Investigation of the Antibacterial Activity and Underlying Mechanism of Terminalia chebula Retz. Against Helicobacter pylori

Investigation of the Antibacterial Activity and Underlying Mechanism of Terminalia chebula Retz. Against Helicobacter pylori | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Investigation of the Antibacterial Activity and Underlying Mechanism of Terminalia chebula Retz. Against Helicobacter pylori Yangchenze Fan, Xuanru Kang, Xianmei Meng, Zhenyu Jiang, Chi Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8635950/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Objective To investigate the antibacterial activity and underlying mechanism of Terminalia chebula Retz. against Helicobacter pylori (H. pylori). Methods Core targets of T. chebula for inhibiting H. pylori were obtained via network pharmacology, followed by enrichment analysis. Molecular docking was performed to verify the binding activity between the active components of T. chebula and the core targets. A rat model of H. pylori infection was established, and the rats were randomly divided into the blank control group, model control group, and high-, medium-, and low-dose T. chebula groups. Routine blood test results of each group were examined, and the H. pylori infection status, the contents of interleukin-12 (IL-12), interleukin-17 (IL-17), interleukin-23 (IL-23), interferon-γ (IFN-γ), tumor necrosis factor-α (TNF-α) in gastric mucosa, as well as the protein expression levels of related targets ABL1, PTPN11, and Ras were determined. Minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) tests were conducted using the Hp SS1 strain. Results Network pharmacology identified 11 key targets of T. chebula for H. pylori inhibition. Enrichment analysis indicated that the inhibitory mechanism of T. chebula against H. pylori may be associated with the RAS signaling pathway, signal transduction pathways and functions of the Eph receptor family and its ligands, and cancer pathways. Molecular docking results showed that the main active components, including ellagic acid, quinidine sulfate, and sennosides, exhibited high docking affinity with the core targets ABL1, PTPN11, and PARP1. Animal experiments revealed obvious gastric mucosal damage in H. pylori-infected rats. After treatment with medium and high doses of T. chebula, the number of inflammatory cells in the gastric mucosa of rats significantly decreased, swelling was alleviated, and mucosal thickness returned to normal. Meanwhile, the serum levels of TNF-α, IL-12, IL-17, IL-23, and IFN-γ were reduced (P < 0.05), and the protein expression levels of ABL1, PTPN11, and Ras were downregulated (P < 0.05), with the most significant reduction and the best anti-inflammatory effect observed in the high-dose T. chebula group. Routine blood test results demonstrated that T. chebula treatment significantly decreased the white blood cell count (P < 0.05). Drug sensitivity tests showed that quinidine sulfate, ellagic acid, sennosides, (R)-(6-methoxy-4-quinolyl)-[(2R,4R,5S)-5-vinylquinuclidin-2-yl]methanol, and Sennoside E_qt exerted significant in vitro antibacterial activity. Conclusion Terminalia chebula exerts a therapeutic effect on H. pylori infection by acting on targets such as ABL1, PTPN11, and c-k-Ras through components including ellagic acid, quinidine sulfate, and sennosides. It inhibits related pathways to downregulate the expression of various cytokines, thereby alleviating gastric mucosal inflammatory damage. Terminalia chebula Retz. Helicobacter pylori network pharmacology ABL1 PTPN11 c-k-Ras ellagic acid signaling pathway anti-inflammatory effect Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction The global infection rate of Helicobacter pylori (H. pylori) exceeds 50%. H. pylori is the primary cause of chronic gastritis and peptic ulcers, and also the most important modifiable risk factor for gastric cancer. However, current mainstream therapies exhibit significant side effects, and drug resistance has become a prominent issue, highlighting an urgent need to develop safer and more effective novel drugs or vaccines. Terminalia chebula Retz., the mature fruit of a plant belonging to the genus Terminalia in the family Combretaceae, is a highly representative research object in traditional medical systems and the history of cross-cultural exchanges. Numerous studies have confirmed its excellent antibacterial activity [17, 18]. Our research team has previously conducted relevant explorations; drug sensitivity tests on 13 commonly used single Chinese-Mongolian medicines showed that T. chebula has good antibacterial activity against the standard H. pylori strain (ATCC43504) [5]. Nevertheless, the antibacterial mechanism of T. chebula against H. pylori remains unclear and requires further in-depth investigation. This study intends to utilize the characteristics of network pharmacology—including integrity, systematicness, and consistency with the multi-component and multi-target properties of traditional Chinese medicines—to predict the targets and pathways underlying the antibacterial effect of T. chebula against H. pylori. Molecular docking and animal experiments will be combined to verify the reliability of the results, thereby providing a reference for the in-depth clinical research and development of T. chebula. Materials and Methods 1.1 Network Pharmacology Analysis of Terminalia chebula Retz. and Helicobacter pylori 1.1.1 Collection of Active Components and Targets of Terminalia chebula Retz. "HE ZI" was used as the search term to screen for active components. The Traditional Chinese Medicine Systems Pharmacology Database (TCMSP, https://old.tcmsp-e.com/tcmsp.php ) was queried to obtain active components of T. chebula that met the criteria of oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18. The structural files of these active components were then imported into the PharmMapper database ( http://www.lilab-ecust.cn/pharmmapper/ ) to acquire their corresponding targets. 1.1.2 Construction of the "Active Component-Target" Network The active components and their targets of T. chebula obtained in Section 1.1.1 were imported into Cytoscape software to construct the "Active Component-Target" network. 1.1.3 Collection of Helicobacter pylori-Related Targets GeneCards database ( https://www.genecards.org/ ) was searched with the keywords "H. pylori" and "inflammation" to collect target information related to H. pylori-induced diseases. The targets of T. chebula active components and the pathogenic targets of H. pylori were intersected using the online tool Venny 2.1.0 ( http://www.liuxiaoyuyuan.cn/ ) to obtain the common genes associated with both T. chebula and H. pylori. 1.1.4 Construction of Protein-Protein Interaction (PPI) Network The intersection target genes obtained in Section 1.1.3 were imported into the String database, with "Homo sapiens" set as the species and the interaction score threshold set at 0.15, to generate the PPI network. 1.1.5 GO and KEGG Pathway Enrichment Analyses The key targets obtained in Section 1.1.4 were imported into the DAVID database ( https://david.ncifcrf.gov/ ). "Official Gene Symbol" and "Gene List" were clicked sequentially, and "Homo sapiens" was selected as the species. After uploading the data, KEGG pathway enrichment analysis was performed, and Gene Ontology (GO) analysis was conducted from three aspects: biological process (BP), cellular component (CC), and molecular function (MF). A P-value < 0.05 indicated statistically significant differences. The data were exported to Excel for processing and then imported into the Bioinformatics website ( http://www.bioinformatics.com.cn/ ) to generate bar charts for GO analysis and bubble charts for KEGG pathway analysis. 1.1.6 Molecular Docking Verification The top-ranked targets in the PPI network (based on degree value) were used as receptors, and the top-ranked active components of T. chebula in the "Active Component-Target" network were used as ligands to predict their binding ability. A lower binding energy value indicated a stronger binding ability. The gene names of key target proteins were entered into the UniProt database ( https://www.uniprot.org/ ) to retrieve their UniProt IDs. These UniProt IDs were then input into the RCSB PDB database ( https://www.rcsb.org/ ) to find the corresponding target proteins, whose 3D structures were downloaded and saved in PDB format. The structures were imported into PyMOL software for dehydration and ligand removal, followed by hydrogenation using AutoDock software and export in PDBQT format. The mol2 structures of T. chebula active components were downloaded from TCMSP, imported into ChemBio3D Ultra 14.0 for energy minimization (with the Minimum RMS Gradient set to 0.001), and saved in mol2 format. The optimized small molecules were imported into AutoDockTools-1.5.6 for hydrogenation, charge calculation, charge assignment, and rotatable bond setting, then saved in PDBQT format. The proteins were imported into PyMOL 2.3.0 to remove crystal water and original ligands, then imported into AutoDockTools (v1.5.6) for hydrogenation, charge calculation, charge assignment, and atom type specification, followed by saving in PDBQT format. POCASA 1.1 was used to predict protein binding sites, and AutoDock Vina 1.1.2 was employed for docking. PyMOL 2.3.0 was utilized to analyze the interaction modes of the docking results. 1.2 Animal Model Validation 1.2.1 Animals and Strains Forty specific pathogen-free (SPF) male Sprague-Dawley (SD) rats, weighing 170–190 g, were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. (License No.: SCXK (Jing) 2024-0001). All rats were subjected to a 7-day acclimatized feeding period. This study was approved by the Ethics Committee of Baotou Medical College, Inner Mongolia University of Science and Technology. Helicobacter pylori (H. pylori) SS1 strain was purchased from Hangzhou Hongsai Biotechnology Co., Ltd. and stored at -80℃. Reagents used included BHI medium (HB8478, Qingdao Haibo Biotechnology Co., Ltd.), defibrinated sheep blood (1001339-1, Qingdao Haibo Biotechnology Co., Ltd.), and agar (A1296, Sigma). 1.2.2 Drugs Forty grams of Terminalia chebula Retz. powder, prepared by the Traditional Chinese Medicine Pharmacy of the Second Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology, was made into a water decoction by conventional decoction method and stored at 4℃ under refrigeration. 1.2.3 Main Reagents HE staining kit (Batch No.: BT-P107), rabbit monoclonal antibody against c-Abl (260 kDa, Batch No.: A22082), rabbit polyclonal antibodies against PTPN11 (68 kDa, Batch No.: 20145-1-AP) and c-k-Ras (21 kDa, Batch No.: 12063-1-AP), rat TNF-α, IL-12, IL-17, IL-23, and IFN-γ ELISA kits (Batch Nos.: ER1393, ER1086, ER0035, ER1096, ER0012 respectively), and 0.22 µm NC membrane (PALL, Cat. No.: P-N66485-30). 1.2.4 Animal Modeling, Administration, and Grouping Rats were intragastrically administered 2.5% amoxicillin solution (0.5 ml per time, twice a day) for 3 consecutive days to eliminate miscellaneous bacteria in the digestive tract. After a 2-day rest, modeling was performed via intragastric gavage. All rats were fasted for 12 hours with free access to water before modeling. Pretreatment drugs, including 0.1 mmol/L NaHCO₃ solution, 56% ethanol, 5 mg/ml indomethacin solution, and 2 g/L NaHCO₃ + indomethacin solution, were intragastrically administered at 0.5 ml per rat. Fasting with free access to water was continued, and 6 hours later, H. pylori bacterial solution (1.5 ml per rat, concentration of 10⁹ CFU/ml) was given by gavage. All rats were fasted and deprived of water for 4 hours after H. pylori gavage, followed by normal feeding. Rats were gavaged every other day for a total of 5 times with H. pylori bacterial solution. Two rats were randomly sacrificed after the last gavage, and gastric antrum mucosa was collected for Gram staining and rapid urease test. Double positive results were considered as successful H. pylori colonization. Model rats were randomly divided into the model control group, low-dose, medium-dose, and high-dose T. chebula groups, with 6 rats in each group (8 rats died during modeling due to individual factors and feeding conditions). Administration was initiated on the 2nd day after modeling. Rats in the low-, medium-, and high-dose T. chebula groups were given T. chebula aqueous extract at 30, 60, and 90 mg/kg respectively (dose design was based on published literature [ 1 ]). Rats in the blank control group and model control group were given an equal volume of normal saline by gavage. All rats were gavaged every other day for 4 consecutive weeks. 1.2.5 Detection of Whole Blood Indicators in Rats After the end of administration, whole blood of rats in each group was collected into EDTA-K2 anticoagulant tubes and detected using a Mindray veterinary automatic hematology analyzer (Model: BC-5000vet). Changes in the counts of white blood cells, red blood cells, neutrophils, lymphocytes, and other indicators were recorded. 1.2.6 HE Staining Paraffin sectioning and HE staining: Tissues were fixed with 4% paraformaldehyde, subjected to gradient ethanol dehydration, and embedded in paraffin. Then 5 µm-thick sections were cut, and pathological morphology was observed after HE staining. 1.2.7 ELISA Detection of Serum TNF-α, IL-12, IL-17, IL-23, and IFN-γ Levels Blood was collected from the orbital venous plexus, centrifuged to collect the upper serum, and the contents of TNF-α, IL-12, IL-17, IL-23, and IFN-γ in rat serum were detected in accordance with the ELISA kit instructions. 