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Hindawi Journal of Healthcare Engineering Volume 2021, Article ID 1536337, 11 pages https://doi.org/10.1155/2021/1536337 Research Article Exploring the Potential Mechanism of Xiaokui Jiedu Decoction for Ulcerative Colitis Based on Network Pharmacology and Molecular Docking Bin Wang, Yang Liu , Jianhui Sun, Nailin Zhang, Xiaojia Zheng, and Qiquan Liu Department of Spleen and Stomach Diseases, e First Affiliated Hospital of Hebei College of Traditional Chinese Medicine, Shijiazhuang, China Correspondence should be addressed to Qiquan Liu; 973194580@qq.com Received 21 August 2021; Revised 8 September 2021; Accepted 9 October 2021; Published 25 October 2021 Academic Editor: Osamah Ibrahim Khalaf Copyright © 2021 Bin Wang et al. 0is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Introduction. Network pharmacology is in line with the holistic characteristics of TCM and can be used to elucidate the complex network of interactions between disease-specific genes and compounds in TCM herbal medicines. Here, we investigate the pharmacological mechanism of Xiaokui Jiedu decoction (XJD) for the treatment of ulcerative colitis (UC). Methods. 0e Computational Systems Biology Laboratory Platform (TCMSP) database was searched and screened for the active ingredients of all drugs in XJD. 0e Uniport database was used to retrieve possible gene targets for the therapeutic effects of XJD. GeneCards, PharmGKB, TTD, and OMIM databases were used to retrieve XJD-related gene targets. A herb-compound-protein network and a protein-protein interaction (PPI) network were constructed, and hub genes were screened for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Finally, molecular docking was performed to validate the interrelationship between disease target proteins and active drug components. Results. A total of 135 XJD potential action targets, 5097 UC-related gene targets, and 103 XJD-UC intersection gene targets were screened. 0e hub gene targets of XJD that exert therapeutic effects on UC are RB1, MAPK1, TP53, JUN, NR3C1, MAPK3, and ESR1. GO enrichment analysis showed 741 biofunctional enrichments, and KEGG enrichment analysis showed 124 related pathway enrichments. Molecular docking showed that the active components of XJD (β-sitosterol, kaempferol, formononetin, quercetin, and luteolin) showed good binding activities to five of the six hub gene targets. Discussion. 0e active ingredients of XJD (β-sitosterol, kaempferol, formononetin, quercetin, and luteolin) may regulate the inflammatory and oxidative stress-related pathways of colon cells during the course of UC by binding to the hub gene targets. 0is may be a potential mechanism of XJD in the treatment of UC. and the annual costs for UC-related screening and treatment 1. Introduction amount to 12.5–29.1 billion euros and 8.1–14.9 billion Ulcerative colitis (UC) is a chronic nonspecific inflamma- dollars [3]. Aminosalicylic acid preparations, commonly tory disease of the rectum and colon of unknown etiology used in the treatment of UC, exert anti-inflammatory effects [1, 2]. 0e lesions mainly invade the mucosa and submucosa mainly by affecting the metabolism of arachidonic acid and of the large intestine, with a continuous diffuse distribution, inhibiting prostaglandin synthesis, but their adverse effects and the main clinical manifestations are diarrhea, muco- are more frequent and their therapeutic effects are less purulent stools, and abdominal pain. satisfactory. Traditional Chinese medicine (TCM) has been In recent years, the incidence of ulcerative colitis (UC) shown to have potential advantages in UC treatment [1, 4, 5]. has increased significantly, with 505 and 214 cases per Hu et al. recently found that XJD was effective in treating UC 100,000 people in Europe and the United States, respectively, by effectively regulating neuroendocrine factors, improving 2 Journal of Healthcare Engineering the intestinal immune response, and reducing patient further screening of the hub genes that were used to con- symptoms [6]. However, the mechanisms involved in the struct