Diabetic Retinopathy(DR)is a type of disease in eyes as a result of a diabetic condition that ends up damaging the retina,leading to blindness or loss of vision.Morphological and physiological retinal variations invol...Diabetic Retinopathy(DR)is a type of disease in eyes as a result of a diabetic condition that ends up damaging the retina,leading to blindness or loss of vision.Morphological and physiological retinal variations involving slowdown of blood flow in the retina,elevation of leukocyte cohesion,basement membrane dystrophy,and decline of pericyte cells,develop.As DR in its initial stage has no symptoms,early detection and automated diagnosis can prevent further visual damage.In this research,using a Deep Neural Network(DNN),segmentation methods are proposed to detect the retinal defects such as exudates,hemorrhages,microaneurysms from digital fundus images and then the conditions are classified accurately to identify the grades as mild,moderate,severe,no PDR,PDR in DR.Initially,saliency detection is applied on color images to detect maximum salient foreground objects from the background.Next,structure tensor is applied powerfully to enhance the local patterns of edge elements and intensity changes that occur on edges of the object.Finally,active contours approximation is performed using gradient descent to segment the lesions from the images.Afterwards,the output images from the proposed segmentation process are subjected to evaluate the ratio between the total contour area and the total true contour arc length to label the classes as mild,moderate,severe,No PDR and PDR.Based on the computed ratio obtained from segmented images,the severity levels were identified.Meanwhile,statistical parameters like the mean and the standard deviation of pixel intensities,mean of hue,saturation and deviation clustering,are estimated through K-means,which are computed as features from the output images of the proposed segmentation process.Using these derived feature sets as input to the classifier,the classification of DR was performed.Finally,a VGG-19 deep neural network was trained and tested using the derived feature sets from the KAGGLE fundus image dataset containing 35,126 images in total.The VGG-19 is trained with features extracted from 20,000 images and tested with features extracted from 5,000 images to achieve a sensitivity of 82%and an accuracy of 96%.The proposed system was able to label and classify DR grades automatically.展开更多
As a representative drug for the treatment of severe community-acquired pneumonia and sepsis,Xuebijing(XBJ)injection is also one of the recommended drugs for the prevention and treatment of coronavirus disease 2019(CO...As a representative drug for the treatment of severe community-acquired pneumonia and sepsis,Xuebijing(XBJ)injection is also one of the recommended drugs for the prevention and treatment of coronavirus disease 2019(COVID-19),but its treatment mechanism for COVID-19 is still unclear.Therefore,this study aims to explore the potential mechanism of XBJ injection in the treatment of COVID-19 employing network pharmacology and molecular docking methods.The corresponding target genes of 45 main active ingredients in XBJ injection and COVID-19 were obtained by using multiple database retrieval and literature mining.102 overlapping targets of them were screened as the core targets for analysis.Then built the PPI network,TCM-compound-target-disease,and disease-target-pathway networks with the help of Cytoscape 3.6.1 software.After that,utilized DAVID to perform gene ontology(GO)function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis to predict the action mechanism of overlapping targets.Finally,by applying molecular docking technology,all compounds were docked with COVID-193 CL protease(3 CLpro),spike protein(S protein),and angiotensin-converting enzyme II(ACE2).The results indicated that quercetin,luteolin,apigenin and other compounds in XBJ injection could affect TNF,MAPK1,IL6 and other overlapping targets.Meanwhile,anhydrosafflor yellow B(AHSYB),salvianolic acid B(SAB),and rutin could combine with COVID-19 crucial proteins,and then played the role of anti-inflammatory,antiviral and immune response to treat COVID-19.This study revealed the multiple active components,multiple targets,and multiple pathways of XBJ injection in the treatment of COVID-19,which provided a new perspective for the study of the mechanism of traditional Chinese medicine(TCM)in the treatment of COVID-19.展开更多
