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

Systemic investigation of the mechanism underlying the therapeutic effect of Astragalus membranaceus in ulcerative colitis

BACKGROUND

Whether Astragalus membranaceus is an effective drug in the treatment of ulcerative colitis (UC) is unknown and how it exhibits activity in UC is unclear.

METHODS

TCMSP, GeneCards, String, and DAVID databases were used to screen target genes in PPI network and we performed GO and KEGG pathway enrichment analysis. Molecular docking and animal experiments were performed. The body weight and disease activity index (DAI) of mice were recorded. ELISA kits were used to detect the levels of CAT, SOD, MDA and IL-6, IL-10, TNF-α in the blood of mice. Western blot kits were utilized to measure the expression of MAPK14, RB1, MAPK1, JUN, ATK1, and IL2 proteins.

RESULTS

The active components of Astragalus membranaceus mainly include 7-O-methylisomucronulatol, quercetin, kaempferol, formononetin and isrhamnetin. Astragalus membranaceus may inhibit the expression of TNF-α, IL-6, MDA, while promoting the expression of CAT, SOD, and IL-10. The expression levels of MAPK14, RB1, MAPK1, JUN and ATK1 proteins were significantly decreased while IL2 protein increased after administration of Astragalus membranaceus.

CONCLUSIONS

Astragalus membranaceus may be an effective drug in the treatment of UC by acting on targets with anti-UC effect via its antioxidant action and by regulating the balance of pro-inflammatory and anti-inflammatory factors.

TCR-ESM: Employing protein language embeddings to predict TCR-peptide-MHC binding

Cognate target identification for T-cell receptors (TCRs) is a significant barrier in T-cell therapy development, which may be overcome by accurately predicting TCR interaction with peptide-bound major histocompatibility complex (pMHC). In this study, we have employed peptide embeddings learned from a large protein language model- Evolutionary Scale Modeling (ESM), to predict TCR-pMHC binding. The TCR-ESM model presented outperforms existing predictors. The complementarity-determining region 3 (CDR3) of the hypervariable TCR is located at the center of the paratope and plays a crucial role in peptide recognition. TCR-ESM trained on paired TCR data with both CDR3α and CDR3β chain information performs significantly better than those trained on data with only CDR3β, suggesting that both TCR chains contribute to specificity, the relative importance however depends on the specific peptide-MHC targeted. The study illuminates the importance of MHC information in TCR-peptide binding which remained inconclusive so far and was thought dependent on the dataset characteristics. TCR-ESM outperforms existing approaches on external datasets, suggesting generalizability. Overall, the potential of deep learning for predicting TCR-pMHC interactions and improving the understanding of factors driving TCR specificity are highlighted. The prediction model is available at http://tcresm.dhanjal-lab.iiitd.edu.in/ as an online tool.

Based on network pharmacology, molecular docking and experimental verification to reveal the mechanism of Andrographis paniculata against solar dermatitis

BACKGROUND

Solar dermatitis (SD) is an acute, damaging inflammation of the skin caused by UV exposure, especially UVB. Therefore, the discovery of novel anti-SD therapeutic agents is crucial. Andrographis paniculata (AP) is a medicinal plant with a wide range of pharmacological effects. Increased evidence shows that AP has potential therapeutic effects on SD. However, the therapeutic mechanisms of AP against SD have not yet been completely elucidated, which is an unexplored field.

PURPOSE

This study employed network pharmacology, molecular docking and experimental verification to ascertain the active constituents, possible targets, and biological pathways associated with AP in the treatment of SD.

METHODS

AP-related active ingredients and their potential targets were screened from TCMSP and Swiss Target Prediction database, respectively. Potential therapeutic targets of SD were collected using the GeneCards, DrugBank and OMIM databases. Then, we established protein-protein interaction (PPI), compound-target-disease (D-C-T-D) through Cytoscape to identify the major components, core targets of AP against SD. Next, the GO and KEGG pathway was identified by the David database of AP in the treatment of SD. Molecular docking techniques were used to estimate the binding force between the components and the hub genes. In this paper, we used UVB-irradiated HaCaT keratinocytes as an in vitro model and established the dorsal skin of UVB-irradiated ICR mice as an in vivo model to explore the mechanism for further verification.

RESULTS

There were 24 active components and 63 related target genes in AP against SD. PPI analysis showed that AKT-1, TNF-α, IL6, MMP9, EGFR, and PTGS2 shared the highest centrality among all target genes. KEGG pathway analysis revealed that the PI3K-Akt signaling pathway may be central in the anti-SD system. The molecular docking results showed that the main active components of AP have strong binding affinity with hub genes. In vitro results showed that WG had a protective effect on UVB-intervened HaCat cells. Western blot analysis showed that WG intervention achieved anti-inflammation by reducing the phosphorylated expression of AKT, PI3K proteins in the PI3K-AKT signaling pathway and downregulating the expression of TNF-α, IL-6, EGFR. Furthermore, Histological analysis confirmed that administration of WG to ICR mice significantly ameliorated UVB-induced skin roughness, epidermal thickening, disturbed collagen fiber alignment and wrinkles. Meanwhile, immunohistochemistry showed that administration of WG to ICR mice significantly reduced UVB-induced expression of MMP9, MPO, F4/80 in the skin. These results provide new insights into the contribution of WG to the development of clinical treatment modalities for UVB-induced SD.

CONCLUSION

The crucial element in the fight against SD is WG, with the primary route being PI3K/Akt. The main components and hub genes had robust binding abilities. In vitro and vivo experiments showed that WG could inhibit the expression level of the hub genes by inhibiting the PI3K/Akt pathway. In summary, the information presented in this study indicates that WG might be utilised as a treatment for UVB-induced SD.

Anti-anemic potential of Eruca sativa L. in iron-deficient rat model; network pharmacology profiling

Iron deficiency anemia is a global health concern, affecting around 2 billion people. Oral iron therapy often causes severe gastro-intestinal issues. Eruca sativa, member of the Brassicaceae family, is valued in traditional medicine and renowned for its rich iron and vitamin C content. This study aims to evaluate the anti-anemic properties of E. sativa extract in vivo and identify its compounds targeting anemia mechanisms using network pharmacology. Thirty-two Sprague-Dawley rats (200 ± 250 g) were split into two distinct groups, iron-deficient and iron-sufficient. Three different doses (200, 400, and 800 mg/kg) of aqueous extract of E. sativa were checked against anemia by studying hematological, oxidative stress, and histopathological parameters. GC-MS analysis of E. sativa revealed its phytochemical profile, followed by ADME screening. Network pharmacology explored targets related to iron deficiency anemia, with oral bioavailability and drug likeness assessment for compounds. The administration of extracts significantly improved various blood parameters, including osmotic fragility, Hb, RBCs, MCV, PCV, and alkaline phosphatase; catalase activity; and histopathological parameters such as liver in both iron-deficient and iron-sufficient rats (p < .001). Seventy-nine compounds were identified in E. sativa aqueous extract, with only six of them found to be bioavailable and drug-like against multiple targets. Gene ontology and pathway analysis revealed their diverse molecular, biological, and cellular functions. One gene EGFR was found to have functional association with ID anemia, suggesting potential for using E. sativa extracts. The study concludes that E. sativa extract has potential for iron deficiency anemia treatment, offering hope for future pharmaceutical interventions.

Colchicine, serotobenine, and kinobeon A: novel therapeutic compounds in Carthamus tinctorius L. for the management of diabetes

Diabetes, a global health concern, poses increasing mortality risks. The pathogenesis of diabetes involves multiple mechanisms, with oxidative stress being one of the key contributors. As synthetic drugs have various side effects, which can be minimized by using herbal plants. This study focuses on the In vitro antioxidant potential, α-amylase inhibition potential, identification of bioactive compounds, and hub genes in diabetes treatment mechanism by using C. tinctorius Extraction of C. tinctorious lead and flower was performed using different solvents (Distilled water, methanol, chloroform, and Dimethyl ether). After extraction different concentrations range from 25–200 mg/mL) was made and checked against activities. The antioxidant potential was assessed using 2, 2-diphenyl-1-picrylhydrazyl (DPPH), total phenolic contents (TPC), and total antioxidant capacity (TAC) assays, while antidiabetic activity was evaluated through α-amylase inhibition assay. Phytochemicals was identified by GC–MS analysis, followed by ADMET screening and network pharmacology analysis using Swiss Target Prediction, Gene Card, DesGeNet, DAVID, STRING, Cytoscape, and drug revitalization databases. Results revealed positive correlations with DPPH, TAC, and TPC. Methanol extract exhibited the highest inhibitory concentration. Screening of 46 compounds was performed by studying their pharmacokinetic properties which revealed 9 compounds effective against 204 diabetes targets. Moreover, their network analysis identified four hub genes, including AKT1, JUN, EGFR, and MMP9. These genes found highly associated with drugs like Colchicine and Serotobenine. Revitalization analysis also highlighted four genes (EGFR, PTGS2, AKT1, and MMP9) strongly correlated with FDA-approved drugs. The study suggests C. tinctorius methanol extract is a potential source for novel drugs.

Graphical Abstract

Biomarkers of Arginine Methylation in Diabetic Nephropathy: Novel Insights from Bioinformatics Analysis

Background

Diabetic nephropathy (DN) is a severe complication of diabetes influenced by arginine methylation. This study aimed to elucidate the role of protein arginine methylation-related genes (PRMT-RGs) in DN and identify potential biomarkers.

Methods

Differentially expressed genes in two GEO datasets (GSE30122 and GSE104954) were integrated with 9 PRMT-RGs. Candidate genes were identified using WGCNA and differential expression analysis, then screened using support vector machine-recursive feature elimination and least absolute shrinkage and selection operator. Biomarkers were defined as genes with consistent differential expression across both datasets. Regulatory networks were constructed using the miRNet and Network Analyst databases. Gene set enrichment analysis was performed to identify the signaling pathways in which the biomarkers were enriched in DN. Different immune cells in DN were identified using immune infiltration analysis. Meanwhile, drug prediction and molecular docking identified potential DN therapies. Finally, qRT-PCR and immunohistochemistry validated two biomarkers in STZ-induced DN mice and DN patients.

