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Article ; Online: Oxyimperatorin attenuates LPS-induced microglial activation in vitro and in vivo via suppressing NF-κB p65 signaling.

Lu, Changcheng / Huang, Chen / Qu, Shuhui / Lin, Huiyuan / Zhong, Hai-Jing / Chong, Cheong-Meng

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie

2024 Volume 173, Page(s) 116379

Abstract: Background: Microglia-mediated neuroinflammation is an important pathological feature in many neurological diseases; thus, suppressing microglial activation is considered a possible therapeutic strategy for reducing neuronal damage. Oxyimperatorin (OIMP) ...

Abstract	Background: Microglia-mediated neuroinflammation is an important pathological feature in many neurological diseases; thus, suppressing microglial activation is considered a possible therapeutic strategy for reducing neuronal damage. Oxyimperatorin (OIMP) is a member of furanocoumarin, isolated from the medicinal herb Glehnia littoralis. However, it is unknown whether OIMP can suppress the neuroinflammation. Purpose: To investigate the neuroprotective activity of oxyimperatorin (OIMP) in LPS-induced neuroinflammation in vitro and in vivo models. Methods: In vitro inflammation-related assays were performed with OIMP in LPS-induced BV-2 microglia. In addition, intraperitoneal injection of LPS-induced microglial activation in the mouse brain was used to validate the anti-neuroinflammatory activity of OIMP. Results: OIMP was found to suppress LPS-induced neuroinflammation in vitro and in vivo. OIMP significantly attenuated LPS-induced the production of free radicals, inducible nitric oxide synthase, cyclooxygenase-2, and pro-inflammatory cytokines in BV-2 microglia without causing cytotoxicity. In addition, OIMP could reduce the M1 pro-inflammatory transition in LPS-stimulated BV-2 microglia. The mechanistic study revealed that OIMP inhibited LPS-induced NF-κB p65 phosphorylation and nuclear translocation. However, OIMP did not affect LPS-induced IκB phosphorylation and degradation. In addition, OIMP also was able to reduce LPS-induced microglial activation in mice brain. Conclusion: Our findings suggest that OIMP suppresses microglia activation and attenuates the production of pro-inflammatory mediators and cytokines via inhibition of NF-κB p65 signaling.
MeSH term(s)	Animals ; Mice ; NF-kappa B/metabolism ; Microglia/metabolism ; Lipopolysaccharides/pharmacology ; Neuroinflammatory Diseases ; Cell Line ; Inflammation/chemically induced ; Inflammation/drug therapy ; Inflammation/metabolism ; Cytokines/metabolism ; Nitric Oxide Synthase Type II/metabolism ; Nitric Oxide/metabolism
Chemical Substances	NF-kappa B ; Lipopolysaccharides ; Cytokines ; Nitric Oxide Synthase Type II (EC 1.14.13.39) ; Nitric Oxide (31C4KY9ESH)
Language	English
Publishing date	2024-03-06
Publishing country	France
Document type	Journal Article
ZDB-ID	392415-4
ISSN	1950-6007 ; 0753-3322 ; 0300-0893
ISSN (online)	1950-6007
ISSN	0753-3322 ; 0300-0893
DOI	10.1016/j.biopha.2024.116379
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Article ; Online: A weighted bilinear neural collaborative filtering approach for drug repositioning.

Meng, Yajie / Lu, Changcheng / Jin, Min / Xu, Junlin / Zeng, Xiangxiang / Yang, Jialiang

Briefings in bioinformatics

2022 Volume 23, Issue 2

Abstract: Drug repositioning is an efficient and promising strategy for traditional drug discovery and development. Many research efforts are focused on utilizing deep-learning approaches based on a heterogeneous network for modeling complex drug-disease ... ...

