Drivers for Inter-city Innovation Networks Across Chinese Cities:Modelling Physical Versus Intangible Effects

Cross-region innovation is widely recognized as an important source of the long-term regional innovation capacity.In the recent past,a growing number of studies has investigated the network structure and mechanisms of cross-region innovation collaboration in various contexts.However,existing research mainly focuses on physical effects,such as geographical distance and high-speed railway connections.These studies ignore the intangible drivers in a changing environment,the more digitalized economy and the increasingly solidified innovation network structure.Thus,the focus of this study is on estimating determinants of innovation networks,especially on intangible drivers,which have been largely neglected so far.Using city-level data of Chinese patents(excluding Hong Kong,Macao,and Taiwan Province of China),we trace innovation networks across Chinese cities over a long period of time.By integrating a measure on Information and Communications Technology(ICT)development gap and network structural effects into the general proximity framework,this paper explores the changing mechanisms of Chinese innovation networks from a new perspective.The results show that the structure of cross-region innovation networks has changed in China.As mechanisms behind this development,the results confirm the increasingly important role of intangible drivers in Chinese inter-city innovation collaboration when controlling for effects of physical proximity,such as geographical distance.Since digitalization and coordinated development are the mainstream trends in China and other developing countries,these countries'inter-city innovation collaboration patterns will witness dramatic changes under the influence of intangible drivers.

Source localization in signed networks with effective distance

While progress has been made in information source localization,it has overlooked the prevalent friend and adversarial relationships in social networks.This paper addresses this gap by focusing on source localization in signed network models.Leveraging the topological characteristics of signed networks and transforming the propagation probability into effective distance,we propose an optimization method for observer selection.Additionally,by using the reverse propagation algorithm we present a method for information source localization in signed networks.Extensive experimental results demonstrate that a higher proportion of positive edges within signed networks contributes to more favorable source localization,and the higher the ratio of propagation rates between positive and negative edges,the more accurate the source localization becomes.Interestingly,this aligns with our observation that,in reality,the number of friends tends to be greater than the number of adversaries,and the likelihood of information propagation among friends is often higher than among adversaries.In addition,the source located at the periphery of the network is not easy to identify.Furthermore,our proposed observer selection method based on effective distance achieves higher operational efficiency and exhibits higher accuracy in information source localization,compared with three strategies for observer selection based on the classical full-order neighbor coverage.

Studying the co-evolution of information diffusion,vaccination behavior and disease transmission in multilayer networks with local and global effects

Today,with the rapid development of the internet,a large amount of information often accompanies the rapid transmission of disease outbreaks,and increasing numbers of scholars are studying the relationship between information and the disease transmission process using complex networks.In fact,the disease transmission process is very complex.Besides this information,there will often be individual behavioral measures and other factors to consider.Most of the previous research has aimed to establish a two-layer network model to consider the impact of information on the transmission process of disease,rarely divided into information and behavior,respectively.To carry out a more in-depth analysis of the disease transmission process and the intrinsic influencing mechanism,this paper divides information and behavior into two layers and proposes the establishment of a complex network to study the dynamic co-evolution of information diffusion,vaccination behavior,and disease transmission.This is achieved by considering four influential relationships between adjacent layers in multilayer networks.In the information layer,the diffusion process of negative information is described,and the feedback effects of local and global vaccination are considered.In the behavioral layer,an individual's vaccination behavior is described,and the probability of an individual receiving a vaccination is influenced by two factors:the influence of negative information,and the influence of local and global disease severity.In the disease layer,individual susceptibility is considered to be influenced by vaccination behavior.The state transition equations are derived using the micro Markov chain approach(MMCA),and disease prevalence thresholds are obtained.It is demonstrated through simulation experiments that the negative information diffusion is less influenced by local vaccination behavior,and is mainly influenced by global vaccination behavior;vaccination behavior is mainly influenced by local disease conditions,and is less influenced by global disease conditions;the disease transmission threshold increases with the increasing vaccination rate;and the scale of disease transmission increases with the increasing negative information diffusion rate and decreases with the increasing vaccination rate.Finally,it is found that when individual vaccination behavior considers both the influence of negative information and disease,it can increase the disease transmission threshold and reduce the scale of disease transmission.Therefore,we should resist the diffusion of negative information,increase vaccination proportions,and take appropriate protective measures in time.