1.2.8 Western Blot Analysis of c-Abl, PTPN11, and c-k-Ras Protein Levels Total protein was extracted from rat gastric tissues using RIPA lysis buffer, and protein quantification was performed with a BCA protein assay kit. Then 40 µg of protein was separated by 15% SDS-PAGE gel electrophoresis and transferred to a PVDF membrane. The membrane was incubated overnight at 4℃ with primary antibodies against GAPDH (1:50000), c-Abl (1:3000), PTPN11 (1:5000), and c-k-Ras (1:5000). After membrane washing, HRP-labeled secondary antibodies (goat anti-rabbit, 1:1000; goat anti-mouse, 1:10000) were added, and the PVDF membrane was soaked in the secondary antibody incubation solution and incubated on a shaker at room temperature for 2 hours. 1.3 In Vitro Antibacterial Assay of Active Components from Terminalia chebula Retz. Against H. pylori Drug stock solutions were prepared for Sennoside E_qt, ellagic acid, and quinidine sulfate. ((R)-(6-methoxy-4-quinolyl)-[(2R,4R,5S)-5-vinylquinuclidin-2-yl]methanol). BHI solid medium supplemented with defibrinated sheep blood was prepared as follows: 26 g of BHI powder and 7.5 g of agar dissolved in 500 mL of distilled water, autoclaved at 121°C for 15 min, and supplemented with 5% defibrinated sheep blood prior to use. Drug-containing medium was prepared via serial dilution (in 24-well plates, with the 8th well as the control). NC membranes were placed into each well. H. pylori was verified by 16S rRNA sequencing, adjusted to an OD₆₀₀ value of 0.3, and then diluted 1000-fold. A 20 µL aliquot of the diluted bacterial suspension was spotted onto the NC membranes, followed by incubation under microaerophilic conditions at 37°C for 7 days. The minimum inhibitory concentration (MIC) was defined as the lowest concentration at which no visible colonies grew on the NC membranes. For the determination of the minimum bactericidal concentration (MBC), bacterial cultures were incubated at a concentration equal to the MIC; the MBC was defined as the lowest concentration yielding fewer than 10 colonies. 1.4 Statistical Analysis Statistical analyses were performed using SPSS 26.0 software, and graphs were plotted with GraphPad Prism 8.0.2 software (note: corrected the typo “Priam” to the standard software name “Prism”). Measurement data were expressed as mean ± standard deviation (xˉ±s). One-way analysis of variance (ANOVA) was used for comparisons among multiple groups with a single factor, and pairwise comparisons between groups were conducted using the least significant difference (LSD) test. A P-value < 0.05 was considered statistically significant. Results 2.1 Results of Network Pharmacology Analysis of Terminalia chebula Retz. and Helicobacter pylori 2.1.1 Active Components of Terminalia chebula Retz. and Their Corresponding Targets A total of 8 active components were obtained from the TCMSP database, namely Sennoside E_qt, ellagic acid, 7-Dehydrosigmasterol, chebulic acid, Ellipticine, (R)-(6-methoxy-4-quinolyl)-[(2R,4R,5S)-5-vinylquinuclidin-2-yl]methanol, Peraksine, and Cheilanthifoline, corresponding to a total of 421 targets. Detailed information is presented in Table 1 . Table 1 Active Components and Corresponding Targets of Terminalia chebula Retz. No. Name Molecular Formula Number of Targets 1 ellagic acid C₁₄H₆O₈ 291 2 Sennoside E_qt C₄₂H₃₈O₂₀ 290 3 7-Dehydrosigmasterol C₂₉H₅₀O 213 4 chebulic acid C₁₄H₁₂O₁₁ 290 5 Ellipticine C₁₇H₁₄N₂ 185 6 (R)-(6-methoxy−4-quinolyl)-[(2R,4R,5S)−5-vinylquinuclidin−2-yl]methanol C₂₀H₂₄N₂O₂ 278 7 Peraksine C₁₉H₂₂N₂O₂ 236 8 Cheilanthifoline C₁₉H₁₉NO₄ 246 2.1.2 Construction of the "Active Component-Target-Disease" Network The "Active Component-Target" diagram is shown in Fig. 1 . The relevant data were imported into Cytoscape software to visualize the targets of the active components of Terminalia chebula Retz. and the pathogenic targets of Helicobacter pylori. 2.1.3 Collection of H. pylori Targets and Acquisition of Drug-Disease Targets A total of 99 pathogenic targets of Helicobacter pylori were identified via screening with the GeneCards database. Finally, 11 potential therapeutic targets of Terminalia chebula Retz. against H. pylori were obtained using the Venn database 2.1.0, with detailed information presented in Fig. 2 . 2.1.4 Construction of Protein-Protein Interaction (PPI) Network The obtained intersection target genes were imported into the String database. The resulting data were visualized using Cytoscape 3.8.0 software, and the protein-protein interaction network as well as the core target network were plotted, as shown in Fig. 3 . The PPI network contained 11 nodes, and several key genes including ABL1, PTPN11, and PARP1 were identified via screening. 2.1.5 GO and KEGG Pathway Enrichment Analysis GO functional enrichment analysis and KEGG pathway analysis were performed on the 11 key targets obtained in Section 2.1.4 using the DAVID 6.8 database. GO analysis was conducted from three aspects, namely Biological Process (BP), Cellular Component (CC), and Molecular Function (MF), yielding a total of 60 GO analysis results. Bar charts of GO analysis were plotted according to the P-value, as shown in Figs. 4 , 5, and 6 ; the taller the bar, the greater the number of genes involved in the corresponding process. Among these results, 47 GO terms had a P-value < 0.05. The BP-related terms included signal transduction pathways and functions of the Eph receptor family and its ligands, positive regulation of peptidyl-tyrosine phosphorylation, negative regulation of the apoptotic process, glutathione derivative biosynthesis, and cellular response to lipopolysaccharide. The CC-related terms included ficolin-1-rich granule lumen, mitochondrion, macromolecular complex, extracellular space, and extracellular region. The MF-related terms included serine-type endopeptidase activity, protein binding, glutathione transferase activity, enzyme binding, and phosphotyrosine binding. A total of 15 KEGG pathways were identified, and bubble charts of KEGG pathway analysis were plotted according to the P-value; the darker the bubble color, the higher the correlation between the pathway and the disease, as shown in Fig. 7. Among these pathways, 7 had a P-value < 0.05, namely chemical carcinogenesis-reactive oxygen species, Ras signaling pathway, cancer pathways, atherosclerosis, hepatocellular carcinoma, axon guidance, and proteoglycans in cancer. The main involved targets included ABL1, GSTM1, and PTPN11. 2.1.6 Molecular Docking Verification In this study, the binding affinities between the top-ranked targets (ABL1, PARP1, and PTPN11) in the PPI network and the active components (ellagic acid, quinidine sulfate, and Sennoside E_qt) were predicted, with the target proteins serving as receptors and the active components as ligands. Generally, it is recognized that the more stable the binding conformation between the receptor and ligand, the lower the binding energy and the stronger the interaction. According to the docking results, the binding energies between all components and targets were negative, indicating that these components and targets exhibited favorable binding activities. Ellagic acid, quinidine sulfate, and Sennoside E_qt showed relatively low binding energies with ABL1, PARP1, and PTPN11, suggesting good binding capabilities. All the docking binding energy results are presented in Table 2 . The molecular docking diagrams are shown in Fig. 8 . Table 2 Molecular Docking Binding Energies Component Target Binding Energy (kcal/mol) ellagic acid ABL1 −7.5 ellagic acid PARP1 −9.4 ellagic acid PTPN11 −8.2 quinidine sulfate ABL1 −7.5 quinidine sulfate PARP1 −8.6 quinidine sulfate PTPN11 −7.3 Sennoside E_qt ABL1 −9 Sennoside E_qt PARP1 −8.4 Sennoside E_qt PTPN11 −8 2.2 Animal Experiment Results 2.2.1 Routine Blood Test Results Analysis of routine blood test indicators showed that statistical analyses were performed on the differences in multiple blood parameters among different treatment groups, including the Normal Group, Model Group, Terminalia chebula Low-Dose Group, Terminalia chebula Medium-Dose Group, and Terminalia chebula High-Dose Group. The results indicated that compared with the Normal Group, the white blood cell (WBC) counts in the Model Group, as well as the low-, medium-, and high-dose groups of Terminalia chebula, were significantly increased (P < 0.05). In contrast, compared with the Model Group, the WBC counts in the low-, medium-, and high-dose groups of Terminalia chebula were significantly decreased (P < 0.05). Additionally, there were differences in some indicators among the low-, medium-, and high-dose groups of Terminalia chebula. Details are shown in Table 3 . Table 3 Routine Blood Test Results Variable Name Normal Model 1 Terminalia chebula Retz.-Low-Dose* Terminalia chebula Retz.-Medium-Dose* Terminalia chebula Retz.-High-Dose* F/Z P Value White Blood Cell (WBC) Count 2.44 ± 0.36 2.41 ± 0.03a 2.44 ± 0.36abc 4.07 ± 0.08abc 3.3 ± 0.08bcd 439.462 1 ) 0.001 Neutrophil Count 0.4 ± 0.2 0.18 ± 0 0.4 ± 0.2 0.48 ± 0.03 0.46 ± 0.02 11.153(2) 0.025 Lymphocyte Count 1.74 ± 0.56 1.98 ± 0.01 1.74 ± 0.56 2.99 ± 0.01b 1.95 ± 0.07 11.142 2 ) 0.025 Monocyte Count 0.28 ± 0.16 0.25 ± 0.03 0.28 ± 0.16 0.57 ± 0.1b 0.85 ± 0.13 11.265 2 ) 0.024 Eosinophil Count 0.01 ± 0.02 0.01 ± 0.01 0.01 ± 0.02 0.03 ± 0.01 0.04 ± 0.01 5.409 2 ) 0.248 Basophil Count 0 ± 0.01 0 ± 0 0 ± 0.01 0 ± 0 0 ± 0.01 5.327 2 ) 0.255 Neutrophil Percentage 16.63 ± 9.61 7.53 ± 0.12 16.63 ± 9.61 11.73 ± 1.01 13.73 ± 0.38 8.181 2 ) 0.085 Lymphocyte Percentage 70.67 ± 17.08 81.7 ± 0.87 70.67 ± 17.08 73.50 ± 1.39 59.07 ± 3.76 7.747 2 ) 0.101 Monocyte Percentage 11.97 ± 8.08 10.3 ± 1.13 11.97 ± 8.08 13.93 ± 2.15 25.77 ± 3.46 5.844 2 ) 0.211 Eosinophil Percentage 0.53 ± 0.76 0.4 ± 0.3 0.53 ± 0.76 0.83 ± 0.21 1.23 ± 0.4 8.763 2 ) 0.067 Eosinophil Percentage 0.2 ± 0.1 0.07 ± 0.12 0.2 ± 0.1 0 ± 0 0.2 ± 0.17 8.636 2 ) 0.071 Red Blood Cell (RBC) Count 6.64 ± 0.69 7.66 ± 0.13 6.64 ± 0.69 8.6 ± 0.22 8.73 ± 0.16 11.233 2 ) 0.024 Hemoglobin (HGB) 144 ± 11.53 156.33 ± 0.58 144 ± 11.53 168 ± 1.00b 169.67 ± 0.58 11.457 2 ) 0.022 Hematocrit (HCT) 42.63 ± 3.5 46.27 ± 0.74 42.63 ± 3.5 49.4 ± 1.15 50.23 ± 0.75 9.851 2 ) 0.043 Mean Corpuscular Volume (MCV) 64.4 ± 3.75 60.43 ± 0.06 64.4 ± 3.75 57.43 ± 0.12 57.57 ± 0.21 7.702 2 ) 0.103 Mean Corpuscular Hemoglobin (MCH) 21.77 ± 1.21 20.43 ± 0.31 21.77 ± 1.21 19.53 ± 0.46b 19.43 ± 0.31 11.855 2 ) 0.018 Mean Corpuscular Hemoglobin Concentration (MCHC) 337.67 ± 1.53 338.33 ± 4.04 337.67 ± 1.53 340.00 ± 6.93 337.67 ± 4.16 5.011 2 ) 0.286 Red Blood Cell Distribution Width-Coefficient of Variation (RDW-CV) 14.03 ± 0.65 14.7 ± 0.2 14.03 ± 0.65 13.93 ± 0.06b 14.07 ± 0.12 10.841 2 ) 0.028 Red Blood Cell Distribution Width-Standard Deviation (RDW-SD) 35.43 ± 1.36 34.93 ± 0.35 35.43 ± 1.36b 31 ± 0.17b 32 ± 0.35 12.894 2 ) 0.012 Platelet (PLT) Count 768.67 ± 3.51 662.67 ± 10.6 768.67 ± 3.51 582.67 ± 15.89b 643 ± 12.17 13.233 2 ) 0.01 Mean Platelet Volume (MPV) 6.17 ± 0.4 6.47 ± 0.06 6.17 ± 0.4 6.8 ± 0.1 6.33 ± 0.06 11.621 2 ) 0.02 Platelet Distribution Width (PDW) 15.37 ± 0.15 15.33 ± 0.12 15.37 ± 0.15 15.53 ± 0.12 15.6 ± 0 11.729 2 ) 0.019 Note: 1) denotes F value; 2) denotes Z value; a denotes P < 0.05 vs. Control Group; b denotes P < 0.05 vs. Model Group; c denotes P < 0.05 vs. Terminalia chebula Retz. Low-Dose Group; d denotes P < 0.05 vs. Terminalia chebula Retz. Medium-Dose Group. 2.2.2 Pathological Analysis of Gastric Mucosa in Rats In this study, Hematoxylin-Eosin (HE) staining was employed to analyze the pathological changes of gastric mucosa in rats. Compared with rats in the Model Group, those in the Terminalia chebula Low-Dose Group showed clearly distinguishable structures of all layers of gastric tissue under light microscopy: the mucosal epithelial cells were regularly arranged, various cell types were abundant, mild cell detachment was observed locally, and no significant inflammatory infiltration was found overall. For rats in the Terminalia chebula Medium-Dose and High-Dose Groups, the structures of all layers of gastric tissue were also clearly distinguishable under light microscopy, with regularly arranged mucosal epithelial cells, abundant various cell types, appropriate mucosal thickness, and no significant inflammatory infiltration overall. Among these groups, the aforementioned improvement effects were more pronounced in the High-Dose Group. However, under light microscopy, abnormalities were detected in the liver tissue of rats in all Terminalia chebula Low-Dose, Medium-Dose, and High-Dose Groups. Cytoplasmic transparency was observed in hepatocytes within a large area of hepatic parenchyma; numerous hepatocytes exhibited obscure structures with local nuclear fragmentation, and the overall tissue structure showed significant changes. These findings suggest that Terminalia chebula may have hepatotoxicity [ 18 ]. Representative images are shown in Fig. 9 . 