Biodiversity metrics on ecological networks: Demonstrated with animal gastrointestinal microbiomes

Biodiversity has become a terminology familiar to virtually every citizen in modern societies.It is said that ecology studies the economy of nature,and economy studies the ecology of humans;then measuring biodiversity should be similar with measuring national wealth.Indeed,there have been many parallels between ecology and economics,actually beyond analogies.For example,arguably the second most widely used biodiversity metric,Simpson(1949)’s diversity index,is a function of familiar Gini-index in economics.One of the biggest challenges has been the high“diversity”of diversity indexes due to their excessive“speciation”-there are so many indexes,similar to each country’s sovereign currency-leaving confused diversity practitioners in dilemma.In 1973,Hill introduced the concept of“numbers equivalent”,which is based on Renyi entropy and originated in economics,but possibly due to his abstruse interpretation of the concept,his message was not widely received by ecologists until nearly four decades later.What Hill suggested was similar to link the US dollar to gold at the rate of$35 per ounce under the Bretton Woods system.The Hill numbers now are considered most appropriate biodiversity metrics system,unifying Shannon,Simpson and other diversity indexes.Here,we approach to another paradigmatic shift-measuring biodiversity on ecological networks-demonstrated with animal gastrointestinal microbiomes representing four major invertebrate classes and all six vertebrate classes.The network diversity can reveal the diversity of species interactions,which is a necessary step for understanding the spatial and temporal structures and dynamics of biodiversity across environmental gradients.

Insights into microbiota community dynamics and flavor development mechanism during golden pomfret(Trachinotus ovatus)fermentation based on single-molecule real-time sequencing and molecular networking analysis

Popular fermented golden pomfret(Trachinotus ovatus)is prepared via spontaneous fermentation;however,the mechanisms underlying the regulation of its flavor development remain unclear.This study shows the roles of the complex microbiota and the dynamic changes in microbial community and flavor compounds during fish fermentation.Single-molecule real-time sequencing and molecular networking analysis revealed the correlations among different microbial genera and the relationships between microbial taxa and volatile compounds.Mechanisms underlying flavor development were also elucidated via KEGG based functional annotations.Clostridium,Shewanella,and Staphylococcus were the dominant microbial genera.Forty-nine volatile compounds were detected in the fermented fish samples,with thirteen identified as characteristic volatile compounds(ROAV>1).Volatile profiles resulted from the interactions among the microorganisms and derived enzymes,with the main metabolic pathways being amino acid biosynthesis/metabolism,carbon metabolism,and glycolysis/gluconeogenesis.This study demonstrated the approaches for distinguishing key microbiota associated with volatile compounds and monitoring the industrial production of high-quality fermented fish products.

Multiscale Characteristics and Connection Mechanisms of Attraction Networks:A Trajectory Data Mining Approach Leveraging Geotagged Data

Urban tourism is considered a complex system,and multiscale exploration of the organizational patterns of attraction networks has become a topical issue in urban tourism,so exploring the multiscale characteristics and connection mechanisms of attraction networks is important for understanding the linkages between attractions and even the future destination planning.This paper uses geotagging data to compare the links between attractions in Beijing,China during four different periods:the pre-Olympic period(2004–2007),the Olympic Games and subsequent‘heat period’(2008–2013),the post-Olympic period(2014–2019),and the COVID-19(Corona Virus Disease 2019)pandemic period(2020–2021).The aim is to better understand the evolution and patterns of attraction networks at different scales in Beijing and to provide insights for tourism planning in the destination.The results show that the macro,meso-,and microscales network characteristics of attraction networks have inherent logical relationships that can explain the commonalities and differences in the development process of tourism networks.The macroscale attraction network degree Matthew effect is significant in the four different periods and exhibits a morphological monocentric structure,suggesting that new entrants are more likely to be associated with attractions that already have high value.The mesoscale links attractions according to the common purpose of tourists,and the results of the community segmentation of the attraction networks in the four different periods suggest that the functional polycentric structure describes their clustering effect,and the weak links between clusters result from attractions bound by incomplete information and distance,and the functional polycentric structure with a generally more efficient network of clusters.The pattern structure at the microscale reveals the topological transformation relationship of the regional collaboration pattern,and the attraction network structure in the four different periods has a very similar importance profile structure suggesting that the attraction network has the same construction rules and evolution mechanism,which aids in understanding the attraction network pattern at both macro and micro scales.Important approaches and practical implications for planners and managers are presented.