Assessing target optical camouflage effects using brain functional networks:A feasibility study

Brain functional networks model the brain's ability to exchange information across different regions,aiding in the understanding of the cognitive process of human visual attention during target searching,thereby contributing to the advancement of camouflage evaluation.In this study,images with various camouflage effects were presented to observers to generate electroencephalography(EEG)signals,which were then used to construct a brain functional network.The topological parameters of the network were subsequently extracted and input into a machine learning model for training.The results indicate that most of the classifiers achieved accuracy rates exceeding 70%.Specifically,the Logistic algorithm achieved an accuracy of 81.67%.Therefore,it is possible to predict target camouflage effectiveness with high accuracy without the need to calculate discovery probability.The proposed method fully considers the aspects of human visual and cognitive processes,overcomes the subjectivity of human interpretation,and achieves stable and reliable accuracy.

Single event effects evaluation on convolution neural network in Xilinx 28 nm system on chip

Convolutional neural networks(CNNs) exhibit excellent performance in the areas of image recognition and object detection, which can enhance the intelligence level of spacecraft. However, in aerospace, energetic particles, such as heavy ions, protons, and alpha particles, can induce single event effects(SEEs) that lead CNNs to malfunction and can significantly impact the reliability of a CNN system. In this paper, the MNIST CNN system was constructed based on a 28 nm systemon-chip(SoC), and then an alpha particle irradiation experiment and fault injection were applied to evaluate the SEE of the CNN system. Various types of soft errors in the CNN system have been detected, and the SEE cross sections have been calculated. Furthermore, the mechanisms behind some soft errors have been explained. This research will provide technical support for the design of radiation-resistant artificial intelligence chips.

The Effect and Mechanism of Fructus lycii on Improvement of Exercise Fatigue Using a Network Pharmacological Approach with in vitro Experimental Verification

Objective This study aimed to investigate the effect and underlying mechanism of Fructus lycii in improving exercise fatigue.Methods A network pharmacological approach was used to explore potential mechanisms of action of Fructus lycii.Skeletal muscle C2C12 cells and immunofluorescence were employed to verify the effect and mechanism of the representative components in Fructus lycii predicted by network pharmacological analysis.Results Six potential active components,namely quercetin,β-sitosterol,stigmasterol,7-Omethylluteolin-6-C-beta-glucoside_qt,atropine,and glycitein,were identified to have potency in improving exercise fatigue via multiple pathways,such as the PI3K-Akt,neuroactive ligand-receptor interaction,IL-17,TNF,and MAPK signaling pathways.The immunofluorescence results indicated that quercetin,a significant active component in Fructus lycii,increased the mean staining area of 2-NBDG,TMRM,and MitoTracker,and decreased the area of CellRox compared to the control.Furthermore,the protein expression levels of p-38 MAPK,p-MAPK,p-JNK,p-PI3K,and p-AKT markedly increased after quercetin treatment.Conclusion Fructus lycii might alleviate exercise fatigue through multiple components and pathways.Among these,quercetin appears to improve exercise fatigue by enhancing energy metabolism and reducing oxidative stress.The PI3K-AKT and MAPK signaling pathways also appear to play a role in this process.

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.