2.2.3 Effects of Different Doses of Terminalia chebula Retz. on Serum Levels of TNF-α, IL-12, IL-17, IL-23, and IFN-γ in Model Rats Helicobacter pylori (H. pylori) infection significantly increased the concentrations of pro-inflammatory cytokines, including TNF-α, IL-12, IL-17, IL-23, and IFN-γ. After intervention with Terminalia chebula Retz., the concentrations of these pro-inflammatory cytokines were significantly reduced (P < 0.05) in a dose-dependent manner: the most significant reduction was observed in the High-Dose Group, followed by the Medium-Dose Group, while the effect was relatively weaker in the Low-Dose Group. Details are shown in Table 4 . Table 4 Comparison of Serum Levels of TNF-α, IL-12, IL-17, IL-23, and IFN-γ Among Different Groups of Rats Group Number of Animals TNF-a IL12 IL17 IL23 IFN-Y Control Group 6 153.070 ± 17.50 192.658 ± 32.337 355.422 ± 41.936 306.175 ± 49.566 270.501 ± 55.407 55Model Group 6 759.954 ± 84.590★★★ 967.426 ± 108.6★★★34 1680.499 ± 180.350★★★ 1562.661 ± 159.022★★★ 1362.990 ± 90.166★★★ Terminalia chebula Retz. High-Dose Group 6 291.246 ± 22.806★★▲▲▲●●● 359.466 ± 28.371★★▲▲▲●●● 552.715 ± 46.361★▲▲▲●●● 552.243 ± 46.110★★▲▲▲●●● 524.196 ± 28.882★★★▲▲▲●●● Terminalia chebula Retz. Medium-Dose Group 6 431.890 ± 28.925★★★▲▲▲●● 558.660 ± 44.076★★★▲▲▲●●● 1048.366 ± 66.816★★★●● 768.972 ± 38.191★▲▲▲●●● 680.951 ± 28.765★★★▲▲▲●●● Terminalia chebula Retz. Low-Dose Group 6 579.700 ± 28.925★★★▲▲ 804.164 ± 30.898★★★▲▲ 1418.140 ± 73.910★★★▲▲ 1272.884 ± 65.440★★▲▲ 1182.799 ± 55.037★★★▲▲ 2.2.4 Effects of Terminalia chebula Retz. on the Protein Expression of c-Abl, PTPN11, and c-k-Ras Compared with the Control Group, the protein levels of c-Abl, PTPN11, and c-k-Ras in the H. pylori Model Group were significantly elevated. In comparison with the Model Group, the protein levels of c-Abl, PTPN11, and c-k-Ras were decreased in the Medium-Dose and High-Dose Groups of Terminalia chebula Retz. (P < 0.05), with a more significant reduction observed in the High-Dose Group (P < 0.01). Western Blot (WB) band images and protein level bar charts showed that the gray value changes of protein bands in each group were consistent with the quantitative results, as shown in Figs. 10 and 11 . Note CON = Control Group; Hp = H. pylori Infection Group; H-L = Terminalia chebula Retz. Low-Dose Group; H-M = Terminalia chebula Retz. Medium-Dose Group; H-H = Terminalia chebula Retz. High-Dose Group 2.3 Antimicrobial Susceptibility Test Results The growth of bacterial cells was observed by visual inspection. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of (R)-(6-methoxy-4-quinolyl)-[(2R,4R,5S)-5-vinylquinuclidin-2-yl]methanol were 0.625 mg/ml and 1.25 mg/ml, respectively; those of ellagic acid were 0.0625 mg/ml and 0.25 mg/ml, respectively; and those of Sennoside E_qt were 2.5 mg/ml and 10 mg/ml, respectively. Details are shown in Table 5 . Table 5 Antimicrobial Susceptibility Test of Terminalia chebula Retz. Active Components Against H.pylori Number Molecular name Solvent MIC(mg/ml) MBC(mg/ml) 1 (R)-(6-methoxy−4-quinolyl)-[(2R,4R,5S)−5-vinylquinuclidin−2-yl]methanol DMSO 0.625 1.25 2 ellagic acid DMSO 0.0625 0.25 3 Sennoside E gt DMSO 2.5 10 Discussion Helicobacter pylori (H. pylori) infection is globally prevalent, and the growing problem of drug resistance has made the development of novel and efficient therapeutic strategies an urgent priority. As a medicinal herb commonly used for treating gastrointestinal disorders in traditional Chinese-Mongolian medicine, Terminalia chebula Retz. has been confirmed by modern studies to possess anti-inflammatory and broad-spectrum antibacterial activities, which provides a potential basis for its anti-H. pylori research. However, current research on the anti-H. pylori effects of T. chebula is still limited to preliminary in vitro reports, and its systematic pharmacological mechanism remains unclear. To address this gap, the present study established an integrated research framework of "target prediction–molecular interaction–in vitro and in vivo validation", aiming to systematically elucidate the multi-component, multi-target, and multi-pathway synergistic mechanism underlying the anti-H. pylori activity of T. chebula, thereby providing scientific evidence for the development of new anti-H. pylori drugs based on Chinese-Mongolian medicinal herbs. Network pharmacology approaches identified 11 core overlapping targets associated with H. pylori pathogenicity for the active components of T. chebula. KEGG pathway analysis revealed that the Ras signaling pathway serves as a core mechanism, whose role in H. pylori-induced gastritis and carcinogenesis has been well-documented [ 13 ]. This prediction was verified in our animal experiments: H. pylori infection significantly upregulated the protein expression of c-Abl1, PTPN11 (SHP2), and c-k-Ras in gastric mucosa, whereas treatment with medium and high doses of T. chebula aqueous extract significantly downregulated their expression. This is consistent with the growing recognition in the academic community that the ABL1/SHP2/Ras module acts as a key signaling node in cell growth and inflammation [ 14 , 16 , 15 ]. Our work is the first to directly link the anti-H. pylori activity of T. chebula to the inhibition of this specific signaling axis, which not only suggests its potential in eradicating the bacterium but also implies its possible role in blocking the progression of precancerous lesions. Furthermore, the improvement of hematological parameters (including the normalization of white blood cell counts) and antimicrobial susceptibility tests further confirmed the systemic anti-inflammatory effects of T. chebula, in contrast to traditional therapies that may overlook hematological indicators. Meanwhile, the integration of network pharmacology into the present study has provided a more in-depth mechanistic interpretation. Furthermore, the significant reduction in the levels of key pro-inflammatory cytokines (TNF-α, IL-12, IL-17, IL-23, and IFN-γ) in the serum of rats from the T. chebula treatment groups provides compelling evidence for its potent anti-inflammatory activity. The excessive production of these cytokines, particularly IL-17 and TNF-α, is a hallmark of H. pylori-associated inflammation and contributes to tissue damage [ 4 ]. The dose-dependent inhibitory effect of T. chebula on these cytokines is consistent with the findings of Ou et al. [ 10 ], who demonstrated that the aqueous extract of T. chebula can inhibit H. pylori-induced inflammatory responses by modulating inflammasome signaling pathways. Our findings extend this understanding by linking cytokine inhibition to the downregulation of upstream signaling proteins such as PTPN11/SHP2. The role of SHP2 in inflammation is complex; for instance, Imai S et al. [ 2 ] demonstrated that the oncogenic phosphatase SHP2 is released during H. pylori infection, thereby promoting gastric carcinogenesis. Our study further reveals that the aqueous extract of T. chebula can effectively inhibit the inflammatory responses driven by PTPN11 (SHP2), which highlights the therapeutic potential of targeting this protein in mucosal immunity. ABL1, PTPN11/SHP2, and Ras constitute a crucial signaling transduction module that plays an indispensable role under both physiological and pathological conditions. Aberrant activation of this signaling axis is one of the core mechanisms driving the malignant phenotypes of cells in malignant tumors (e.g., leukemia) and infection-associated carcinogenesis (e.g., H. pylori-associated gastric cancer). ABL1 initiates the signaling cascade, SHP2 acts as a key transducer and amplifier, and Ras ultimately executes the pro-proliferative and pro-survival instructions [ 12 – 16 ]. An in-depth understanding of the network interactions among these three molecules not only unravels the intrinsic logic underlying disease initiation and progression but also provides a solid theoretical basis for developing combined targeted therapies against this signaling axis. Future studies should aim to explore the optimal strategies for disrupting the synergistic effects of this signaling axis in specific cancer contexts and translate this knowledge into effective clinical therapeutic regimens. The present study still has several limitations: the differences between the animal model and human chronic infection may affect the extrapolation of conclusions; network pharmacology may omit certain components or targets; the interactions among various active components have not been elucidated; and the hepatotoxicity risk indicated by animal experiments requires further clarification [19]. Future research should conduct clinical studies to verify the efficacy and safety, employ multi-omics technologies to further unravel the underlying mechanisms, and isolate and optimize active components for the development of new compound preparations. Meanwhile, exploring the synergistic effects of Terminalia chebula Retz. with existing antibiotics is expected to provide new strategies for overcoming H. pylori resistance. In conclusion, the anti-H. pylori activity of Terminalia chebula Retz. does not depend on a single component or mechanism, but rather reflects a comprehensive pharmacological mode of multi-component, multi-target, and multi-pathway synergistic actions. At the molecular level, its active components, such as ellagic acid and quinine sulfate, can not only directly act on bacteria to inhibit their growth and proliferation, exerting a bacteriostatic effect, but also effectively suppress the excessive activation of the Ras signaling pathway and related inflammatory pathways through interacting with the PTPN11 (SHP2) target, thereby regulating the release of downstream inflammatory factors and alleviating the inflammatory damage of gastric mucosa. The synergistic operation of this dual mechanism of bacteriostasis and anti-inflammation highlights the potential advantage of Terminalia chebula Retz. in addressing both the symptoms and root causes during the intervention of H. pylori infection, and also provides a theoretical basis for further developing it as an adjuvant therapeutic agent. Declarations Author Contribution Yangchenze Fan and Xuanru Kang drafted the main manuscript text.All authors reviewed the manuscript. References Liu H, Shen J, Liu WB, et al. Protective effect and mechanism of aqueous extract of Terminalia chebula Retz. on rats with hyperuricemic nephropathy[J]. Pharmacology and Clinics of Chinese Materia Medica, 2024, 40(10): 56–64. DOI: 10.13412/j.cnki.zyyl.20240412.004 . Imai S, Ooki T, Murata-Kamiya N, et al. Helicobacter pylori CagA elicits BRCAness to induce genome instability that may underlie bacterial gastric carcinogenesis. Cell Host Microbe. 2021;29(6):941–958.e10. doi: 10.1016/j.chom.2021.04.006 Kashyap D, Baral B, Jakhmola S, Singh AK, Jha HC. Helicobacter pylori and Epstein-Barr Virus Coinfection Stimulates Aggressiveness in Gastric Cancer through the Regulation of Gankyrin. mSphere. 2021;6(5):e0075121. doi: 10.1128/mSphere.00751-21 . Epub 2021 Sep 29. PMID: 34585958; PMCID: PMC8550222. Fei X, Chen S, Li L, Xu X, Wang H, Ke H, He C, Xie C, Wu X, Liu J, Xie Y, Lu N, Zhu Y, Li N. Helicobacter pylori infection promotes M1 macrophage polarization and gastric inflammation by activation of NLRP3 inflammasome via TNF/TNFR1 axis. Cell Commun Signal. 2025;23(1):6. doi: 10.1186/s12964-024-02017-7 . PMID: 39762835; PMCID: PMC11705855. Liu C, Meng XM, Nian YY, et al. In vitro bacteriostatic effect of Sino-Mongolian medicines on Helicobacter pylori[J]. Journal of Modern Integrated Traditional Chinese and Western Medicine, 2024, 33(16): 2244–2249. Zhang M, Cui S, Mao B, Zhang Q, Zhao J, Zhang H, Tang X, Chen W. Ellagic acid and intestinal microflora metabolite urolithin A: A review on its sources, metabolic distribution, health benefits, and biotransformation. Crit Rev Food Sci Nutr. 2023;63(24):6900–6922. doi: 10.1080/10408398.2022.2036693. Epub 2022 Feb 10. PMID: 35142569. Zhang XJ, He LJ, Lu Q, Li DY. [Pharmacological activity of Terminalia chebula]. Zhongguo Zhong Yao Za Zhi. 2016;41(4):619–623. Chinese. doi: 10.4268/cjcmm20160412 . PMID: 28871682. Ou L, Liu HR, Shi XY, Peng C, Zou YJ, Jia JW, Li H, Zhu ZX, Wang YH, Su BM, Lai YQ, Chen MY, Zhu WX, Feng Z, Zhang GM, Yao MC. Terminalia chebula Retz. aqueous extract inhibits the Helicobacter pylori-induced inflammatory response by regulating the inflammasome signaling and ER-stress pathway. J Ethnopharmacol. 2024;320:117428. doi: 10.1016/j.jep.2023.117428 . Epub 2023 Nov 18. PMID: 37981121. Liang XL, Hu HB, Jia AP, et al. Correlation study between gastric cancer, PTPN11 gene polymorphism and Helicobacter pylori infection[J]. Journal of Youjiang Medical University for Nationalities, 2011, 33(3): 261–263. Wu WC, Xu LM, Lai YQ, et al. Relationship between Helicobacter pylori OipA and expression of p53 and ras proteins in gastric cancer tissues[J]. Journal of Practical Medicine, 2012, 28(13): 2156–2158. Kazemi-Sefat GE, Keramatipour M, Vaezi M, et al. Integrated genomic sequencing in myeloid blast crisis chronic myeloid leukemia (MBC-CML), identified potentially important findings in the context of leukemogenesis model. Sci Rep. 2022;12(1):12816. Published 2022 Jul 27. doi: 10.1038/s41598-022-17232-w Craig VJ, Cogliatti SB, Rehrauer H, Wündisch T, Müller A. Epigenetic silencing of microRNA-203 dysregulates ABL1 expression and drives Helicobacter-associated gastric lymphomagenesis. Cancer Res. 2011;71(10):3616–3624. doi: 10.1158/0008-5472.CAN-10-3907 Goto Y, Ando T, Yamamoto K, et al. Association between serum pepsinogens and polymorphismof PTPN11 encoding SHP-2 among Helicobacter pylori seropositive Japanese. Int J Cancer. 2006;118(1):203–208. doi: 10.1002/ijc.21338 He C, Tu H, Sun L, Xu Q, Gong Y, Jing J, Dong N, Yuan Y. SNP interactions of Helicobacter pylori-related host genes PGC, PTPN11, IL1B, and TLR4 in susceptibility to gastric carcinogenesis. Oncotarget. 