the PPI network. Next, the clusterProfiler package in R was used to perform Gene Ontology and KEGG pathway field of UC treatment with XJD are still unknown. Ulcerative colitis belongs to the category of “diarrhea” enrichment analyses on these hub genes (inclusion criteria: and “dysentery” in TCM and is mainly caused by damp-heat p value <0.05) [14, 15]. and a diet that damages the spleen and stomach. Network pharmacology is in line with the holistic characteristics of 2.5. Molecular Docking. Molecular docking refers to the TCM and can elucidate the complex network of interactions “docking theory” that relies on receptor-ligand interactions between disease-specific genes and compounds in TCM to predict the potential binding mode of a compound to a herbal medicines [7–10]. protein [16–18]. Hub genes in the PPI network were selected In summary, our study combines network pharmacol- for molecular docking validation. We searched the Pub- ogy, molecular docking, and cellular experimental analysis Chem database for 2D structures of possible active ingre- to investigate the pharmacological mechanism of XJD for dients in XJD and the PDB database for 3D structures of the UC treatment. target proteins of the hub genes in the PPI network. 0e PyMOL software package was then used to remove water 2. Methods molecules and small molecule ligands from the target protein structures of the hub genes. AutoDockTools software 2.1. Screening for Active Ingredients and Potential Targets of was used to determine the active pockets at potential sites for XJD. In the Computational Systems Biology Laboratory molecular docking. Molecular docking was performed using Platform (TCMSP), herbs are searched separately (baitou- AutoDock Vina software, and then PyMOL software was weng (Pulsatilliae radix), huanglian (Coptidis rhizoma), ultimately used to map and analyze the results of the lowest huangbai (Phellodendri chinensis cortex), qinpi (Fraxini binding energy docking. cortex), kusheng (Sophorae flavescentis radix), baiji (Bletilla striata), gegen (Radix puerariae), huaihua (Sophora japonica L.), xianhecao (Agrimonia eupatoria), and gancao (licorice)) 2.6. Animals and Drug Preparation. XJD is a combination of [11]. 0e screening conditions are set as OB ≥ 30% and baitouweng (Pulsatilliae radix), huanglian (Coptidis rhi- DL≥ 0.18, and the active ingredients and related target in- zoma), huangbai (Phellodendri chinensis cortex), qinpi formation of the Chinese medicines were searched separately (Fraxini cortex), kusheng (Sophorae flavescentis radix), baiji and screened based on pharmacokinetic absorption, distri- (Bletilla striata), gegen (Radix puerariae), huaihua (Sophora bution, metabolism, and excretion parameters. All potential japonica L.), xianhecao (Agrimonia eupatoria), and gancao targets were combined, and duplicates were excluded to (licorice). Animal welfare was carried out in strict accor- obtain potential targets for the active ingredients of XJD. dance with the internationally accepted principles for lab- oratory animals (EEC Directive; 86/609/EEC). Sprague Dawley (SD) rats with a weight range of 270± 10 g were 2.2. XJD Active Ingredient Target Prediction. In the Uniport randomly divided into three groups according to body mass: database (https://www.uniprot.org/), the Uniport KB func- blank group, XJD treatment group, and model group. tion was used to search for human gene abbreviations related Dosages administered were calculated with reference to the to the targets of the active ingredients of XJD and to obtain previous literature [19, 20]. 0e treatment group was given possible gene targets for the therapeutic effects of XJD. different doses of antidiarrhea pills by gavage, while the blank group and the model group were given an equal volume of saline by gavage twice a day for seven days. One 2.3. UC-Related Disease Target Prediction. 0e GeneCards, hour after the last dose, blood was collected from the ab- OMIM, DrugBank, and PharmGKB databases were searched dominal aorta under aseptic conditions. All animals were for relevant gene targets using the keyword “ulcerative euthanised with thiopental sodium at the end of the ex- colitis,” as shown in previous studies [4]. 