MicroRNA–disease Network Analysis Repurposes Methotrexate for the Treatment of Abdominal Aortic Aneurysm in Mice

Abdominal aortic aneurysm(AAA)is a permanent dilatation of the abdominal aorta and is highly lethal.The main purpose of the current study is to search for noninvasive medical therapies for AAA,for which there is currently no effective drug therapy.Network medicine represents a cuttingedge technology,as analysis and modeling of disease networks can provide critical clues regarding the etiology of specific diseases and therapeutics that may be effective.Here,we proposed a novel algorithm to quantify disease relations based on a large accumulated microRNA–disease association dataset and then built a disease network covering 15 disease classes and 304 diseases.Analysis revealed some patterns for these diseases.For instance,diseases tended to be clustered and coherent in the network.Surprisingly,we found that AAA showed the strongest similarity with rheumatoid arthritis and systemic lupus erythematosus,both of which are autoimmune diseases,suggesting that AAA could be one type of autoimmune diseases in etiology.Based on this observation,we further hypothesized that drugs for autoimmune diseases could be repurposed for the prevention and therapy of AAA.Finally,animal experiments confirmed that methotrexate,a drug for autoimmune diseases,was able to alleviate the formation and development of AAA.

Dynamic analysis of a sexually transmitted disease model on complex networks

In this paper,a sexually transmitted disease model is proposed on complex networks,where contacts between humans are treated as a scale-free social network.There are three groups in our model,which are dangerous male,non-dangerous male,and female.By mathematical analysis,we obtain the basic reproduction number for the existence of endemic equilibrium and study the effects of various immunization schemes about different groups.Furthermore,numerical simulations are undertaken to verify more conclusions.

Role of presynaptic calcium stores for neural network dysfunction in Alzheimer＇s disease

Alzheimer’s disease（AD）is the most common form of dementia representing a major problem for public health.In 2017 there were an estimated 50 million patients worldwide and this number is expected to almost double every 20years,reaching 75 million in 2030 and 131.5 million in 2050（https：//www.alz.co.uk/research/statistics）.

Layer-Constrained Triangulated Irregular Network Algorithm Based on Ground Penetrating Radar Data and Its Application

In this paper,a layer-constrained triangulated irregular network（ LC-TIN） algorithm is proposed for three-dimensional（ 3 D） modelling,and applied to construct a 3 D model for geological disease information based on ground penetrating radar（ GPR） data. Compared with the traditional TIN algorithm,the LCTIN algorithm introduced a layer constraint to the discrete data points during the 3 D modelling process,and it can dynamically construct networks from layer to layer and implement 3 D modelling for arbitrary shapes with high precision. The experimental results validated this method,the proposed algorithm not only can maintain the rationality of triangulation network,but also can obtain a good generation speed. In addition,the algorithm is also introduced to our self-developed 3 D visualization platform,which utilized GPR data to model geological diseases. Therefore the feasibility of the algorithm is verified in the practical application.

Modulatory effects of acupuncture on brain networks in mild cognitive impairment patients

Functional magnetic resonance imaging has been widely used to investigate the effects of acupuncture on neural activity. However, most functional magnetic resonance imaging studies have focused on acute changes in brain activation induced by acupuncture. Thus, the time course of the therapeutic effects of acupuncture remains unclear. In this study, 32 patients with amnestic mild cognitive impairment were randomly divided into two groups, where they received either Tiaoshen Yizhi acupuncture or sham acupoint acupuncture. The needles were either twirled at Tiaoshen Yizhi acupoints, including Sishencong（EX-HN1）, Yintang（EX-HN3）, Neiguan（PC6）, Taixi（KI3）, Fenglong（ST40）, and Taichong（LR3）, or at related sham acupoints at a depth of approximately 15 mm, an angle of ± 60°, and a rate of approximately 120 times per minute. Acupuncture was conducted for 4 consecutive weeks, five times per week, on weekdays. Resting-state functional magnetic resonance imaging indicated that connections between cognition-related regions such as the insula, dorsolateral prefrontal cortex, hippocampus, thalamus, inferior parietal lobule, and anterior cingulate cortex increased after acupuncture at Tiaoshen Yizhi acupoints. The insula, dorsolateral prefrontal cortex, and hippocampus acted as central brain hubs. Patients in the Tiaoshen Yizhi group exhibited improved cognitive performance after acupuncture. In the sham acupoint acupuncture group, connections between brain regions were dispersed, and we found no differences in cognitive function following the treatment. These results indicate that acupuncture at Tiaoshen Yizhi acupoints can regulate brain networks by increasing connectivity between cognition-related regions, thereby improving cognitive function in patients with mild cognitive impairment.

Exploring Mechanisms of Panax notoginseng Saponins in Treating Coronary Heart Disease by Integrating Gene Interaction Network and Functional Enrichment Analysis

Objective： To investigate the mechanisms of Panax notoginseng saponins（PNS） in treating coronary heart disease（CHD） by integrating gene interaction network and functional enrichment analysis. Methods： Text mining was used to get CHD and PNS associated genes. Gene–gene interaction networks of CHD and PNS were built by the Gene MANIA Cytoscape plugin. Advanced Network Merge Cytoscape plugin was used to analyze the two networks. Their functions were analyzed by gene functional enrichment analysis via DAVID Bioinformatics. Joint subnetwork of CHD network and PNS network was identified by network analysis. Results： The 11 genes of the joint subnetwork were the direct targets of PNS in CHD network and enriched in cytokine-cytokine receptor interaction pathway. PNS could affect other 85 genes by the gene–gene interaction of joint subnetwork and these genes were enriched in other 7 pathways. The direct mechanisms of PNS in treating CHD by targeting cytokines to relieve the inflammation and the indirect mechanisms of PNS in treating CHD by affecting other 7 pathways through the interaction of joint subnetwork of PNS and CHD network. The genes in the 7 pathways could be potential targets for the immunologic adjuvant, anticoagulant, hypolipidemic, anti-platelet and anti-hypertrophic activities of PNS. Conclusion： The key mechanisms of PNS in treating CHD could be anticoagulant and hypolipidemic which are indicated by analyzing biological functions of hubs in the merged network.