2015;6(22):19017–26. doi: 10.18632/oncotarget.4231 . PMID: 26158864; PMCID: PMC4662472. Kashyap D, Baral B, Jakhmola S, Singh AK, Jha HC. Helicobacter pylori and Epstein-Barr Virus Coinfection Stimulates Aggressiveness in Gastric Cancer through the Regulation of Gankyrin. mSphere. 2021;6(5):e0075121. doi: 10.1128/mSphere.00751-21 . Epub 2021 Sep 29. PMID: 34585958; PMCID: PMC8550222. Yang F. Study on Chemical Components and Toxic Mechanism of Hepatotoxic Extraction Fractions from Terminalia chebula Retz.[D]. Northeast Agricultural University, 2022. DOI: 10.27010/d.cnki.gdbnu.2022.000502 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 09 Feb, 2026 Editor assigned by journal 22 Jan, 2026 Submission checks completed at journal 22 Jan, 2026 First submitted to journal 19 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8635950","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588430165,"identity":"8bc0ab31-a72f-462a-9ed2-3af675deb626","order_by":0,"name":"Yangchenze Fan","email":"","orcid":"","institution":"Baotou Medical College, Inner Mongolia University of Science and Technology，Baotou","correspondingAuthor":false,"prefix":"","firstName":"Yangchenze","middleName":"","lastName":"Fan","suffix":""},{"id":588430166,"identity":"e3aa325e-a7cd-4780-bbd0-2ba4427c8324","order_by":1,"name":"Xuanru Kang","email":"","orcid":"","institution":"Baotou Medical College, Inner Mongolia University of Science and Technology，Baotou","correspondingAuthor":false,"prefix":"","firstName":"Xuanru","middleName":"","lastName":"Kang","suffix":""},{"id":588430167,"identity":"213da705-d8bd-47d8-993b-6e340ead8e0f","order_by":2,"name":"Xianmei Meng","email":"","orcid":"","institution":"Baotou Medical College, Inner Mongolia University of Science and Technology，Baotou","correspondingAuthor":false,"prefix":"","firstName":"Xianmei","middleName":"","lastName":"Meng","suffix":""},{"id":588430168,"identity":"c0cff659-7700-4cfc-a573-16e8f426a14a","order_by":3,"name":"Zhenyu Jiang","email":"","orcid":"","institution":"Baotou Medical College, Inner Mongolia University of Science and Technology，Baotou","correspondingAuthor":false,"prefix":"","firstName":"Zhenyu","middleName":"","lastName":"Jiang","suffix":""},{"id":588430169,"identity":"2907c044-414c-4836-9e19-2774045d9f51","order_by":4,"name":"Chi Wang","email":"","orcid":"","institution":"Baotou Medical College, Inner Mongolia University of Science and Technology，Baotou","correspondingAuthor":false,"prefix":"","firstName":"Chi","middleName":"","lastName":"Wang","suffix":""},{"id":588430170,"identity":"66351d0b-4111-4c54-94bb-61e5df1d9622","order_by":5,"name":"Jiaying Zhu","email":"","orcid":"","institution":"Baotou Medical College, Inner Mongolia University of Science and Technology，Baotou","correspondingAuthor":false,"prefix":"","firstName":"Jiaying","middleName":"","lastName":"Zhu","suffix":""},{"id":588430171,"identity":"ba766497-244e-4a90-9b8b-e751f1b5948f","order_by":6,"name":"Yuanyuan Nian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYDACZhSeAQMDPzPz4QekaZFsZ0szIM1ag/M8ChL4FJizMz97zNtml2dwI/nZ44KCO/LGh3mAltXYROPSYtnMZm7M25ZcbHAjzdx4hsEzw22HeQ88YDiWltuAyxmHGcykeduYEzfcSDCT5jE4zLjtMF+CAWPDYTxa2L8BtdQDtaR/A2mx39zMYyCBXwsPyJbDQC05YFsSNzAT1lImOefc8cSZZ96UgbQkzzgMDOQEfH45f3ybxJuy6sS+4+nbpHn+HLbt7z98+MGHGhucWkCAiQdIKBxAFkrAoxwEGH8ACXl8ho6CUTAKRsHIBgBxa1kX9gonSwAAAABJRU5ErkJggg==","orcid":"","institution":"Baotou Medical College, Inner Mongolia University of Science and Technology，Baotou","correspondingAuthor":true,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Nian","suffix":""}],"badges":[],"createdAt":"2026-01-19 08:25:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8635950/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8635950/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102443460,"identity":"1e9e0d18-d9e7-41f7-8a80-7abdd84d2b56","added_by":"auto","created_at":"2026-02-11 17:21:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":531201,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eActive Component-Target Network Diagram\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8635950/v1/06a5c115b5a201e553faee2a.png"},{"id":102443467,"identity":"048a0909-a839-4d0f-b50c-9635b0c09a1b","added_by":"auto","created_at":"2026-02-11 17:21:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":45930,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVenn Diagram of Targets of Active Components from Terminalia chebula Retz. and Pathogenic Targets of Helicobacter pylori\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8635950/v1/be8cb5b7488a4c986fbf689b.png"},{"id":102443461,"identity":"84fdc1ee-fb5d-4bbf-ab45-9350c2a9a73e","added_by":"auto","created_at":"2026-02-11 17:21:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":113034,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePPI Network Diagram of Interaction Targets between Terminalia chebula Retz. and Helicobacter pylori\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8635950/v1/d704c25f42b06e3febecab83.png"},{"id":102443462,"identity":"ac777f91-743c-4fe9-9d82-df0adc506f17","added_by":"auto","created_at":"2026-02-11 17:21:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":434376,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGO-BP Bar Chart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8635950/v1/a7788c5ce0e68058e95a12b1.png"},{"id":102443470,"identity":"53d321f8-b7ce-48dc-8b4a-81d31bc56068","added_by":"auto","created_at":"2026-02-11 17:21:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":111369,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGO-CC Bar Chart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8635950/v1/6ef9f52158a34afddbd7a022.png"},{"id":102443469,"identity":"a76b4463-09d2-4bde-9953-7a3568244f04","added_by":"auto","created_at":"2026-02-11 17:21:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":287610,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGO-MF Bar Chart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8635950/v1/973cb5a59432ea308537b80a.png"},{"id":102745955,"identity":"75fcf4fd-2b21-487d-91f9-635c6592d08b","added_by":"auto","created_at":"2026-02-16 08:54:54","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":230981,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKEGG Bubble Chart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8635950/v1/8564556b7a7906f775ea6a5b.png"},{"id":102443468,"identity":"5f73241e-e4d5-41a2-8b2e-7c19357a4586","added_by":"auto","created_at":"2026-02-11 17:21:08","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":293937,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOptimal Docking Diagram of Key Targets Inhibiting Helicobacter pylori and Active Components (ellagic acid and PARP1)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8635950/v1/4fe7a99089ca9fc4444d42bc.png"},{"id":102443463,"identity":"f6eac869-93c7-40d2-8f21-fd20c7079d76","added_by":"auto","created_at":"2026-02-11 17:21:08","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":681033,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNote: N = Normal Group; C = H. pylori Infection Group; H-L = Terminalia chebula Low-Dose Group; H-M = Terminalia chebula Medium-Dose Group\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8635950/v1/938ce21d9a750691de0391aa.png"},{"id":102443464,"identity":"40a753b9-0212-4c46-af6f-b77fc4068458","added_by":"auto","created_at":"2026-02-11 17:21:08","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":190177,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWB Band Image\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-8635950/v1/ff149a4c6c5912a80ccc12a6.png"},{"id":102443465,"identity":"185f3ad0-4ab1-4a9a-9320-22ab4783b27d","added_by":"auto","created_at":"2026-02-11 17:21:08","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":69318,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHorizontal Bar Chart\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: CON = Control Group; Hp = H. pylori Infection Group; H-L = Terminalia chebula Retz. Low-Dose Group; H-M = Terminalia chebula Retz. Medium-Dose Group; H-H = Terminalia chebula Retz. High-Dose Group\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-8635950/v1/c025f2b8b145847b8d51a847.png"},{"id":102750551,"identity":"109b7e49-64dd-4deb-9057-a6ef3368b0f4","added_by":"auto","created_at":"2026-02-16 09:20:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5654719,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8635950/v1/229b2a20-dc1e-4bc5-9c04-ecc3e9c5e294.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigation of the Antibacterial Activity and Underlying Mechanism of Terminalia chebula Retz. Against Helicobacter pylori","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe global infection rate of Helicobacter pylori (H. pylori) exceeds 50%. H. pylori is the primary cause of chronic gastritis and peptic ulcers, and also the most important modifiable risk factor for gastric cancer. However, current mainstream therapies exhibit significant side effects, and drug resistance has become a prominent issue, highlighting an urgent need to develop safer and more effective novel drugs or vaccines.\u003c/p\u003e\n\u003cp\u003eTerminalia chebula Retz., the mature fruit of a plant belonging to the genus Terminalia in the family Combretaceae, is a highly representative research object in traditional medical systems and the history of cross-cultural exchanges. Numerous studies have confirmed its excellent antibacterial activity [17, 18]. Our research team has previously conducted relevant explorations; drug sensitivity tests on 13 commonly used single Chinese-Mongolian medicines showed that T. chebula has good antibacterial activity against the standard H. pylori strain (ATCC43504) [5]. Nevertheless, the antibacterial mechanism of T. chebula against H. pylori remains unclear and requires further in-depth investigation.\u003c/p\u003e\n\u003cp\u003eThis study intends to utilize the characteristics of network pharmacology\u0026mdash;including integrity, systematicness, and consistency with the multi-component and multi-target properties of traditional Chinese medicines\u0026mdash;to predict the targets and pathways underlying the antibacterial effect of T. chebula against H. pylori. Molecular docking and animal experiments will be combined to verify the reliability of the results, thereby providing a reference for the in-depth clinical research and development of T. chebula.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Network Pharmacology Analysis of Terminalia chebula Retz. and Helicobacter pylori\u003c/h2\u003e \u003cdiv id=\"Sec3\" class=\"Section3\"\u003e \u003ch2\u003e1.1.1 Collection of Active Components and Targets of Terminalia chebula Retz.\u003c/h2\u003e \u003cp\u003e\"HE ZI\" was used as the search term to screen for active components. The Traditional Chinese Medicine Systems Pharmacology Database (TCMSP, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://old.tcmsp-e.com/tcmsp.php\u003c/span\u003e\u003cspan address=\"https://old.tcmsp-e.com/tcmsp.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was queried to obtain active components of T. chebula that met the criteria of oral bioavailability (OB)\u0026thinsp;\u0026ge;\u0026thinsp;30% and drug-likeness (DL)\u0026thinsp;\u0026ge;\u0026thinsp;0.18. The structural files of these active components were then imported into the PharmMapper database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.lilab-ecust.cn/pharmmapper/\u003c/span\u003e\u003cspan address=\"http://www.lilab-ecust.cn/pharmmapper/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to acquire their corresponding targets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e1.1.2 Construction of the \"Active Component-Target\" Network\u003c/h2\u003e \u003cp\u003eThe active components and their targets of T. chebula obtained in Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e1.1.1\u003c/span\u003e were imported into Cytoscape software to construct the \"Active Component-Target\" network.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e1.1.3 Collection of Helicobacter pylori-Related Targets\u003c/h2\u003e \u003cp\u003eGeneCards database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was searched with the keywords \"H. pylori\" and \"inflammation\" to collect target information related to H. pylori-induced diseases. The targets of T. chebula active components and the pathogenic targets of H. pylori were intersected using the online tool Venny 2.1.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.liuxiaoyuyuan.cn/\u003c/span\u003e\u003cspan address=\"http://www.liuxiaoyuyuan.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to obtain the common genes associated with both T. chebula and H. pylori.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e1.1.4 Construction of Protein-Protein Interaction (PPI) Network\u003c/h2\u003e \u003cp\u003eThe intersection target genes obtained in Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e1.1.3\u003c/span\u003e were imported into the String database, with \"Homo sapiens\" set as the species and the interaction score threshold set at 0.15, to generate the PPI network.