Prediction of Porous Media Fluid Flow with Spatial Heterogeneity Using Criss-Cross Physics-Informed Convolutional Neural Networks

Recent advances in deep neural networks have shed new light on physics,engineering,and scientific computing.Reconciling the data-centered viewpoint with physical simulation is one of the research hotspots.The physicsinformedneural network(PINN)is currently the most general framework,which is more popular due to theconvenience of constructing NNs and excellent generalization ability.The automatic differentiation(AD)-basedPINN model is suitable for the homogeneous scientific problem;however,it is unclear how AD can enforce fluxcontinuity across boundaries between cells of different properties where spatial heterogeneity is represented bygrid cells with different physical properties.In this work,we propose a criss-cross physics-informed convolutionalneural network(CC-PINN)learning architecture,aiming to learn the solution of parametric PDEs with spatialheterogeneity of physical properties.To achieve the seamless enforcement of flux continuity and integration ofphysicalmeaning into CNN,a predefined 2D convolutional layer is proposed to accurately express transmissibilitybetween adjacent cells.The efficacy of the proposedmethodwas evaluated through predictions of several petroleumreservoir problems with spatial heterogeneity and compared against state-of-the-art(PINN)through numericalanalysis as a benchmark,which demonstrated the superiority of the proposed method over the PINN.

Metabologenomics and network pharmacology to understand the molecular mechanism of cancer research

In this editorial I comment on the article“Network pharmacological and molecular docking study of the effect of Liu-Wei-Bu-Qi capsule on lung cancer”published in the recent issue of the World Journal of Clinical Cases 2023 November 6;11(31):7593-7609.Almost all living forms are able to manufacture particular chemicals-metabolites that enable them to differentiate themselves from one another and to overcome the unique obstacles they encounter in their natural habitats.Numerous methods for chemical warfare,communication,nutrition acquisition,and stress prevention are made possible by these specialized metabolites.Metabolomics is a popular technique for collecting direct mea-surements of metabolic activity from many biological systems.However,con-fusing metabolite identification is a typical issue,and biochemical interpretation is frequently constrained by imprecise and erroneous genome-based estimates of enzyme activity.Metabolite annotation and gene integration uses a biochemical reaction network to obtain a metabolite-gene association so called metabologe-nomics.This network uses an approach that emphasizes metabolite-gene consensus via biochemical processes.Combining metabolomics and genomics data is beneficial.Furthermore,computer networking proposes that using meta-bolomics data may improve annotations in sequenced species and provide testable hypotheses for specific biochemical processes.CONCLUSION The genome and metabolites of biological organisms are not fully characterized with current technologies.However,increasing high-throughput metabolomics and genomics data provide promising generation of paired data sets to understand the molecular mechanism of biochemical processes as well as determining targets for pharmaceutical drug design.Contemporary network infrastructures to integrate omics analysis can provide molecular mechanism of biochemical pathways.Furthermore,clinical data may be integrated to gene expression–metabolite expression by system genetics approach.Calculating pair-wise correlations and weighted correlation network analysis provide the basis of this integration[11-13].The occurrence of strong correlations between classified metabolites and co-expression transcripts implies either various roles of metabolites or linkages between metabolic pathways and the immune system.