Effect of land use on soil nematode community composition and co-occurrence network relationship

Land use influences soil biota community composition and diversity,and then belowground ecosystem processes and functions.To characterize the effect of land use on soil biota,soil nematode communities in crop land,forest land and fallow land were investigated in six regions of northern China.Generic richness,diversity,abundance and biomass of soil nematodes was the lowest in crop land.The richness and diversity of soil nematodes were 28.8and 15.1%higher in fallow land than in crop land,respectively.No significant differences in soil nematode indices were found between forest land and fallow land,but their network keystone genera composition was different.Among the keystone genera,50%of forest land genera were omnivores-predators and 36%of fallow land genera were bacterivores.The proportion of fungivores in forest land was 20.8%lower than in fallow land.The network complexity and the stability were lower in crop land than forest land and fallow land.Soil pH,NH_(4)^(+)-N and NO_(3)^(–)-N were the major factors influencing the soil nematode community in crop land while soil organic carbon and moisture were the major factors in forest land.Soil nematode communities in crop land influenced by artificial management practices were more dependent on the soil environment than communities in forest land and fallow land.Land use induced soil environment variation and altered network relationships by influencing trophic group proportions among keystone nematode genera.

Effects of different doses of glucose and fructose on central metabolic pathways and intercellular wireless communication networks in humans

Fructose and glucose are often widely used in food processing and may contribute to many metabolic diseases.To observe the effects of different doses of glucose and fructose on human metabolism and cellular communication,volunteers were given low,medium,and high doses of glucose and fructose.Serum cytokines,glucose,lactate,nicotinamide adenine dinucleotide(NADH)and metabolic enzymes were assayed,and central carbon metabolic pathway networks and cytokine communication networks were constructed.The results showed that the glucose and fructose groups basically maintained the trend of decreasing catabolism and increasing anabolism with increasing dose.Compared with glucose,low-dose fructose decreased catabolism and increased anabolism,significantly enhanced the expression of the inflammatory cytokine interferon-γ(IFN-γ),macrophage-derived chemokine(MDC),induced protein-10(IP-10),and eotaxin,and significantly reduced the activity of isocitrate dehydrogenase(ICDH)and pyruvate dehydrogenase complexes(PDHC).Both medium and high doses of fructose increase catabolism and anabolism,and there are more cytokines and enzymes with significant changes.Furthermore,multiple cytokines and enzymes show strong relevance to metabolic regulation by altering the transcription and expression of enzymes in central carbon metabolic pathways.Therefore,excessive intake of fructose should be reduced to avoid excessive inflammatory responses,allergic reactions and autoimmune diseases.

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.

Using Network Pharmacology to Explore Therapeutic Effect of Polygonum capitatum on Renal Calculus in Rats

[Objectives]To explore the therapeutic effect of Polygonum capitatum on renal calculus in rats based on network pharmacology.[Methods]Through the preliminary construction of P.capitatum-urolithiasis disease target network,explore the active components,action pathway and action target of P.capitatum-urolithiasis treatment,and use 1%ethylene glycol+2%ammonium chloride to induce SD rat kidney calcium oxalate stone model to verify the efficacy of P.capitatum-urolithiasis treatment.[Results]Through the network pharmacological prediction,it is found that the important active components in P.capitatum were quercetin,gallic acid,rutin,silybin,catechin,kaempferol and so on;potential active targets include INS,CAT,IL-6,MOCOS,etc.The results also suggest that forkhead transcription factor signaling pathway(FoxO signaling pathway),tumor necrosis factor signaling pathway(TNF signaling pathway)and hypoxia-inducible factor signaling pathway(HIF-1 signaling pathway)are the core pathways.The results of biochemical indicators in animal experiment showed that the contents of serum urea nitrogen(BUN),creatinine(Cr)and malondialdehyde(MDA)in renal tissue in the treatment group(200,500 mg/kg)were significantly lower than those in the model group,while the content of superoxide dismutase(SOD)was significantly higher than that in the model group.In addition,the kidney tissue H&E staining sections showed that P.capitatum alcohol extract administration group rats kidney calcium oxalate crystals were significantly reduced compared with the model group,the degree of renal tubular lumen expansion was lighter than the model group,suggesting that P.capitatum alcohol extract has the effect of improving renal calculus in rats.[Conclusions]This study provides a theoretical reference for the deep development of P.capitatum in the treatment of renal calculus.

