, MAPK3, MAPK1, HSP90AA1, STAT3, and BCL2L1) may be the therapeutic targets of Dida in cholecystitis. Analysis of the KEGG pathway indicated that the anti-cholecystitis effect of Dida may its regulation of a few crucial pathways, such as apoptosis, as well as toll-like  receptor, T cell receptor, NOD-like receptor, and MAPK signaling pathways. Furthermore, molecular docking simulation revealed that CASP3, CAPDH, HSP90AA1, MAPK3, MAPK1, and STAT3 had well-characterized interactions with the corresponding compounds.Conclusion: The mechanism underlying the anti-cholecystitis effect of Dida was successfully predicted and verified using a combination of network pharmacology and molecular docking simulation. This provides a firm basis for the experimental verification of the use of Dida in the treatment of cholecystitis, and enhances its rational application in clinical medication. Keywords: Tibetan medicine, Dida, Cholecystitis, Mechanism of effect, Network pharmacology, Molecular docking simulation

scholarly journals Mechanisms of Core Chinese Herbs against Colorectal Cancer: A Study Based on Data Mining and Network Pharmacology

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Tong Lin ◽  
Caijun Liang ◽  
Wenya Peng ◽  
Yuqin Qiu ◽  
Lisheng Peng
Keyword(s):  
Colorectal Cancer ◽  
Data Mining ◽  
Bioactive Compounds ◽  
Target Genes ◽  
Prognostic Significance ◽  
Therapeutic Targets ◽  
Docking Simulation ◽  
Functional Enrichment ◽  
Chinese Herbs ◽  
Network Pharmacology

Colorectal cancer (CRC) is now the second most deadly cancer globally. Chinese herbal medicine (CHM) plays an indispensable role in CRC treatment in China. However, the core herbs (the CHs) in the treatment of CRC and their underlying therapeutic mechanisms remain unclear. This study aims to uncovering the CHs and their mechanisms of action of CRC treatment, applying data mining and network pharmacology approach. First, CHM prescriptions treating CRC were collected from clinical studies from the Chinese National Knowledge Infrastructure (CNKI) and MEDLINE databases, and the CHs were identified through data mining. Then, the bioactive compounds and the corresponding putative targets of the CHs were obtained from three traditional Chinese medicine (TCM) databases. CRC related targets were acquired from three disease databases; the overlapping targets between the CHs and CRC were identified as the therapeutic targets. Subsequently, functional enrichment analysis was performed to elucidate the mechanisms of the CHs on CRC. Moreover, networks were constructed to screen the major bioactive compounds and therapeutic targets. Finally, prognostic values of the major target genes were evaluated by survival analysis, and molecular docking simulation was performed to assess the binding affinity of key targets and major bioactive compounds. It came out that 10 the CHs from 113 prescriptions and 190 bioactive compounds with 118 therapeutic targets were identified. The therapeutic targets were mainly enriched in the biological progress of transcription, apoptosis, and response to cytokine. Various cancer-associated signaling pathways, including microRNAs, TNF, apoptosis, PI3K-Akt, and p53, were involved. Furthermore, 15 major bioactive compounds and five key target genes (VEGFA, CASP3, MYC, CYP1Y1, and NFKB1) with prognostic significance were identified. Additionally, most major bioactive compounds might bind firmly to the key target proteins. This study provided an overview of the anti-CRC mechanisms of the CHs, which might refer to the regulation of apoptosis, transcription, and inflammation.

scholarly journals Identification of the active substances and mechanisms of ginger for the treatment of colon cancer based on network pharmacology and molecular docking

2020 ◽  
Author(s):  
MengMeng Zhang ◽  
Dan Wang ◽  
Feng Lu ◽  
Rong Zhao ◽  
Xun Ye ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Molecular Docking ◽  
Signaling Pathway ◽  
Cellular Response ◽  
Protein Localization ◽  
Docking Simulation ◽  
Estrogen Signaling ◽  
Receptor Protein ◽  
Network Pharmacology ◽  
Active Compounds