Abstract	Drug repositioning is an efficient and promising strategy for traditional drug discovery and development. Many research efforts are focused on utilizing deep-learning approaches based on a heterogeneous network for modeling complex drug-disease associations. Similar to traditional latent factor models, which directly factorize drug-disease associations, they assume the neighbors are independent of each other in the network and thus tend to be ineffective to capture localized information. In this study, we propose a novel neighborhood and neighborhood interaction-based neural collaborative filtering approach (called DRWBNCF) to infer novel potential drugs for diseases. Specifically, we first construct three networks, including the known drug-disease association network, the drug-drug similarity and disease-disease similarity networks (using the nearest neighbors). To take the advantage of localized information in the three networks, we then design an integration component by proposing a new weighted bilinear graph convolution operation to integrate the information of the known drug-disease association, the drug's and disease's neighborhood and neighborhood interactions into a unified representation. Lastly, we introduce a prediction component, which utilizes the multi-layer perceptron optimized by the α-balanced focal loss function and graph regularization to model the complex drug-disease associations. Benchmarking comparisons on three datasets verified the effectiveness of DRWBNCF for drug repositioning. Importantly, the unknown drug-disease associations predicted by DRWBNCF were validated against clinical trials and three authoritative databases and we listed several new DRWBNCF-predicted potential drugs for breast cancer (e.g. valrubicin and teniposide) and small cell lung cancer (e.g. valrubicin and cytarabine).
MeSH term(s)	Algorithms ; Computational Biology ; Databases, Factual ; Drug Discovery ; Drug Repositioning ; Neural Networks, Computer
Language	English
Publishing date	2022-01-17
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbab581
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Article ; Online: Graph embedding and Gaussian mixture variational autoencoder network for end-to-end analysis of single-cell RNA sequencing data.

Xu, Junlin / Xu, Jielin / Meng, Yajie / Lu, Changcheng / Cai, Lijun / Zeng, Xiangxiang / Nussinov, Ruth / Cheng, Feixiong

Cell reports methods

2023 Volume 3, Issue 1, Page(s) 100382

Abstract: Single-cell RNA sequencing (scRNA-seq) is a revolutionary technology to determine the precise gene expression of individual cells and identify cell heterogeneity and subpopulations. However, technical limitations of scRNA-seq lead to heterogeneous and ... ...

Abstract	Single-cell RNA sequencing (scRNA-seq) is a revolutionary technology to determine the precise gene expression of individual cells and identify cell heterogeneity and subpopulations. However, technical limitations of scRNA-seq lead to heterogeneous and sparse data. Here, we present autoCell, a deep-learning approach for scRNA-seq dropout imputation and feature extraction. autoCell is a variational autoencoding network that combines graph embedding and a probabilistic depth Gaussian mixture model to infer the distribution of high-dimensional, sparse scRNA-seq data. We validate autoCell on simulated datasets and biologically relevant scRNA-seq. We show that interpolation of autoCell improves the performance of existing tools in identifying cell developmental trajectories of human preimplantation embryos. We identify disease-associated astrocytes (DAAs) and reconstruct DAA-specific molecular networks and ligand-receptor interactions involved in cell-cell communications using Alzheimer's disease as a prototypical example. autoCell provides a toolbox for end-to-end analysis of scRNA-seq data, including visualization, clustering, imputation, and disease-specific gene network identification.
MeSH term(s)	Humans ; Antiviral Agents ; Single-Cell Analysis/methods ; Gene Regulatory Networks/genetics ; Models, Statistical ; Sequence Analysis, RNA/methods
Chemical Substances	Antiviral Agents
Language	English
Publishing date	2023-01-05
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Intramural ; Research Support, N.I.H., Extramural
ISSN	2667-2375
ISSN (online)	2667-2375
DOI	10.1016/j.crmeth.2022.100382
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Article ; Online: Enhancing Drug Repositioning through Local Interactive Learning with Bilinear Attention Networks.