Results

Two biomarkers (FAM98A and FAM13B) of DN were identified in this study. The molecular regulatory network revealed that FAM98A and FAM13B were co-regulated by 6 microRNAs and 1 transcription factor and were enriched in signaling pathways. Immune infiltration and correlation analyses revealed that FAM98A and FAM13B were involved in developing DN along with PRMT-RGs and immune cells. The expression levels of Fam98a and Fam13b were significantly upregulated in the kidneys of DN mice revealed by qRT-PCR analysis. The expression levels of FAM98A were significantly upregulated in the kidneys of DN patients revealed by immunohistochemistry staining. Molecular docking showed that estradiol and rotenone exerted potential therapeutic effects on DN by targeting FAM98A.

Conclusion

Comprehensive bioinformatics analysis revealed that FAM98A and FAM13B were potential DN biomarkers correlated with PRMT-RGs and immune cells. This study provided useful insights for elucidating the molecular mechanisms and developing targeted therapy for DN.

Based on network pharmacology and molecular docking to explore the potential mechanism of shikonin in periodontitis

OBJECTIVES

To investigate the potential mechanisms of shikonin in preventing and treating periodontitis using network pharmacology and molecular docking methods.

MATERIALS AND METHODS

The targets of shikonin were obtained in TCMSP and SEA databases, and targets of periodontitis were gathered from the OMIM, GeneCards and Drugbank Databases. The intersecting targets were entered into the DAVID database to obtain the relevant biological functions and pathways by GO and KEGG enrichment analysis. The obtained targets were analysed the protein-protein interaction (PPI) in STRING platform. In Cytoscape 3.8.0, the network analysis function with the MCODE plug-in were used to obtain the key targets, of shikonin and periodontitis. Molecular docking and molecular dynamics simulation (MD) were used to assess the affinity between the shikonin and the key targets.

RESULTS

Shikonin was screened for 22 targets and periodontitis was screened for 944 targets, the intersecting targets were considered as potential therapeutic targets. The targets played important roles in cellular response to hypoxia, response to xenobiotic stimulus and positive regulates of apoptotic process by GO enrichment analysis. 10 significant pathways were analyzed by KEGG, such as human cytomegalovirus infection and PI3K-Akt signaling pathway, etc. Cytoscape software screened the key genes including AKT1, CCL5, CXCR4, PPARG, PTEN, PTGS2 and TP53. Molecular docking and MD results showed that shikonin could bind stably to the targets.

CONCLUSIONS

The present study enriched the molecular mechanisms in periodontitis with shikonin, providing potential therapeutic targets for periodontitis.

Integration of network pharmacology and experimental verifications reveals the Bian-Se-Tong mixture can alleviate constipation in STC rats by reducing apoptosis of Cajal cells via activating PI3K-Akt signaling pathway

Bian-Se-Tong mixture (BSTM) is an optimized formulation based on the classical prescription "Zhizhu pill", which is widely used in the clinical treatment of slow-transit constipation (STC). The potential molecular mechanism of BSTM therapy for STC was investigated by network pharmacology prediction combined with animal experiments. The active components of BSTM were screened via the TCMSP platform. The GeneCards, OMIM and DrugBank databases were used to search for STC targets. With the help of the Biogenet tool, a protein interaction network between drugs and disease targets was constructed, and the intersection network of the two was extracted to obtain the key targets of BSTM in the treatment of STC. GO and KEGG enrichment analyses of key targets were carried out with Metascape. Loperamide hydrochloride was used to establish an STC rat model, and the key targets and related pathways were preliminarily verified. The important signaling pathways included the PI3K-Akt, MAPK, IL-17, cAMP, and cell cycle signaling pathways. The experimental results showed that BSTM treatment increased the body weight of STC rats and increased the fecal particle number, fecal water content and intestinal carbon ink promotion rate within 24 h. Further pathological changes in the colon of the rats were also observed. In-depth mechanistic studies have shown that BSTM can significantly reduce the apoptosis of intestinal Cajal cells, downregulate the expression of Bax and c-Caspase 3, upregulate the expression of Bcl-2 and c-kit, and promote the phosphorylation of AKT. The results showed that BSTM can significantly relieve constipation in STC rats via a mechanism related to activating the PI3K-Akt signaling pathway and improving Cajal cell apoptosis.

Active substance and mechanisms of Actinidia chinensis Planch for the treatment of breast cancer was explored based on network pharmacology and in silico study

In this paper, our objective was to investigate the potential mechanisms of Actinidia chinensis Planch (ACP) for breast cancer treatment with the application of network pharmacology, molecular docking, and molecular dynamics. "Mihoutaogen" was used as a key word to query the Traditional Chinese Medicine Systems Pharmacology database for putative ingredients of ACP and its related targets. DrugBank, GeneCards, Online Mendelian Inheritance in Man, and therapeutic target databases were used to search for genes associated with "breast cancer." Using Cytoscape 3.9.0 we then constructed the protein-protein interaction and drug-ingredient-target-disease networks. An enrichment analysis of Kyoto encyclopedia of genes and genomes pathway and gene ontology were performed to exploration of the signaling pathways associated with ACP for breast cancer treatment. Discovery Studio software was applied to molecular docking. Finally, the ligand-receptor complex was subjected to a 50-ns molecular dynamics simulation using the Desmond_2020.4 tools. Six main active ingredients and 176 targets of ACP and 2243 targets of breast cancer were screened. There were 118 intersections of targets for both active ingredients and diseases. Tumor protein P53 (TP53), AKT serine/threonine kinase 1 (AKT1), estrogen receptor 1 (ESR1), Erb-B2 receptor tyrosine kinase 2 (ERBB2), epidermal growth factor receptor (EGFR), Jun Proto-Oncogene (JUN), and Heat Shock Protein 90 Alpha Family Class A Member 1 (HSP90AA1) selected as the most important genes were used for verification by molecular docking and molecular dynamics simulation. The primary active compounds of ACP against breast cancer were predicted preliminarily, and its mechanism was studied, thereby providing a theoretical basis for future clinical studies.

Recent advances in the area of plant-based anti-cancer drug discovery using computational approaches

Phytocompounds are a well-established source of drug discovery due to their unique chemical and functional diversities. In the area of cancer therapeutics, several phytocompounds have been used till date to design and develop new drugs. One of the desired interests of pharmaceutical companies and researchers globally is that new anti-cancer leads are discovered, for which phytocompounds can be considered a valuable source. Simultaneously, in recent years, the growth of computational approaches like virtual screening (VS), molecular dynamics (MD), pharmacophore modelling, Quantitative structure-activity relationship (QSAR), Absorption Distribution Metabolism Excretion and Toxicity (ADMET), network biology, and machine learning (ML) has gained importance due to their efficiency, reduced time-consuming nature, and cost-effectiveness. Therefore, the present review amalgamates the information on plant-based molecules identified for cancer lead discovery from in silico approaches. The mandate of this review is to discuss studies published in the last 5-6 years that aim to identify the phytomolecules as leads against cancer with the help of traditional computational approaches as well as newer techniques like network pharmacology and ML. This review also lists the databases and webservers available in the public domain for phytocompounds related information that can be harnessed for drug discovery. It is expected that the present review would be useful to pharmacologists, medicinal chemists, molecular biologists, and other researchers involved in the development of natural products (NPs) into clinically effective lead molecules.

Mechanisms of action of Shizhenqing granules for eczema treatment: Network pharmacology analysis and experimental validation

Background

Jiuwan decoction has been used to treat chronic eczema since the Qing Dynasty. According to clinical experience, Shizhenqing granules (SZQG), derived from the Jiuwan decoction, exert beneficial clinical effects on acute eczema and reduce recurrence. Therefore, we elucidated the underlying mechanisms of SZQG through network pharmacology, molecular docking, and experimental validation.

Methods

The main chemical components of SZQG were identified by ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). And the targets of SZQG against eczema were screened out through online databases. Then, the regulatory network map of the "herbal compound-potential target" and the target protein-protein interaction (PPI) network was constructed. The Gene Ontology (GO) analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using by R language. Additionally, the interaction between the active compounds and the targets was verified by molecular docking technology. Finally, an experiment in vivo was used to verify the effect and mechanism of SZQG on eczema.

Results

Using UHPLC-MS/MS, 158 main chemical compounds of SZQG were identified, and 72 compounds were selected according to the criteria for further analysis. All 237 potential targets of SZQG in eczema were explored using multiple online databases. The network with 14 core targets was screened out, including STAT3, RELA, TNF, JUN, MAPK3, IL-6, PIK3CA, STAT1, MAPK14, MAPK1, IL-4, NFKBIA, IL1B, and MYC. KEGG analyses indicated that the therapeutic effects of SZQG on eczema were predominantly associated with cytokine-cytokine receptor interaction, TNF, MAPK, NF-κB, toll-like receptor, T cell receptor, and Th1 and Th2 cell differentiation signaling pathways. Furthermore, the good affinity between the core compounds and core targets was verified by molecular docking technology, particularly for RELA and MAPK. Animal experiments revealed that SZQG downregulated MAPK14, RELA, T-bet, and GATA3 mRNA expression, reduced immunoglobulin E (IgE) and interleukin-4 (IL-4) serum concentrations, and improved eczema-like lesions in model rats.

Conclusion

This study identified potential targets and signaling pathways of SZQG in the treatment of eczema, whereby RELA and MAPK14 may constitute the main therapeutic targets of SZQG in cytokine regulation and reduction of inflammatory responses.

