A network pharmacology approach and experimental validation to investigate the anticancer mechanism of Qi-Qin-Hu-Chang formula against colitis-associated colorectal cancer through induction of apoptosis via JNK/p38 MAPK signaling pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: The Qi-Qin-Hu-Chang Formula (QQHCF) is a traditional Chinese medicine prescription that is clinically used at the Affiliated Hospital of Nanjing University of Chinese Medicine for the treatment of colitis-associated colorectal cancer (CAC). AIM OF THE STUDY: To evaluate the potential therapeutic effects of QQHCF on a CAC mouse model and investigate its underlying mechanisms using network pharmacology and experimental validation. MATERIALS AND METHODS: The active components and potential targets of QQHCF were obtained from Traditional Chinese Medicine Systems Pharmacology (TCMSP) and herb-ingredient-targets gene network were constructed by Cytoscape 3.9.2. Target genes of CAC were obtained from GeneCards, Online Mendelian Inheritance in Man, and DrugBank database. The drug disease target protein-protein interaction (PPI) network was constructed and the core targets were visualized and identified using Cytoscape. The Metascape database was used for GO and KEGG enrichment analysis. UHPLC-MS/MS was used to further identify the active compounds in QQHCF. Subsequently, the therapeutic effects and potential mechanism of QQHCF against CAC were investigated in AOM/DSS-induced CAC mouse in vivo, and HT-29 and HCT116 cells in vitro. Finally, interactions between JNK, p38, and active ingredients were assessed by molecular docking. RESULTS: A total of 176 active compounds, 273 potential therapeutic targets, and 2460 CAC-related target genes were obtained. The number of common targets between QQHCF and CAC were 165. KEGG pathway analysis indicated that the MAPK signaling pathway was closely associated with CAC, which may be the potential mechanism of QQHCF against CAC. Network pharmacology and UHPLC-MS/MS analyses showed that the active compounds of QQHCF included quercetin, kaempferol, luteolin, wogonin, oxymatrine, lupanine, and baicalin. Animal experiments demonstrated that QQHCF reduced tumor load, number, and size in AOM/DSS-treated mice, and induced apoptosis in colon tissue. In vitro experiments further showed that QQHCF induced apoptosis and inhibited cell viability, migration, and invasion in HCT116 and HT-29 cells. Notably, QQHCF activated the JNK/p38 MAPK signaling pathway both in vivo and in vitro. Molecular docking analysis revealed an ability for the main components of QQHCF and JNK/p38 to bind. CONCLUSION: The present study demonstrated that QQHCF could ameliorate AOM/DSS-induced CAC in mice by activating the JNK/p38 MAPK signaling pathway. These results have important implications for the development of effective treatment strategies for CAC.

Integrated Network Pharmacology, Molecular Docking and Animal Experiment to Explore the Efficacy and Potential Mechanism of Baiyu Decoction Against Ulcerative Colitis by Enema.

Background: Baiyu Decoction (BYD), a clinical prescription of traditional Chinese medicine, has been proven to be valuable for treating ulcerative colitis (UC) by enema. However, the mechanism of BYD against UC remains unclear. Purpose: A combination of bioinformatics methods including network pharmacology and molecular docking and animal experiments were utilized to investigate the potential mechanism of BYD in the treatment of UC. Materials and Methods: Firstly, the representative compounds of each herb in BYD were detected by liquid chromatography-mass spectrometry. Subsequently, we predicted the core targets and potential pathways of BYD for treating UC through network pharmacology. And rat colitis model was established with dextran sodium sulfate. UC rats were subjected to BYD enema administration, during which we recorded body weight changes, disease activity index, and colon length to assess the effectiveness of BYD. Besides, quantitative real-time PCR, western blotting, ELISA and immunofluorescence were used to detect intestinal inflammatory factors, intestinal barrier biomarkers and TOLL-like receptor pathway in rats. Finally, the core components and targets of BYD were subjected to molecular docking so as to further validate the results of network pharmacology. Results: A total of 41 active compositions and 203 targets related to BYD-UC were subjected to screening. The results of bioinformatics analysis showed that quercetin and kaempferol may be the main compounds. Additionally, AKT1, IL-6, TP53, TNF and IL-1ß were regarded as potential therapeutic targets. KEGG results explained that TOLL-like receptor pathway might play a pivotal role in BYD protecting against UC. In addition, animal experiments and molecular docking validated the network pharmacology results. BYD enema treatment can reduce body weight loss, lower disease activity index score, reverse colon shortening, relieve intestinal inflammation, protect intestinal barrier, and inhibit TOLL-like receptor pathway in UC rats. Besides, molecular docking suggested that quercetin and kaempferol docked well with TLR4, AKT1, IL-6, TP53. Conclusion: Utilizing network pharmacology, animal studies, and molecular docking, enema therapy with BYD was confirmed to have anti-UC efficacy by alleviating intestinal inflammation, protecting the intestinal barrier, and inhibiting the TOLL-like receptor pathway. Researchers should focus not only on oral medications but also on the rectal administration of medications in furtherance of the cure of ulcerative colitis.