Tang, Xianfang / Zhou, Chang / Lu, Changcheng / Meng, Yajie / Xu, Junlin / Hu, Xinrong / Tian, Geng / Yang, Jialiang

IEEE journal of biomedical and health informatics

2023 Volume PP

Abstract: Drug repositioning has emerged as a promising strategy for identifying new therapeutic applications for existing drugs. In this study, we present DRGBCN, a novel computational method that integrates heterogeneous information through a deep bilinear ... ...

Abstract	Drug repositioning has emerged as a promising strategy for identifying new therapeutic applications for existing drugs. In this study, we present DRGBCN, a novel computational method that integrates heterogeneous information through a deep bilinear attention network to infer potential drugs for specific diseases. DRGBCN involves constructing a comprehensive drug-disease network by incorporating multiple similarity networks for drugs and diseases. Firstly, we introduce a layer attention mechanism to effectively learn the embeddings of graph convolutional layers from these networks. Subsequently, a bilinear attention network is constructed to capture pairwise local interactions between drugs and diseases. This combined approach enhances the accuracy and reliability of predictions. Finally, a multi-layer perceptron module is employed to evaluate potential drugs. Through extensive experiments on three publicly available datasets, DRGBCN demonstrates better performance over baseline methods in 10-fold cross-validation, achieving an average area under the receiver operating characteristic curve (AUROC) of 0.9399. Furthermore, case studies on bladder cancer and acute lymphoblastic leukemia confirm the practical application of DRGBCN in real-world drug repositioning scenarios. Importantly, our experimental results from the drug-disease network analysis reveal the successful clustering of similar drugs within the same community, providing valuable insights into drug-disease interactions. In conclusion, DRGBCN holds significant promise for uncovering new therapeutic applications of existing drugs, thereby contributing to the advancement of precision medicine.
Language	English
Publishing date	2023-11-21
Publishing country	United States
Document type	Journal Article
ZDB-ID	2695320-1
ISSN	2168-2208 ; 2168-2194
ISSN (online)	2168-2208
ISSN	2168-2194
DOI	10.1109/JBHI.2023.3335275
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Article ; Online: A comprehensive review of the classical prescription Yiguan Jian: Phytochemistry, quality control, clinical applications, pharmacology, and safety profile.

Lu, Changcheng / Zhang, Siyuan / Lei, Si San / Wang, Danni / Peng, Bo / Shi, Ruipeng / Chong, Cheong-Meng / Zhong, Zhangfeng / Wang, Yitao

Journal of ethnopharmacology

2023 Volume 319, Issue Pt 2, Page(s) 117230

Abstract: Ethnopharmacological relevance: Yiguan Jian (YGJ) is a classical prescription, which employs 6 kinds of medicinal herbs including Rehmanniae Radix, Lycii Fructus, Angelicae sinensis Radix, Glehniae Radix, Ophiopogonis Radix, and Toosendan Fructus. YGJ ... ...