Structural Characteristics and Influencing Factors of Carbon Emission Spatial Association Network:A Case Study of Yangtze River Delta City Cluster,China

City cluster is an effective platform for encouraging regionally coordinated development.Coordinated reduction of carbon emissions within city cluster via the spatial association network between cities can help coordinate the regional carbon emission management,realize sustainable development,and assist China in achieving the carbon peaking and carbon neutrality goals.This paper applies the improved gravity model and social network analysis(SNA)to the study of spatial correlation of carbon emissions in city clusters and analyzes the structural characteristics of the spatial correlation network of carbon emissions in the Yangtze River Delta(YRD)city cluster in China and its influencing factors.The results demonstrate that:1)the spatial association of carbon emissions in the YRD city cluster exhibits a typical and complex multi-threaded network structure.The network association number and density show an upward trend,indicating closer spatial association between cities,but their values remain generally low.Meanwhile,the network hierarchy and network efficiency show a downward trend but remain high.2)The spatial association network of carbon emissions in the YRD city cluster shows an obvious‘core-edge’distribution pattern.The network is centered around Shanghai,Suzhou and Wuxi,all of which play the role of‘bridges’,while cities such as Zhoushan,Ma'anshan,Tongling and other cities characterized by the remote location,single transportation mode or lower economic level are positioned at the edge of the network.3)Geographic proximity,varying levels of economic development,different industrial structures,degrees of urbanization,levels of technological innovation,energy intensities and environmental regulation are important influencing factors on the spatial association of within the YRD city cluster.Finally,policy implications are provided from four aspects:government macro-control and market mechanism guidance,structural characteristics of the‘core-edge’network,reconfiguration and optimization of the spatial layout of the YRD city cluster,and the application of advanced technologies.

Dynamics and synchronization in a memristor-coupled discrete heterogeneous neuron network considering noise

Research on discrete memristor-based neural networks has received much attention.However,current research mainly focuses on memristor–based discrete homogeneous neuron networks,while memristor-coupled discrete heterogeneous neuron networks are rarely reported.In this study,a new four-stable discrete locally active memristor is proposed and its nonvolatile and locally active properties are verified by its power-off plot and DC V–I diagram.Based on two-dimensional(2D)discrete Izhikevich neuron and 2D discrete Chialvo neuron,a heterogeneous discrete neuron network is constructed by using the proposed discrete memristor as a coupling synapse connecting the two heterogeneous neurons.Considering the coupling strength as the control parameter,chaotic firing,periodic firing,and hyperchaotic firing patterns are revealed.In particular,multiple coexisting firing patterns are observed,which are induced by different initial values of the memristor.Phase synchronization between the two heterogeneous neurons is discussed and it is found that they can achieve perfect synchronous at large coupling strength.Furthermore,the effect of Gaussian white noise on synchronization behaviors is also explored.We demonstrate that the presence of noise not only leads to the transition of firing patterns,but also achieves the phase synchronization between two heterogeneous neurons under low coupling strength.

The Effect of Key Nodes on theMalware Dynamics in the Industrial Control Network

As industrialization and informatization becomemore deeply intertwined,industrial control networks have entered an era of intelligence.The connection between industrial control networks and the external internet is becoming increasingly close,which leads to frequent security accidents.This paper proposes a model for the industrial control network.It includes a malware containment strategy that integrates intrusion detection,quarantine,and monitoring.Basedonthismodel,the role of keynodes in the spreadofmalware is studied,a comparisonexperiment is conducted to validate the impact of the containment strategy.In addition,the dynamic behavior of the model is analyzed,the basic reproduction number is computed,and the disease-free and endemic equilibrium of the model is also obtained by the basic reproduction number.Moreover,through simulation experiments,the effectiveness of the containment strategy is validated,the influence of the relevant parameters is analyzed,and the containment strategy is optimized.In otherwords,selective immunity to key nodes can effectively suppress the spread ofmalware andmaintain the stability of industrial control systems.The earlier the immunization of key nodes,the better.Once the time exceeds the threshold,immunizing key nodes is almost ineffective.The analysis provides a better way to contain the malware in the industrial control network.