0ese potential periment. 0e samples were placed in 10 mL centrifuge XJD target genes were then linked to UC target genes to tubes, and the serum was inactivated in a 56 C water bath for identify candidate targets. 30 min, then filtered through a 0.22 μm microporous membrane, and stored at −20 C for quantitative real-time 2.4. Construction of the Herb-Compound-Protein Network PCR (qPCR). Followed by cDNA synthesis using the Prime- and the Protein-Protein Interaction (PPI) Network. 0e R Script RT Master Mix (TaKaRa), total mRNA was isolated software package was used to plot the Venn diagram to from the cell cultures using the Mini-BEST Universal RNA obtain the intersection of drug and disease gene targets to extraction kit (TaKaRa, Kyoto, Japan). And, qPCR assays achieve the likely therapeutic targets. 0ese were then en- were detected with PCR LightCycler480. tered into the string system, and a protein-protein inter- action (PPI) network was constructed with a confidence 3. Results interval of ±0.950 [12]. Cytoscape was used for the herb- compound-protein network. Cytoscape’s plugin CytoNCA 3.1. XJD Active Ingredients and Potential Targets. 0e study calculated the parameters required to evaluate the functional flow chart is described in Figure 1. 0e preliminary search importance of each node in the network [13]. 0is allowed yielded 92 active ingredients of gancao (licorice), five active Journal of Healthcare Engineering 3 Xiaokui Jiedu Fang GeneCards Drug Compouds PharmGkb Get US disease genes TTD Herb-Compound network OMIM Herb-Compound-Protein Network PPI Network Go and KEGG e docking model of Enrichment Analysis compounds with proteins Experimental Verification Xiaokui Jiedu Fang down-regulate the relative RNA level of FOS, TNF-α and IL-1 Figure 1: Abstract diagram of the research flow of this study. ingredients of xianhecao (Agrimonia eupatoria), six active 0e herb-compound-protein network had 145 active in- ingredients of huaihua (Sophora japonica L.), four active gredients associated with XJD for UC treatment, of which ingredients of gegen (Radix puerariae), nine active ingre- five with the highest number of corresponding gene targets dients of baiji (Bletilla striata), 45 active ingredients of included β-sitosterol, kaempferol, formononetin, querce- kusheng (Sophorae flavescentis radix), three active ingre- tin, and luteolin (Figure 3). 0ey were used to construct the dients of qinpi (Fraxini cortex), 27 active ingredients of PPI network of possible gene targets for XJD in the huangbai (Phellodendri chinensis cortex), 11 active ingre- treatment of UC, as shown in Figure 4(a) (Figure S1). Figure 4(a) shows 78 nodes and 202 edges, Figure 4(b) dients of huanglian (Coptidis rhizoma), and 12 active in- gredients of baitouweng (Pulsatilliae radix). A total of 168 shows 18 nodes and 72 edges, and Figure 4(c) shows seven human gene targets corresponding to the active ingredients nodes and 38 edges. 0e seven hub gene targets, RB1, were searched through the Uniport database. MAPK1, TP53, JUN, NR3C1, MAPK3, and ESR1, were obtained after two analytical screens (Figures 4(b) and 4(c)). 3.2. UC-Related Disease Target Prediction. Using the key- word “ulcerative colitis,” the GeneCards database retrieved 3.4. Enrichment Analysis of Hub Genes. Gene screening and 5,079 disease-related gene targets, the OMIM database enrichment analysis of GO and KEGG were performed. retrieved 19 disease-related gene targets, the DrugBank Seven target genes were also analyzed for KEGG and GO database retrieved 13 disease-related gene targets, and the enrichment, respectively, which is our focus. GO enrichment PharmGKB database retrieved 54 disease-related gene analysis showed that there were 741 biofunctional enrich- targets. A total of 5,097 UC-related gene targets were ments. GO functional enrichment analysis revealed that obtained by combining all disease-related gene targets candidate target genes for UC were shown to be enriched in (Figure 2(a)). BPs (DNA-templated transcription, regulation of DNA- binding transcription factor activity, cellular response to 3.3. Construction of Herb-Compound-Protein Network and starvation, cellular response to cadmium ion, response to PPI Network. 