Comparison of Feature Selection Methods and Machine Learning Classifiers for Predicting Chronic Obstructive Pulmonary Disease Using Texture-Based CT Lung Radiomic Features.

RATIONALE: Texture-based radiomics analysis of lung computed tomography (CT) images has been shown to predict chronic obstructive pulmonary disease (COPD) status using machine learning models. However, various approaches are used and it is unclear which provides the best performance. OBJECTIVES: To compare the most commonly used feature selection and classification methods and determine the optimal models for classifying COPD status in a mild, population-based COPD cohort. MATERIALS AND METHODS: CT images from the multi-center Canadian Cohort Obstructive Lung Disease (CanCOLD) study were pre-processed by resampling the image to a 1mm isotropic voxel volume, segmenting the lung and removing the airways (VIDA Diagnostics Inc.), and applying a threshold of -1000HU-to-0HU. A total of 95 texture features were then extracted from each CT image. Combinations of 17 feature selection methods and 9 classifiers were tested and evaluated. In addition, the role of data cleaning (outlier removal and highly correlated feature removal) was evaluated. The area under the curve (AUC) from the receiver operating characteristic curve was used to evaluate model performance. RESULTS: A total of 1204 participants were evaluated (n = 602 no COPD, n = 602 COPD). There were no significant differences between the groups for female sex (no COPD = 46.3%; COPD = 38.5%; p = 0.77), or body mass index (no COPD = 27.7 kg/m2; COPD = 27.4 kg/m2; p = 0.21). The highest AUC value for predicting COPD status (AUC = 0.78 [0.73, 0.84]) was obtained following data cleaning and feature selection using Elastic Net with the Linear-SVM classifier. CONCLUSION: In a population-based cohort, the optimal combination for radiomics-based prediction of COPD status was Elastic Net as the feature selection method and Linear-SVM as the classifier.

Qing-Chang-Hua-Shi granule ameliorates DSS-induced colitis by activating NLRP6 signaling and regulating Th17/Treg balance.

BACKGROUND: Chinese herbal medicine Qing-Chang-Hua-Shi granule (QCHS) is widely used to treat ulcerative colitis in China. However, the molecular mechanisms of QCHS remains largely unknown. PURPOSE: To assess the therapeutic effects of QCHS on colitis and to reveal its mechanisms of action. METHODS: The main components of QCHS were identified using a UHPLC-QTOF-MS method and the efficacy of QCHS was evaluated using an DSS-induced mice model. The inflammatory responses and mucosal integrity in colon were comprehensively assessed. Flow cytometry was used to analysis the proportion of Th17 and Treg cells. Detect the signal transduction of the NOD-like receptor family pyrin domain containing 6 (NLRP6) both in vitro and in vivo. Furthermore, siNLRP6 transfection was used to validate the functional targets of QCHS. RESULTS: QCHS treatment significantly alleviated colitis in mice by improving symptoms and pathological damage. Moreover, QCHS treatment suppressed the inflammatory response and preserved the integrity of colon tissue. Most importantly, QCHS balanced the Th17/Treg response of UC mice. Mechanistically, by activating NLRP6 inflammasome pathway, QCHS regulated the maturation of interleukin (IL)-1ß and IL-18 to affect inflammation and drive Th17 cell differentiation. CONCLUSIONS: The effect of QCHS on UC mice is dose-dependent, with high-dose QCHS being superior to 5-Aminosalicylic acid (200 mg/kg/day). QCHS acts through the NLRP6 signaling pathway to modulate Th17/Treg balance, resulting in the protective effects against colitis. This study investigated the relevant pharmacological mechanisms of QCHS, providing further evidence for the application of QCHS in UC treatment.

Baicalin Ameliorates DSS-Induced Colitis by Protecting Goblet Cells through Activating NLRP6 Inflammasomes.