Implementation of IFPTML Computational Models in Drug Discovery Against Flaviviridae Family

The Flaviviridae family consists of single-stranded positive-sense RNA viruses, which contains the genera Flavivirus, Hepacivirus, Pegivirus, and Pestivirus. Currently, there is an outbreak of viral diseases caused by this family affecting millions of people worldwide, leading to significant morbidity and mortality rates. Advances in computational chemistry have greatly facilitated the discovery of novel drugs and treatments for diseases associated with this family. Chemoinformatic techniques, such as the perturbation theory machine learning method, have played a crucial role in developing new approaches based on ML models that can effectively aid drug discovery. The IFPTML models have shown its capability to handle, classify, and process large data sets with high specificity. The results obtained from different models indicates that this methodology is proficient in processing the data, resulting in a reduction of the false positive rate by 4.25%, along with an accuracy of 83% and reliability of 92%. These values suggest that the model can serve as a computational tool in assisting drug discovery efforts and the development of new treatments against Flaviviridae family diseases.

An integrated network pharmacology approach to discover therapeutic mechanisms of Commiphora wightii for the treatment of Bell's palsy

Bell's palsy (BP) can result in facial paralysis. Inflammation or injury to the cranial nerves that regulate the facial muscles is primarily responsible for that disease. Commiphora wightii remains recognized as a cure for a few human ailments. This study focused on therapeutic phenomena of C. wightii for the treatment of Bell's palsy, utilizing the network drug discovery and molecular docking techniques. Active biological constituents of C. wightii were retrieved from literature and independent databases. Potential therapeutic targets (431) of 13 bioactive phytochemicals were fetched via SwissTargetPrediction tool. Putative intersecting targets (855) of Bell's palsy were computed through the DisGeNET and GeneCards datasets. Subsequently, by the analysis of potential shared targets (87) of C. wightii and Bell's palsy, a Venn diagram was drawn. DAVID database was used to evaluate gene functional annotations and enriched pathways that are involved in Bell's palsy. STRING database was used for generating the protein-protein relationship complex. Visual presentations of the interactions of potential targets to active chemical constituents were done by the Cytoscape. Whereas, the conformational research sorted out 10 key targets through the protein-protein interactions network. Moreover, the capacity of therapeutic ingredients to interact with a target inhibiting Bell's palsy was confirmed by molecular docking, which might ratify the findings of network pharmacology. In the molecular complex of AKT1-cholesterol, a 100-ns simulation unveiled a graceful stability, with a minimal 0.167 Å ligand shift and resilient hydrogen bonds (ASN54 and SER205). The final 20 ns showcased a P1 motif pirouette, gracefully forming aromatic bonds with H165 and W186, underscoring the complex's dynamic finesse. This study evaluated compound-target interactions and their impact on disease-related genes. It revealed that five genes (AKT1, TNF, MAPK3, EGFR and SRC) of C. wightii might be useful therapeutic targets for the treatment of Bell's palsy, as well as helping in lowering down the blood pressure.Communicated by Ramaswamy H. Sarma.

Understanding metric-related pitfalls in image analysis validation

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.

Understanding metric-related pitfalls in image analysis validation

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although taking into account the individual strengths, weaknesses and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multistage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides a reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Although focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation.

Ginger volatile oil inhibits the growth of MDA-MB-231 in the bisphenol A environment by altering gut microbial diversity

To examine the impact of ginger volatile oil (GVO) on the growth of MDA-MB-231 breast cancer cells in the presence of bisphenol A (BPA) by modulating the diversity of gut microbiota.

METHODS

MDA-MB-231 breast cancer cells were injected subcutaneously into the right armpit of female BALB/c Nude (nu/nu) mice to create a triple negative breast cancer model. Thirty nude mice were randomly divided into 5 groups: control group (distilled water every day), BPA control group (distilled PEG-400+ DMSO + cyclodextrin every day), BPA + GVO (0.25 mL/kg) group, BPA + GVO (0.5 mL/kg) group, BPA + GVO (1 mL/kg) group, 6 mice in each group; The drug was given by gavage once a day for 4 weeks. At the end of the experiment, the changes of tumor mass and tumor volume were observed and compared in 5 groups of tumor-bearing mice. High-throughput sequencing (16S rRNA) was used to detect the changes of gut microflora in each group.

RESULTS

The volume and weight of breast cancer decreased in the low, medium and high dose groups of GVO. Among them, the difference between the high-dose group and the BPA group reached a significant level (P < 0.05). The species and abundance of gut flora decreased following BPA treatment, but increased after combined treatment of BPA with GVO. In the tumor control group, the ratio of Firmicutes(F) and Bacteroidea(B) respectively was 0.10:0.79 at the phylum level, while the ratio of BPA group further decreased (0.04:0.88). After feeding GVO, the number of Firmicutes and Bacteroidea increased, the F/B ratio increased, and the level of Lactobacillus and alistipes increased. In the BPA and GVO treatment group, the predominant gut microflora functions are cell membrane biogenesis, carbohydrate transport and metabolism. This is followed by amino acid transport and metabolism, and transcription function. After GVO administration, the Gram-positive bacteria (G+) ratio had an increasing trend and the Gram-negative bacteria (G-)ratio had a decreasing trend.

CONCLUSION

The species and abundance of gut flora decreased following BPA treatment, but increased after combined treatment of BPA with GVO.

Study of Therapeutic Mechanisms of Bupi Yichang Formula against Colon Cancer Based on Network Pharmacology, Machine Learning, and Experimental Verification

Bupi Yichang formula (BPYCF) has shown the anti-cancer potential; however, its effects on colon cancer and the mechanisms remain unknown. This study intended to explore the effects of BPYC on colon cancer and its underlying mechanisms. BPYCF-related and colon cancer-related targets were acquired from public databases, followed by differentially expressed genes (DEG) identification. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using clusterProfiler. A protein-protein interaction (PPI) network was constructed using STRING database. CytoHubba and MCODE to screen the hub targets. A diagnostic model was built using random forest algorithm. Molecular docking was conducted using PyMOL and AutoDock. High-performance liquid chromatograph-mass spectrometry (HPLC-MS) analysis and in vitro validation were performed. Forty-six overlapping targets of BPYCF-related, colon cancer-related targets, and DEGs were obtained. GO and KEGG analyses showed that the targets were mainly enriched in response to lipopolysaccharide, neuronal cell body, protein serine/threonine/tyrosine, as well as C-type lectin receptor, NOD-like receptor, and TNF signaling pathways. Five targets were identified as the pivotal targets, among which, NOS3, CASP8, RIPK3, and TNFRSF10B were stably docked with the core active component, naringenin. Naringenin was also identified from the BPYCF sample through HPLC-MS analysis. In vitro experiments showed that BPYCF inhibited cell viability, reduced NOS3 expression, and elevated CASP8, RIPK3, and TNFRSF10B expression in colon cancer cells. BPYCF might treat colon cancer mainly by regulating NOS3, CASP8, RIPK3, and TN-FRSF10B. This study first revealed the therapeutic effects and mechanisms of BPYCF against colon cancer, paving the path for the development of targeted therapeutic strategies for this cancer in the clinic.

Deciphering Multi-target Pharmacological Mechanism of Cucurbita pepo Seeds against Kidney Stones: Network Pharmacology and Molecular Docking Approach

BACKGROUND

Urolithiasis is a prevalent condition with significant morbidity and economic implications. The economic burden associated with urolithiasis primarily stems from medical expenses. Previous literature suggests that herbal plants, including Cucurbita pepo, have lithotriptic capabilities. C. pepo is an annual, herbaceous, widely grown, and monoecious vegetative plant known for its antioxidants, fibers, and fatty acids. Recent studies on C. pepo seeds have shown therapeutic potential in reducing bladder stones and urodynamic illnesses, like kidney stones. However, the precise molecular and pharmacological mechanisms are unclear.

OBJECTIVE

In this research, we employed network pharmacology and molecular docking to examine the active compounds and biological mechanisms of Cucurbita pepo against kidney stones.

METHODS

Active constituents were obtained from previous studies and the IMPPAT database, with their targets predicted using Swiss target prediction. Kidney stone-associated genes were collected from DisGeNET and GeneCards. The active constituent-target-pathway network was constructed using Cytoscape, and the target protein-protein interaction network was generated using the STRING database. Gene enrichment analysis of C. pepo core targets was conducted using DAVID. Molecular docking was performed to identify potential kidney stone-fighting agents.

RESULTS

The findings revealed that Cucurbita pepo contains 18 active components and has 192 potential gene targets, including AR, EGFR, ESR1, AKT1, MAPK3, SRC, and MTOR. Network analysis demonstrated that C. pepo seeds may prevent kidney stones by influencing disease-related signaling pathways. Molecular docking indicated that key kidney stone targets (mTOR, EGFR, AR, and ESR1) effectively bind with active constituents of C. pepo.

CONCLUSION

These findings provide insight into the anti-kidney stone effects of Cucurbita pepo at a molecular level. In conclusion, this study contributes to understanding the potential of Cucurbita pepo in combating kidney stones and lays the foundation for further research.

Exploring active ingredients and mechanisms of Coptidis Rhizoma-ginger against colon cancer using network pharmacology and molecular docking

BACKGROUND

Colon cancer is the most prevalent and rapidly increasing malignancy globally. It has been suggested that some of the ingredients in the herb pair of Coptidis Rhizoma and ginger (Zingiber officinale), a traditional Chinese medicine, have potential anti-colon cancer properties.

OBJECTIVE

This study aimed to investigate the molecular mechanisms underlying the effects of the Coptidis Rhizoma-ginger herb pair in treating colon cancer, using an integrated approach combining network pharmacology and molecular docking.

