scholarly journals Summarizing the Effective Herbs for the Treatment of Hypertensive Nephropathy by Complex Network and Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jia-Ming Huan ◽  
Wen-Ge Su ◽  
Wei Li ◽  
Chao Gao ◽  
Peng Zhou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Signaling Pathways ◽  
Complex Network ◽  
Renal Damage ◽  
Clustering Algorithm ◽  
Mapk Signaling ◽  
Prescription Data ◽  
Hypertensive Nephropathy

Hypertensive nephropathy is a common complication of hypertension. Traditional Chinese medicine has been used in the clinical treatment of hypertensive nephropathy for a long time, but the commonly used prescriptions have not been summarized, and the basic therapeutic approaches have not been discussed. Based on data from 3 years of electronic medical records of traditional Chinese medicine used at the Affiliated Hospital of Shandong University of Traditional Chinese Medicine, a complex network and machine learning algorithm was used to explore the prescribed herbs of traditional Chinese medicine in the treatment of hypertensive nephropathy (HN). In this study, complex network algorithms were used to describe traditional Chinese medicine prescriptions for HN treatment. The Apriori algorithm was used to analyze the compatibility of these treatments with modern medicine. Data on the targets and regulatory genes related to hypertensive nephropathy and the herbs that affect their expression were obtained from public databases, and then, the signaling pathways enriched with these genes were identified on the basis of their participation in biological processes. A clustering algorithm was used to analyze the therapeutic pathways at multiple levels. A total of 1499 prescriptions of traditional Chinese medicines used for the treatment of hypertensive renal damage were identified. Fourteen herbs used to treat hypertensive nephropathy act through different biological pathways: huangqi, danshen, dangshen, fuling, baizhu, danggui, chenpi, banxia, gancao, qumai, cheqianzi, ezhu, qianshi, and niuxi. We found the formulae of these herbs and observed that they could downregulate the expression of inflammatory cytokines such as TNF, IL1B, and IL6 and the NF-κB and MAPK signaling pathways to reduce the renal inflammatory damage caused by excessive activation of RAAS. In addition, these herbs could facilitate the deceleration in the decline of renal function and relieve the symptoms of hypertensive nephropathy. In this study, the traditional Chinese medicine approach for treating hypertensive renal damage is summarized and effective treatment prescriptions were identified and analyzed. Data mining technology provided a feasible method for the collation and extraction of traditional Chinese medicine prescription data and provided an objective and reliable tool for use in determining the TCM treatments of hypertensive nephropathy.

A network pharmacology approach to explore the mechanisms of action of Epimrdii Herba-Coicis Semen for treatment of Alzheimer's disease

2020 ◽  
Author(s):  
Yuxuan Zhou
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Signaling Pathways ◽  
Mapk Signaling ◽  
Venn Diagram ◽  
Network Pharmacology ◽  
Pharmacokinetic Parameters ◽  
Neuron Death

Abstract Background: Traditional Chinese medicine (TCM) can treat diseases through its “multi-component, multi-target, multi-pathway” mechanisms. Especially have advantages in the treatment of diseases with complicated pathogenesis, such as Alzheimer’s disease (AD). Tonifying the kidney and strengthening the spleen is one of the common methods of Chinese Medicine to treat AD. The TCM combination of Epimrdii Herba and Coicis Semen can be used as the main drugs of a prescription for tonifying the kidney and strengthening the spleen. However, the mechanisms for Epimrdii Herba-Coicis Semen (EH-CS) to treat AD is vague. The purpose of this study was to explore the mechanisms of EH-CS on AD using a network pharmacological method.Methods: We retrieved the chemical compounds and targets of Epimrdii Herba-Coicis Semen from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). We screened the active ingredients based on the pharmacokinetic parameters (ADME). The Human Gene Database (GeenCards) was used to obtain disease targets of Alzheimer’s disease. Then we drew a venn diagram to obtain common targets of Chinese medicine and disease. Based on the topological properties, we screened the key targets. The protein-protein interaction (PPI) network was constructed using the STRING database, and the "Traditional Chinese Medicine-active ingredient-target" network was constructed using Cytoscape software. The key targets were respectively uploaded to the Metascape and DAVID database for GO and KEGG pathway analysis.Results: We obtained 31 active compounds for EH-CS. Flavonoids play important roles in the treatment of AD. A total of 29 key targets, including AKT1, MAPK1, and TP53, etc. The biological processes involve response to lipopolysaccharide, neuron death, neuroinflammatory response, etc. The main pathways include TNF signaling pathways, MAPK signaling pathways, PI3K-Akt signaling pathways and other signaling pathways.Conclusion: The network pharmacology method is an effective tool for exploring the mechanisms of TCM. Based on network pharmacology, this study systematically explained the potential mechanisms of EH-CS on AD. It provides a valuable reference for the development of AD drugs.