Mechanism of Anti-tumor Effects of Viola Medicinal Materials Based on Network Pharmacology and Molecular Docking

[Objectives]To explore the relationship between anti-tumor components,targets,and pathways involved in Viola medicinal materials,study its main active components,and evaluate its inhibitory activity.[Methods]Through network pharmacological analysis,molecular docking simulation experiments and in vitro experiments,the main components and corresponding targets of Viola were screened out,and their anti-tumor signaling pathways were confirmed.MTT colorimetric assay was used to investigate the inhibitory effect of different extraction layers of Viola on the growth of tumor cells.[Results]18 anti-tumor active components such as 2α,19α-Dihydroxyursolic acid,Corlumine,Madolin U,Trifolirhizin and etc.,and 52 action targets such as PTGS2,PTGS1,P2RX7,MMP1,and GABRB3,and anti-tumor signaling pathways were confirmed.The results of molecular docking showed that all the selected Viola compounds had good binding activity.The results of MTT colorimetric assay showed that the petroleum ether layer and n-butanol layer had a good inhibitory effect on the growth of tumor cell lines.[Conclusions]Viola medicinal materials have the potential of anti-tumor,triterpenoids and flavonoids may be the main active components,and the petroleum ether layer and n-butanol layer have better inhibitory effect on the growth of tumor cells.

Effect of different interventions on orthodontic tooth movement acceleration:A network meta-analysis

Objective: To explore the effectiveness of various interventions in accelerating tooth movement, a systematic review and net-work meta analysis were used to draw a conclusion. Methods: MEDLINE, EMBASE, Willey Library, EBSCO, Web of Science Databases, and Cochrane Central Register of Controlled Trials databases to identify relevant studies. ADDIS 1.16.6 and Stata 16.0 software were used for NMA. Results: Five thousand five hundred and forty-two articles were searched out. After screening by two independent investigators, forty-seven randomized controlled trials, 1 390 participants, were included in this network meta-analysis. A total of 11 interventions involving Piezocision (Piezo), Photobiomodulation therapy (PBMT), Plate- let-rich plasma(PRP), Electromagnetic field(EF), Low intensity laser therapy(LLLT), Low intensity pulsed ultrasound(LI-PUS), Low-frequency vibrations(LFV), Distraction osteogenesis(DAD), Corticotomy(Corti), Microosteoperforations (MOPS), Traditional orthodontic(OT)were identified and classified into 3 classes including surgical treatment, non-surgical treatment and traditional orthodontic treatment. According to SUCRA probability ranking of the best intervention effect, when orthodontic treatment lasted for 1 month, PBMT (90.6%), Piezo(87.4%) and MOPs(73.6%)were the top three interventions to improve the efficiency of canine tooth movement. When orthodontic treatment lasted for 2 months, Corti (75.7%), Piezo (69.6%) and LFV(58.9%)were the top three interventions for improving the mobility efficiency of canine tooth movement. When orthodontic treatment lasted for 3 months, Cort (73.3%), LLLT(68.4%)and LFV(60.8%)were the top three interventions for improving the mobility efficiency of canine tooth movement. Conclusion: PBMT and Piezo can improve the efficiency of canine tooth movement significantly after 1 month, while Corti and LFV can improve the efficiency of canine tooth movement better after 2 and 3 months.

DCFNet:An Effective Dual-Branch Cross-Attention Fusion Network for Medical Image Segmentation