Objective: Lung-toxin Dispelling Formula No. 1, referred to as Respiratory Detox Shot(RDS), was developed based on a classical prescription of traditional Chinese medicine(TCM) and the theoretical understanding of her...Objective: Lung-toxin Dispelling Formula No. 1, referred to as Respiratory Detox Shot(RDS), was developed based on a classical prescription of traditional Chinese medicine(TCM) and the theoretical understanding of herbal properties within TCM. Therapeutic benefits of using RDS for both disease control and prevention, in the effort to contain the coronavirus disease 2019(COVID-19), have been shown. However,the biochemically active constituents of RDS and their mechanisms of action are still unclear. The goal of the present study is to clarify the material foundation and action mechanism of RDS.Methods: To conduct an analysis of RDS, an integrative analytical platform was constructed, including target prediction, protein–protein interaction(PPI) network, and cluster analysis;further, the hub genes involved in the disease-related pathways were identified, and the their corresponding compounds were used for in vitro validation of molecular docking predictions. The presence of these validated compounds was also measured in samples of the RDS formula to quantify the abundance of the biochemically active constituents. In our network pharmacological study, a total of 26 bioinformatic programs and databases were used, and six networks, covering the entire Zang-fu viscera, were constructed to comprehensively analyze the intricate connections among the compounds-targets-disease pathways-meridians of RDS.Results: For all 1071 known chemical constituents of the nine ingredients in RDS, identified from established TCM databases, 157 passed drug-likeness screening and led to 339 predicted targets in the constituent–target network. Forty-two hub genes with core regulatory effects were extracted from the PPI network, and 134 compounds and 29 crucial disease pathways were implicated in the target–constitu ent–disease network. Twelve disease pathways attributed to the Lung–Large Intestine meridians, with six and five attributed to the Kidney–Urinary Bladder and Stomach–Spleen meridians, respectively. Onehundred and eighteen candidate constituents showed a high binding affinity with SARS-coronavirus-23-chymotrypsin-like protease(3 CLpro), as indicated by molecular docking using computational pattern recognition. The in vitro activity of 22 chemical constituents of RDS was validated using the 3 CLproinhibition assay. Finally, using liquid chromatography mass spectrometry in data-independent analysis mode,the presence of seven out of these 22 constituents was confirmed and validated in an aqueous decoction of RDS, using reference standards in both non-targeted and targeted approaches.Conclusion: RDS acts primarily in the Lung–Large Intestine, Kidney–Urinary Bladder and Stomach–Spleen meridians, with other Zang-fu viscera strategically covered by all nine ingredients. In the context of TCM meridian theory, the multiple components and targets of RDS contribute to RDS’s dual effects of healthstrengthening and pathogen-eliminating. This results in general therapeutic effects for early COVID-19 control and prevention.展开更多
(Aim)To make a more accurate and precise COVID-19 diagnosis system,this study proposed a novel deep rank-based average pooling network(DRAPNet)model,i.e.,deep rank-based average pooling network,for COVID-19 recognitio...(Aim)To make a more accurate and precise COVID-19 diagnosis system,this study proposed a novel deep rank-based average pooling network(DRAPNet)model,i.e.,deep rank-based average pooling network,for COVID-19 recognition.(Methods)521 subjects yield 1164 slice images via the slice level selection method.All the 1164 slice images comprise four categories:COVID-19 positive;community-acquired pneumonia;second pulmonary tuberculosis;and healthy control.Our method firstly introduced an improved multiple-way data augmentation.Secondly,an n-conv rankbased average pooling module(NRAPM)was proposed in which rank-based pooling—particularly,rank-based average pooling(RAP)—was employed to avoid overfitting.Third,a novel DRAPNet was proposed based on NRAPM and inspired by the VGGnetwork.Grad-CAM was used to generate heatmaps and gave our AI model an explainable analysis.(Results)Our DRAPNet achieved a micro-averaged F1 score of 95.49%by 10 runs over the test set.The sensitivities of the four classes were 95.44%,96.07%,94.41%,and 96.07%,respectively.The precisions of four classes were 96.45%,95.22%,95.05%,and 95.28%,respectively.The F1 scores of the four classes were 95.94%,95.64%,94.73%,and 95.67%,respectively.Besides,the confusion matrix was given.(Conclusions)The DRAPNet is effective in diagnosing COVID-19 and other chest infectious diseases.The RAP gives better results than four other methods:strided convolution,l2-norm pooling,average pooling,and max pooling.展开更多