scholarly journals Exploring the mechanism of (-)-Epicatechin on premature ovarian insufficiency based on network pharmacology and experimental evaluation

2021 ◽  
Vol 41 (2) ◽  
Author(s):  
Fei Yan ◽  
Qi Zhao ◽  
Huanpeng Gao ◽  
Xiaomei Wang ◽  
Ke Xu ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Signaling Pathways ◽  
Signaling Pathway ◽  
Mapk Signaling ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Related Factor ◽  
Nrf2 Signaling Pathway

Abstract Methods: Relevant potential targets for EC were obtained based on Traditional Chinese Medicine System Pharmacology Database (TCMSP), a bioinformatics analysis tool for molecular mechanism of Traditional Chinese Medicine (BATMAN-TCM) and STITCH databases. The Online Mendelian Inheritance in Man (OMIM) and GeneCards databases were utilized to screen the known POI-related targets, while Cytoscape software was used for network construction and visualization. Then, the Gene Ontology (GO) and pathway enrichment analysis were carried out by the Database for Annotation, Visualization and Integrated Discovery (DAVID) database. Furthermore, KGN cells were performed to validate the predicted results in oxidative stress (OS) model, and antioxidant effect was examined. Results: A total of 70 potential common targets for EC in the treatment of POI were obtained through network pharmacology. Metabolic process, response to stimulus and antioxidant activity occupied a leading position of Gene Ontology (GO) enrichment. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that PI3K/protein kinase B (AKT), TNF, estrogen, VEGF and MAPK signaling pathways were significantly enriched. In addition, cell experiments showed that EC exhibited antioxidant effects in an H2O2-mediated OS model in ovarian granulosa cells by regulating the expression of PI3K/AKT/nuclear factor erythroid 2-related factor 2 (Nrf2) signaling pathway and multiple downstream antioxidant enzymes. Conclusion: EC could regulate multiple signaling pathways and several biological processes (BPs). EC had the ability to down-regulate elevated OS level through the PI3K/AKT/Nrf2 signaling pathway and represented a potential novel treatment for POI.

scholarly journals Theoretical Study of the anti-NCP Molecular Mechanism of Traditional Chinese Medicine Lianhua-Qingwen Formula (LQF)

2020 ◽  
Author(s):  
Chenghao Ye ◽  
Meina Gao ◽  
Wangqiang Lin ◽  
Kunqian Yu ◽  
Peng Li ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Molecular Mechanism ◽  
Hydrophobic Interactions ◽  
Killer Cell ◽  
Cell Receptor ◽  
The United States ◽  
Mapk Signaling ◽  
Binding Modes ◽  
Pathway Network