Automatic segmentation of medical images provides a reliable scientific basis for disease diagnosis and analysis.Notably,most existing methods that combine the strengths of convolutional neural networks(CNNs)and Transformers have made significant progress.However,there are some limitations in the current integration of CNN and Transformer technology in two key aspects.Firstly,most methods either overlook or fail to fully incorporate the complementary nature between local and global features.Secondly,the significance of integrating the multiscale encoder features from the dual-branch network to enhance the decoding features is often disregarded in methods that combine CNN and Transformer.To address this issue,we present a groundbreaking dual-branch cross-attention fusion network(DCFNet),which efficiently combines the power of Swin Transformer and CNN to generate complementary global and local features.We then designed the Feature Cross-Fusion(FCF)module to efficiently fuse local and global features.In the FCF,the utilization of the Channel-wise Cross-fusion Transformer(CCT)serves the purpose of aggregatingmulti-scale features,and the Feature FusionModule(FFM)is employed to effectively aggregate dual-branch prominent feature regions from the spatial perspective.Furthermore,within the decoding phase of the dual-branch network,our proposed Channel Attention Block(CAB)aims to emphasize the significance of the channel features between the up-sampled features and the features generated by the FCFmodule to enhance the details of the decoding.Experimental results demonstrate that DCFNet exhibits enhanced accuracy in segmentation performance.Compared to other state-of-the-art(SOTA)methods,our segmentation framework exhibits a superior level of competitiveness.DCFNet’s accurate segmentation of medical images can greatly assist medical professionals in making crucial diagnoses of lesion areas in advance.

Exploring the effect and mechanisms of Epimedium on diabetic testicular injury using network pharmacology,molecular docking,and cell experiments

Background:Studying the potential targets and mechanisms of Epimedium for anti-diabetic testicular injury using network pharmacology,molecular docking,and cell experiments.Methods:Acquisition of major components and targets of Epimedium was based on TCMSP,TCMID,and Symmap databases and retrieval of diabetic testicular injury targets by OMIM,GeneCards,Pharmgkb,and Drugbank databases.Intersecting targets were obtained from the Venny 2.1.0 database and input SRTING data to construct a protein-protein interaction(PPI)network,and key targets were screened in Cytoscape 3.8.0 software.Then the Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analyses of intersecting targets were conducted through the DAVID database.Further,AutoDock software was used to verify docking between the main components and the core target proteins.In addition,a Cell Counting Kit-8(CCK-8)assay was used to determine the survival effect of quercetin,the main component of Epimedium,on TM4 sertoli-like cells exposed to palmitic acid(PA).Results:Quercetin,kaempferol and luteolin in epimedium were identified as the main components in the treatment of diabetic testicular injury.It has core target proteins including MMP9,AKT1,and TNF.The biological process mainly involves the regulation of the apoptotic signaling pathway.The key pathways of KEGG are the AGE-RAGE signaling pathway in diabetic complications,PI3K-Akt and MAPK signaling pathway.Molecular docking results showed that quercetin had the strongest binding ability to MMP9.Also,PA-challenged cells had a lower survival rate,which was alleviated by the administration of quercetin.Conclusion:Our findings suggest that Epimedium attenuates diabetes mellitus(DM)-induced testicular injury through AGE-RAGE,PI3K-Akt and MAPK signaling pathway.These insights offer a potential therapeutic strategy for managing DM-induced testicular injury,will be the basis for future clinical research.

Therapeutic Effects and Potential Mechanisms of Glyasperin A against Myocardial Ischemia Based on Network Pharmacology and Molecular Docking

[Objectives]To explore the therapeutic effects and potential mechanisms of Glyasperin A(GAA)on myocardial ischemia(MI)based on network pharmacology and molecular docking.[Methods]The molecular structure of GAA was downloaded from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP),and all targets of GAA were predicted by converting 3D model molecules into SMILES online tool and Swiss target prediction.Genecards database and DisGeNET database were used to find the targets related to MI,and then Venny 2.1.0 was used to generate the corresponding Wayne diagram,and then Cytoscape 3.9.1 software was used to construct the protein-protein interaction(PPI)network.With the help of DAVID database and Microbiology,the selected core targets were enriched and analyzed by gene ontology(GO),biological process(BP),and Kyoto Encyclopedia of Genes and Genomes(KEGG),and then the molecular docking between GAA and core targets was verified by AutoDock and Pymol software.[Results]A total of 1883 MI targets were screened,and in the protein-protein interaction network,AKT1,PTGS2,PPARG,ESR1,GSK3B were the proteins with higher values.Gene ontology and KEEG enrichment analysis showed that the biological processes involved mainly included inflammatory response,negative regulation of gene expression,and response to exogenous stimuli.Signaling pathways mainly include IL-17 signaling pathway,HIF-1 signaling pathway,and so on.The results of molecular docking showed that the binding energy of GAA and core protein was less than-5 Kcal/mol in four groups.These indicated that GAA with good binding had a certain therapeutic effect on myocardial ischemia.[Conclusions]Based on the systematic network pharmacology method,this study predicts the basic pharmacological effects and potential mechanisms of GAA in the treatment of MI,and reveals that GAA may treat MI through multiple targets and signaling pathways.It is expected to provide a basis for further study of its pharmacological mechanisms.