Abstract Background: Colon cancer is increasing recently but the high cost and adverse side effects experienced always leads to treatment drop out. Zingiber officinale, commonly known as ginger, is a popular herbal medicine and this study was aimed to identify the active compounds from ginger and to investigate its anti-cancer mechanisms through network pharmacology construction. Results: Ginger compounds were discerned through the TCMSP, which were filtered by the metrics of oral bioavailability and drug likeness, and its related targets were searched. After that, the targets interacting with colon cancer were collected using Genecards, OMIM, and Drugbank databases. Six potential active compounds, 288 interacting targets in addition to 1356 disease-related targets were collected, of which 114 intersection targets were obtained. The PPI network showed that 32 targets including SRC, PIK3R1, and TP53 were identified as key targets. These targets were mainly associated with the biological processes like transmembrane receptor protein tyrosine kinase signaling pathway, regulation of cellular protein localization, cellular response to oxidative stress. KEGG enrichment manifested that ginger probably produced preventive effects against colon cancer by regulating significant signaling pathway like pathway in cancer, hepatitis B, and estrogen signaling pathway. TP53, HSP90AA1, MAPK8, JAK2, CASP3, and ERBB2 could be viewed as the most potential target proteins, which were validated by molecular docking simulation.Conclusion: This study demonstrated the multi-component, multi-target, and multi-pathway characteristics of ginger, providing novel insight for ginger compounds developed as new drug for anti-colon cancer.

scholarly journals Identification of the active substances and mechanisms of ginger for the treatment of colon cancer based on network pharmacology and molecular docking

2020 ◽  
Author(s):  
MengMeng Zhang ◽  
Dan Wang ◽  
Feng Lu ◽  
Rong Zhao ◽  
Xun Ye ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Molecular Docking ◽  
Cancer Prevention ◽  
Signaling Pathways ◽  
Cellular Response ◽  
Endocrine Resistance ◽  
Docking Simulation ◽  
Network Pharmacology ◽  
Integrated Network ◽  
Colon Cancer Prevention

Abstract Background and objective: Colon cancer is increasing in people recently and ginger (Zingiber officinale), as a commonly used herbal medicine, has been suggested as a potential agent against colon cancer. This study was aimed to identify the bioactive compounds and potential mechanisms of ginger for colon cancer prevention by an integrated network pharmacology approach.Methods: Putative ingredients of ginger and its related targets were discerned from the TCMSP database. After that, the targets interacting with colon cancer were collected using Genecards, OMIM, and Drugbank databases. KEGG pathway and GO enrichment analysis were performed to explore the signaling pathways related to ginger for colon cancer treatments. The PPI and compound-target-disease networks were constructed using Cytoscape.Results: Six potential active compounds, 285 interacting targets in addition to 1356 disease-related targets were collected, of which 118 intersection targets were obtained. A total of 34 key targets including PIK3CA, SRC, and TP53 were identified. These targets were mainly focused on the biological processes of phosphatidylinositol 3-kinase signaling, cellular response to oxidative stress, and cellular response to peptide hormone stimulus. The KEGG enrichment manifested that three signaling pathways were closely related to colon cancer prevention of ginger, including cancer, endocrine resistance, and hepatitis B pathways. TP53, HSP90AA1, MAPK8, JAK2, CASP3, and ERBB2 were viewed as the most important genes, which were validated by molecular docking simulation.Conclusion: This study demonstrated that ginger produced preventive effects against colon cancer by regulating multi-target and multi-pathway with multi-components. And, the combined data provide novel insight for ginger compounds developed as new drug for anti-colon cancer.

Exploring the Potential of Capsaicin Against Cancer Metastasis Based on TGF-β Signaling Modulation Through Module-based Network Pharmacology Approach

2020 ◽  
Vol 17 (5) ◽  
pp. 647-660 ◽  
Author(s):  
Shivananda Kandagalla ◽  
Sharath Belenahalli Shekarappa ◽  
Gollapalli Pavan ◽  
Umme Hani ◽  
Manjunatha Hanumanthappa
Keyword(s):  
Amino Acid ◽  
Amino Acid Composition ◽  
Signaling Pathway ◽  
Acid Composition ◽  
Protein Interaction ◽  
Cancer Metastasis ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Mode Analysis ◽  
Network Pharmacology