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e1.1.5 GO and KEGG Pathway Enrichment Analyses\u003c/h2\u003e \u003cp\u003eThe key targets obtained in Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e1.1.4\u003c/span\u003e were imported into the DAVID database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). \"Official Gene Symbol\" and \"Gene List\" were clicked sequentially, and \"Homo sapiens\" was selected as the species. After uploading the data, KEGG pathway enrichment analysis was performed, and Gene Ontology (GO) analysis was conducted from three aspects: biological process (BP), cellular component (CC), and molecular function (MF). A P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated statistically significant differences. The data were exported to Excel for processing and then imported into the Bioinformatics website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioinformatics.com.cn/\u003c/span\u003e\u003cspan address=\"http://www.bioinformatics.com.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to generate bar charts for GO analysis and bubble charts for KEGG pathway analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e1.1.6 Molecular Docking Verification\u003c/h2\u003e \u003cp\u003eThe top-ranked targets in the PPI network (based on degree value) were used as receptors, and the top-ranked active components of T. chebula in the \"Active Component-Target\" network were used as ligands to predict their binding ability. A lower binding energy value indicated a stronger binding ability. The gene names of key target proteins were entered into the UniProt database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to retrieve their UniProt IDs. These UniProt IDs were then input into the RCSB PDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to find the corresponding target proteins, whose 3D structures were downloaded and saved in PDB format. The structures were imported into PyMOL software for dehydration and ligand removal, followed by hydrogenation using AutoDock software and export in PDBQT format. The mol2 structures of T. chebula active components were downloaded from TCMSP, imported into ChemBio3D Ultra 14.0 for energy minimization (with the Minimum RMS Gradient set to 0.001), and saved in mol2 format. The optimized small molecules were imported into AutoDockTools-1.5.6 for hydrogenation, charge calculation, charge assignment, and rotatable bond setting, then saved in PDBQT format. The proteins were imported into PyMOL 2.3.0 to remove crystal water and original ligands, then imported into AutoDockTools (v1.5.6) for hydrogenation, charge calculation, charge assignment, and atom type specification, followed by saving in PDBQT format. POCASA 1.1 was used to predict protein binding sites, and AutoDock Vina 1.1.2 was employed for docking. PyMOL 2.3.0 was utilized to analyze the interaction modes of the docking results.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Animal Model Validation\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e1.2.1 Animals and Strains\u003c/h2\u003e \u003cp\u003eForty specific pathogen-free (SPF) male Sprague-Dawley (SD) rats, weighing 170\u0026ndash;190 g, were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. (License No.: SCXK (Jing) 2024-0001). All rats were subjected to a 7-day acclimatized feeding period. This study was approved by the Ethics Committee of Baotou Medical College, Inner Mongolia University of Science and Technology. Helicobacter pylori (H. pylori) SS1 strain was purchased from Hangzhou Hongsai Biotechnology Co., Ltd. and stored at -80℃. Reagents used included BHI medium (HB8478, Qingdao Haibo Biotechnology Co., Ltd.), defibrinated sheep blood (1001339-1, Qingdao Haibo Biotechnology Co., Ltd.), and agar (A1296, Sigma).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e1.2.2 Drugs\u003c/h2\u003e \u003cp\u003eForty grams of Terminalia chebula Retz. powder, prepared by the Traditional Chinese Medicine Pharmacy of the Second Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology, was made into a water decoction by conventional decoction method and stored at 4℃ under refrigeration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e1.2.3 Main Reagents\u003c/h2\u003e \u003cp\u003eHE staining kit (Batch No.: BT-P107), rabbit monoclonal antibody against c-Abl (260 kDa, Batch No.: A22082), rabbit polyclonal antibodies against PTPN11 (68 kDa, Batch No.: 20145-1-AP) and c-k-Ras (21 kDa, Batch No.: 12063-1-AP), rat TNF-α, IL-12, IL-17, IL-23, and IFN-γ ELISA kits (Batch Nos.: ER1393, ER1086, ER0035, ER1096, ER0012 respectively), and 0.22 \u0026micro;m NC membrane (PALL, Cat. No.: P-N66485-30).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e1.2.4 Animal Modeling, Administration, and Grouping\u003c/h2\u003e \u003cp\u003eRats were intragastrically administered 2.5% amoxicillin solution (0.5 ml per time, twice a day) for 3 consecutive days to eliminate miscellaneous bacteria in the digestive tract. After a 2-day rest, modeling was performed via intragastric gavage. All rats were fasted for 12 hours with free access to water before modeling. Pretreatment drugs, including 0.1 mmol/L NaHCO₃ solution, 56% ethanol, 5 mg/ml indomethacin solution, and 2 g/L NaHCO₃ + indomethacin solution, were intragastrically administered at 0.5 ml per rat. Fasting with free access to water was continued, and 6 hours later, H. pylori bacterial solution (1.5 ml per rat, concentration of 10⁹ CFU/ml) was given by gavage. All rats were fasted and deprived of water for 4 hours after H. pylori gavage, followed by normal feeding. Rats were gavaged every other day for a total of 5 times with H. pylori bacterial solution. Two rats were randomly sacrificed after the last gavage, and gastric antrum mucosa was collected for Gram staining and rapid urease test. Double positive results were considered as successful H. pylori colonization.\u003c/p\u003e \u003cp\u003eModel rats were randomly divided into the model control group, low-dose, medium-dose, and high-dose T. chebula groups, with 6 rats in each group (8 rats died during modeling due to individual factors and feeding conditions). Administration was initiated on the 2nd day after modeling. Rats in the low-, medium-, and high-dose T. chebula groups were given T. chebula aqueous extract at 30, 60, and 90 mg/kg respectively (dose design was based on published literature [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]). Rats in the blank control group and model control group were given an equal volume of normal saline by gavage. All rats were gavaged every other day for 4 consecutive weeks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e1.2.5 Detection of Whole Blood Indicators in Rats\u003c/h2\u003e \u003cp\u003eAfter the end of administration, whole blood of rats in each group was collected into EDTA-K2 anticoagulant tubes and detected using a Mindray veterinary automatic hematology analyzer (Model: BC-5000vet). Changes in the counts of white blood cells, red blood cells, neutrophils, lymphocytes, and other indicators were recorded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e1.2.6 HE Staining\u003c/h2\u003e \u003cp\u003eParaffin sectioning and HE staining: Tissues were fixed with 4% paraformaldehyde, subjected to gradient ethanol dehydration, and embedded in paraffin. Then 5 \u0026micro;m-thick sections were cut, and pathological morphology was observed after HE staining.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e1.2.7 ELISA Detection of Serum TNF-α, IL-12, IL-17, IL-23, and IFN-γ Levels\u003c/h2\u003e \u003cp\u003eBlood was collected from the orbital venous plexus, centrifuged to collect the upper serum, and the contents of TNF-α, IL-12, IL-17, IL-23, and IFN-γ in rat serum were detected in accordance with the ELISA kit instructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e1.2.8 Western Blot Analysis of c-Abl, PTPN11, and c-k-Ras Protein Levels\u003c/h2\u003e \u003cp\u003eTotal protein was extracted from rat gastric tissues using RIPA lysis buffer, and protein quantification was performed with a BCA protein assay kit. Then 40 \u0026micro;g of protein was separated by 15% SDS-PAGE gel electrophoresis and transferred to a PVDF membrane. The membrane was incubated overnight at 4℃ with primary antibodies against GAPDH (1:50000), c-Abl (1:3000), PTPN11 (1:5000), and c-k-Ras (1:5000). After membrane washing, HRP-labeled secondary antibodies (goat anti-rabbit, 1:1000; goat anti-mouse, 1:10000) were added, and the PVDF membrane was soaked in the secondary antibody incubation solution and incubated on a shaker at room temperature for 2 hours.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e1.3 In Vitro Antibacterial Assay of Active Components from Terminalia chebula Retz. Against H. pylori\u003c/h2\u003e \u003cp\u003eDrug stock solutions were prepared for Sennoside E_qt, ellagic acid, and quinidine sulfate.\u003c/p\u003e \u003cp\u003e((R)-(6-methoxy-4-quinolyl)-[(2R,4R,5S)-5-vinylquinuclidin-2-yl]methanol). BHI solid medium supplemented with defibrinated sheep blood was prepared as follows: 26 g of BHI powder and 7.5 g of agar dissolved in 500 mL of distilled water, autoclaved at 121\u0026deg;C for 15 min, and supplemented with 5% defibrinated sheep blood prior to use. Drug-containing medium was prepared via serial dilution (in 24-well plates, with the 8th well as the control). NC membranes were placed into each well. H. pylori was verified by 16S rRNA sequencing, adjusted to an OD₆₀₀ value of 0.3, and then diluted 1000-fold. A 20 \u0026micro;L aliquot of the diluted bacterial suspension was spotted onto the NC membranes, followed by incubation under microaerophilic conditions at 37\u0026deg;C for 7 days. The minimum inhibitory concentration (MIC) was defined as the lowest concentration at which no visible colonies grew on the NC membranes. For the determination of the minimum bactericidal concentration (MBC), bacterial cultures were incubated at a concentration equal to the MIC; the MBC was defined as the lowest concentration yielding fewer than 10 colonies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e1.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using SPSS 26.0 software, and graphs were plotted with GraphPad Prism 8.0.2 software (note: corrected the typo \u0026ldquo;Priam\u0026rdquo; to the standard software name \u0026ldquo;Prism\u0026rdquo;). Measurement data were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (xˉ\u0026plusmn;s). One-way analysis of variance (ANOVA) was used for comparisons among multiple groups with a single factor, and pairwise comparisons between groups were conducted using the least significant difference (LSD) test. A P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Results of Network Pharmacology Analysis of Terminalia chebula Retz. and Helicobacter pylori\u003c/h2\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Active Components of Terminalia chebula Retz. and Their Corresponding Targets\u003c/h2\u003e \u003cp\u003eA total of 8 active components were obtained from the TCMSP database, namely Sennoside E_qt, ellagic acid, 7-Dehydrosigmasterol, chebulic acid, Ellipticine, (R)-(6-methoxy-4-quinolyl)-[(2R,4R,5S)-5-vinylquinuclidin-2-yl]methanol, Peraksine, and Cheilanthifoline, corresponding to a total of 421 targets. Detailed information is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eActive Components and Corresponding Targets of Terminalia chebula Retz.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMolecular Formula\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of Targets\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eellagic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC₁₄H₆O₈\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e291\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSennoside E_qt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC₄₂H₃₈O₂₀\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7-Dehydrosigmasterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC₂₉H₅₀O\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echebulic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC₁₄H₁₂O₁₁\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEllipticine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC₁₇H₁₄N₂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(R)-(6-methoxy\u0026minus;4-quinolyl)-[(2R,4R,5S)\u0026minus;5-vinylquinuclidin\u0026minus;2-yl]methanol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC₂₀H₂₄N₂O₂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e278\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeraksine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC₁₉H₂₂N₂O₂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCheilanthifoline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC₁₉H₁₉NO₄\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Construction of the \"Active Component-Target-Disease\" Network\u003c/h2\u003e \u003cp\u003eThe \"Active Component-Target\" diagram is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The relevant data were imported into Cytoscape software to visualize the targets of the active components of Terminalia chebula Retz. and the pathogenic targets of Helicobacter pylori.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3 Collection of H. pylori Targets and Acquisition of Drug-Disease Targets\u003c/h2\u003e \u003cp\u003eA total of 99 pathogenic targets of Helicobacter pylori were identified via screening with the GeneCards database. Finally, 11 potential therapeutic targets of Terminalia chebula Retz. against H. pylori were obtained using the Venn database 2.1.0, with detailed information presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e2.1.4 Construction of Protein-Protein Interaction (PPI) Network\u003c/h2\u003e \u003cp\u003eThe obtained intersection target genes were imported into the String database. The resulting data were visualized using Cytoscape 3.8.0 software, and the protein-protein interaction network as well as the core target network were plotted, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The PPI network contained 11 nodes, and several key genes including ABL1, PTPN11, and PARP1 were identified via screening.