0e Venn plot obtained the intersection of starvation, regulation of telomerase activity, cellular re- drug and disease gene targets and yielded 103 potential sponse to nutrient levels, and cellular response to extra- gene targets for XJD in the treatment of UC (Figure 2(b)). cellular stimulus), CCs (nuclear chromatin, pseudopodium, 4 Journal of Healthcare Engineering PharmGkb Disease Drug TTD GeneCards OMIM 5 0 5023 0 0 3 4994 103 32 41 0 (a) (b) Figure 2: (a) Venn diagrams of UC-related genes searched in the GeneCards, OMIM, DrugBank, and PharmGKB databases. (b) Venn diagram of drug target genes and UC-related target genes. Baiji Baitouweng Huanglian Huangbai Qinpi Kusheng Gegen Huaihua Xianhecao Gancao Figure 3: Herb-compound-protein network of UC and XJD. nuclear/RNA polymerase II transcription factor complex, (Figure 6(a) and Figure S2), implying that they could be spindle, caveola, PML body, plasma membrane raft, and late involved in the pathogenesis of UC. endosome), and MFs (RNA polymerase II transcription factor binding, phosphoprotein binding, MAP kinase ac- tivity, phosphatase binding, disordered domain-specific 3.5. Molecular Docking Analysis. Based on the above results, binding, and phosphotyrosine residue binding) (P< 0.05; the active ingredients in XJD (β-sitosterol, kaempferol, Figure 6(a)). KEGG enrichment showed that 124 related formononetin, quercetin, and luteolin) were found to exhibit pathways were enriched. KEGG pathway analysis showed the same effects as ESR1, JUN, NR3C1, MAPK1, MAPK3, that those target genes were mainly involved in endocrine RB1, and TP53 (Figure 7). 0ese seven proteins were entered resistance, breast cancer, MAPK signaling pathway, hepatitis into the PDB database. For MOE molecular docking anal- B, viral carcinogenesis, Kaposi’s sarcoma-associated her- ysis, seven key target proteins (ESR1, JUN, NR3C1, MAPK1, pesvirus infection, melanoma, thyroid hormone signaling MAPK3, RB1, and TP53) were selected (Figures 8(a)–8(e)) pathway, non-small-cell lung cancer, chemical carcinogen- and were found to be related to β-sitosterol, kaempferol, esis-receptor activation, pancreatic cancer, chronic myeloid formononetin, quercetin, and luteolin. Among them, β-si- leukemia, colorectal cancer, prostate cancer, neurotrophin tosterol binds to ESR1 with a binding energy of −5.8 kcal/ signaling pathway, apoptosis, estrogen signaling pathway, mol, JUN at −5.4 kcal/mol, and NR3C1 at − 4.3 kcal/mol. thyroid cancer, gastric cancer, etc. (P< 0.05; Figure 6(b)). Six Kaempferol bound to ESR1 with a binding energy of target genes (ESR1, JUN, TP53, MAPK3, RB1, and MAPK1) −7.1 kcal/mol and JUN at −5.5 kcal/mol. Formononetin were enriched in the endocrine resistance pathway bound to ESR1 with a binding energy of −6.7 kcal/mol and to Journal of Healthcare Engineering 5 MAPK14 RB1 CDK1 MARK8 AR DRD6 SELE GSK3B ADRA18 CDK2 TP53 JUN CHRM3 ADRB2 ESR1 MAPK1 VCAM1 PRSS1 PPARG LDLR NR3C1 MAPK3 PLAU GSTA1 CHRM1 MMP3 EGF SOD1 RXRA MMP1 GSTA2 CCL2 EGFR KDR CAT NCOA1 EGFR IL6R BCL2 IFNG VEGFA MMP2 JUN RXRB HMOX1 PPARG ESR2 ESR1 NQO1 ODC1 CTSD IL1B MAPK14 RXRB MAPK8 (b) PPARD MAPK1 MAPK3 IL2RA MAPK10 COL1A1 RXRA NCOA1 NR3C1 GJA1 COL3A1 MET PTGS2 AR CDK2 PGR RB1 CYP1A2 CDK1 GSK3B MAPK1 NR3C2 CDK4 CHEK1 MPO ALOX5 CCNA2 CYP3A4 OPRM1 PTGER3 F7 RB1 LTA4H MAOA TP53 CYP2B6 PTGS1 NR3C1 ADRA2A DRD3 JUN SULT1E1 F3 AKR1C1 OPRD1 TP53 (a) ESR1 MAPK3 (c) Figure 4: PPI network and clustering of gene combinations at different levels (a–c). JUN at −5.7 kcal/mol. Quercetin bound to ESR1 with a downregulated after XJD administration. Our study reveals, binding energy of −6.3 kcal/mol, MAPK1 at −8.9 kcal/mol, to some extent, the pharmacological mechanism of XJD in RB1 at −8.4 kcal/mol, and TP53 at −8.3 kcal/mol. Luteolin the treatment of UC. bound to ESR1 with a binding energy of −7.0 kcal/mol, MAPK1 at −9.1 kcal/mol, RB1 at −8.3 kcal/mol, and TP53 at 4. Discussion −8.3 kcal/mol (Table 