METHODS

The ingredients of the herb pair Coptidis Rhizoma-ginger, along with their corresponding protein targets, were obtained from the Traditional Chinese Medicine System Pharmacology and Swiss Target Prediction databases. Target genes associated with colon cancer were retrieved from the GeneCards and OMIM databases. Then, the protein targets of the active ingredients in the herb pair were identified, and the disease-related overlapping targets were determined using the Venn online tool. The protein-protein interaction (PPI) network was constructed using STRING database and analyzed using Cytoscape 3.9.1 to identify key targets. Then, a compound-target-disease-pathway network map was constructed. The intersecting target genes were subjected to Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses for colon cancer treatment. Molecular docking was performed using the Molecular Operating Environment (MOE) software to predict the binding affinity between the key targets and active compounds.

RESULTS

Besides 1922 disease-related targets, 630 targets associated with 20 potential active compounds of the herb pair Coptidis Rhizoma-ginger were collected. Of these, 229 intersection targets were obtained. Forty key targets, including STAT3, Akt1, SRC, and HSP90AA1, were further analyzed using the ClueGO plugin in Cytoscape. These targets are involved in biological processes such as miRNA-mediated gene silencing, phosphatidylinositol 3-kinase (PI3K) signaling, and telomerase activity. KEGG enrichment analysis showed that PI3K-Akt and hypoxia-inducible factor 1 (HIF-1) signaling pathways were closely related to colon cancer prevention by the herb pair Coptidis Rhizoma-ginger. Ten genes (Akt1, TP53, STAT3, SRC, HSP90AA1, JAK2, CASP3, PTGS2, BCl2, and ESR1) were identified as key genes for validation through molecular docking simulation.

CONCLUSIONS

This study demonstrated that the herb pair Coptidis Rhizoma-ginger exerted preventive effects against colon cancer by targeting multiple genes, utilizing various active compounds, and modulating multiple pathways. These findings might provide the basis for further investigations into the molecular mechanisms underlying the therapeutic effects of Coptidis Rhizoma-ginger in colon cancer treatment, potentially leading to the development of novel drugs for combating this disease.

The Mechanism of Plantaginis Semen in the Treatment of Diabetic Nephropathy based on Network Pharmacology and Molecular Docking Technology

BACKGROUND

Diabetic nephropathy (DN) is one of the common complications of diabetes. Plantaginis Semen (PS) has a variety of therapeutic effects, however its mechanism on DN is unclear.

OBJECTIVE

This paper aims to find the ingredients, the key targets, and the action pathways of PS on DN from the perspective of network pharmacology.

METHODS

The databases of network pharmacology, such as Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Pharmmapper, OMIM, DrugBank, Gene- Cards, TTD, Disgenet, STRING, and Cytoscape software, were used to find the main ingredients and targets. Gene Ontology (GO) function and Kyoto Encyclopedia of Genome and Genomes (KEGG) pathway enrichment analysis were used to reveal the potential pathways of the PS on DN. The GEO database was used to find the targets of DN based on valid experimental research. The molecular docking technology was used to evaluate the combination between ingredients of PS and the targets.

RESULTS

A total of 9 active ingredients and 216 potential therapeutic targets were obtained for PS on DN. Hub targets were discovered by the Cytoscape software analysis. CASP3 was screened by Venn diagram by making intersection between GSE30529 and hub genes. Moreover, CASP3 was combined with one of the nine active ingredients, quercetin, by molecular docking analysis. The KEGG pathways were mainly involved in diabetic nephropathy, and were simultaneously associated with CASP3 as followed: AGE-RAGE signaling pathway in diabetic complications, apoptosis, lipid and atherosclerosis, MAPK signaling pathway, TNF signaling pathway, IL-17 signaling pathway, and p53 signaling pathway.

CONCLUSION

PS can have the treatment on DN through CASP3. Quercetin, as one of the nine active ingredients, can be bounded to CASP3 to inhibit apoptosis in DN. PS can also take action on DN probably through many pathways. The role of PS on DN through other pathways still needs to be further elaborated.

Ginger Oil Nanoemulsion Formulation Augments Its Antiproliferative Effect in Ehrlich Solid Tumor Model

Cancer is a disease that is characterized by uncontrolled cell proliferation. Breast cancer is the most prevalent cancer among women. Ginger oil is a natural cancer fighter and anti-oxidant. However, the minimal absorption of ginger oil from the gastrointestinal tract accounts for its limited medicinal efficacy. The present study was designed to evaluate the efficacy of a nanoemulsion preparation of ginger oil on its oral bioavailability and in vivo anti-cancer efficacy. Ginger oil nanoemulsion was prepared by a high-pressure homogenization technique using different surfactants (Tween 20, 40, and 80). The prepared formulations were evaluated for droplet size, polydispersity index (PDI), zeta potential (ZP), pH, viscosity, and stability by calculating the creaming index percentage. The best formulation was evaluated for shape by TEM. The antitumor activity of the best nano-formulation was determined in comparison with the free oil using the in vivo Ehrlich solid tumor (EST) model. The prepared ginger oil nanoemulsion formulations exhibited acceptable droplet size in the range from 56.67 ± 3.10 nm to 357.17 ± 3.62 nm. A PDI of less than 0.5 indicates the homogeneity of size distribution. The oil globules possessed a negative charge ranging from -12.33 ± 1.01 to -39.33 ± 0.96 mV. The pH and viscosity were in the acceptable range. The TEM image of the best formulation appeared to be spherical with a small size. The ginger oil nanoemulsion reduced in vivo tumor volume and weight, extended animals' life span, and ameliorated liver and kidney function in EST-bearing mice. These effects were superior to using free ginger oil. Collectively, the present study demonstrated that the ginger oil nanoemulsion improved oral absorption with a subsequent enhancement of its anti-proliferative efficacy in vivo, suggesting a nano-formulation of ginger oil for better therapeutic outcomes in breast cancer patients.

Network pharmacology: a bright guiding light on the way to explore the personalized precise medication of traditional Chinese medicine

Network pharmacology can ascertain the therapeutic mechanism of drugs for treating diseases at the level of biological targets and pathways. The effective mechanism study of traditional Chinese medicine (TCM) characterized by multi-component, multi-targeted, and integrative efficacy, perfectly corresponds to the application of network pharmacology. Currently, network pharmacology has been widely utilized to clarify the mechanism of the physiological activity of TCM. In this review, we comprehensively summarize the application of network pharmacology in TCM to reveal its potential of verifying the phenotype and underlying causes of diseases, realizing the personalized and accurate application of TCM. We searched the literature using "TCM network pharmacology" and "network pharmacology" as keywords from Web of Science, PubMed, Google Scholar, as well as Chinese National Knowledge Infrastructure in the last decade. The origins, development, and application of network pharmacology are closely correlated with the study of TCM which has been applied in China for thousands of years. Network pharmacology and TCM have the same core idea and promote each other. A well-defined research strategy for network pharmacology has been utilized in several aspects of TCM research, including the elucidation of the biological basis of diseases and syndromes, the prediction of TCM targets, the screening of TCM active compounds, and the decipherment of mechanisms of TCM in treating diseases. However, several factors limit its application, such as the selection of databases and algorithms, the unstable quality of the research results, and the lack of standardization. This review aims to provide references and ideas for the research of TCM and to encourage the personalized and precise use of Chinese medicine.

CTCN: a novel credit card fraud detection method based on Conditional Tabular Generative Adversarial Networks and Temporal Convolutional Network

Credit card fraud can lead to significant financial losses for both individuals and financial institutions. In this article, we propose a novel method called CTCN, which uses Conditional Tabular Generative Adversarial Networks (CTGAN) and temporal convolutional network (TCN) for credit card fraud detection. Our approach includes an oversampling algorithm that uses CTGAN to balance the dataset, and Neighborhood Cleaning Rule (NCL) to filter out majority class samples that overlap with the minority class. We generate synthetic minority class samples that conform to the original data distribution, resulting in a balanced dataset. We then employ TCN to analyze transaction sequences and capture long-term dependencies between data, revealing potential relationships between transaction sequences, thus achieving accurate credit card fraud detection. Experiments on three public datasets demonstrate that our proposed method outperforms current machine learning and deep learning methods, as measured by recall, F1-Score, and AUC-ROC.

Investigations of the gingerol oil colon targeting pellets for the treatment of ulcerative colitis

The clinical treatment of ulcerative colitis (UC) faces great challenges due to lifetime medication. In this study, Gingerol oil was extracted and purified by the process easily scale-up and cost effective, with productivity 2.72 ± 0.38% (w/w, versus crude drugs). The quality control of gingerol oil was fully established by HPLC fingerprint with 4 common peaks identified as 6-gingerol, 8-gingerol, 6-shogaol and 10-gingerol. The similarities of 6 batches of gingerol oil are within 0.931-0.999. The protective effects of gingerol oil are equivalent to or even stronger than that of 6-gingerol on inflammation and oxidative stress of HT-29 cells induced by lipopolysaccharide and H2O2, as well as on UC in mice caused by dextran sulfate sodium salt (DSS). Our research conclusions coincide well with the holistic view of Traditional Chinese Medicine and network pharmacology. The absorption kinetics of gingerol oil were conducted using the in situ intestinal perfusion in rats and comparable absorption were achieved in the jejunum, ileum and colon segments within 2 h. Thus, gingerol oil colon targeting pellets were prepared by extrusion-spherization technique. The cumulative dissolution behaviors and mechanisms were observed and analyzed by fitting to dissolution model. Our studies provided reliable theoretical and experimental support for the gingerol oil as reliable therapeutic choice of UC.

Amelioration of ovalbumin gel properties by EGCG via protein aggregation, hydrogen, and van der Waals force

The mechanism of epigallocatechin gallate (EGCG)-modified ovalbumin gel (EMOG) was investigated. Results indicated that, with the increase of EGCG concentration from 0% to 0.05%, the opacity, hardness, and cohesiveness of EMOG increased significantly from 0.058 to 0.133, 321.0 g to 377.6 g, and 0.879 to 0.951, respectively, while the soluble protein, surface hydrophobicity, and free sulfhydryl decreased significantly by 41.74%, 28.26%, and 39.36%, respectively. Moreover, EGCG promoted the formation of dense and stable microstructures of EMOG, changed the expansion rate, and improved the stability of EMOG. Moreover, the results of silico simulation showed that EGCG would insert into ovalbumin and interact with the amino acids through van der Waals force and hydrogen bonds, leading to a compact and stable protein structure. In this paper, the mechanism of modification of ovalbumin by EGCG was elucidated at the macro and micro levels, providing insights into the action of polyphenols and proteins.