Effects of data smoothing and recurrent neural network(RNN)algorithms for real-time forecasting of tunnel boring machine(TBM)performance

Tunnel boring machines(TBMs)have been widely utilised in tunnel construction due to their high efficiency and reliability.Accurately predicting TBM performance can improve project time management,cost control,and risk management.This study aims to use deep learning to develop real-time models for predicting the penetration rate(PR).The models are built using data from the Changsha metro project,and their performances are evaluated using unseen data from the Zhengzhou Metro project.In one-step forecast,the predicted penetration rate follows the trend of the measured penetration rate in both training and testing.The autoregressive integrated moving average(ARIMA)model is compared with the recurrent neural network(RNN)model.The results show that univariate models,which only consider historical penetration rate itself,perform better than multivariate models that take into account multiple geological and operational parameters(GEO and OP).Next,an RNN variant combining time series of penetration rate with the last-step geological and operational parameters is developed,and it performs better than other models.A sensitivity analysis shows that the penetration rate is the most important parameter,while other parameters have a smaller impact on time series forecasting.It is also found that smoothed data are easier to predict with high accuracy.Nevertheless,over-simplified data can lose real characteristics in time series.In conclusion,the RNN variant can accurately predict the next-step penetration rate,and data smoothing is crucial in time series forecasting.This study provides practical guidance for TBM performance forecasting in practical engineering.

Effects of ginkgo flavone aglycone on atherosclerosis based on network pharmacology,molecular docking,and in vitro experiments

Background:Ginkgo flavone aglycones(GA),a Ginkgo(Ginkgo biloba)extract,has been proven to have good biological activity in atherosclerosis(AS)treatment.Moreover,its active compounds and the corresponding mechanism for the treatment of AS remain unclear.Methods:To evaluate and identify the potential pharmacological mechanisms of GA in AS treatment,the program Cytoscape was used to generate network mappings of the GA-AS-potential target gene.GO and KEGG enrichment analyses were performed to further investigate the potential mechanism of AS and the pharmacological properties of GA.A molecular docking approach was utilized to determine the GA components that interact with Akt.In vitro experiments were carried out to identify the anti-atherosclerotic effects of GA by targeting Akt.Results:Network pharmacological research determined that the active components of GA(quercetin,kaempferol,and isorhamnetin)correlated with AS target genes such as AKT1,EGFR,SRC,ESR1,PTGS2,MMP9,KDR,GSK3B,APP,and MMP2,respectively.GO enrichment and KEGG analysis showed that PI3K-Akt signaling may play an important role in GA treatment.Molecular docking experiments indicated that quercetin,kaempferol,and isorhamnetin integrate into the binding pockets of the most potentially beneficial GA-AS target protein(Akt).Consequently,cell experiments were conducted to support the anti-atherosclerotic activity of GA on AS by inhibiting the phosphorylation of AKT1 and its downstream signaling molecules,which regulated the proliferation of HASMCs.Conclusion:Our results detailed GA's active ingredients,potential targets,and molecular basis against AS.GA may exert anti-atherosclerotic effects by suppressing Akt phosphorylation and inhibiting the proliferation of HASMCs.It also proposed a viable approach to determining the scientific foundation and therapeutic mechanism of Chinese herbal medicine extracts in disease therapy.