1). Molecular docking binding free energy less than Hu et al. recently found that XJD was effective in treating −5.0 kcal/mol indicates strong binding activity, and that UC, with its ability to effectively regulate neuroendocrine less than −7.0 kcal/mol indicates very strong binding ac- factors, improve the intestinal immune response, and reduce tivity. In the present study, 47% of targets (n � 8) had a patient symptoms [6]. Network pharmacology, animal ex- molecular docking binding free energy less than −7.0 kcal/ periments, and molecular docking approaches were used to mol and 94% had less than −5 kcal/mol, indicating that the systematically investigate the potential pharmacological targets had good binding power with the components. mechanisms of XJD in UC treatment in this study. 0erefore, we suggest that XJD binds to these genes (ESR1, A total of 135 potential action targets of XJD, 5,097 UC- JUN, NR3C1, MAPK1, MAPK3, RB1, and TP53) very well related gene targets, and 103 XJD-UC intersection gene and this may be the potential mechanism of XJD in the targets were screened, and 103 potential protein targets were treatment of UC. found to be possible UC-related genes. 0e hub gene targets of XJD that exert therapeutic effects on UC are RB1, MAPK1, 3.6. Effects of XJD Administration on the Expression Levels of TP53, JUN, NR3C1, MAPK3, and ESR1. GO enrichment FOS, TNF-α, and IL-1 mRNA in a UC Rat Model. FOS, TNF- analysis showed 741 biofunctional enrichments, and KEGG α, and IL-1 are key proteins in the development of UC. RT- enrichment analysis showed 124 related pathway enrich- qPCR showed that the mRNA expression of FOS, TNF-α, ments. Molecular docking showed that the active compo- and IL-1 was reduced in the XJD-treated group compared nents of XJD (β-sitosterol, kaempferol, formononetin, with the model group (p< 0.01) (Figure 9). 0e expression quercetin, and luteolin) showed good binding activity to six levels of FOS, TNF-α, and IL-1 mRNA in venous blood of the seven hub gene targets (ESR1, JUN, NR3C1, MAPK1, samples were significantly higher in the model group than in RB1, and TP53). β-Sitosterol, kaempferol, formononetin, the blank group (p< 0.01). 0erefore, the expression of key quercetin, and luteolin have shown potential functions in the mRNAs (FOS, TNF-α, and IL-1) in UC was significantly treatment of UC in many previous studies [21–25]. 6 Journal of Healthcare Engineering DNA-templated transcription, initiation cellular response to steroid hormone stimulus steroid hormone mediated signaling pathway response to cadmium ion hormone–mediated signaling pathway response to steroid hormone peptidyl–serine phosphorylation cellular response to reactive oxygen species intracellular receptor signaling pathway cellular response to oxidative stress nuclear chromatin RNA polymerase II transcription factor complex qvalue transcription factor complex nuclear transcription factor complex 1e–04 cyclin–dependent protein kinase holoenzyme complex 2e–04 spindle 3e–04 serine/threonine protein kinase complex 4e–04 transferase complex, transferring phosphorus–containing groups 5e–04 pseudopodium protein kinase complex nuclear receptor activity transcription factor activity, direct ligand regulated sequence–specific DNA binding steroid hormone receptor activity RNA polymerase II transcription factor binding MAP kinase activity protein serine/threonine kinase activity phosphatase binding transcription factor activity, RNA polymerase II proximal promoter sequence–specific DNA binding nuclear hormone receptor binding MAP kinase kinase activity 0.0 2.5 5.0 7.5 (a) Endocrine resistance Lipid and atherosclerosis Breast cancer Gastric cancer Prostate cancer yroid hormone signaling pathway Non–small cell lung cancer yroid cancer Chemical carcinogenesis – receptor activation Colorectal cancer Hepatitis C Hepatitis B qvalue 17 cell differentiation Kaposi sarcoma–associated herpesvirus infection 5.0e–08 Neurotrophin signaling pathway 1.0e–07 Prolactin signaling pathway Pancreatic cancer 1.5e–07 ErbB signaling pathway 2.0e–07 Bladder cancer Shigellosis Cellular senescence GnRH signaling