Since the outbreak of the novel corona virus disease 2019(COVID-19)at the end of 2019,specific antiviral drugs have been lacking.A Chinese patent medicine Toujiequwen granules has been promoted in the treatment of COV...Since the outbreak of the novel corona virus disease 2019(COVID-19)at the end of 2019,specific antiviral drugs have been lacking.A Chinese patent medicine Toujiequwen granules has been promoted in the treatment of COVID-19.The present study was designed to reveal the molecular mechanism of Toujiequwen granules against COVID-19.A network pharmacological method was applied to screen the main active ingredients of Toujiequwen granules.Network analysis of 149 active ingredients and 330 drug targets showed the most active ingredient interacting with many drug targets is quercetin.Drug targets most ffected by the active ingredients were PTGS2,PTGS1,and DPP4.Drug target disease enrichment analysis showed drug targets were significantly enriched in cardiovascular diseases and digestive tract diseases.An"active ingredient-target-disease"network showed that 57 active ingredients from Toujiequwen granules interacted with 15 key targets of COVID-19.There were 53 ingredients that could act on DPP4,suggesting that DPP4 may become a potential new key target for the treatment of COVID-19.GO analysis results showed that key targets were mainly enriched in the cellular response to lipopolysaccharide,cytokine activity and other functions.KEGG analysis showed they were mainly concentrated in viral protein interaction with cytokine and cytokine receptors and endocrine resistance pathway.The evidence suggests that Toujiequwen granules might play an effective role by improving the symptoms of underlying diseases in patients with COVID-19 and multi-target interventions against mutiple signaling pathways related to the pathogenesis of COVID-19.展开更多
Objective:Severe cases of coronavirus disease 2019(COVID-19)are expected to have a worse prognosis than mild cases.Shenhuang Granule(SHG)has been shown to be a safe and effective treatment for severe COVID-19 in a pre...Objective:Severe cases of coronavirus disease 2019(COVID-19)are expected to have a worse prognosis than mild cases.Shenhuang Granule(SHG)has been shown to be a safe and effective treatment for severe COVID-19 in a previous randomized clinical trial,but the active chemical constituents and underlying mechanisms of action remain unknown.The goal of this study is to explore the chemical basis and mechanisms of SHG in the treatment of severe COVID-19,using network pharmacology.Methods:Ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry was employed to screen chemical constituents of SHG.Putative therapeutic targets were predicted by searching traditional Chinese medicine system pharmacology database and analysis platform,Swiss Target Prediction,and Gene Expression Omnibus(GEO)databases.The target protein–protein interaction network and enrichment analysis were performed to investigate the hub genes and presumptive mechanisms.Molecular docking and molecular dynamics simulations were used to verify the stability and interaction between the key chemical constituents of SHG and COVID-19 protein targets.Results:Forty-five chemical constituents of SHG were identified along with 131 corresponding therapeutic targets,including hub genes such as HSP90AA1,MMP9,CXCL8,PTGS2,IFNG,DNMT1,TYMS,MDM2,HDAC3 and ABCB1.Functional enrichment analysis indicated that SHG mainly acted on the neuroactive ligand-receptor interaction,calcium signaling pathway and c AMP signaling pathway.Molecular docking showed that the key constituents had a good affinity with the severe acute respiratory syndrome coronavirus 2 protein targets.Molecular dynamics simulations indicated that ginsenoside Rg4 formed a stable protein-ligand complex with helicase.Conclusion:Multiple components of SHG regulated multiple targets to inhibit virus invasion and cytokine storm through several signaling pathways;this provides a scientific basis for clinical applications and further experiments.展开更多
The latest studies with radiological imaging techniques indicate that X-ray images provide valuable details on the Coronavirus disease 2019(COVID-19).The usage of sophisticated artificial intelligence technology(AI)an...The latest studies with radiological imaging techniques indicate that X-ray images provide valuable details on the Coronavirus disease 2019(COVID-19).The usage of sophisticated artificial intelligence technology(AI)and the radiological images can help in diagnosing the disease reliably and addressing the problem of the shortage of trained doctors in remote villages.In this research,the automated diagnosis of Coronavirus disease was performed using a dataset of X-ray images of patients with severe bacterial pneumonia,reported COVID-19 disease,and normal cases.The goal of the study is to analyze the achievements for medical image recognition of state-of-the-art neural networking architectures.Transfer Learning technique has been implemented in this work.Transfer learning is an ambitious task,but it results in impressive outcomes for identifying distinct patterns in tiny datasets of medical images.The findings indicate that deep learning with X-ray imagery could retrieve important biomarkers relevant for COVID-19 disease detection.Since all diagnostic measures show failure levels that pose questions,the scientific profession should determine the probability of integration of X-rays with the clinical treatment,utilizing the results.The proposed model achieved 96.73%accuracy outperforming the ResNet50 and traditional Resnet18 models.Based on our findings,the proposed system can help the specialist doctors in making verdicts for COVID-19 detection.展开更多