Identification of anti-gastric cancer effects and molecular mechanisms of resveratrol: From network pharmacology and bioinformatics to experimental validation

BACKGROUND Gastric cancer(GC)is one of the most aggressive malignancies with limited therapeutic options and a poor prognosis.Resveratrol,a non-flavonoid poly-phenolic compound found in a variety of Chinese medicinal materials,has shown excellent anti-GC effect.However,its exact mechanisms of action in GC have not been clarified.AIM To identify the effects of resveratrol on GC progression and explore the related molecular mechanisms.METHODS Action targets of resveratrol and GC-related targets were screened from public databases.The overlapping targets between the two were confirmed using a Venn diagram,and a“Resveratrol-Target-GC”network was constructed using Cyto-scape software version 3.9.1.The protein-protein interaction(PPI)network was constructed using STRING database and core targets were identified by PPI network analysis.The Database for Annotation,Visualization and Integrated A total of 378 resveratrol action targets and 2154 GC disease targets were obtained from public databases,and 181 intersection targets between the two were screened by Venn diagram.The top 20 core targets were identified by PPI network analysis of the overlapping targets.GO function analysis mainly involved protein binding,identical protein binding,cytoplasm,nucleus,negative regulation of apoptotic process and response to xenobiotic stimulus.KEGG enrichment analysis suggested that the involved signaling pathways mainly included PI3K-AKT signaling pathway,MAPK signaling pathway,IL-17 signaling pathway,TNF signaling pathway,ErbB signaling pathway,etc.FBJ murine osteosarcoma viral oncogene homolog(FOS)and matrix metallopeptidase 9(MMP9)were selected by differential expression analysis,and they were closely associated with immune infiltration.Molecular docking results showed that resveratrol docked well with these two targets.Resveratrol treatment arrested the cell cycle at the S phase,induced apoptosis,and weakened viability,migration and invasion in a dose-dependent manner.Furthermore,resveratrol could exhibit anti-GC effect by regulating FOS and MMP9 expression.CONCLUSION The anti-GC effects of resveratrol are related to the inhibition of cell proliferation,migration,invasion and induction of cell cycle arrest and apoptosis by targeting FOS and MMP9.

MetaPINNs:Predicting soliton and rogue wave of nonlinear PDEs via the improved physics-informed neural networks based on meta-learned optimization

Efficiently solving partial differential equations(PDEs)is a long-standing challenge in mathematics and physics research.In recent years,the rapid development of artificial intelligence technology has brought deep learning-based methods to the forefront of research on numerical methods for partial differential equations.Among them,physics-informed neural networks(PINNs)are a new class of deep learning methods that show great potential in solving PDEs and predicting complex physical phenomena.In the field of nonlinear science,solitary waves and rogue waves have been important research topics.In this paper,we propose an improved PINN that enhances the physical constraints of the neural network model by adding gradient information constraints.In addition,we employ meta-learning optimization to speed up the training process.We apply the improved PINNs to the numerical simulation and prediction of solitary and rogue waves.We evaluate the accuracy of the prediction results by error analysis.The experimental results show that the improved PINNs can make more accurate predictions in less time than that of the original PINNs.

Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model

Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function,a brand-new tristable locally active memristor model is first proposed in this paper.Here,a novel four-dimensional fractional-order memristive cellular neural network(FO-MCNN)model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its performance.Then,its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation platforms.Subsequently,it is used toward secure communication application scenarios.Taking it as the pseudo-random number generator(PRNG),a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing(ASR-CS)model.Eventually,the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance.

Multi-head neural networks for simulating particle breakage dynamics

The breakage of brittle particulate materials into smaller particles under compressive or impact loads can be modelled as an instantiation of the population balance integro-differential equation.In this paper,the emerging computational science paradigm of physics-informed neural networks is studied for the first time for solving both linear and nonlinear variants of the governing dynamics.Unlike conventional methods,the proposed neural network provides rapid simulations of arbitrarily high resolution in particle size,predicting values on arbitrarily fine grids without the need for model retraining.The network is assigned a simple multi-head architecture tailored to uphold monotonicity of the modelled cumulative distribution function over particle sizes.The method is theoretically analyzed and validated against analytical results before being applied to real-world data of a batch grinding mill.The agreement between laboratory data and numerical simulation encourages the use of physics-informed neural nets for optimal planning and control of industrial comminution processes.