Background: Capsaicin is an active alkaloid /principal component of red pepper responsible for the pungency of chili pepper. Capsaicin by changing the intracellular redox homeostasis regulate a variety of signaling pathways ultimately producing a divergent cellular outcome. Several reports showed the potential of capsaicin against cancer metastasis, however unexplored molecular mechanism is still an active part of the research. Several growth factors have a critical role during cancer metastasis among them TGF- β signaling play a vital role. Methods: The present study aimed at analyzing capsaicin modulation of TGF-β signaling using network pharmacology approach. The chemical and protein interaction data of capsaicin was curated and abstracted using STITCH4.0, PubChem and ChEMBL database. Further, the compiled data set was subjected to the pathway and functional enrichment analysis using Protein Analysis THrough Evolutionary Relationship (PANTHER) and, Database for Annotation, Visualization, and Integrated Discovery (DAVID) database. Meanwhile, the pattern of amino acid composition across the capsaicin targets was analyzed using the EMBOSS Pepstat tool. Capsaicin targets involved in TGF- β were identified and their Protein-Protein Interaction (PPI) network constructed using STRING v10 and Cytoscape (v 3.2.1). From the above-constructed network, the clusters were mined using the MCODE clustering algorithm and finally binding affinity of capsaicin with its targets involved in TGF-β signaling pathway was analyzed using Autodock Vina. Results: The analysis explored capsaicin targets and, their associated functional and pathway annotations. Besides, the analysis also provides a detailed distinct pattern of amino acid composition across the capsaicin targets. The capsaicin targets described as MAPK14, JUN, SMAD3, MAPK3, MAPK1 and MYC involved in TGF-β signaling pathway through pathway enrichment analysis. The binding mode analysis of capsaicin with its targets has shown high affinity with MAPK3, MAPK1, JUN and MYC. Conclusion: The study explores the potential of capsaicin as a potent modulator of TGF-β signaling pathway during cancer metastasis and proposes new methodology and mechanism of action of capsaicin against TGF- β signaling pathway.

scholarly journals Insights into the molecular mechanisms of Huangqi decoction on liver fibrosis via computational systems pharmacology approaches

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Biting Wang ◽  
Zengrui Wu ◽  
Weihua Li ◽  
Guixia Liu ◽  
Yun Tang
Keyword(s):  
Molecular Docking ◽  
Liver Fibrosis ◽  
Molecular Mechanisms ◽  
Docking Simulation ◽  
Chinese Herbs ◽  
Network Pharmacology ◽  
Bipartite Network ◽  
Metabolomics Data ◽  
Metabolic Products ◽  
Compound Target

Abstract Background The traditional Chinese medicine Huangqi decoction (HQD) consists of Radix Astragali and Radix Glycyrrhizae in a ratio of 6: 1, which has been used for the treatment of liver fibrosis. In this study, we tried to elucidate its action of mechanism (MoA) via a combination of metabolomics data, network pharmacology and molecular docking methods. Methods Firstly, we collected prototype components and metabolic products after administration of HQD from a publication. With known and predicted targets, compound-target interactions were obtained. Then, the global compound-liver fibrosis target bipartite network and the HQD-liver fibrosis protein–protein interaction network were constructed, separately. KEGG pathway analysis was applied to further understand the mechanisms related to the target proteins of HQD. Additionally, molecular docking simulation was performed to determine the binding efficiency of compounds with targets. Finally, considering the concentrations of prototype compounds and metabolites of HQD, the critical compound-liver fibrosis target bipartite network was constructed. Results 68 compounds including 17 prototype components and 51 metabolic products were collected. 540 compound-target interactions were obtained between the 68 compounds and 95 targets. Combining network analysis, molecular docking and concentration of compounds, our final results demonstrated that eight compounds (three prototype compounds and five metabolites) and eight targets (CDK1, MMP9, PPARD, PPARG, PTGS2, SERPINE1, TP53, and HIF1A) might contribute to the effects of HQD on liver fibrosis. These interactions would maintain the balance of ECM, reduce liver damage, inhibit hepatocyte apoptosis, and alleviate liver inflammation through five signaling pathways including p53, PPAR, HIF-1, IL-17, and TNF signaling pathway. Conclusions This study provides a new way to understand the MoA of HQD on liver fibrosis by considering the concentrations of components and metabolites, which might be a model for investigation of MoA of other Chinese herbs.