Many people around the world have lost their lives due to COVID-19.The symptoms of most COVID-19 patients are fever,tiredness and dry cough,and the disease can easily spread to those around them.If the infected people...Many people around the world have lost their lives due to COVID-19.The symptoms of most COVID-19 patients are fever,tiredness and dry cough,and the disease can easily spread to those around them.If the infected people can be detected early,this will help local authorities control the speed of the virus,and the infected can also be treated in time.We proposed a six-layer convolutional neural network combined with max pooling,batch normalization and Adam algorithm to improve the detection effect of COVID-19 patients.In the 10-fold cross-validation methods,our method is superior to several state-of-the-art methods.In addition,we use Grad-CAM technology to realize heat map visualization to observe the process of model training and detection.展开更多
The coronavirus disease 2019(COVID-19)has been widely spread around the world,and the control and behavior dynamics are still one of the important research directions in the world.Based on the characteristics of COVID...The coronavirus disease 2019(COVID-19)has been widely spread around the world,and the control and behavior dynamics are still one of the important research directions in the world.Based on the characteristics of COVID-19’s spread,a coupled disease-awareness model on multiplex networks is proposed in this paper to study and simulate the interaction between the spreading behavior of COVID-19 and related information.In the layer of epidemic spreading,the nodes can be divided into five categories,where the topology of the network represents the physical contact relationship of the population.The topological structure of the upper network shows the information interaction among the nodes,which can be divided into aware and unaware states.Awareness will make people play a positive role in preventing the epidemic diffusion,influencing the spread of the disease.Based on the above model,we have established the state transition equation,through the microscopic Markov chain approach(MMCA),and proposed the propagation threshold calculation method under the epidemic model.Furthermore,MMCA iteration and the Monte Carlo method are simulated on the static network and dynamic network,respectively.The current results will be beneficial to the study of COVID-19,and propose a more rational and effective model for future research on epidemics.展开更多
Coronavirus disease 2019(Covid-19)is a life-threatening infectious disease caused by a newly discovered strain of the coronaviruses.As by the end of 2020,Covid-19 is still not fully understood,but like other similar v...Coronavirus disease 2019(Covid-19)is a life-threatening infectious disease caused by a newly discovered strain of the coronaviruses.As by the end of 2020,Covid-19 is still not fully understood,but like other similar viruses,the main mode of transmission or spread is believed to be through droplets from coughs and sneezes of infected persons.The accurate detection of Covid-19 cases poses some questions to scientists and physicians.The two main kinds of tests available for Covid-19 are viral tests,which tells you whether you are currently infected and antibody test,which tells if you had been infected previously.Rou-tine Covid-19 test can take up to 2 days to complete;in reducing chances of false negative results,serial testing is used.Medical image processing by means of using Chest X-ray images and Computed Tomography(CT)can help radiologists detect the virus.This imaging approach can detect certain characteristic changes in the lung associated with Covid-19.In this paper,a deep learning model or tech-nique based on the Convolutional Neural Network is proposed to improve the accuracy and precisely detect Covid-19 from Chest Xray scans by identifying structural abnormalities in scans or X-ray images.The entire model proposed is categorized into three stages:dataset,data pre-processing andfinal stage being training and classification.展开更多
Traditional Chinese medicine,as a complementary and alternative medicine,has been practiced for thousands of years in China and possesses remarkable clinical efficacy.Thus,systematic analysis and examination of the me...Traditional Chinese medicine,as a complementary and alternative medicine,has been practiced for thousands of years in China and possesses remarkable clinical efficacy.Thus,systematic analysis and examination of the mechanistic links between Chinese herbal medicine(CHM)and the complex human body can benefit contemporary understandings by carrying out qualitative and quantitative analysis.With increasing attention,the approach of network pharmacology has begun to unveil the mystery of CHM by constructing the heterogeneous network relationship of“herb-compound-target-pathway,”which corresponds to the holistic mechanisms of CHM.By integrating computational techniques into network pharmacology,the efficiency and accuracy of active compound screening and target fishing have been improved at an unprecedented pace.This review dissects the core innovations to the network pharmacology approach that were developed in the years since 2015 and highlights how this tool has been applied to understanding the coronavirus disease 2019 and refining the clinical use of CHM to combat it.展开更多