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e2.1.5 GO and KEGG Pathway Enrichment Analysis\u003c/h2\u003e \u003cp\u003eGO functional enrichment analysis and KEGG pathway analysis were performed on the 11 key targets obtained in Section \u003cspan refid=\"Sec25\" class=\"InternalRef\"\u003e2.1.4\u003c/span\u003e using the DAVID 6.8 database. GO analysis was conducted from three aspects, namely Biological Process (BP), Cellular Component (CC), and Molecular Function (MF), yielding a total of 60 GO analysis results. Bar charts of GO analysis were plotted according to the P-value, as shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, 5, and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e; the taller the bar, the greater the number of genes involved in the corresponding process. Among these results, 47 GO terms had a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The BP-related terms included signal transduction pathways and functions of the Eph receptor family and its ligands, positive regulation of peptidyl-tyrosine phosphorylation, negative regulation of the apoptotic process, glutathione derivative biosynthesis, and cellular response to lipopolysaccharide. The CC-related terms included ficolin-1-rich granule lumen, mitochondrion, macromolecular complex, extracellular space, and extracellular region. The MF-related terms included serine-type endopeptidase activity, protein binding, glutathione transferase activity, enzyme binding, and phosphotyrosine binding. A total of 15 KEGG pathways were identified, and bubble charts of KEGG pathway analysis were plotted according to the P-value; the darker the bubble color, the higher the correlation between the pathway and the disease, as shown in Fig.\u0026nbsp;7. Among these pathways, 7 had a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, namely chemical carcinogenesis-reactive oxygen species, Ras signaling pathway, cancer pathways, atherosclerosis, hepatocellular carcinoma, axon guidance, and proteoglycans in cancer. The main involved targets included ABL1, GSTM1, and PTPN11.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e2.1.6 Molecular Docking Verification\u003c/h2\u003e \u003cp\u003eIn this study, the binding affinities between the top-ranked targets (ABL1, PARP1, and PTPN11) in the PPI network and the active components (ellagic acid, quinidine sulfate, and Sennoside E_qt) were predicted, with the target proteins serving as receptors and the active components as ligands. Generally, it is recognized that the more stable the binding conformation between the receptor and ligand, the lower the binding energy and the stronger the interaction. According to the docking results, the binding energies between all components and targets were negative, indicating that these components and targets exhibited favorable binding activities. Ellagic acid, quinidine sulfate, and Sennoside E_qt showed relatively low binding energies with ABL1, PARP1, and PTPN11, suggesting good binding capabilities. All the docking binding energy results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The molecular docking diagrams are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMolecular Docking Binding Energies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTarget\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinding Energy (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eellagic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eABL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;7.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eellagic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePARP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;9.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eellagic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTPN11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;8.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003equinidine sulfate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eABL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;7.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003equinidine sulfate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePARP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;8.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003equinidine sulfate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTPN11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;7.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSennoside E_qt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eABL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSennoside E_qt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePARP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;8.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSennoside E_qt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTPN11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Animal Experiment Results\u003c/h2\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Routine Blood Test Results\u003c/h2\u003e \u003cp\u003eAnalysis of routine blood test indicators showed that statistical analyses were performed on the differences in multiple blood parameters among different treatment groups, including the Normal Group, Model Group, Terminalia chebula Low-Dose Group, Terminalia chebula Medium-Dose Group, and Terminalia chebula High-Dose Group. The results indicated that compared with the Normal Group, the white blood cell (WBC) counts in the Model Group, as well as the low-, medium-, and high-dose groups of Terminalia chebula, were significantly increased (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, compared with the Model Group, the WBC counts in the low-, medium-, and high-dose groups of Terminalia chebula were significantly decreased (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, there were differences in some indicators among the low-, medium-, and high-dose groups of Terminalia chebula. Details are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRoutine Blood Test Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTerminalia chebula Retz.-Low-Dose*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTerminalia chebula Retz.-Medium-Dose*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTerminalia chebula Retz.-High-Dose*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF/Z\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite Blood Cell (WBC) Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36abc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08abc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08bcd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e439.462\u003csup\u003e1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.153(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.142\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocyte Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.265\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEosinophil Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.409\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasophil Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.327\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil Percentage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e16.63\u0026thinsp;\u0026plusmn;\u0026thinsp;9.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.63\u0026thinsp;\u0026plusmn;\u0026thinsp;9.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.181\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte Percentage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e70.67\u0026thinsp;\u0026plusmn;\u0026thinsp;17.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.67\u0026thinsp;\u0026plusmn;\u0026thinsp;17.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.07\u0026thinsp;\u0026plusmn;\u0026thinsp;3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.747\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocyte Percentage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e11.97\u0026thinsp;\u0026plusmn;\u0026thinsp;8.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.97\u0026thinsp;\u0026plusmn;\u0026thinsp;8.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.93\u0026thinsp;\u0026plusmn;\u0026thinsp;2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.77\u0026thinsp;\u0026plusmn;\u0026thinsp;3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.844\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEosinophil Percentage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.763\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEosinophil Percentage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.636\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed Blood Cell (RBC) Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.233\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (HGB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e144\u0026thinsp;\u0026plusmn;\u0026thinsp;11.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144\u0026thinsp;\u0026plusmn;\u0026thinsp;11.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e168\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e169.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.457\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematocrit (HCT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e42.63\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.63\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.851\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Corpuscular Volume (MCV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e64.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.702\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Corpuscular Hemoglobin (MCH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e21.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.855\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Corpuscular Hemoglobin Concentration (MCHC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e337.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e338.33\u0026thinsp;\u0026plusmn;\u0026thinsp;4.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e337.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e340.00\u0026thinsp;\u0026plusmn;\u0026thinsp;6.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e337.67\u0026thinsp;\u0026plusmn;\u0026thinsp;4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.011\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed Blood Cell Distribution Width-Coefficient of Variation (RDW-CV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e14.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.841\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed Blood Cell Distribution Width-Standard Deviation (RDW-SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e35.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.894\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet (PLT) Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e768.67\u0026thinsp;\u0026plusmn;\u0026thinsp;3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e662.67\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e768.67\u0026thinsp;\u0026plusmn;\u0026thinsp;3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e582.67\u0026thinsp;\u0026plusmn;\u0026thinsp;15.89b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e643\u0026thinsp;\u0026plusmn;\u0026thinsp;12.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.233\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Platelet Volume (MPV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.621\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet Distribution Width (PDW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e15.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.729\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: 1) denotes F value; 2) denotes Z value; a denotes P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 vs. Control Group; b denotes P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 vs. Model Group; c denotes P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 vs. Terminalia chebula Retz. Low-Dose Group; d denotes P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 vs. Terminalia chebula Retz. Medium-Dose Group.