<div>Due to the good clinical efficacy in treating Novel Coronavirus Pneumonia (NCP) resulted from </div><div> </div><div>SARS-CoV-2, as the traditional Chinese medicine(TCM) prescription, Lianhua Qingwen Formula </div><div>(LQF) was composed into the Diagnosis and Treatment Programs of 2019 New Coronavirus<br></div><div> </div><div>Pneumonia (from fourth to seventh editions) formulated by the National Health Commission of China. </div><div> </div><div>Aiming to prevent and treat viral influenza, LQF was patented from 2003 in China, and passed the </div><div> </div><div>Phase II clinical trial by FDA in the United States in 2015. However, the molecular mechanism of LQF </div><div> </div><div>anti SARS-CoV-2 pneumonia is still not clear. It is shown that the docking scores of three components </div><div> </div><div>in LQF including Rutin, Forsythoside E, and Hyperoside to main protease of SARS-CoV-2 are very </div><div>large as -9.1, -9.0 and -8.7 kcal/mol, respectively, which are even better than those of Lopinavir at -7.3<br></div><div> </div><div>kcal/mol. Importantly, the binding modes between active compounds and protein were verified via </div><div> </div><div>molecular dynamics (MD) simulation and calculation all the binding free energies at MM-PBSA level. </div><div> </div><div>Note that these donor-acceptor systems were stabilized by non-polar interactions including hydrogen </div><div> </div><div>bonds and hydrophobic interactions. At last, from the constructed component-target-pathway network, </div><div> </div><div>it is shown that the components in LQF are related important pathways to improve the human immunity </div><div> </div><div>such as T cell, B cell receptor signaling, natural killer cell mediated cytotoxicity, as well as anti</div><div> </div><div>inflammatory pathways including Fc epsilon RI, ErbB, MAPK signaling and so on. The present </div><div> </div><div>investigation represents the first report on the molecular mechanism of LQF as NCP inhibitor</div>

scholarly journals Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Changbo Zhao ◽  
Guo-Zheng Li ◽  
Chengjun Wang ◽  
Jinling Niu
Keyword(s):  
Machine Learning ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Machine Learning Algorithms ◽  
Diagnostic Methods ◽  
Machine Learning Techniques ◽  
Syndrome Differentiation ◽  
Reference List ◽  
Patient Classification ◽  
Research Issues

As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification.

scholarly journals Evaluation and Identification of the Neuroprotective Compounds of Xiaoxuming Decoction by Machine Learning: A Novel Mode to Explore the Combination Rules in Traditional Chinese Medicine Prescription

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Shilun Yang ◽  
Yanjia Shen ◽  
Wendan Lu ◽  
Yinglin Yang ◽  
Haigang Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Cross Validation ◽  
Bayesian Models ◽  
Machine Learning Algorithms ◽  
Therapeutic Effects ◽  
Test Set ◽  
Screening Experiments ◽  
Fold Cross Validation

Xiaoxuming decoction (XXMD), a classic traditional Chinese medicine (TCM) prescription, has been used as a therapeutic in the treatment of stroke in clinical practice for over 1200 years. However, the pharmacological mechanisms of XXMD have not yet been elucidated. The purpose of this study was to develop neuroprotective models for identifying neuroprotective compounds in XXMD against hypoxia-induced and H2O2-induced brain cell damage. In this study, a phenotype-based classification method was designed by machine learning to identify neuroprotective compounds and to clarify the compatibility of XXMD components. Four different single classifiers (AB, kNN, CT, and RF) and molecular fingerprint descriptors were used to construct stacked naïve Bayesian models. Among them, the RF algorithm had a better performance with an average MCC value of 0.725±0.014 and 0.774±0.042 from 5-fold cross-validation and test set, respectively. The probability values calculated by four models were then integrated into a stacked Bayesian model. In total, two optimal models, s-NB-1-LPFP6 and s-NB-2-LPFP6, were obtained. The two validated optimal models revealed Matthews correlation coefficients (MCC) of 0.968 and 0.993 for 5-fold cross-validation and of 0.874 and 0.959 for the test set, respectively. Furthermore, the two models were used for virtual screening experiments to identify neuroprotective compounds in XXMD. Ten representative compounds with potential therapeutic effects against the two phenotypes were selected for further cell-based assays. Among the selected compounds, two compounds significantly inhibited H2O2-induced and Na2S2O4-induced neurotoxicity simultaneously. Together, our findings suggested that machine learning algorithms such as combination Bayesian models were feasible to predict neuroprotective compounds and to preliminarily demonstrate the pharmacological mechanisms of TCM.