Background:To evaluate the mechanism of Chinese patent drug Xuebijing(XBJ)injection in the treatment of a new coronavirus disease 2019(COVID-19)based on network pharmacology and molecular docking technology.Methods:Th...Background:To evaluate the mechanism of Chinese patent drug Xuebijing(XBJ)injection in the treatment of a new coronavirus disease 2019(COVID-19)based on network pharmacology and molecular docking technology.Methods:The TCMSP database was employed to collect and screen the active ingredients of the Chinese herb contained in the XBJ injection.The GeneCards database and STRING database were applied to collect and expand the targets of COVID-19 and compare and screen the related targets of COVID-19 by XBJ injection.Cytoscape was employed to build a network connecting Chinese medicine,compounds,targets,disease,and topology analysis was performed via the Network Analyzer to screen the key ingredients and targets.The software of Schrödinger molecular docking was used to verify the binding activity of the key ingredients of XBJ injection and the key targets of COVID-19.Metascape platform and DAVID database were utilized to conduct Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes analysis on the key targets of COVID-19 treated by XBJ injection.Results:Eight key compounds and 15 key targets were screened and verified by molecular docking;these key compounds included luteolin,quercetin,baicalein,and kaempferol.The key targets included DPP4,AR,ESR1,CALM1,and protein kinase 1.Gene Ontology analysis involved an apoptosis and hypoxia reaction and the changes in blood vessel morphology.Kyoto Encyclopedia of Genes and Genomes analysis involved signaling pathways of hypoxia inducible factor-1,VEGF,and PI3K/AKT/NF-κB.Conclusion:The mechanism of XBJ injection when used to treat COVID-19 should be further investigated as the key compounds in XBJ regulated the expression of key targets such as protein kinase 1,VEGF-A,B-cell lymphoma-2,and TNF,which affected the COVID-19 receptors such as angiotensin-converting enzyme 2 and signaling pathways like hypoxia inducible factor-1,PI3K-Akt,and NF-κB,which alleviated the inflammation,respiratory distress,and hypoxia caused by COVID-19 infection.展开更多
The COVID-19 pandemic poses an additional serious public health threat due to little or no pre-existing human immunity,and developing a system to identify COVID-19 in its early stages will save millions of lives.This ...The COVID-19 pandemic poses an additional serious public health threat due to little or no pre-existing human immunity,and developing a system to identify COVID-19 in its early stages will save millions of lives.This study applied support vector machine(SVM),k-nearest neighbor(K-NN)and deep learning convolutional neural network(CNN)algorithms to classify and detect COVID-19 using chest X-ray radiographs.To test the proposed system,chest X-ray radiographs and CT images were collected from different standard databases,which contained 95 normal images,140 COVID-19 images and 10 SARS images.Two scenarios were considered to develop a system for predicting COVID-19.In the first scenario,the Gaussian filter was applied to remove noise from the chest X-ray radiograph images,and then the adaptive region growing technique was used to segment the region of interest from the chest X-ray radiographs.After segmentation,a hybrid feature extraction composed of 2D-DWT and gray level co-occurrence matrix was utilized to extract the features significant for detecting COVID-19.These features were processed using SVM and K-NN.In the second scenario,a CNN transfer model(ResNet 50)was used to detect COVID-19.The system was examined and evaluated through multiclass statistical analysis,and the empirical results of the analysis found significant values of 97.14%,99.34%,99.26%,99.26%and 99.40%for accuracy,specificity,sensitivity,recall and AUC,respectively.Thus,the CNN model showed significant success;it achieved optimal accuracy,effectiveness and robustness for detecting COVID-19.展开更多