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Pathological Analysis of Gastric Mucosa in Rats\u003c/h2\u003e \u003cp\u003eIn this study, Hematoxylin-Eosin (HE) staining was employed to analyze the pathological changes of gastric mucosa in rats. Compared with rats in the Model Group, those in the Terminalia chebula Low-Dose Group showed clearly distinguishable structures of all layers of gastric tissue under light microscopy: the mucosal epithelial cells were regularly arranged, various cell types were abundant, mild cell detachment was observed locally, and no significant inflammatory infiltration was found overall. For rats in the Terminalia chebula Medium-Dose and High-Dose Groups, the structures of all layers of gastric tissue were also clearly distinguishable under light microscopy, with regularly arranged mucosal epithelial cells, abundant various cell types, appropriate mucosal thickness, and no significant inflammatory infiltration overall. Among these groups, the aforementioned improvement effects were more pronounced in the High-Dose Group. However, under light microscopy, abnormalities were detected in the liver tissue of rats in all Terminalia chebula Low-Dose, Medium-Dose, and High-Dose Groups. Cytoplasmic transparency was observed in hepatocytes within a large area of hepatic parenchyma; numerous hepatocytes exhibited obscure structures with local nuclear fragmentation, and the overall tissue structure showed significant changes. These findings suggest that Terminalia chebula may have hepatotoxicity [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Representative images are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e2.2.3 Effects of Different Doses of Terminalia chebula Retz. on Serum Levels of TNF-α, IL-12, IL-17, IL-23, and IFN-γ in Model Rats\u003c/p\u003e \u003cp\u003eHelicobacter pylori (H. pylori) infection significantly increased the concentrations of pro-inflammatory cytokines, including TNF-α, IL-12, IL-17, IL-23, and IFN-γ. After intervention with Terminalia chebula Retz., the concentrations of these pro-inflammatory cytokines were significantly reduced (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in a dose-dependent manner: the most significant reduction was observed in the High-Dose Group, followed by the Medium-Dose Group, while the effect was relatively weaker in the Low-Dose Group. Details are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Serum Levels of TNF-α, IL-12, IL-17, IL-23, and IFN-γ Among Different Groups of Rats\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Animals\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTNF-a\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIL12\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIL17\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIL23\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIFN-Y\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e153.070\u0026thinsp;\u0026plusmn;\u0026thinsp;17.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e192.658\u0026thinsp;\u0026plusmn;\u0026thinsp;32.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e355.422\u0026thinsp;\u0026plusmn;\u0026thinsp;41.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e306.175\u0026thinsp;\u0026plusmn;\u0026thinsp;49.566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e270.501\u0026thinsp;\u0026plusmn;\u0026thinsp;55.407\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55Model Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e759.954\u0026thinsp;\u0026plusmn;\u0026thinsp;84.590★★★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e967.426\u0026thinsp;\u0026plusmn;\u0026thinsp;108.6★★★34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1680.499\u0026thinsp;\u0026plusmn;\u0026thinsp;180.350★★★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e1562.661\u0026thinsp;\u0026plusmn;\u0026thinsp;159.022★★★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1362.990\u0026thinsp;\u0026plusmn;\u0026thinsp;90.166★★★\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerminalia chebula Retz. High-Dose Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e291.246\u0026thinsp;\u0026plusmn;\u0026thinsp;22.806★★▲▲▲●●●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e359.466\u0026thinsp;\u0026plusmn;\u0026thinsp;28.371★★▲▲▲●●●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e552.715\u0026thinsp;\u0026plusmn;\u0026thinsp;46.361★▲▲▲●●●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e552.243\u0026thinsp;\u0026plusmn;\u0026thinsp;46.110★★▲▲▲●●●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e524.196\u0026thinsp;\u0026plusmn;\u0026thinsp;28.882★★★▲▲▲●●●\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerminalia chebula Retz. Medium-Dose Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e431.890\u0026thinsp;\u0026plusmn;\u0026thinsp;28.925★★★▲▲▲●●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e558.660\u0026thinsp;\u0026plusmn;\u0026thinsp;44.076★★★▲▲▲●●●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1048.366\u0026thinsp;\u0026plusmn;\u0026thinsp;66.816★★★●●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e768.972\u0026thinsp;\u0026plusmn;\u0026thinsp;38.191★▲▲▲●●●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e680.951\u0026thinsp;\u0026plusmn;\u0026thinsp;28.765★★★▲▲▲●●●\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerminalia chebula Retz. Low-Dose Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e579.700\u0026thinsp;\u0026plusmn;\u0026thinsp;28.925★★★▲▲\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e804.164\u0026thinsp;\u0026plusmn;\u0026thinsp;30.898★★★▲▲\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1418.140\u0026thinsp;\u0026plusmn;\u0026thinsp;73.910★★★▲▲\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e1272.884\u0026thinsp;\u0026plusmn;\u0026thinsp;65.440★★▲▲\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1182.799\u0026thinsp;\u0026plusmn;\u0026thinsp;55.037★★★▲▲\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Effects of Terminalia chebula Retz. on the Protein Expression of c-Abl, PTPN11, and c-k-Ras\u003c/h2\u003e \u003cp\u003eCompared with the Control Group, the protein levels of c-Abl, PTPN11, and c-k-Ras in the H. pylori Model Group were significantly elevated. In comparison with the Model Group, the protein levels of c-Abl, PTPN11, and c-k-Ras were decreased in the Medium-Dose and High-Dose Groups of Terminalia chebula Retz. (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with a more significant reduction observed in the High-Dose Group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Western Blot (WB) band images and protein level bar charts showed that the gray value changes of protein bands in each group were consistent with the quantitative results, as shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e10\u003c/span\u003e and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e11\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eCON\u0026thinsp;=\u0026thinsp;Control Group; Hp\u0026thinsp;=\u0026thinsp;H. pylori Infection Group; H-L\u0026thinsp;=\u0026thinsp;Terminalia chebula Retz. Low-Dose Group; H-M\u0026thinsp;=\u0026thinsp;Terminalia chebula Retz. Medium-Dose Group; H-H\u0026thinsp;=\u0026thinsp;Terminalia chebula Retz. High-Dose Group\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Antimicrobial Susceptibility Test Results\u003c/h2\u003e \u003cp\u003eThe growth of bacterial cells was observed by visual inspection. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of (R)-(6-methoxy-4-quinolyl)-[(2R,4R,5S)-5-vinylquinuclidin-2-yl]methanol were 0.625 mg/ml and 1.25 mg/ml, respectively; those of ellagic acid were 0.0625 mg/ml and 0.25 mg/ml, respectively; and those of Sennoside E_qt were 2.5 mg/ml and 10 mg/ml, respectively. Details are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAntimicrobial Susceptibility Test of Terminalia chebula Retz. Active Components Against H.pylori\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMolecular name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSolvent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMIC(mg/ml)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMBC(mg/ml)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(R)-(6-methoxy\u0026minus;4-quinolyl)-[(2R,4R,5S)\u0026minus;5-vinylquinuclidin\u0026minus;2-yl]methanol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDMSO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eellagic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDMSO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSennoside E gt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDMSO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eHelicobacter pylori (H. pylori) infection is globally prevalent, and the growing problem of drug resistance has made the development of novel and efficient therapeutic strategies an urgent priority. As a medicinal herb commonly used for treating gastrointestinal disorders in traditional Chinese-Mongolian medicine, Terminalia chebula Retz. has been confirmed by modern studies to possess anti-inflammatory and broad-spectrum antibacterial activities, which provides a potential basis for its anti-H. pylori research. However, current research on the anti-H. pylori effects of T. chebula is still limited to preliminary in vitro reports, and its systematic pharmacological mechanism remains unclear. To address this gap, the present study established an integrated research framework of \"target prediction\u0026ndash;molecular interaction\u0026ndash;in vitro and in vivo validation\", aiming to systematically elucidate the multi-component, multi-target, and multi-pathway synergistic mechanism underlying the anti-H. pylori activity of T. chebula, thereby providing scientific evidence for the development of new anti-H. pylori drugs based on Chinese-Mongolian medicinal herbs.\u003c/p\u003e \u003cp\u003eNetwork pharmacology approaches identified 11 core overlapping targets associated with H. pylori pathogenicity for the active components of T. chebula. KEGG pathway analysis revealed that the Ras signaling pathway serves as a core mechanism, whose role in H. pylori-induced gastritis and carcinogenesis has been well-documented [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This prediction was verified in our animal experiments: H. pylori infection significantly upregulated the protein expression of c-Abl1, PTPN11 (SHP2), and c-k-Ras in gastric mucosa, whereas treatment with medium and high doses of T. chebula aqueous extract significantly downregulated their expression. This is consistent with the growing recognition in the academic community that the ABL1/SHP2/Ras module acts as a key signaling node in cell growth and inflammation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Our work is the first to directly link the anti-H. pylori activity of T. chebula to the inhibition of this specific signaling axis, which not only suggests its potential in eradicating the bacterium but also implies its possible role in blocking the progression of precancerous lesions. Furthermore, the improvement of hematological parameters (including the normalization of white blood cell counts) and antimicrobial susceptibility tests further confirmed the systemic anti-inflammatory effects of T. chebula, in contrast to traditional therapies that may overlook hematological indicators. Meanwhile, the integration of network pharmacology into the present study has provided a more in-depth mechanistic interpretation.\u003c/p\u003e \u003cp\u003eFurthermore, the significant reduction in the levels of key pro-inflammatory cytokines (TNF-α, IL-12, IL-17, IL-23, and IFN-γ) in the serum of rats from the T. chebula treatment groups provides compelling evidence for its potent anti-inflammatory activity. The excessive production of these cytokines, particularly IL-17 and TNF-α, is a hallmark of H. pylori-associated inflammation and contributes to tissue damage [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The dose-dependent inhibitory effect of T. chebula on these cytokines is consistent with the findings of Ou et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e10\u003c/span\u003e], who demonstrated that the aqueous extract of T. chebula can inhibit H. pylori-induced inflammatory responses by modulating inflammasome signaling pathways. Our findings extend this understanding by linking cytokine inhibition to the downregulation of upstream signaling proteins such as PTPN11/SHP2. The role of SHP2 in inflammation is complex; for instance, Imai S et al. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] demonstrated that the oncogenic phosphatase SHP2 is released during H. pylori infection, thereby promoting gastric carcinogenesis. Our study further reveals that the aqueous extract of T. chebula can effectively inhibit the inflammatory responses driven by PTPN11 (SHP2), which highlights the therapeutic potential of targeting this protein in mucosal immunity.\u003c/p\u003e \u003cp\u003eABL1, PTPN11/SHP2, and Ras constitute a crucial signaling transduction module that plays an indispensable role under both physiological and pathological conditions. Aberrant activation of this signaling axis is one of the core mechanisms driving the malignant phenotypes of cells in malignant tumors (e.g., leukemia) and infection-associated carcinogenesis (e.g., H. pylori-associated gastric cancer). ABL1 initiates the signaling cascade, SHP2 acts as a key transducer and amplifier, and Ras ultimately executes the pro-proliferative and pro-survival instructions [\u003cspan additionalcitationids=\"CR13 CR14 CR15\" citationid=\"CR10\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAn in-depth understanding of the network interactions among these three molecules not only unravels the intrinsic logic underlying disease initiation and progression but also provides a solid theoretical basis for developing combined targeted therapies against this signaling axis. Future studies should aim to explore the optimal strategies for disrupting the synergistic effects of this signaling axis in specific cancer contexts and translate this knowledge into effective clinical therapeutic regimens.