Prospects for the application of traditional Chinese medicine network pharmacology in food science research

There has always been a particular difficulty with in-depth research on the mechanisms of food nutrition and bioactivity. The main function of food is to meet the nutritional needs of the human body, rather than to exert a therapeutic effect. Its relatively modest biological activity makes it difficult to study from the perspective of general pharmacological models. With the popularity of functional foods and the concept of dietary therapy, and the development of information and multi-omics technology in food research, research into these mechanisms is moving towards a more microscopic future. Network pharmacology has accumulated nearly 20 years of research experience in traditional Chinese medicine (TCM), and there has been no shortage of work from this perspective on the medicinal functions of food. Given the similarity between the concept of 'multi-component-multi-target' properties of food and TCM, we think that network pharmacology is applicable to the study of the complex mechanisms of food. Here we review the development of network pharmacology, summarize its application to 'medicine and food homology', and propose a methodology based on food characteristics for the first time, demonstrating its feasibility for food research. © 2023 Society of Chemical Industry.

Fabrication of high strength cold-set sodium alginate/whey protein nanofiber double network hydrogels and their interaction with curcumin

Enhancing the bio-based hydrogels strength is fundamental to extend their engineering applications. In this study, high strength cold-set sodium alginate/whey protein nanofiber (SA/WPN) double network hydrogels were prepared and their interaction with curcumin (Cur) was studied. Our results indicated that the rheological and textural properties of SA/WPN double network hydrogels were enhanced with increasing WPN by forming SA-COO^--Ca²⁺-^-OOC-WPN bridge through electrostatic interactions. The storage modulus (768.2 Pa), hardness (273.3 g), adhesiveness (318.7 g·sec) and cohesiveness (0.464) of SA/WPN50 (WPN concentration of 50 mg/mL) double network hydrogels were 3.75, 2.26, 3.76 and 2.19 times higher than those of SA hydrogels, respectively. Cur was combined with SA/WPN hydrogels through hydrogen bonding, van der Waals forces and hydrophobic interactions with an encapsulation efficiency of 91.6 ± 0.8 %, and the crystalline state was changed after binding. In conclusion, SA/WPN double network hydrogels can be enhanced by the addition of WPN and have potential as carriers for hydrophobic bioactive substances.

SSELM-neg: spherical search-based extreme learning machine for drug-target interaction prediction

BACKGROUND

The experimental verification of a drug discovery process is expensive and time-consuming. Therefore, efficiently and effectively identifying drug-target interactions (DTIs) has been the focus of research. At present, many machine learning algorithms are used for predicting DTIs. The key idea is to train the classifier using an existing DTI to predict a new or unknown DTI. However, there are various challenges, such as class imbalance and the parameter optimization of many classifiers, that need to be solved before an optimal DTI model is developed.

METHODS

In this study, we propose a framework called SSELM-neg for DTI prediction, in which we use a screening approach to choose high-quality negative samples and a spherical search approach to optimize the parameters of the extreme learning machine.

RESULTS

The results demonstrated that the proposed technique outperformed other state-of-the-art methods in 10-fold cross-validation experiments in terms of the area under the receiver operating characteristic curve (0.986, 0.993, 0.988, and 0.969) and AUPR (0.982, 0.991, 0.982, and 0.946) for the enzyme dataset, G-protein coupled receptor dataset, ion channel dataset, and nuclear receptor dataset, respectively.

CONCLUSION

The screening approach produced high-quality negative samples with the same number of positive samples, which solved the class imbalance problem. We optimized an extreme learning machine using a spherical search approach to identify DTIs. Therefore, our models performed better than other state-of-the-art methods.

Mechanism-Driven and Clinically Focused Development of Botanical Foods as Multitarget Anticancer Medicine: Collective Perspectives and Insights from Preclinical Studies, IND Applications and Early-Phase Clinical Trials

Cancer progression and mortality remain challenging because of current obstacles and limitations in cancer treatment. Continuous efforts are being made to explore complementary and alternative approaches to alleviate the suffering of cancer patients. Epidemiological and nutritional studies have indicated that consuming botanical foods is linked to a lower risk of cancer incidence and/or improved cancer prognosis after diagnosis. From these observations, a variety of preclinical and clinical studies have been carried out to evaluate the potential of botanical food products as anticancer medicines. Unfortunately, many investigations have been poorly designed, and encouraging preclinical results have not been translated into clinical success. Botanical products contain a wide variety of chemicals, making them more difficult to study than traditional drugs. In this review, with the consideration of the regulatory framework of the USFDA, we share our collective experiences and lessons learned from 20 years of defining anticancer foods, focusing on the critical aspects of preclinical studies that are required for an IND application, as well as the checkpoints needed for early-phase clinical trials. We recommend a developmental pipeline that is based on mechanisms and clinical considerations.

Investigation of the Active Compounds and Important Pathways of Huaiqihuang Granule for the Treatment of Immune Thrombocytopenia Using Network Pharmacology and Molecular Docking

Materials and Methods

Compounds of HQHG were scanned by LC-MS/MS, and the target profiles of compounds were identified based on SwissTarget Prediction. ITP target proteins were collected from various databases. Then, KEGG pathway and GO enrichment analyses were performed to explore the signaling pathways related to HQHG for ITP. The PPI and drug-herbs-compounds-targets-pathways network were constructed using Cytoscape 3.7.2. Finally, Discovery studio software was used to confirm the key targets and active compounds from HQHG.

Results

A total of 187 interacting targets of 19 potentially active compounds in HQHG and 3837 ITP-related targets were collected. Then, 187 intersection targets were obtained. A total of 20 key targets including EGFR, CASP3, TNF, STAT3, and ERBB2 were identified through PPI network analysis. These targets were mainly focused on the biological processes of positive regulation of protein phosphorylation, cellular response to organonitrogen compound, and cellular response to nitrogen compound. 20 possible pathways of HQHG in the treatment of ITP were identified through KEGG enrichment. EGFR, CASP3, TNF, and STAT3 are the four most important target proteins, while adenosine, caffeic acid, ferulic acid, quercetin-3β-D-glucoside, rutin, scopoletin, and tianshic acid are the most important active compounds, which were validated by molecular docking simulation.

Conclusion

This study demonstrated that HQHG produced relief effects against ITP by regulating multitargets and multipathways with multicompounds. And the combined data provide novel insight of drug developing for ITP.

Investigation on the mechanism of Shaoyao-Gancao Decoction in the treatment of gastric carcinoma based on network pharmacology and experimental verification

BACKGROUND

Shaoyao-Gancao Decoction (SG-D) is a famous classical Chinese prescription that has been used in the treatment of numerous kinds of diseases. However, its mechanism of action in the treatment of Gastric carcinoma (GC) is not clear.

METHODS

The active ingredients and targets of SG-D were screened using network pharmacology, and GC-related targets were retrieved through several databases. The protein-protein interaction network was then further constructed and GO and KEGG enrichment analysis were performed. Subsequently, molecular docking was carried out. Finally, we validated the results of the network pharmacology by performing in vitro cell experiments on CCK-8, apoptosis, cell cycle, platelet clone formation, and Western blotting with AGS cells.

RESULTS

Three key active ingredients and 8 core targets were screened through a network pharmacological analysis, and the results of the KEGG indicated that the PI3K/Akt and MAPK signaling pathways are critical signaling pathways for SG-D to treat GC. Experimental results revealed that SG-D was able to inhibit AGS cells proliferation, induce apoptosis and arrest the cell cycle, and reduce the ability of cell clone formation by regulating the PI3K/Akt and MAPK signaling pathways.

CONCLUSIONS

Network pharmacology has shown that SG-D can act on multiple targets through multiple ingredients and treat GC by regulating multiple signaling pathways. In vitro cell experiments have also confirmed this, so as to provide a reference for subsequent related research.

Investigation of the Antibacterial, Anti-Biofilm, and Antioxidative Effect of Piper betle Leaf Extract against Bacillus gaemokensis MW067143 Isolated from Dental Caries, an In Vitro-In Silico Approach

Among oral diseases, dental caries is one of the most frequent to affect human health. The current research work aimed to ascertain the antibacterial, anti-biofilm, and antioxidative potential of Piper betle leaf extract against bacteria isolated from dental caries. Analysis for the presence of phytochemical compounds revealed compounds, such as tannins, steroids, phenolic compounds, and alkaloids, which were also confirmed by TLC and FTIR. GC-MS analysis elucidated the presence of 20 phytocompounds, among which were some well-reported bioactive compounds. The chloroform extract of P. betle demonstrated good antibacterial activity (7 mm) and minimum inhibitory concentration (MIC) (100 mg mL-1) against Bacillus gaemokensis MW067143, which was the frequent biofilm producer among isolated bacterial strains. Fractions of the extract were isolated through column chromatography, after which the antibacterial activity was again evaluated. Spirost-8-en-11-one,3-hydroxy(3β,5α,14β,20β,22β,25R), an oxosteroid in nature, was observed to exhibit remarkable antibacterial potential (12 mm) against B. gaemokensis. Bacterial cells treated with P. betle extract had elevated SOD, APOX, POX, and GR activity, while its proteolytic activity against whole bacterial proteins was pronounced with the suppression of several proteins (50, 40, 15, and 10 kDa) in SDS-PAGE. Bacterial cells treated with P. betle extract demonstrated decreased growth, while the extract was also observed to exhibit inhibition of biofilm formation (70.11%) and demolition of established B. gaemokensis biofilms (57.98%). SEM analysis revealed significant changes to bacterial morphology post treatment with P. betle, with cellular disintegration being prominent. In silico network pharmacology analysis elucidated proteins like ESR1 and IL6 to be majorly involved in biological pathways of dental caries, which also interact with the protective ability of P. betle. Gene Ontology (GO) terms and KEGG pathways were also screened using enrichment analysis. Molecular docking demonstrated the highest binding affinity of Spirost-8-en-11-one,3-hydroxy-,(3β,5α,14β,20β,22β,25R) with bacterial proteins FabI (-12 kcal/mol), MurB (-17.1 kcal/mol), and FtsZ (-14.9 kcal/mol). Therefore, it is suggested that P. betle can serve a potentially therapeutic role and could be used in the preparation of herbal formulations for managing bacterial flora.