In this study,we have proposed an artificial neural network(ANN)model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17,2020.The proposed model is...In this study,we have proposed an artificial neural network(ANN)model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17,2020.The proposed model is based on the existing data(training data)published in the Saudi Arabia Coronavirus disease(COVID-19)situation—Demographics.The Prey-Predator algorithm is employed for the training.Multilayer perceptron neural network(MLPNN)is used in this study.To improve the performance of MLPNN,we determined the parameters of MLPNN using the prey-predator algorithm(PPA).The proposed model is called the MLPNN–PPA.The performance of the proposed model has been analyzed by the root mean squared error(RMSE)function,and correlation coefficient(R).Furthermore,we tested the proposed model using other existing data recorded in Saudi Arabia(testing data).It is demonstrated that the MLPNN-PPA model has the highest performance in predicting the number of infected and recovering in Saudi Arabia.The results reveal that the number of infected persons will increase in the coming days and become a minimum of 9789.The number of recoveries will be 2000 to 4000 per day.展开更多
Currently,COVID-19 is spreading all over the world and profoundly impacting people’s lives and economic activities.In this paper,a novel approach called the COVID-19 Quantum Neural Network(CQNN)for predicting the sev...Currently,COVID-19 is spreading all over the world and profoundly impacting people’s lives and economic activities.In this paper,a novel approach called the COVID-19 Quantum Neural Network(CQNN)for predicting the severity of COVID-19 in patients is proposed.It consists of two phases:In the first,the most distinct subset of features in a dataset is identified using a Quick Reduct Feature Selection(QRFS)method to improve its classification performance;and,in the second,machine learning is used to train the quantum neural network to classify the risk.It is found that patients’serial blood counts(their numbers of lymphocytes from days 1 to 15 after admission to hospital)are associated with relapse rates and evaluations of COVID-19 infections.Accordingly,the severity of COVID-19 is classified in two categories,serious and non-serious.The experimental results indicate that the proposed CQNN’s prediction approach outperforms those of other classification algorithms and its high accuracy confirms its effectiveness.展开更多
The paper is proposed to explore the potential effects of Shufeng Jiedu Capsule against COVID-19. The ingredients and targets of Shufeng Jiedu Capsule were collected by the Traditional Chinese Medicine Systems Pharmac...The paper is proposed to explore the potential effects of Shufeng Jiedu Capsule against COVID-19. The ingredients and targets of Shufeng Jiedu Capsule were collected by the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and the gene names of potential targets were extracted by UniProtKB. Then we did protein-protein interaction networks functional enrichment analysis by the STRING platform, reconstructed drug-target pathways and networks to predict the likely protein targets of the capsule against COVID-19 with software Cytoscape 3.6.1, and carried out GO enrichment analysis and KEGG analysis with R 5.3.2 software. At last we validated our predictions on molecular docking. The results suggested that Shufeng Jiedu Capsule contained 155 ingredients and 237 targets, including 26 main active ingredients and 45 key targets. There were 2334 biological processes (BP), 103 cell composition (CC) and 198 molecular functions (MF) in GO Enrichment Analysis, and 177 pathways in the KEGG analysis. The molecular docking analysis showed that binding energy for 26 main active ingredients ranged from -32.21 to -25.94 kJ·mol-1, and the main targets bind to SARS-CoV-2 3CL hydrolase by acting on CASP9, PRKCA, RELA and others. Our study suggested that Shufeng Jiedu Capsule has potential therapeutic effects on COVID-19.展开更多
Network pharmacology is an emerging technology based on systems biology and computer information technology,with the help of databases and related auxiliary software,to carry out new drug development and the screening...Network pharmacology is an emerging technology based on systems biology and computer information technology,with the help of databases and related auxiliary software,to carry out new drug development and the screening analysis of drug active ingredients and targets.At present,the network pharmacology has been used widely in the research of prevention and treatment drugs for coronavirus disease 2019(COVID-19).This paper reviews the research methods of network pharmacology in the field of prevention and treatment of COVID-19 by traditional Chinese medicine(TCM)and the development of its specific drugs and further explores the concrete application ideas of this technology.The necessary databases and tools of necessary for screening the active components and targets to molecular docking are summarized.In addition,the practical application of network pharmacology in the study of several potential TCM and active components against COVID-19 is reviewed,mainly including the screening of active components,the discovery of target,and the elucidation of action mechanism.The diversification of research ideas of network pharmacology in the field of TCM was realized,in particular,with two specific ideas in the study of active ingredients of