\u003c/p\u003e \u003cp\u003eThe present study still has several limitations: the differences between the animal model and human chronic infection may affect the extrapolation of conclusions; network pharmacology may omit certain components or targets; the interactions among various active components have not been elucidated; and the hepatotoxicity risk indicated by animal experiments requires further clarification [19]. Future research should conduct clinical studies to verify the efficacy and safety, employ multi-omics technologies to further unravel the underlying mechanisms, and isolate and optimize active components for the development of new compound preparations. Meanwhile, exploring the synergistic effects of Terminalia chebula Retz. with existing antibiotics is expected to provide new strategies for overcoming H. pylori resistance.\u003c/p\u003e \u003cp\u003eIn conclusion, the anti-H. pylori activity of Terminalia chebula Retz. does not depend on a single component or mechanism, but rather reflects a comprehensive pharmacological mode of multi-component, multi-target, and multi-pathway synergistic actions. At the molecular level, its active components, such as ellagic acid and quinine sulfate, can not only directly act on bacteria to inhibit their growth and proliferation, exerting a bacteriostatic effect, but also effectively suppress the excessive activation of the Ras signaling pathway and related inflammatory pathways through interacting with the PTPN11 (SHP2) target, thereby regulating the release of downstream inflammatory factors and alleviating the inflammatory damage of gastric mucosa. The synergistic operation of this dual mechanism of bacteriostasis and anti-inflammation highlights the potential advantage of Terminalia chebula Retz. in addressing both the symptoms and root causes during the intervention of H. pylori infection, and also provides a theoretical basis for further developing it as an adjuvant therapeutic agent.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYangchenze Fan and Xuanru Kang drafted the main manuscript text.All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLiu H, Shen J, Liu WB, et al. Protective effect and mechanism of aqueous extract of Terminalia chebula Retz. on rats with hyperuricemic nephropathy[J]. Pharmacology and Clinics of Chinese Materia Medica, 2024, 40(10): 56\u0026ndash;64. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.13412/j.cnki.zyyl.20240412.004\u003c/span\u003e\u003cspan address=\"10.13412/j.cnki.zyyl.20240412.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImai S, Ooki T, Murata-Kamiya N, et al. Helicobacter pylori CagA elicits BRCAness to induce genome instability that may underlie bacterial gastric carcinogenesis. Cell Host Microbe. 2021;29(6):941\u0026ndash;958.e10. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.chom.2021.04.006\u003c/span\u003e\u003cspan address=\"10.1016/j.chom.2021.04.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKashyap D, Baral B, Jakhmola S, Singh AK, Jha HC. Helicobacter pylori and Epstein-Barr Virus Coinfection Stimulates Aggressiveness in Gastric Cancer through the Regulation of Gankyrin. mSphere. 2021;6(5):e0075121. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1128/mSphere.00751-21\u003c/span\u003e\u003cspan address=\"10.1128/mSphere.00751-21\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2021 Sep 29. PMID: 34585958; PMCID: PMC8550222.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFei X, Chen S, Li L, Xu X, Wang H, Ke H, He C, Xie C, Wu X, Liu J, Xie Y, Lu N, Zhu Y, Li N. Helicobacter pylori infection promotes M1 macrophage polarization and gastric inflammation by activation of NLRP3 inflammasome via TNF/TNFR1 axis. Cell Commun Signal. 2025;23(1):6. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12964-024-02017-7\u003c/span\u003e\u003cspan address=\"10.1186/s12964-024-02017-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 39762835; PMCID: PMC11705855.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu C, Meng XM, Nian YY, et al. In vitro bacteriostatic effect of Sino-Mongolian medicines on Helicobacter pylori[J]. Journal of Modern Integrated Traditional Chinese and Western Medicine, 2024, 33(16): 2244\u0026ndash;2249.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang M, Cui S, Mao B, Zhang Q, Zhao J, Zhang H, Tang X, Chen W. Ellagic acid and intestinal microflora metabolite urolithin A: A review on its sources, metabolic distribution, health benefits, and biotransformation. Crit Rev Food Sci Nutr. 2023;63(24):6900\u0026ndash;6922. doi: 10.1080/10408398.2022.2036693. Epub 2022 Feb 10. PMID: 35142569.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang XJ, He LJ, Lu Q, Li DY. [Pharmacological activity of Terminalia chebula]. Zhongguo Zhong Yao Za Zhi. 2016;41(4):619\u0026ndash;623. Chinese. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4268/cjcmm20160412\u003c/span\u003e\u003cspan address=\"10.4268/cjcmm20160412\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 28871682.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOu L, Liu HR, Shi XY, Peng C, Zou YJ, Jia JW, Li H, Zhu ZX, Wang YH, Su BM, Lai YQ, Chen MY, Zhu WX, Feng Z, Zhang GM, Yao MC. Terminalia chebula Retz. aqueous extract inhibits the Helicobacter pylori-induced inflammatory response by regulating the inflammasome signaling and ER-stress pathway. J Ethnopharmacol. 2024;320:117428. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jep.2023.117428\u003c/span\u003e\u003cspan address=\"10.1016/j.jep.2023.117428\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2023 Nov 18. PMID: 37981121.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang XL, Hu HB, Jia AP, et al. Correlation study between gastric cancer, PTPN11 gene polymorphism and Helicobacter pylori infection[J]. Journal of Youjiang Medical University for Nationalities, 2011, 33(3): 261\u0026ndash;263.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu WC, Xu LM, Lai YQ, et al. Relationship between Helicobacter pylori OipA and expression of p53 and ras proteins in gastric cancer tissues[J]. Journal of Practical Medicine, 2012, 28(13): 2156\u0026ndash;2158.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKazemi-Sefat GE, Keramatipour M, Vaezi M, et al. Integrated genomic sequencing in myeloid blast crisis chronic myeloid leukemia (MBC-CML), identified potentially important findings in the context of leukemogenesis model. Sci Rep. 2022;12(1):12816. Published 2022 Jul 27. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-022-17232-w\u003c/span\u003e\u003cspan address=\"10.1038/s41598-022-17232-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCraig VJ, Cogliatti SB, Rehrauer H, W\u0026uuml;ndisch T, M\u0026uuml;ller A. Epigenetic silencing of microRNA-203 dysregulates ABL1 expression and drives Helicobacter-associated gastric lymphomagenesis. Cancer Res. 2011;71(10):3616\u0026ndash;3624. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/0008-5472.CAN-10-3907\u003c/span\u003e\u003cspan address=\"10.1158/0008-5472.CAN-10-3907\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoto Y, Ando T, Yamamoto K, et al. Association between serum pepsinogens and polymorphismof PTPN11 encoding SHP-2 among Helicobacter pylori seropositive Japanese. Int J Cancer. 2006;118(1):203\u0026ndash;208. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ijc.21338\u003c/span\u003e\u003cspan address=\"10.1002/ijc.21338\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe C, Tu H, Sun L, Xu Q, Gong Y, Jing J, Dong N, Yuan Y. SNP interactions of Helicobacter pylori-related host genes PGC, PTPN11, IL1B, and TLR4 in susceptibility to gastric carcinogenesis. Oncotarget. 2015;6(22):19017\u0026ndash;26. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18632/oncotarget.4231\u003c/span\u003e\u003cspan address=\"10.18632/oncotarget.4231\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 26158864; PMCID: PMC4662472.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKashyap D, Baral B, Jakhmola S, Singh AK, Jha HC. Helicobacter pylori and Epstein-Barr Virus Coinfection Stimulates Aggressiveness in Gastric Cancer through the Regulation of Gankyrin. mSphere. 2021;6(5):e0075121. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1128/mSphere.00751-21\u003c/span\u003e\u003cspan address=\"10.1128/mSphere.00751-21\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2021 Sep 29. PMID: 34585958; PMCID: PMC8550222.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang F. Study on Chemical Components and Toxic Mechanism of Hepatotoxic Extraction Fractions from Terminalia chebula Retz.[D]. Northeast Agricultural University, 2022. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.27010/d.cnki.gdbnu.2022.000502\u003c/span\u003e\u003cspan address=\"10.27010/d.cnki.gdbnu.2022.000502\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"antonie-van-leeuwenhoek","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"anto","sideBox":"Learn more about [Antonie van Leeuwenhoek](https://www.springer.com/journal/10482)","snPcode":"10482","submissionUrl":"https://submission.nature.com/new-submission/10482/3","title":"Antonie van Leeuwenhoek","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Terminalia chebula Retz., Helicobacter pylori, network pharmacology, ABL1, PTPN11, c-k-Ras, ellagic acid, signaling pathway, anti-inflammatory effect","lastPublishedDoi":"10.21203/rs.3.rs-8635950/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8635950/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective To investigate the antibacterial activity and underlying mechanism of Terminalia chebula Retz. against Helicobacter pylori (H. pylori).\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods Core targets of T. chebula for inhibiting H. pylori were obtained via network pharmacology, followed by enrichment analysis. Molecular docking was performed to verify the binding activity between the active components of T. chebula and the core targets. A rat model of H. pylori infection was established, and the rats were randomly divided into the blank control group, model control group, and high-, medium-, and low-dose T. chebula groups. Routine blood test results of each group were examined, and the H. pylori infection status, the contents of interleukin-12 (IL-12), interleukin-17 (IL-17), interleukin-23 (IL-23), interferon-γ (IFN-γ), tumor necrosis factor-α (TNF-α) in gastric mucosa, as well as the protein expression levels of related targets ABL1, PTPN11, and Ras were determined. Minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) tests were conducted using the Hp SS1 strain.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults Network pharmacology identified 11 key targets of T. chebula for H. pylori inhibition. Enrichment analysis indicated that the inhibitory mechanism of T. chebula against H. pylori may be associated with the RAS signaling pathway, signal transduction pathways and functions of the Eph receptor family and its ligands, and cancer pathways. Molecular docking results showed that the main active components, including ellagic acid, quinidine sulfate, and sennosides, exhibited high docking affinity with the core targets ABL1, PTPN11, and PARP1. Animal experiments revealed obvious gastric mucosal damage in H. pylori-infected rats. After treatment with medium and high doses of T. chebula, the number of inflammatory cells in the gastric mucosa of rats significantly decreased, swelling was alleviated, and mucosal thickness returned to normal. Meanwhile, the serum levels of TNF-α, IL-12, IL-17, IL-23, and IFN-γ were reduced (P \u0026lt; 0.05), and the protein expression levels of ABL1, PTPN11, and Ras were downregulated (P \u0026lt; 0.05), with the most significant reduction and the best anti-inflammatory effect observed in the high-dose T. chebula group. Routine blood test results demonstrated that T. chebula treatment significantly decreased the white blood cell count (P \u0026lt; 0.05). Drug sensitivity tests showed that quinidine sulfate, ellagic acid, sennosides, (R)-(6-methoxy-4-quinolyl)-[(2R,4R,5S)-5-vinylquinuclidin-2-yl]methanol, and Sennoside E_qt exerted significant in vitro antibacterial activity.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion Terminalia chebula exerts a therapeutic effect on H. pylori infection by acting on targets such as ABL1, PTPN11, and c-k-Ras through components including ellagic acid, quinidine sulfate, and sennosides. It inhibits related pathways to downregulate the expression of various cytokines, thereby alleviating gastric mucosal inflammatory damage.\u003c/strong\u003e\u003c/p\u003e","manuscriptTitle":"Investigation of the Antibacterial Activity and Underlying Mechanism of Terminalia chebula Retz. Against Helicobacter pylori","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 17:20:59","doi":"10.21203/rs.3.rs-8635950/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-02-09T15:54:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-22T15:58:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-22T15:57:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Antonie van Leeuwenhoek","date":"2026-01-19T06:43:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"antonie-van-leeuwenhoek","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"anto","sideBox":"Learn more about [Antonie van Leeuwenhoek](https://www.springer.com/journal/10482)","snPcode":"10482","submissionUrl":"https://submission.nature.com/new-submission/10482/3","title":"Antonie van Leeuwenhoek","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"036db5cc-03f1-4d46-99c1-ef3b056a75ff","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-11T17:20:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-11 17:20:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8635950","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8635950","identity":"rs-8635950","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}