Towards a potential pan-cancer prognostic signature for gene expression based on probesets and ensemble machine learning

Cancer is one of the leading causes of death worldwide and can be caused by environmental aspects (for example, exposure to asbestos), by human behavior (such as smoking), or by genetic factors. To understand which genes might be involved in patients’ survival, researchers have invented prognostic genetic signatures: lists of genes that can be used in scientific analyses to predict if a patient will survive or not. In this study, we joined together five different prognostic signatures, each of them related to a specific cancer type, to generate a unique pan-cancer prognostic signature, that contains 207 unique probesets related to 187 unique gene symbols, with one particular probeset present in two cancer type-specific signatures (203072_at related to the MYO1E gene). We applied our proposed pan-cancer signature with the Random Forests machine learning method to 57 microarray gene expression datasets of 12 different cancer types, and analyzed the results. We also compared the performance of our pan-cancer signature with the performances of two alternative prognostic signatures, and with the performances of each cancer type-specific signature on their corresponding cancer type-specific datasets. Our results confirmed the effectiveness of our prognostic pan-cancer signature. Moreover, we performed a pathway enrichment analysis, which indicated an association between the signature genes and a protein-protein interaction analysis, that highlighted PIK3R2 and FN1 as key genes having a fundamental relevance in our signature, suggesting an important role in pan-cancer prognosis for both of them.

In Vitro, Molecular Docking and In Silico ADME/Tox Studies of Emodin and Chrysophanol against Human Colorectal and Cervical Carcinoma

Anthraquinones (AQs) are present in foods, dietary supplements, pharmaceuticals, and traditional treatments and have a wide spectrum of pharmacological activities. In the search for anti-cancer drugs, AQ derivatives are an important class. In this study, anthraquinone aglycons chrysophanol (Chr), emodin (EM) and FDA-approved anticancer drug fluorouracil were analyzed by molecular docking studies against receptor molecules caspase-3, apoptosis regulator Bcl-2, TRAF2 and NCK-interacting protein kinase (TNIK) and cyclin-dependent protein kinase 2 (CDK2) as novel candidates for future anticancer therapeutic development. The ADMET SAR database was used to predict the toxicity profile and pharmacokinetics of the Chr and EM. Furthermore, in silico results were validated by the in vitro anticancer activity against HCT-116 and HeLa cell lines to determine the anticancer effect. According to the docking studies simulated by the docking program AutoDock Vina 4.0, Chr and EM had good binding energies against the target proteins. It has been observed that Chr and EM show stronger molecular interaction than that of the FDA-approved anticancer drug fluorouracil. In the in vitro results, Chr and EM demonstrated promising anticancer activity in HCT-116 and HeLa cells. These findings lay the groundwork for the potential use of Chr and EM in the treatment of human colorectal and cervical carcinomas.

Mechanism of Glycitein in the Treatment of Colon Cancer Based on Network Pharmacology and Molecular Docking

INTRODUCTION

The prevalence of colon cancer remains high across the world. The early diagnosis of colon cancer is challenging. Moreover, patients with colon cancer frequently suffer from poor prognoses.

METHODS

Differentially expressed genes (DEGs) in colon cancer were acquired based on TCGA-COAD dataset screening. DEGs were input into the Connectivity Map (CMap) database to screen small molecule compounds with the potential to reverse colon cancer pathological function. Glycitein ranked first among the screened small-molecule compounds. We downloaded the main targets of glycitein from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database and constructed protein-protein interaction (PPI) networks of those which were closely related to targets by the Search Tool for the Retrieval of Interaction Gene/Proteins (STRING). Five potential targets of glycitein for treating colon cancer were identified (CCNA2, ESR1, ESR2, MAPK14, and PTGS2). These targets were used as seeds for random walk with restart (RWR) analysis of PPI networks. Then, the interaction network of glycitein-colon cancer-related genes was constructed based on the top 50 genes in affinity coefficients. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted on the potential genes targeted by glycitein in colon cancer treatment and those that were closely bound up with targets.

RESULTS

GO analysis demonstrated that the enrichment of these genes was primarily discovered in biological functions including regulation of fibroblast proliferation, response to oxygen levels, and epithelial cell proliferation. The KEGG analysis results illustrated that the signaling pathways where these genes were mostly involved consisted of the mitogen-activated protein kinase signaling pathway, the phosphatidylinositol-3-kinase-Akt signaling pathway, and the p53 signaling pathway. Finally, stable binding of glycitein to five potential targets in colon cancer was verified by molecular docking.

CONCLUSION

This study elucidated the key targets and main pathways of glycitein on the basis of network pharmacology and preliminarily analyzed molecular mechanisms in the treatment of colon cancer. A scientific basis is provided for glycitein application in treating colon cancer.

Identification and Verification of Potential Biomarkers in Gastric Cancer By Integrated Bioinformatic Analysis

Background: Gastric cancer (GC) is a common cancer with high mortality. This study aimed to identify its differentially expressed genes (DEGs) using bioinformatics methods.

Methods: DEGs were screened from four GEO (Gene Expression Omnibus) gene expression profiles. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. A protein–protein interaction (PPI) network was constructed. Expression and prognosis were assessed. Meta-analysis was conducted to further validate prognosis. The receiver operating characteristic curve (ROC) was analyzed to identify diagnostic markers, and a nomogram was developed. Exploration of drugs and immune cell infiltration analysis were conducted.

Results: Nine up-regulated and three down-regulated hub genes were identified, with close relations to gastric functions, extracellular activities, and structures. Overexpressed Collagen Type VIII Alpha 1 Chain (COL8A1), Collagen Type X Alpha 1 Chain (COL10A1), Collagen Triple Helix Repeat Containing 1 (CTHRC1), and Fibroblast Activation Protein (FAP) correlated with poor prognosis. The area under the curve (AUC) of ADAM Metallopeptidase With Thrombospondin Type 1 Motif 2 (ADAMTS2), COL10A1, Collagen Type XI Alpha 1 Chain (COL11A1), and CTHRC1 was >0.9. A nomogram model based on CTHRC1 was developed. Infiltration of macrophages, neutrophils, and dendritic cells positively correlated with COL8A1, COL10A1, CTHRC1, and FAP. Meta-analysis confirmed poor prognosis of overexpressed CTHRC1.

Conclusion: ADAMTS2, COL10A1, COL11A1, and CTHRC1 have diagnostic values in GC. COL8A1, COL10A1, CTHRC1, and FAP correlated with worse prognosis, showing prognostic and therapeutic values. The immune cell infiltration needs further investigations.

Syringin exerts anti-breast cancer effects through PI3K-AKT and EGFR-RAS-RAF pathways

Background

Breast cancer (BC) is one of the most common malignant tumors with the highest mortality in the world. Modern pharmacological studies have shown that Syringin has an inhibitory effect on many tumors, but its anti-BC efficacy and mechanism are still unclear.

Methods

First, Syringin was isolated from Acanthopanax senticosus (Rupr. & Maxim.) Harms (ASH) by systematic solvent extraction and silica gel chromatography column. The plant name is composed of genus epithet, species additive words and the persons’ name who give its name. Then, the hub targets of Syringin against BC were revealed by bioinformatics. To provide a more experimental basis for later research, the hub genes which could be candidate biomarkers of BC and a ceRNA network related to them were obtained. And the potential mechanism of Syringin against BC was proved in vitro experiments.

Results

Syringin was obtained by liquid chromatography-mass spectrometry (LC–MS), nuclear magnetic resonance (NMR), and high-performance liquid chromatography (HPLC). Bioinformatics results showed that MAP2K1, PIK3CA, HRAS, EGFR, Caspase3, and PTGS2 were the hub targets of Syringin against BC. And PIK3CA and HRAS were related to the survival and prognosis of BC patients, the PIK3CA-hsa-mir-139-5p-LINC01278 and PIK3CA-hsa-mir-375 pathways might be closely related to the mechanism of Syringin against BC. In vitro experiments confirmed that Syringin inhibited the proliferation and migration and promoted apoptosis of BC cells through the above hub targets.

Conclusions

Syringin against BC via PI3K-AKT-PTGS2 and EGFR-RAS-RAF-MEK-ERK pathways, and PIK3CA and HRAS are hub genes for adjuvant treatment of BC.

Graphical Abstract

Deep neural network trained on gigapixel images improves lymph node metastasis detection in clinical settings

The pathological identification of lymph node (LN) metastasis is demanding and tedious. Although convolutional neural networks (CNNs) possess considerable potential in improving the process, the ultrahigh-resolution of whole slide images hinders the development of a clinically applicable solution. We design an artificial-intelligence-assisted LN assessment workflow to facilitate the routine counting of metastatic LNs. Unlike previous patch-based approaches, our proposed method trains CNNs by using 5-gigapixel images, obviating the need for lesion-level annotations. Trained on 5907 LN images, our algorithm identifies metastatic LNs in gastric cancer with a slide-level area under the receiver operating characteristic curve (AUC) of 0.9936. Clinical experiments reveal that the workflow significantly improves the sensitivity of micrometastasis identification (81.94% to 95.83%, P < .001) and isolated tumor cells (67.95% to 96.15%, P < .001) in a significantly shorter review time (−31.5%, P < .001). Cross-site evaluation indicates that the algorithm is highly robust (AUC = 0.9829).