TCM.Finally,the difference of control effect among several TCM and Western medicines on COVID-19 and the limitation and challenge of network pharmacology in TCM,i.e.,the insufficient integrity and accuracy of the database,the uncertain complexity of components analysis,the unclear mechanism of component-target action,and some new challenges due to the characteristics of TCM,are discussed.In view of the importance of TCM in the field of control of COVID-19,the combination of TCM and network pharmacology will continue to play an important role in the development of specific drugs of COVID-19 in the future,in particular,to save time and reduce the workload of drug developers,which is also a direction of TCM development.This study provides theoretical reference and methodological basis for the prevention and treatment of COVID-19 by TCM.展开更多
文摘Diabetic Retinopathy(DR)is a type of disease in eyes as a result of a diabetic condition that ends up damaging the retina,leading to blindness or loss of vision.Morphological and physiological retinal variations involving slowdown of blood flow in the retina,elevation of leukocyte cohesion,basement membrane dystrophy,and decline of pericyte cells,develop.As DR in its initial stage has no symptoms,early detection and automated diagnosis can prevent further visual damage.In this research,using a Deep Neural Network(DNN),segmentation methods are proposed to detect the retinal defects such as exudates,hemorrhages,microaneurysms from digital fundus images and then the conditions are classified accurately to identify the grades as mild,moderate,severe,no PDR,PDR in DR.Initially,saliency detection is applied on color images to detect maximum salient foreground objects from the background.Next,structure tensor is applied powerfully to enhance the local patterns of edge elements and intensity changes that occur on edges of the object.Finally,active contours approximation is performed using gradient descent to segment the lesions from the images.Afterwards,the output images from the proposed segmentation process are subjected to evaluate the ratio between the total contour area and the total true contour arc length to label the classes as mild,moderate,severe,No PDR and PDR.Based on the computed ratio obtained from segmented images,the severity levels were identified.Meanwhile,statistical parameters like the mean and the standard deviation of pixel intensities,mean of hue,saturation and deviation clustering,are estimated through K-means,which are computed as features from the output images of the proposed segmentation process.Using these derived feature sets as input to the classifier,the classification of DR was performed.Finally,a VGG-19 deep neural network was trained and tested using the derived feature sets from the KAGGLE fundus image dataset containing 35,126 images in total.The VGG-19 is trained with features extracted from 20,000 images and tested with features extracted from 5,000 images to achieve a sensitivity of 82%and an accuracy of 96%.The proposed system was able to label and classify DR grades automatically.
基金supported by the Double-Class University project(No.CPU2018GY19)the National Natural Science Foundation of China(No.81874331)。
文摘As a representative drug for the treatment of severe community-acquired pneumonia and sepsis,Xuebijing(XBJ)injection is also one of the recommended drugs for the prevention and treatment of coronavirus disease 2019(COVID-19),but its treatment mechanism for COVID-19 is still unclear.Therefore,this study aims to explore the potential mechanism of XBJ injection in the treatment of COVID-19 employing network pharmacology and molecular docking methods.The corresponding target genes of 45 main active ingredients in XBJ injection and COVID-19 were obtained by using multiple database retrieval and literature mining.102 overlapping targets of them were screened as the core targets for analysis.Then built the PPI network,TCM-compound-target-disease,and disease-target-pathway networks with the help of Cytoscape 3.6.1 software.After that,utilized DAVID to perform gene ontology(GO)function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis to predict the action mechanism of overlapping targets.Finally,by applying molecular docking technology,all compounds were docked with COVID-193 CL protease(3 CLpro),spike protein(S protein),and angiotensin-converting enzyme II(ACE2).The results indicated that quercetin,luteolin,apigenin and other compounds in XBJ injection could affect TNF,MAPK1,IL6 and other overlapping targets.Meanwhile,anhydrosafflor yellow B(AHSYB),salvianolic acid B(SAB),and rutin could combine with COVID-19 crucial proteins,and then played the role of anti-inflammatory,antiviral and immune response to treat COVID-19.This study revealed the multiple active components,multiple targets,and multiple pathways of XBJ injection in the treatment of COVID-19,which provided a new perspective for the study of the mechanism of traditional Chinese medicine(TCM)in the treatment of COVID-19.