scholarly journals A Network Pharmacology Approach to Explore the Mechanisms of Shugan Jianpi Formula in Liver Fibrosis

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Chang Fan ◽  
Fu Rong Wu ◽  
Jia Fu Zhang ◽  
Hui Jiang
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Liver Fibrosis ◽  
Signaling Pathways ◽  
Target Genes ◽  
Janus Kinase ◽  
Target Proteins ◽  
Effective Components ◽  
Search Tool ◽  
Common Target

Purpose. We explored the mechanism of Shugan Jianpi Formula (SGJPF) and its effective components for the treatment of liver fibrosis (LF). Materials and Methods. We collected the active ingredients in SGJPF through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform and screened the effective components by absorption, distribution, metabolism, and excretion. Herb-associated target proteins were predicted and screened based on the Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine and Search Tool for Interactions of Chemicals databases. LF-associated target proteins were predicted and screened based on the Online Mendelian Inheritance in Man® Database and Comparative Toxicogenomics Database. Common genes with LF and herbs were selected, and Cytoscape 3.5.1 software was used to construct an herb pathway and component-LF common target network. The Search Tool for the Retrieval of Interacting Genes/Proteins was used to build a protein-protein interaction, and quantitative PCR was used to verify the related target genes. Finally, clusterProfiler was applied for the analysis of Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways. Results. The pharmacological network contained 252 active compounds (e.g., Astragaloside A, saikosaponin, linoleic acid, and Poria acid A), 84 common target genes, and 94 significant signaling pathways. Among them, interleukin 6 (IL-6), tumor protein 53 p53 (TP53), prostaglandin-endoperoxide synthase 2 (PTGS2), AKT1, IL-1β, and the nucleotide-binding and oligomerization domain-like receptor and Janus kinase-signal transducer and activator of transcription signaling pathways were selected as the critical target gene and critical signal pathway, respectively. Conclusion. The mechanisms of SGJPF in protecting against LF include the regulation of multiple targets such as IL-6, TP53, PTGS2, and AKT1. These target proteins affect LF through various signal transduction pathways.

scholarly journals Automated Tongue Feature Extraction for ZHENG Classification in Traditional Chinese Medicine

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Ratchadaporn Kanawong ◽  
Tayo Obafemi-Ajayi ◽  
Tao Ma ◽  
Dong Xu ◽  
Shao Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Color Space ◽  
Image Features ◽  
Machine Learning Algorithms ◽  
Disease Classification ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Control Group

ZHENG, Traditional Chinese Medicine syndrome, is an integral and essential part of Traditional Chinese Medicine theory. It defines the theoretical abstraction of the symptom profiles of individual patients and thus, used as a guideline in disease classification in Chinese medicine. For example, patients suffering from gastritis may be classified as Cold or Hot ZHENG, whereas patients with different diseases may be classified under the same ZHENG. Tongue appearance is a valuable diagnostic tool for determining ZHENG in patients. In this paper, we explore new modalities for the clinical characterization of ZHENG using various supervised machine learning algorithms. We propose a novel-color-space-based feature set, which can be extracted from tongue images of clinical patients to build an automated ZHENG classification system. Given that Chinese medical practitioners usually observe the tongue color and coating to determine a ZHENG type and to diagnose different stomach disorders including gastritis, we propose using machine-learning techniques to establish the relationship between the tongue image features and ZHENG by learning through examples. The experimental results obtained over a set of 263 gastritis patients, most of whom suffering Cold Zheng or Hot ZHENG, and a control group of 48 healthy volunteers demonstrate an excellent performance of our proposed system.

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