The pathological identification of lymph node metastasis in whole-slide images is demanding and tedious. Here, the authors design an artificial-intelligence-assisted assessment workflow to facilitate the routine counting of metastatic LNs.

Targeting protein tyrosine phosphatase 1B in obesity-associated colon cancer: Possible role of sweet potato (Ipomoea batatas)

Protein tyrosine phosphatase 1B (PTP1B) has emerged as one of the links between obesity and colon cancer (CC). Anti-obesity and anti-CC attributes of sweet potato (Ipomoea batatas) reported sparsely. Here, we aimed to study the potential of PTP1B as a target in CC, particularly in obese population. Expression and genomic alteration frequency of PTPN1 (PTP1B) were checked in CC. Interacting partners of PTP1B through STRING and hub genes through Cytoscape (MCODE) were identified. Hub genes were subjected to functional enrichment analyses (via Metascape), differential gene expression, copy number variation, and single nucleotide variation analyses (GSCA database). Cancer-related pathways and associated immune infiltrates of the hub genes were checked too. Eleven sweet potato-derived compounds selected through drug likeness (DL) and toxicity filters were explored via molecular docking (AutoDock Vina) to reveal the interactions with PTP1B. Genomic alteration frequency of the PTPN1 was highest in CC compared to all the other TCGA cancers, and a high expression (RNA and protein) is also observed in CC that correlated well to a poor overall survival (OS). Furthermore, PTP1B and related proteins were enriched in different biological processes and signaling pathways related to carcinogenesis including epithelial-mesenchymal transition. Overall, PTP1B identified as a potential target in obesity-linked CC and sweet potato might exert its protective action by targeting the PTP1B. Sweet potato compounds (e.g., pelargonidin and luteolin) interacted with the catalytic P loop and the WPD loop of the PTP1B. Furthermore, MD simulation study ascertained that luteolin has the highest affinity against the PTP1B, whereas pelargonidin and quercetin showed good binding affinity too, thus can be explored further.

Diagnostic Value, Prognostic Value, and Immune Infiltration of LOX Family Members in Liver Cancer: Bioinformatic Analysis

Background

Liver cancer (LC) is well known for its prevalence as well as its poor prognosis. The aberrant expression of lysyl oxidase (LOX) family is associated with liver cancer, but their function and prognostic value in LC remain largely unclear. This study aimed to explore the function and prognostic value of LOX family in LC through bioinformatics analysis and meta-analysis.

Results

The expression levels of all LOX family members were significantly increased in LC. Area under the receiver operating characteristic curve (AUC) of LOXL2 was 0.946 with positive predictive value (PPV) of 0.994. LOX and LOXL3 were correlated with worse prognosis. Meta-analysis also validated effect of LOX on prognosis. Nomogram of these two genes and other predictors was also plotted. There was insufficient data from original studies to conduct meta-analysis on LOXL3. The functions of LOX family members in LC were mostly involved in extracellular and functions and structures. The expressions of LOX family members strongly correlated with various immune infiltrating cells and immunomodulators in LC.

Conclusions

For LC patients, LOXL2 may be a potential diagnostic biomarker, while LOX and LOXL3 have potential prognostic and therapeutic values. Positive correlation between LOX family and infiltration of various immune cells and immunomodulators suggests the need for exploration of their roles in the tumor microenvironment and for potential immunotherapeutic to target LOX family proteins.

Isolation and identification of the components in Cybister chinensis Motschulsky against inflammation and their mechanisms of action based on network pharmacology and molecular docking

ETHNOPHARMACOLOGICAL RELEVANCE

Cybister chinensis Motschulsky belongs to the family Dytiscidae. As a traditional Chinese medicine, the insect is called Longshi in the folk and is commonly used to treat enuresis in children and frequent urination in the elderly.

AIM OF THE STUDY

Inflammation is involved in chronic kidney disease. The previous study proved ethanol extract of C. chinensis exhibited anti-inflammation effects in the Doxorubicin-induced kidney disease. However, the material basis and their possible mechanism of the insect were still unclear. Thus, we aimed to separate the active compounds of the ethanol extract from C. chinensis and to investigate their possible mechanism of anti-inflammation by network pharmacology and molecular docking.

MATERIALS AND METHODS

The insect was extracted with 75% ethanol to produce ethanol extracts and then were extracted by petroleum ether, ethyl acetate and n-butanol respectively. Silica gel column chromatography and preparative HPLC were applied to separate the compounds of the extract. The compounds were characterized and identified by NMR and mass. The compound associated genes were collected by BATMAN-TCM database and the inflammation associated genes were obtained through DigSee database. The protein-protein interaction (PPI) network was carried out via Search Tool for the Retrieval of Interacting Genes/Protein (STRING) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) target pathway analysis was performed in Database for Annotation, Visualization and Integrated Discovery (DAVID). The possible mechanism of compounds against inflammation was investigated by molecular docking. Finally, the anti-inflammatory effect of the representative compound was verified by the LPS-induced Raw 264.7 cell inflammatory model. TNF-α, IL-1β and IL-6 of the cell supernatants were analyzed via using ELISA kits and the key proteins in JAK2/STAT3 signaling pathway were verified via the Western blot assays.

RESULTS

Among crude extracts from C. chinensis, ethyl acetate extract showed the obvious anti-inflammatory effects. Nine compounds were isolated from ethyl acetate extract of Cybister chinensis for the first time, including benzoic acid (1), hydroxytyrosol (2), protocatechualdehyde (3), N-[2-(4-hydroxyphenyl)ethyl]acetamide (4), (2E)-3-phenylprop-2-enoic acid (5), 3-phenylpropionic acid (6), methyl 3,4-dihydroxybenzoate (7), 1,4-diphenyl butane-2,3-diol (8) and p-N,N-dimethylaminobenzaldehyde (9). After searching in the database, 1079 compound associated genes and 467 inflammation associated genes were found. The 137 common targets covered 77 signaling pathways, in which HIF-1 signaling pathway, TNF signaling pathway, influenza A, PI3K/Akt signaling pathway, NOD-like receptor signaling pathway, MAPK signaling pathway, Toll-like receptor signaling pathway and Jak-STAT signaling pathway were important for inflammation. Molecular docking studies showed compound 1, 4, 5, 6, 7 and 8 were the potential inhibitors of JAK2 protein. In addition, the in vitro test showed compound 5 reduced the expression of tumor necrosis factor (TNF)-α, interleukin (IL)-6 and IL-1β in lipopolysaccharide (LPS)-stimulated RAW264.7 cells in a dose-dependent manner. Furthermore, it was found that compound 5 inhibited the expression of p-JAK2 and p-STAT3 in LPS-induced RAW264.7 cells in a dose-dependent manner.

CONCLUSIONS

Based on the network pharmacology and molecular docking, the study suggested that C. chinensis could relieve the inflammation based on the multi-compounds and multi-pathways, which provided the foundation for the medicinal application of C. chinensis.

Diabetes mellitus risk prediction in the presence of class imbalance using flexible machine learning methods

Background

Early detection and prediction of type two diabetes mellitus incidence by baseline measurements could reduce associated complications in the future. The low incidence rate of diabetes in comparison with non-diabetes makes accurate prediction of minority diabetes class more challenging.

Methods

Deep neural network (DNN), extremely gradient boosting (XGBoost), and random forest (RF) performance is compared in predicting minority diabetes class in Tehran Lipid and Glucose Study (TLGS) cohort data. The impact of changing threshold, cost-sensitive learning, over and under-sampling strategies as solutions to class imbalance have been compared in improving algorithms performance.

Results

DNN with the highest accuracy in predicting diabetes, 54.8%, outperformed XGBoost and RF in terms of AUROC, g-mean, and f1-measure in original imbalanced data. Changing threshold based on the maximum of f1-measure improved performance in g-mean, and f1-measure in three algorithms. Repeated edited nearest neighbors (RENN) under-sampling in DNN and cost-sensitive learning in tree-based algorithms were the best solutions to tackle the imbalance issue. RENN increased ROC and Precision-Recall AUCs, g-mean and f1-measure from 0.857, 0.603, 0.713, 0.575 to 0.862, 0.608, 0.773, 0.583, respectively in DNN. Weighing improved g-mean and f1-measure from 0.667, 0.554 to 0.776, 0.588 in XGBoost, and from 0.659, 0.543 to 0.775, 0.566 in RF, respectively. Also, ROC and Precision-Recall AUCs in RF increased from 0.840, 0.578 to 0.846, 0.591, respectively.

Conclusion

G-mean experienced the most increase by all imbalance solutions. Weighing and changing threshold as efficient strategies, in comparison with resampling methods are faster solutions to handle class imbalance. Among sampling strategies, under-sampling methods had better performance than others.

Implementation of System Pharmacology and Molecular Docking Approaches to Explore Active Compounds and Mechanism of Ocimum Sanctum against Tuberculosis

Worldwide, Tuberculosis (TB) is caused by Mycobacterium tuberculosis bacteria. Ocimum sanctum, commonly known as holy basil (Tulsi), is an herbaceous perennial that belongs to the family Lamiaceae and is considered one of the most important sources of medicine and drugs for the treatment of various diseases. The presented study aims to discover the potential phenomenon of Ocimum sanctum in the medicament of tuberculosis using a network pharmacology approach. Active ingredients of Ocimum sanctum were fetched through two different databases and from literature review and then targets of these compounds were harvested by SwissTargetPrediction. Potential targets of TB were downloaded from GeneCards and DisGNet databases. After screening of mutual targets, enrichment analysis through DAVID was performed. Protein–protein interaction was performed using the String database and visualized by Cytoscape. Then the target-compound-pathway network was constructed with Cytoscape. In the end, molecular docking was performed to get the potential active ingredients against tuberculosis. Eight active ingredients with 776 potential therapeutic targets were obtained from O. sanctum, 632 intersected targets from two databases were found in TB, 72 common potential targets were found from TB and O. sanctum. The topological analysis exposes those ten targets that formed the core PPI network. Furthermore, molecular docking analysis reveals that active compounds have the greater binding ability with the potential target to suppress TB.