Computational Approaches for Prioritizing Candidate Disease Genes Based on PPI Networks

With the continuing development and improvement of genome-wide techniques, a great number of candidate genes are discovered. How to identify the most likely disease genes among a large number of candidates becomes a fundamental challenge in human health. A common view is that genes related to a specific or similar disease tend to reside in the same neighbourhood of biomolecular networks. Recently, based on such observations,many methods have been developed to tackle this challenge. In this review, we firstly introduce the concept of disease genes, their properties, and available data for identifying them. Then we review the recent computational approaches for prioritizing candidate disease genes based on Protein-Protein Interaction（PPI） networks and investigate their advantages and disadvantages. Furthermore, some pieces of existing software and network resources are summarized. Finally, we discuss key issues in prioritizing candidate disease genes and point out some future research directions.

NOGEA: A Network-oriented Gene Entropy Approach for Dissecting Disease Comorbidity and Drug Repositioning

Rapid development of high-throughput technologies has permitted the identification of an increasing number of disease-associated genes(DAGs),which are important for understanding disease initiation and developing precision therapeutics.However,DAGs often contain large amounts of redundant or false positive information,leading to difficulties in quantifying and prioritizing potential relationships between these DAGs and human diseases.In this study,a networkoriented gene entropy approach(NOGEA)is proposed for accurately inferring master genes that contribute to specific diseases by quantitatively calculating their perturbation abilities on directed disease-specific gene networks.In addition,we confirmed that the master genes identified by NOGEA have a high reliability for predicting disease-specific initiation events and progression risk.Master genes may also be used to extract the underlying information of different diseases,thus revealing mechanisms of disease comorbidity.More importantly,approved therapeutic targets are topologically localized in a small neighborhood of master genes in the interactome network,which provides a new way for predicting drug-disease associations.Through this method,11 old drugs were newly identified and predicted to be effective for treating pancreatic cancer and then validated by in vitro experiments.Collectively,the NOGEA was useful for identifying master genes that control disease initiation and co-occurrence,thus providing a valuable strategy for drug efficacy screening and repositioning.NOGEA codes are publicly available at https://github.com/guozihuaa/NOGEA.

A network pharmacology study of Sendeng-4,a Mongolian medicine

We collected the data on the Sendeng-4 chemical composition corresponding targets through the literature and from Drug Bank, SuperT arget, TTD(Therapeutic Targets Database) and other databases and the relevant signaling pathways from the KEGG(Kyoto Encyclopedia of Genes and Genomes) database and established models of the chemical composition-target network and chemical composition-target- disease network using Cytoscape software, the analysis indicated that the chemical composition had at least nine different types of targets that acted together to exert effects on the diseases, suggesting a "multi-component, multi-target" feature of the traditional Mongolian medicine. We also employed the rat model of rheumatoid arthritis induced by Collgen Type II to validate the key targets of the chemical components of Sendeng-4, and three of the key targets were validated through laboratory experiments, further confirming the anti-inflammatory effects of Sendeng-4. In all, this study predicted the active ingredients and targets of Sendeng-4, and explored its mechanism of action, which provided new strategies and methods for further research and development of Sendeng-4 and other traditional Mongolian medicines as well.

Exploring Traditional Chinese Medicine by A Novel Therapeutic Concept of Network Target

Traditional Chinese medicine（TCM） holds a holistic theory, and specializes in balancing disordered human body using numerous natural products, particularly Chinese herbal formulae. TCM has certain treatment advantages for patients suffering from various complex diseases. However, due to the complex nature of TCM, it remains difficult to unveil such holistic medicine by the current reductionism research strategies, which treat both herbal ingredients and targets in isolation. Recently, an emerging network pharmacology approach has been introduced to tackle this bottleneck problem. A TCM-derived novel therapeutic concept, ＂network target＂, which is different from the Western medicine＇s ＂onetarget＂ concept, has been proposed from China. The network target strategy is able to illustrate the complex interactions among the biological systems, drugs, and complex diseases from a network perspective, and thus provides an innovative approach to access ancient remedies in a precision manner and at a systematic level, which also highlights TCM＇s potential in current medical systems.

SEIR-SW, Simulation Model of Influenza Spread Based on the Small World Network

This study modeled the spread of an influenza epidemic in the population of Oran, Algeria. We investigated the mathematical epidemic model, SEIR（Susceptible-Exposed-Infected-Removed）, through extensive simulations of the effects of social network on epidemic spread in a Small World（SW） network, to understand how an influenza epidemic spreads through a human population. A combined SEIR-SW model was built, to help understand the dynamics of infectious disease in a community, and to identify the main characteristics of epidemic transmission and its evolution over time. The model was also used to examine social network effects to better understand the topological structure of social contact and the impact of its properties. Experiments were conducted to evaluate the combined SEIR-SW model. Simulation results were analyzed to explore how network evolution influences the spread of desease, and statistical tests were applied to validate the model. The model accurately replicated the dynamic behavior of the real influenza epidemic data, confirming that the susceptible size and topological structure of social networks in a human population significantly influence the spread of infectious diseases. Our model can provide health policy decision makers with a better understanding of epidemic spread,allowing them to implement control measures. It also provides an early warning of the emergence of influenza epidemics.