Comparative study of anti-inflammatory effects of different processed products through the COX-2/PGE2 signaling pathway: based on network pharmacology and molecular docking

Background:Radix Aconiti Lateralis Preparata(Fu-zi)is a traditional Chinese medicinal herb,which has been widely used in the clinic and has potent anti-inflammatory activities.we aimed to explore the mechanisms of extract containing alkaloids from different Fu-zi Processed Products(FPP)in treating inflammation,especially rheumatoid arthritis(RA).Methods:Firstly,using network pharmacology technology,the ingredients,and targets of Fu-zi were obtained by searching and screening,the targets involving RA were acquired,the intersection targets were constructed a"component-target-pathway"network.A comprehensive investigation was conducted on the anti-rheumatoid arthritis mechanisms of 5 FPPs in lipopolysaccharide(LPS)induced RAW264.7 cells,which serve as a model for RA.The production of NO and inflammatory cytokines were measured by ELISA kit.Quantitative Real-time PCR(qRT-PCR)was utilized to measure the mRNA levels.COX-2/PGE2 signaling pathway-associated proteins were determined by western blot.Results:According to a network pharmacological study,16 chemical components and 43 common targets were found in Fu-zi and 6 key targets including PTGS2 were closely related to the mechanism of Fu-zi in treating RA.The in vitro study revealed that the levels of NO,TNF-α,and IL-1βwere substantially decreased by the 5 FPPs.The 5 FPPs significantly suppressed the expression of proteins COX-2,iNOS,and NF-κB,with particularly notable effects observed for PFZ and XFZ.Conclusion:Altogether,these results demonstrated that the 5 PPS containing alkaloids have a good anti-RA-related inflammatory effect,and the mechanism may be related to COX-2/PGE2 signaling pathway,particularly,Fu-zi prepared utilizing a traditional Chinese technique.

Effectiveness and safety of traditional Chinese medicine decoction for diabetic gastroparesis:A network meta-analysis

BACKGROUND Diabetic gastroparesis(DGP)is a prevalent complication of diabetes that impairs people's quality of life and places a significant financial burden on them.The gastrointestinal symptoms of DGP patients can be improved by several Traditional Chinese Medicine(TCM)decoctions that have been shown to be effective in treating the disease.There are still many unanswered questions regarding the identification of appropriate therapeutic agents for the treatment of DGP in clinical practice.AIM To analyze the efficacy of several TCM decoctions in the treatment of DGP using Bayesian network meta-analysis for reference.METHODS PubMed,EMBASE,Cochrane Library,Web of Science,China National Knowledge Infrastructure,The China Biology Medicine DVD,Wanfang,and CQVIP were searched from inception to September 17,2022,to collect randomized controlled trials(RCTs)about TCM decoctions for DGP.Clinical effects and symptom scores were the primary outcomes.Additionally,we assessed motilin(MOT),somatostatin(SS),gastrin(GAS),gastric emptying rate,gastric emptying time,and adverse drug events as secondary outcomes.RESULTS A total of 67 eligible RCTs involving 4790 DGP patients and 7 TCM decoctions were included.The results of network meta-analysis(NMA)and surface under the cumulative ranking curve showed that with western medicine(WM)as a common control,the Banxia Xiexin Decoction(BXXD)+WM was most effective in clinical effects and enhancing early satiety scores;the Simo decoction(SMD)+WM was most effective in improving nausea and vomiting scores and anorexia scores,bloating scores;the Chaishao Liujunzi Decoction(CSLJD)was most effective in MOT,the Zhishi Xiaopi Decoction(ZSXPD)was most effective in SS and upgrading emptying rate;the Jianpi Xiaozhi Decoction was most effective in GAS;the CSLJD+WM was most effective in improving gastric emptying time.CONCLUSION These NMA results suggest that the BXXD+WM and SMD+WM may be one of the potential optimal treatments.Due to various limitations,further large-sample,double-blind,multi-center randomized RCTs are needed.