Abstract	Ethnopharmacological relevance: Yiguan Jian (YGJ) is a classical prescription, which employs 6 kinds of medicinal herbs including Rehmanniae Radix, Lycii Fructus, Angelicae sinensis Radix, Glehniae Radix, Ophiopogonis Radix, and Toosendan Fructus. YGJ decoction is originally prescribed in Qing Dynasty (1636 CE ∼ 1912 CE) in China, and is commonly used to treat liver diseases. There remain abundant literature investigating YGJ decoction from multiple aspects, but few reviews summarized the research and gave a precise definition, which impedes further applications and commercialization of YGJ decoction. Aim of the review: The aim of this review is to provide comprehensive descriptions of YGJ decoction, tackling with issues in the research and development of YGJ decoction. Materials and methods: The literature and clinical reports were obtained from the databases including Web of Science, Science Direct, PubMed, Google Scholar, China National Knowledge Infrastructure, China Science Periodical Database, China Science and Technology Journal Database, and SinoMed since 2000. The phytochemical characteristics, quality control, pharmaceutical forms, clinical position, pharmacological effects, and toxic events of YGJ decoction were included for analysis. Result: This review firstly summarized the progress of the chemical existences of YGJ decoction and discussed the advanced methods in monitoring quality of YGJ decoction and its herbal ingredients, particularly in the form of granules. Whilst this review aims to identify the pharmacological actions and clinical impacts of YGJ decoction, the medicinal materials that could provide these benefits were observed in the remaining herbs to exert the anti-fibrotic effects, anti-inflammatory activities, anti-cancer, and anti-diabetic effects, and to universally treat liver and gastric diseases. This review provided supplementary descriptions on the safety issues, especially in Glehniae Radix and Toosendan Fructus, to define the alterations between hepatoprotective activities and unclear toxics in YGJ decoction application. Conclusions: Our comprehensively organized review discussed the chemical characteristics and the research in altering or identifying these essences. The effects of YGJ decoction on the non-clinical and clinical tests exert the good management of sophisticated diseases. In this review, current issues are discussed to inform and inspire subsequent research of YGJ decoction and other classical prescriptions.
MeSH term(s)	Medicine, Chinese Traditional ; Drugs, Chinese Herbal/adverse effects ; Quality Control ; Phytochemicals/pharmacology
Chemical Substances	yiguan ; rehmannia root (1BEM3U6LQQ) ; Drugs, Chinese Herbal ; Phytochemicals
Language	English
Publishing date	2023-09-29
Publishing country	Ireland
Document type	Journal Article ; Review
ZDB-ID	134511-4
ISSN	1872-7573 ; 0378-8741
ISSN (online)	1872-7573
ISSN	0378-8741
DOI	10.1016/j.jep.2023.117230
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Article ; Online: Drug repositioning based on weighted local information augmented graph neural network.

Meng, Yajie / Wang, Yi / Xu, Junlin / Lu, Changcheng / Tang, Xianfang / Peng, Tao / Zhang, Bengong / Tian, Geng / Yang, Jialiang

Briefings in bioinformatics

2023 Volume 25, Issue 1

Abstract: Drug repositioning, the strategy of redirecting existing drugs to new therapeutic purposes, is pivotal in accelerating drug discovery. While many studies have engaged in modeling complex drug-disease associations, they often overlook the relevance ... ...

Abstract	Drug repositioning, the strategy of redirecting existing drugs to new therapeutic purposes, is pivotal in accelerating drug discovery. While many studies have engaged in modeling complex drug-disease associations, they often overlook the relevance between different node embeddings. Consequently, we propose a novel weighted local information augmented graph neural network model, termed DRAGNN, for drug repositioning. Specifically, DRAGNN firstly incorporates a graph attention mechanism to dynamically allocate attention coefficients to drug and disease heterogeneous nodes, enhancing the effectiveness of target node information collection. To prevent excessive embedding of information in a limited vector space, we omit self-node information aggregation, thereby emphasizing valuable heterogeneous and homogeneous information. Additionally, average pooling in neighbor information aggregation is introduced to enhance local information while maintaining simplicity. A multi-layer perceptron is then employed to generate the final association predictions. The model's effectiveness for drug repositioning is supported by a 10-times 10-fold cross-validation on three benchmark datasets. Further validation is provided through analysis of the predicted associations using multiple authoritative data sources, molecular docking experiments and drug-disease network analysis, laying a solid foundation for future drug discovery.
MeSH term(s)	Drug Repositioning ; Molecular Docking Simulation ; Benchmarking ; Drug Discovery ; Neural Networks, Computer
Language	English
Publishing date	2023-11-09
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbad431
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Article ; Online: Indicator Regularized Non-Negative Matrix Factorization Method-Based Drug Repurposing for COVID-19.

Tang, Xianfang / Cai, Lijun / Meng, Yajie / Xu, JunLin / Lu, Changcheng / Yang, Jialiang

Frontiers in immunology

2021 Volume 11, Page(s) 603615

Abstract: A novel coronavirus, named COVID-19, has become one of the most prevalent and severe infectious diseases in human history. Currently, there are only very few vaccines and therapeutic drugs against COVID-19, and their efficacies are yet to be tested. Drug ...