Multi-scale physics-informed neural networks for solving high Reynolds number boundary layer flows based on matched asymptotic expansions

Multi-scale system remains a classical scientific problem in fluid dynamics,biology,etc.In the present study,a scheme of multi-scale Physics-informed neural networks is proposed to solve the boundary layer flow at high Reynolds numbers without any data.The flow is divided into several regions with different scales based on Prandtl's boundary theory.Different regions are solved with governing equations in different scales.The method of matched asymptotic expansions is used to make the flow field continuously.A flow on a semi infinite flat plate at a high Reynolds number is considered a multi-scale problem because the boundary layer scale is much smaller than the outer flow scale.The results are compared with the reference numerical solutions,which show that the msPINNs can solve the multi-scale problem of the boundary layer in high Reynolds number flows.This scheme can be developed for more multi-scale problems in the future.

TCAS-PINN:Physics-informed neural networks with a novel temporal causality-based adaptive sampling method

Physics-informed neural networks(PINNs)have become an attractive machine learning framework for obtaining solutions to partial differential equations(PDEs).PINNs embed initial,boundary,and PDE constraints into the loss function.The performance of PINNs is generally affected by both training and sampling.Specifically,training methods focus on how to overcome the training difficulties caused by the special PDE residual loss of PINNs,and sampling methods are concerned with the location and distribution of the sampling points upon which evaluations of PDE residual loss are accomplished.However,a common problem among these original PINNs is that they omit special temporal information utilization during the training or sampling stages when dealing with an important PDE category,namely,time-dependent PDEs,where temporal information plays a key role in the algorithms used.There is one method,called Causal PINN,that considers temporal causality at the training level but not special temporal utilization at the sampling level.Incorporating temporal knowledge into sampling remains to be studied.To fill this gap,we propose a novel temporal causality-based adaptive sampling method that dynamically determines the sampling ratio according to both PDE residual and temporal causality.By designing a sampling ratio determined by both residual loss and temporal causality to control the number and location of sampled points in each temporal sub-domain,we provide a practical solution by incorporating temporal information into sampling.Numerical experiments of several nonlinear time-dependent PDEs,including the Cahn–Hilliard,Korteweg–de Vries,Allen–Cahn and wave equations,show that our proposed sampling method can improve the performance.We demonstrate that using such a relatively simple sampling method can improve prediction performance by up to two orders of magnitude compared with the results from other methods,especially when points are limited.

Bioinformatics and network pharmacology identify the therapeutic role and potential targets of diosgenin in Alzheimer disease and COVID-19

Objective: This study aims to investigate the potential targets of diosgenin for the treatment of Alzheimer's disease (AD) and Coronavirus Disease 2019 (COVID-19) through the utilization of bioinformatics, network pharmacology, and molecular docking techniques. Methods: Differential expression genes (DEGs) shared by AD and COVID-19 were enriched by bioinformatics. Additionally, regulatory networks were analyzed to identify key genes in the Transcription Factor (TF) of both diseases. The networks were visualized using Cytoscape. Utilizing the DGIdb database, an investigation was conducted to identify potential drugs capable of treating both Alzheimer's disease (AD) and COVID-19. Subsequently, a Venn diagram analysis was performed using the drugs associated with AD and COVID-19 in the CTD database, leading to the identification of diosgenin as a promising candidate for the treatment of both AD and COVID-19.SEA, SuperPred, Swiss Target Prediction and TCMSP were used to predict the target of diosgenin in the treatment of AD and COVID-19, and the target of diosgenin in the treatment of AD and COVID-19 was determined by Wayne diagram intersection analysis with the differentially expressed genes of AD and COVID- 19. Their Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were analyzed jointly. Genomes The Protein Protein Interaction (PPI) network of these drug targets was constructed, and core targets with the highest correlation were screened out. The binding of diosgenin to these core targets was analyzed by molecular docking. Results: Through enrichment and cluster analysis, it was found that the biological processes, pathways and diseases enriched by DEGs in AD and COVID-19 were all related to inflammation and immune regulation. These common DEGs and Trust databases were used to construct AD and COVID-19 TFs regulatory networks. Diosgenin was predicted as a potential drug for the treatment of AD and COVID-19 by network pharmacology, and 36 targets of diosgenin for the treatment of AD and 27 targets for COVID-19 were revealed. The six core targets with the highest correlation were selected for molecular docking with diosgenin using CytohHubba to calculate the scores. Conclusions: This study firstly revealed that the common TFs regulatory network of AD and COVID-19, and predicted and verified diosgenin as a potential drug for the treatment of AD and COVID-19. The binding of diosgenin to the core pharmacological targets for the treatment of AD and COVID-19 was determined by molecular docking, which provides a theoretical basis for developing a new approach to clinical treatment of AD and COVID-19.