scholarly journals xCELLanalyzer: A Framework for the Analysis of Cellular Impedance Measurements for Mode of Action Discovery

2019 ◽  
Vol 24 (3) ◽  
pp. 213-223 ◽  
Author(s):  
Raimo Franke ◽  
Bettina Hinkelmann ◽  
Verena Fetz ◽  
Theresia Stradal ◽  
Florenz Sasse ◽  
...  
Keyword(s):  
Data Analysis ◽  
Bioactive Compounds ◽  
Mode Of Action ◽  
Mammalian Cells ◽  
Cellular Response ◽  
Label Free ◽  
Synthesis Inhibitor ◽  
Analysis Pipeline ◽  
Bioactive Natural Products ◽  
Data Analysis Pipeline

Mode of action (MoA) identification of bioactive compounds is very often a challenging and time-consuming task. We used a label-free kinetic profiling method based on an impedance readout to monitor the time-dependent cellular response profiles for the interaction of bioactive natural products and other small molecules with mammalian cells. Such approaches have been rarely used so far due to the lack of data mining tools to properly capture the characteristics of the impedance curves. We developed a data analysis pipeline for the xCELLigence Real-Time Cell Analysis detection platform to process the data, assess and score their reproducibility, and provide rank-based MoA predictions for a reference set of 60 bioactive compounds. The method can reveal additional, previously unknown targets, as exemplified by the identification of tubulin-destabilizing activities of the RNA synthesis inhibitor actinomycin D and the effects on DNA replication of vioprolide A. The data analysis pipeline is based on the statistical programming language R and is available to the scientific community through a GitHub repository.

scholarly journals Studies on the Mechanism of Gegen Qinlian Decoction in Treating Diabetes Mellitus Based on Network Pharmacology

2021 ◽  
Vol 16 (1) ◽  
pp. 1934578X2098213
Author(s):  
Xiaodong Deng ◽  
Yuhua Liang ◽  
Jianmei Hu ◽  
Yuhui Yang
Keyword(s):  
Diabetes Mellitus ◽  
Molecular Docking ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Network Pharmacology ◽  
Hub Genes ◽  
Topological Parameters

Diabetes mellitus (DM) is a chronic disease that is very common and seriously threatens patient health. Gegen Qinlian decoction (GQD) has long been applied clinically, but its mechanism in pharmacology has not been extensively and systematically studied. A GQD protein interaction network and diabetes protein interaction network were constructed based on the methods of system biology. Functional module analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, and Gene Ontology (GO) enrichment analysis were carried out on the 2 networks. The hub nodes were filtered by comparative analysis. The topological parameters, interactions, and biological functions of the 2 networks were analyzed in multiple ways. By applying GEO-based external datasets to verify the results of our analysis that the Gene Set Enrichment Analysis (GSEA) displayed metabolic pathways in which hub genes played roles in regulating different expression states. Molecular docking is used to verify the effective components that can be combined with hub nodes. By comparing the 2 networks, 24 hub targets were filtered. There were 7 complex relationships between the networks. The results showed 4 topological parameters of the 24 selected hub targets that were much higher than the median values, suggesting that these hub targets show specific involvement in the network. The hub genes were verified in the GEO database, and these genes were closely related to the biological processes involved in glucose metabolism. Molecular docking results showed that 5,7,2', 6'-tetrahydroxyflavone, magnograndiolide, gancaonin I, isoglycyrol, gancaonin A, worenine, and glyzaglabrin produced the strongest binding effect with 10 hub nodes. This compound–target mode of interaction may be the main mechanism of action of GQD. This study reflected the synergistic characteristics of multiple targets and multiple pathways of traditional Chinese medicine and discussed the mechanism of GQD in the treatment of DM at the molecular pharmacological level.

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