Abstract	A novel coronavirus, named COVID-19, has become one of the most prevalent and severe infectious diseases in human history. Currently, there are only very few vaccines and therapeutic drugs against COVID-19, and their efficacies are yet to be tested. Drug repurposing aims to explore new applications of approved drugs, which can significantly reduce time and cost compared with
MeSH term(s)	Algorithms ; Antiviral Agents ; Datasets as Topic ; Drug Discovery/methods ; Drug Repositioning/methods ; Humans ; SARS-CoV-2/drug effects ; COVID-19 Drug Treatment
Chemical Substances	Antiviral Agents
Language	English
Publishing date	2021-01-29
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2606827-8
ISSN	1664-3224 ; 1664-3224
ISSN (online)	1664-3224
ISSN	1664-3224
DOI	10.3389/fimmu.2020.603615
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Article ; Online: Drug repositioning based on the heterogeneous information fusion graph convolutional network.

Cai, Lijun / Lu, Changcheng / Xu, Junlin / Meng, Yajie / Wang, Peng / Fu, Xiangzheng / Zeng, Xiangxiang / Su, Yansen

Briefings in bioinformatics

2021 Volume 22, Issue 6

Abstract: In silico reuse of old drugs (also known as drug repositioning) to treat common and rare diseases is increasingly becoming an attractive proposition because it involves the use of de-risked drugs, with potentially lower overall development costs and ... ...

Abstract	In silico reuse of old drugs (also known as drug repositioning) to treat common and rare diseases is increasingly becoming an attractive proposition because it involves the use of de-risked drugs, with potentially lower overall development costs and shorter development timelines. Therefore, there is a pressing need for computational drug repurposing methodologies to facilitate drug discovery. In this study, we propose a new method, called DRHGCN (Drug Repositioning based on the Heterogeneous information fusion Graph Convolutional Network), to discover potential drugs for a certain disease. To make full use of different topology information in different domains (i.e. drug-drug similarity, disease-disease similarity and drug-disease association networks), we first design inter- and intra-domain feature extraction modules by applying graph convolution operations to the networks to learn the embedding of drugs and diseases, instead of simply integrating the three networks into a heterogeneous network. Afterwards, we parallelly fuse the inter- and intra-domain embeddings to obtain the more representative embeddings of drug and disease. Lastly, we introduce a layer attention mechanism to combine embeddings from multiple graph convolution layers for further improving the prediction performance. We find that DRHGCN achieves high performance (the average AUROC is 0.934 and the average AUPR is 0.539) in four benchmark datasets, outperforming the current approaches. Importantly, we conducted molecular docking experiments on DRHGCN-predicted candidate drugs, providing several novel approved drugs for Alzheimer's disease (e.g. benzatropine) and Parkinson's disease (e.g. trihexyphenidyl and haloperidol).
MeSH term(s)	Algorithms ; Biomarkers ; Databases, Pharmaceutical ; Drug Development/methods ; Drug Discovery/methods ; Drug Repositioning ; Humans ; Models, Molecular ; ROC Curve ; Reproducibility of Results ; Structure-Activity Relationship
Chemical Substances	Biomarkers
Language	English
Publishing date	2021-08-02
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbab319
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Article: [Analysis of pulmonary dysfunction of 1 953 coal miners  in Hunan Province].

Lai, Zhiwei / Wang, Xiaoye / Tan, Hongzhuan / Huang, Yaoyu / Lu, Changcheng

Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences

2015 Volume 40, Issue 7, Page(s) 764–769

Abstract: Objective: To explore the effect of dust exposure, type of work, age, length of service and duration of dust exposure on pulmonary function in coal miners by pulmonary function tests. : Methods: A total of 1 953 coal miners, who received occupational ...