Incorporating Lasso Regression to Physics-Informed Neural Network for Inverse PDE Problem

Partial Differential Equation(PDE)is among the most fundamental tools employed to model dynamic systems.Existing PDE modeling methods are typically derived from established knowledge and known phenomena,which are time-consuming and labor-intensive.Recently,discovering governing PDEs from collected actual data via Physics Informed Neural Networks(PINNs)provides a more efficient way to analyze fresh dynamic systems and establish PEDmodels.This study proposes Sequentially Threshold Least Squares-Lasso(STLasso),a module constructed by incorporating Lasso regression into the Sequentially Threshold Least Squares(STLS)algorithm,which can complete sparse regression of PDE coefficients with the constraints of l0 norm.It further introduces PINN-STLasso,a physics informed neural network combined with Lasso sparse regression,able to find underlying PDEs from data with reduced data requirements and better interpretability.In addition,this research conducts experiments on canonical inverse PDE problems and compares the results to several recent methods.The results demonstrated that the proposed PINN-STLasso outperforms other methods,achieving lower error rates even with less data.

Integrated network pharmacology and metabolomics to explore the mechanisms of Shenzao dripping pill against chronic myocardial ischemia

Background:Shenzao dripping pill(SZDP)is empirically prescribed for treating cardiac diseases.Nevertheless,there is a lack of comprehensive knowledge regarding the underlying mechanisms contributing to its therapeutic effects.The objective of this study is to investigate the underlying mechanism of SZDP against chronic myocardial ischemia(CMI)in a rat model.Methods:In this study,we utilized electrocardiographic and echocardiographic detection along with pathological tissue analysis to evaluate the efficacy of SZDP.The integration of network pharmacology and metabolomics was conducted to investigate the mechanisms.Molecular docking and molecular dynamics simulations were used to validate the binding energy between the compounds of SZDP and the associated targets.Results:The results showed that SZDP was able to improve T wave voltage,reverse CMI abnormalities in ejection fraction and fractional shortening,and restore histopathological heart damage.Metabolomics results indicated that disturbances of metabolic profile in CMI rats were partly corrected after SZDP administration,mainly affecting purine metabolism.13-Docosenamide may be the potential metabolic biomarker of the therapeutic application of SZDP for CMI.Integrating network pharmacology and metabolomics,thiopurine S-methyltransferase(TPMT),xanthine dehydrogenase/oxidase(XDH),bifunctional purine biosynthesis protein ATIC(ATIC),and cytochrome p4501A1(CYP1A1)were identified as possible targets of SZDP to exert therapeutic effects by enhancing the metabolic levels of L-Tryptophan,Deoxyribose 1-phosphate and Phosphoribosyl formamidocarboxamide.Conclusion:SZDP has a therapeutic effect on CMI by regulating metabolite levels,acting on the targets of TMPT,XDH,ATIC,and CYP1A1,and reducing cardiomyocyte injury and myocardial fibrosis.

Target layer state estimation in multi-layer complex dynamical networks considering nonlinear node dynamics

In many engineering networks, only a part of target state variables are required to be estimated.On the other hand,multi-layer complex network exists widely in practical situations.In this paper, the state estimation of target state variables in multi-layer complex dynamical networks with nonlinear node dynamics is studied.A suitable functional state observer is constructed with the limited measurement.The parameters of the designed functional observer are obtained from the algebraic method and the stability of the functional observer is proven by the Lyapunov theorem.Some necessary conditions that need to be satisfied for the design of the functional state observer are obtained.Different from previous studies, in the multi-layer complex dynamical network with nonlinear node dynamics, the proposed method can estimate the state of target variables on some layers directly instead of estimating all the individual states.Thus, it can greatly reduce the placement of observers and computational cost.Numerical simulations with the three-layer complex dynamical network composed of three-dimensional nonlinear dynamical nodes are developed to verify the effectiveness of the method.