Pharmacological Effects of Cisplatin Combination with Natural Products in Cancer Chemotherapy

Cisplatin and other platinum-based drugs, such as carboplatin, ormaplatin, and oxaliplatin, have been widely used to treat a multitude of human cancers. However, a considerable proportion of patients often relapse due to drug resistance and/or toxicity to multiple organs including the liver, kidneys, gastrointestinal tract, and the cardiovascular, hematologic, and nervous systems. In this study, we sought to provide a comprehensive review of the current state of the science highlighting the use of cisplatin in cancer therapy, with a special emphasis on its molecular mechanisms of action, and treatment modalities including the combination therapy with natural products. Hence, we searched the literature using various scientific databases., such as MEDLINE, PubMed, Google Scholar, and relevant sources, to collect and review relevant publications on cisplatin, natural products, combination therapy, uses in cancer treatment, modes of action, and therapeutic strategies. Our search results revealed that new strategic approaches for cancer treatment, including the combination therapy of cisplatin and natural products, have been evaluated with some degree of success. Scientific evidence from both in vitro and in vivo studies demonstrates that many medicinal plants contain bioactive compounds that are promising candidates for the treatment of human diseases, and therefore represent an excellent source for drug discovery. In preclinical studies, it has been demonstrated that natural products not only enhance the therapeutic activity of cisplatin but also attenuate its chemotherapy-induced toxicity. Many experimental studies have also reported that natural products exert their therapeutic action by triggering apoptosis through modulation of mitogen-activated protein kinase (MAPK) and p53 signal transduction pathways and enhancement of cisplatin chemosensitivity. Furthermore, natural products protect against cisplatin-induced organ toxicity by modulating several gene transcription factors and inducing cell death through apoptosis and/or necrosis. In addition, formulations of cisplatin with polymeric, lipid, inorganic, and carbon-based nano-drug delivery systems have been found to delay drug release, prolong half-life, and reduce systemic toxicity while other formulations, such as nanocapsules, nanogels, and hydrogels, have been reported to enhance cell penetration, target cancer cells, and inhibit tumor progression.

Using the Isalos platform to develop a (Q)SAR model that predicts metal oxide toxicity utilizing facet-based electronic, image analysis-based, and periodic table derived properties as descriptors

Engineered nanoparticles (NPs) are being studied for their potential to harm humans and the environment. Biological activity, toxicity, physicochemical properties, fate, and transport of NPs must all be evaluated and/or predicted. In this work, we explored the influence of metal oxide nanoparticle facets on their toxicity towards bronchial epithelial (BEAS-2B), Murine myeloid (RAW 264.7), and E. coli cell lines. To estimate the toxicity of metal oxide nanoparticles grown to a low facet index, a quantitative structure–activity relationship ((Q)SAR) approach was used. The novel model employs theoretical (density functional theory calculations) and experimental studies (transmission electron microscopy images from which several particle descriptors are extracted and toxicity data extracted from the literature) to investigate the properties of faceted metal oxides, which are then utilized to construct a toxicity model. The classification mode of the k-nearest neighbour algorithm (EnaloskNN, Enalos Chem/Nanoinformatics) was used to create the presented model for metal oxide cytotoxicity. Four descriptors were identified as significant: core size, chemical potential, enthalpy of formation, and electronegativity count of metal oxides. The relationship between these descriptors and metal oxide facets is discussed to provide insights into the relative toxicities of the nanoparticle. The model and the underpinning dataset are freely available on the NanoSolveIT project cloud platform and the NanoPharos database, respectively.

Integrating bioinformatics with pharmacological evaluation for illustrating the action mechanism of herbal formula Jiao'e mixture in suppressing lung carcinoma

ETHNOPHARMACOLOGICAL RELEVANCE

Lung carcinoma (LC) is not only a kind of disease that seriously threatens human life but also an intractable problem in modern medicine. Jiao'e Mixture (JEM) is an innovative Chinese medicine formula with Chinese patent, which is composed of two herbal extracts with a specific ratio-zedoary turmeric oil and medicinal Zanthoxylum bungeanum Maxim(Z. bungeanum Maxim) seeds oil (ZMSO). Zedoary turmeric oil is extracted from dried rhizomes of Curcuma wenyujin Y.H.Chen et C. Ling, which has been reported have an anti-cancer effects. Medicinal ZMSO is a by-product of Z. bungeanum Maxim, refined from kernel shell separation, modern cold soaking and refining technology; JEM is used to treat Lung carcinoma (LC) patients in folk for many years. However, its therapeutic mechanisms for treating LC have not been fully explored.

AIM OF THE STUDY

The purpose of this study was to explore the therapeutic mechanisms of JEM for treating LC.

MATERIALS AND METHODS

The action mechanism of JEM in LC treatment was analysed by comprehensive network pharmacology approach combined with experimental validation (in vivo and in vitro).

RESULTS

Seventeen active compounds and 457 related targets were collected from the HERB, TCMSP, and Swiss Target Prediction platforms. Nine hundred and thirty-eight LC related targets were obtained from Gene Cards and OMIM databases. Finally, 140 overlapping targets were obtained, which representing the target of JEM in LC treatment. The pathway analysis showed that PI3K-AKT could be a potential pathway for JEM in LC treatment. In vivo results presented that JEM had a good effect in inhibiting the growth of LC tumour cells with high efficacy and low toxicity. In vitro experiments validated that JEM had inhibited LC cells' proliferation, migration and invasion, and had induced cell apoptosis mainly via PI3K/Akt signalling pathways.

CONCLUSION

The anti-LC activity of JEM might via regulating the PI3K-AKT signalling pathways.This study may provide further evidence for the potential use of JEM in LC treatment.

Mechanism of allergic rhinitis treated by Centipeda minima from different geographic areas

CONTEXT

The coriander plant Centipeda minima (L.) A. Braun et Aschers (Compositae) is used for the treatment of allergic rhinitis.

OBJECTIVE

Analyze the difference of the C. minima volatile oil from 7 geographic areas and its therapeutic effect on allergic rhinitis.

MATERIALS AND METHODS

The volatile oils from different geographic areas were extracted and analyzed, the protein and biological pathway for the treatment of allergic rhinitis were predicted by network pharmacology. Established three groups of Sprague-Dawley rat allergic rhinitis models (n = 10). The treatment group was given 100 μL/nostril of 0.1% C. minima volatile oil, the blank and model groups were given the same amount of normal saline. After 15 days, serum inflammatory factors were detected by ELISA. Nasal mucosa tissues were examined by hematoxylineosin staining and immunuhistrochemistry.

RESULTS

There are differences in the content of volatile oil in the seven geographic areas. Experiments showed that the concentration of TNF-α in the serum of the administration group decreased from 63.66 ± 2.06 to 51.01 ± 4.10 (pg/mL), IL-4 decreased from 41.90 ± 3.90 to 28.68 ± 3.39 (pg/mL), IgE decreased from 22.18 ± 1.40 to 17.59 ± 1.60 (pg/mL), IL-2 increased from 314.14 ± 10.32 to 355.90 ± 10.01(pg/mL). Immunohistochemistry showed that compared with the model group, the PTGS2 and MAPK3 proteins in the administration group were significantly reduced.

DISCUSSION AND CONCLUSIONS

C. minima volatile oil is a multi-target and multi-pathway in the treatment of allergic rhinitis, which provides a new research basis and reference for the treatment of allergic rhinitis.

Identification of the active compounds and their mechanisms of medicinal and edible Shanzha based on network pharmacology and molecular docking

Shanzha (Crataegus pinnatifida Bunge), an edible traditional Chinese medicine (TCM), has an effect on dyspepsia. However, the investigations of the pharmacological effects have not been carried out. This study aimed to identify the potential targets and pharmacological mechanisms of Shanzha in the treatment of dyspepsia by network pharmacology and molecular docking. Five active compounds and 13 key targets were obtained by a set of bioinformatics assays. Vitexin 7-glucoside, suchilactone, and 20-hexadecanoylingenol were the main compounds acting on dyspepsia. The key targets were prostaglandin-endoperoxide synthase 2 (PTGS2), serine/threonine-protein kinase mTOR (MTOR), heat shock protein HSP 90-alpha (HSP90AA1), mitogen-activated protein kinase 1 (MAPK1), MAPK3, E3 ubiquitin-protein ligase Mdm2 (MDM2), receptor tyrosine-protein kinase erbB-2 (ERBB2), caspase-3 (CASP3), matrix metalloproteinase-9 (MMP9), estrogen receptor (ESR1), tumor necrosis factor (TNF), phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform (PIK3CA), and peroxisome proliferator-activated receptor gamma (PPARG), which played the vital roles in TNF, prostate cancer, thyroid hormone, hepatitis B and estrogen signaling pathway. The molecular mechanisms of Shanzha regulating dyspepsia were mainly related to reduction of inflammatory response, controlling cell proliferation and survival, increasing intestinal moisture, and promoting intestinal motility. PRACTICAL APPLICATIONS: Shanzha has been used as an edible TCM to improve digestion for a long time. However, the ingredients and mechanisms of Shanzha in the treatment of dyspepsia are not clear. In this research, network pharmacological analysis integrated with molecular docking was conducted to investigate the molecular mechanism. The results suggested that the core targets alleviated dyspepsia by reducing the intestinal inflammatory response, increasing intestinal movement, controlling cell physiological activities, and reducing constipation. In summary, this study demonstrated the multiple compounds, targets, and pathways characteristics of Shanzha in the treatment of dyspepsia, which may provide guidance and foundations for further application of edible medicine.