Abstract	Objective: To explore the effect of dust exposure, type of work, age, length of service and duration of dust exposure on pulmonary function in coal miners by pulmonary function tests.  Methods: A total of 1 953 coal miners, who received occupational healthy examination and pulmonary function tests during June, 2013 and August, 2014 in Hunan Prevention and Treatment Institute, were enrolled for this study.  Results: A total of 1 302 miners (66.7%) displayed pulmonary dysfunction, including 1 139 with mild dysfunction (58.3%) and 163 with moderate or more serious dysfunction (8.3%). The risk factors for pulmonary dysfunction were age (OR=1.329, 95% CI: 1.196-1.620), dust exposure duration (OR=1.267, 95% CI: 1.136-1.413) and type of works (mining workers OR=1.156, 95% CI: 1.033-1.293; all P<0.05).  Conclusion: The incidence rate of pulmonary dysfunction in coal miners is relatively high in Hunan Province. Most of them are mild dysfunction. The incidence rate of pulmonary dysfunction in mining works is statistically higher than that in other work types. Older workers and long duration-exposed workers are more likely to have pulmonary dysfunction.
MeSH term(s)	China ; Coal Mining ; Dust ; Humans ; Incidence ; Lung/physiopathology ; Lung Diseases/epidemiology ; Occupational Exposure ; Respiratory Function Tests ; Risk Factors
Chemical Substances	Dust
Language	Chinese
Publishing date	2015-08-12
Publishing country	China
Document type	Journal Article
ZDB-ID	2168533-2
ISSN	1672-7347
ISSN	1672-7347
DOI	10.11817/j.issn.1672-7347.2015.07.011
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Article: [Effect of underground work on cardiovascular system  in coal miners].

Lai, Zhiwei / Wang, Xiaoye / Tan, Hongzhuan / Huang, Yaoyu / Lu, Changcheng

Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences

2015 Volume 40, Issue 10, Page(s) 1103–1108

Abstract: Objective: To study the effect of underground work on cardiovascular system health in coal miners. : Methods: Male coal miners, who received electrocardiographic examinations between June, 2013 and August, 2014 in Hunan Prevention and Treatment ... ...

Abstract	Objective: To study the effect of underground work on cardiovascular system health in coal miners.  Methods: Male coal miners, who received electrocardiographic examinations between June, 2013 and August, 2014 in Hunan Prevention and Treatment Institute for Occupational Diseases to exclude pneumoconiosis, were enrolled for this study (n=3 134). Miners with 2 years or more underground work experience were selected as the exposed group (n=2 370), while miners without underground work experience were selected as the control group (n=764). The prevalence of electrocardiographic abnormalities and the influential factors were compared between the 2 groups.  Results: The prevalences of electrocardiographic abnormalities, hypertension, heart rate abnormalities and cardiovascular system abnormalities in the exposed group vs the control group were 37.6% vs 25.4%, 20.5% vs 13.4%, 5.7% vs 6.0%, 49.8% vs 35.2%, respectively. The cardiovascular system abnormalities were correlated with the underground work (OR=3.128, 95% CI: 1.969-4.970), the underground work experience (OR=1.205, 95% CI: 1.070-1.358) and the type of works (mining worker OR=1.820, 95% CI: 1.527-2.169; auxiliary worker OR=1.937, 95% CI: 1.511-2.482; other worker OR=3.291, 95%CI: 2.120-5.109).  Conclusion: Underground work may increase the prevalence of cardiovascular system abnormalities for coal miners. The longer the coal miners work in underground, the higher the risk of the cardiovascular system abnormalities they are.
MeSH term(s)	Cardiovascular System/physiopathology ; Case-Control Studies ; Coal Mining ; Electrocardiography ; Humans ; Male ; Miners ; Occupational Diseases/epidemiology ; Pneumoconiosis ; Prevalence
Language	Chinese
Publishing date	2015-11-05
Publishing country	China
Document type	Journal Article
ZDB-ID	2168533-2
ISSN	1672-7347
ISSN	1672-7347
DOI	10.11817/j.issn.1672-7347.2015.10.008
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