Objective: Baicalin is an active compound found in many natural herbs and has been used to treat intestinal disorders such as diarrhea and colon cancer. In this study, we used a dextran sodium sulfate (DSS)-induced colitis mouse model to investigate baicalin's mechanisms in the treatment of colitis. Methods: 3% DSS was administered through the drinking supply for 7 days to induce colitis followed by the administration of 5-aminosalicylic acid and baicalin at three different doses (25, 50, and 100 mg/kg, W/W) for an additional 7 days. Body weight, stool consistency, and colon length were recorded. Colon tissue was stained with hematoxylin and eosin (H&E) to be used for histopathological scoring. Cytokine levels of the colon tissue and serum were evaluated using real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and enzyme-linked immunosorbent assay (ELISA), respectively. mRNA expression and protein levels of tight junctions (TJs) were detected with qRT-PCR and Western blotting. Goblet cells and the mucosal layer of the colon were visualized by Alcian Blue/periodic acid-Schiff (AB/PAS) staining. Mucin 2 (MUC2) was evaluated in both mRNA expression and protein levels. Nod-like receptor pyrin domain-containing protein 6 (NLRP6) inflammasomes were detected by immunohistochemistry and Western blotting. Results: Treatment with baicalin significantly relieved colitis as evidenced by reversing both weight loss and colon length shortening. In addition, baicalin inhibited inflammation by reducing proinflammatory cytokines and protected the intestinal barrier by upregulating tight junction proteins. Moreover, goblet cell count and intestinal mucosa thickness were both significantly increased after baicalin treatment. Giving baicalin could upregulate the expression of NLRP6 and interleukin (IL-18) both in mRNA and protein. Conclusion: Baicalin ameliorates DSS-induced colitis by protecting goblet cells through activating NLRP6 inflammasomes.

Gegen Qinlian decoction ameliorates murine colitis by inhibiting the expansion of Enterobacteriaceae through activating PPAR-γ signaling.

Ulcerative colitis (UC) is a chronic and relapsing inflammatory disease of the intestine. Dysbiosis, especially the expansion of facultative anaerobic Enterobacteriaceae, maybe the main pathogenesis of UC. Gegen Qinlian decoction (GD), a traditional Chinese medicinal formula chronicled in the Shang Han Lun, is commonly used to treat UC and has shown an excellent effect on inducing disease remission. However, the role of GD in regulating gut microbiota has not been fully clarified. Herein, we investigated the potential effect of GD on inhibiting the expansion of Enterobacteriaceae and further explored the potential mechanism of this action. Our study demonstrated that GD remarkably reduced body weight loss of colitis mice, shortening of colon length, and inflammation of the colon. Peroxisome proliferator-activated receptor-γ (PPAR-γ) signaling was inactivated in colitis colon tissue, and the abundance of Escherichia coli (E. coli, family of Enterobacteriaceae) in colonic contents and the concentration of lipopolysaccharide (LPS) in colonic tissue were significantly upregulated after DSS-treatment. Notably, GD administration can result in the activation of PPAR-γ and inactivation of iNOS, which lead to the reduction of nitrate, the inhibition of E. coli, and less production of LPS. Combined GD with PPAR-γ antagonist, the effect of GD on the treatment of UC was weakened, and effectless in inhibiting the expansion of Enterobacteriaceae. Therefore, GD ameliorates UC by preventing a dysbiotic expansion of potentially pathogenic E. coli by reducing nitrate levels in the lumen through activating PPAR-γ signaling.

Impact of image pre-processing methods on computed tomography radiomics features in chronic obstructive pulmonary disease.

Computed tomography (CT) imaging texture-based radiomics analysis can be used to assess chronic obstructive pulmonary disease (COPD). However, different image pre-processing methods are commonly used, and how these different methods impact radiomics features and lung disease assessment, is unknown. The purpose of this study was to develop an image pre-processing pipeline to investigate how various pre-processing combinations impact radiomics features and their use for COPD assessment. Spirometry and CT images were obtained from the multi-centered Canadian Cohort of Obstructive Lung Disease study. Participants were divided based on assessment site and were further dichotomized as No COPD or COPD within their participant groups. An image pre-processing pipeline was developed, calculating 32 grey level co-occurrence matrix radiomics features. The pipeline included lung segmentation, airway segmentation or no segmentation, image resampling or no resampling, and either no pre-processing, binning, edgmentation, or thresholding pre-processing techniques. A three-way analysis of variance was used for method comparison. A nested 10-fold cross validation using logistic regression and multiple linear regression models were constructed to classify COPD and assess correlation with lung function, respectively. Logistic regression performance was evaluated using the area under the receiver operating characteristic curve (AUC). A total of 1210 participants (Sites 1-8: No COPD:n = 447, COPD:n = 413; and Site 9: No COPD:n = 155, COPD:n = 195) were evaluated. Between the two participant groups, at least 16/32 features were different between airway segmentation/no segmentation (P ≤ 0.04), at least 29/32 features were different between no resampling/resampling (P ≤ 0.04), and 32/32 features were different between the pre-processing techniques (P < 0.0001). Features generated using the resampling/edgmentation and resampling/thresholding pre-processing combinations, regardless of airway segmentation, performed the best in COPD classification (AUC ≥ 0.718), and explained the most variance with lung function (R2 ≥ 0.353). Therefore, the image pre-processing methods completed prior to CT radiomics feature extraction significantly impacted extracted features and their ability to assess COPD.