pathway IL–17 signaling pathway Progesterone–mediated oocyte maturation T cell receptor signaling pathway Endometrial cancer Viral carcinogenesis Osteoclast differentiation Oocyte meiosis Relaxin signaling pathway 0.0 2.5 5.0 7.5 10.0 (b) Figure 5: (a) GO and (b) KEGG enrichment analysis of genes in Figure 4(b). MF CC BP Journal of Healthcare Engineering 7 DNA–templated transcription, initiation positive regulation of DNA-templated transcription, initiation regulation of DNA-binding transcription factor activity cellular response to starvation cellular response to cadmium ion regulation of DNA-templated transcription, initiation response to starvation regulation of telomerase activity cellular response to nutrient levels cellular response to extracellular stimulus nuclear chromatin pseudopodium qvalue RNA polymerase II transcription factor complex nuclear transcription factor complex 0.001 spindle 0.002 transcription factor complex 0.003 caveola 0.004 PML body 0.005 Plasma membrane ra late endosome RNA polymerase II transcription factor binding transcription factor activity, RNA polymerase II proximal promoter sequence–specific DNA binding phosphoprotein binding MAP kinase activity DNA–binding transcription activator activity, RNA polymerase II–specific MAP kinase kinase activity RNA polymerase II basal transcription factor binding phosphatase binding disordered domain specific binding phosphotyrosine residue binding (a) Endocrine resistance ESR1/JUN/TP53/MAPK3/RB1/MAPK1 Breast cancer MAPK signaling pathway JUN/TP53/MAPK3/MAPK1 Hepatitis B Kaposi sarcoma–associated herpesvirus infection Viral carcinogenesis Melanoma Non–small cell lung cancer Glioma Chemical carcinogenesis – receptor activation Pancreatic cancer Chronic myeloid leukemia qvalue Human T–cell leukemia virus 1 infection Colorectal cancer 3e-06 Prostate cancer Neurotrophin signaling pathway 6e-06 Thyroid hormone signaling pathway 9e-06 Apoptosis Estrogen signaling pathway Thyroid cancer Gastric cancer Cellular senescence Hepatitis C Hepatocellular carcinoma Endometrial cancer Proteoglycancs in cancer Lipid and atherosclerosis Human cytomegalovirus infection Renal cell carcinoma Prolactin signaling pathway 02 4 6 (b) Figure 6: (a) GO and (b) KEGG enrichment analysis of genes in Figure 4(c). MF CC BP 8 Journal of Healthcare Engineering TP53 Xianhecao RB1 quercetin NR3C1 Qinpi MAPK1 Kusheng luteolin Huangbal Huaihua JUN kaempferol Gegen formononetin Gancao ESR1 beta-sitosterol Baitouweng Drug MolName Target MolName beta-sitosterol luteolin formononetin quercetin kaempferol Figure 7: Drug components-protein network diagram for potential mechanisms of XJD in the treatment of UC. ESR1 JUN NR3C1 (a) ESR1 JUN (b) Figure 8: Continued. Journal of Healthcare Engineering 9 ESR1 JUN (c) ESR1 JUN MAPK1 RB1 TP53 (d) ESR1 JUN MAPK1 RB1 TP53 (e) Figure 8: Schematic diagram of the docking of XJD active ingredients with NLRP3 target. β-Sitosterol (a), kaempferol (b), formononetin (c), quercetin (d), and luteolin (e). Table 1: Virtual docking of five ingredients for UC targets. Binding energy (kcal/mol) Proteins β-Sitosterol Kaempferol Formononetin Quercetin Luteolin ESR1 −5.8 −7.1 −6.7 −6.3 −7.0 JUN −5.4 −5.5 −5.7 −6.1 −5.6 NR3C1 −4.3 NA NA NA NA MAPK1 NA NA NA −8.9 −9.1 RB1 NA NA NA −8.4 −8.3 TP53 NA NA NA −8.3 −7.9 Variability and methylation patterns of ESR1 were associ- inflammatory response and pathways associated with the ated with the development of Crohn’s disease in patients oxidative stress response in colon cells during the course [1, 26]. 0e potential role of JUN and MAPK1 [27, 28] and of UC. NR3C1 [29] on UC is well known, and our study system- Many factors contribute to the development of UC, atically revealed these links between them. Finally, the ex- including genetic factors, environmental factors, bacterial pression of key mRNAs (FOS, TNF-α, and IL-1) in UC was infections, and hormonal drugs [30, 31]. 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Journal of Healthcare Engineering – Hindawi Publishing Corporation
Published: Oct 25, 2021
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