Construction of the underlying circRNA-miRNA-mRNA regulatory network and a new diagnostic model in ulcerative colitis by bioinformatics analysis

BACKGROUND Circular RNAs(circRNAs)are involved in the pathogenesis of many diseases through competing endogenous RNA(ceRNA)regulatory mechanisms.AIM To investigate a circRNA-related ceRNA regulatory network and a new predictive model by circRNA to understand the diagnostic mechanism of circRNAs in ulcerative colitis(UC).METHODS We obtained gene expression profiles of circRNAs,miRNAs,and mRNAs in UC from the Gene Expression Omnibus dataset.The circRNA-miRNA-mRNA network was constructed based on circRNA-miRNA and miRNA-mRNA interactions.Functional enrichment analysis was performed to identify the biological mechanisms involved in circRNAs.We identified the most relevant differential circRNAs for diagnosing UC and constructed a new predictive nomogram,whose efficacy was tested with the C-index,receiver operating characteristic curve(ROC),and decision curve analysis(DCA).RESULTS A circRNA-miRNA-mRNA regulatory network was obtained,containing 12 circRNAs,three miRNAs,and 38 mRNAs.Two optimal prognostic-related differentially expressed circRNAs,hsa_circ_0085323 and hsa_circ_0036906,were included to construct a predictive nomogram.The model showed good discrimination,with a C-index of 1(>0.9,high accuracy).ROC and DCA suggested that the nomogram had a beneficial diagnostic ability.CONCLUSION This novel predictive nomogram incorporating hsa_circ_0085323 and hsa_circ_0036906 can be conveniently used to predict the risk of UC.The circRNa-miRNA-mRNA network in UC could be more clinically significant.

To explore the mechanism of Fuyang Jiebiao granules against viral pneumonia based on network pharmacology and pharmacodynamics

Objective:To investigate the mechanism of Fuyang Jiebiao granule(FYJBKL)in the treatment of viral pneumonia.Methods:Firstly,a network model was constructed using network pharmacology to study the target expression sites of FYJBKL viral pneumonia,so as to determine the main targets and important signal transduction pathways for the treatment of viral pneumonia.Secondly,the main components of the drug and the main target are docked.Then,the fever,sweating and inflammation rat models were established to explore the antipyretic,sweating and anti-inflammatory mechanisms of FYJBKL.Finally,the contents of IL-17,IL-1β,TNF-αand IL-6 in blood samples of rats were analyzed by ELISA method,and the morphological changes of lung tissue were observed by HE staining.Results:Quercetin,luteolin,kaempferol,etc.,and the main mechanism targets are IL-17,IL-1β,TNF-α,IL-6 and so on.Thirty signal pathways were identified by KEGG enrichment analysis,including interleukin-17 signaling pathway(IL-17 signaling pathway),human cytomegalovirus infection pathway(human cytomegalovirus infection),Kaposi's sarcoma associated herpesvirus infection pathway(Kaposi's sarcoma-as-sociated herpesvirus infection)and so on.After the study of molecular docking,we found that the contact efficiency between active substances and possible key targets is good.The high and middle concentration groups of FYJBKL significantly decreased the expression of IL-17,IL-1β,TNF-αand IL-6 in the blood of rats with inflammation(P<0.05).FYJBKL significantly reduced the foot swelling induced by egg white and inhibited the increase of body temperature induced by yeast in rats(P<0.05).HE staining showed that FYJBKL improved pulmonary fibrosis and inflammatory exudation to varying degrees.Conclusion:The effects of FuyangJiebiao granules on the related signal pathways of anti-virus,anti-immune and anti-inflammation as well as biological and cellular processes may be caused by the binding of quercetin,luteolin,kaempferol and other active ingredients to their shared targets.Fuyang Jiebiao granules can improve the related symptoms caused by viral pneumonia,and its mechanism may be related to the activities of TNF,IL-17,IL-6 and other related channels,which are multiple targets of inflammation regulation.