基金the financial support from National Key Research and Development Program of China (No.2018YFC1707900).
文摘Objective: Lung-toxin Dispelling Formula No. 1, referred to as Respiratory Detox Shot(RDS), was developed based on a classical prescription of traditional Chinese medicine(TCM) and the theoretical understanding of herbal properties within TCM. Therapeutic benefits of using RDS for both disease control and prevention, in the effort to contain the coronavirus disease 2019(COVID-19), have been shown. However,the biochemically active constituents of RDS and their mechanisms of action are still unclear. The goal of the present study is to clarify the material foundation and action mechanism of RDS.Methods: To conduct an analysis of RDS, an integrative analytical platform was constructed, including target prediction, protein–protein interaction(PPI) network, and cluster analysis;further, the hub genes involved in the disease-related pathways were identified, and the their corresponding compounds were used for in vitro validation of molecular docking predictions. The presence of these validated compounds was also measured in samples of the RDS formula to quantify the abundance of the biochemically active constituents. In our network pharmacological study, a total of 26 bioinformatic programs and databases were used, and six networks, covering the entire Zang-fu viscera, were constructed to comprehensively analyze the intricate connections among the compounds-targets-disease pathways-meridians of RDS.Results: For all 1071 known chemical constituents of the nine ingredients in RDS, identified from established TCM databases, 157 passed drug-likeness screening and led to 339 predicted targets in the constituent–target network. Forty-two hub genes with core regulatory effects were extracted from the PPI network, and 134 compounds and 29 crucial disease pathways were implicated in the target–constitu ent–disease network. Twelve disease pathways attributed to the Lung–Large Intestine meridians, with six and five attributed to the Kidney–Urinary Bladder and Stomach–Spleen meridians, respectively. Onehundred and eighteen candidate constituents showed a high binding affinity with SARS-coronavirus-23-chymotrypsin-like protease(3 CLpro), as indicated by molecular docking using computational pattern recognition. The in vitro activity of 22 chemical constituents of RDS was validated using the 3 CLproinhibition assay. Finally, using liquid chromatography mass spectrometry in data-independent analysis mode,the presence of seven out of these 22 constituents was confirmed and validated in an aqueous decoction of RDS, using reference standards in both non-targeted and targeted approaches.Conclusion: RDS acts primarily in the Lung–Large Intestine, Kidney–Urinary Bladder and Stomach–Spleen meridians, with other Zang-fu viscera strategically covered by all nine ingredients. In the context of TCM meridian theory, the multiple components and targets of RDS contribute to RDS’s dual effects of healthstrengthening and pathogen-eliminating. This results in general therapeutic effects for early COVID-19 control and prevention.
基金This study is partially supported by the Medical Research Council Confidence in Concept Award,UK(MC_PC_17171)Royal Society International Exchanges Cost Share Award,UK(RP202G0230)+3 种基金Hope Foundation for Cancer Research,UK(RM60G0680)British Heart Foundation Accelerator Award,UKSino-UK Industrial Fund,UK(RP202G0289)Global Challenges Research Fund(GCRF),UK(P202PF11).We thank Dr.Hemil Patel for his help in English correction.
文摘(Aim)To make a more accurate and precise COVID-19 diagnosis system,this study proposed a novel deep rank-based average pooling network(DRAPNet)model,i.e.,deep rank-based average pooling network,for COVID-19 recognition.(Methods)521 subjects yield 1164 slice images via the slice level selection method.All the 1164 slice images comprise four categories:COVID-19 positive;community-acquired pneumonia;second pulmonary tuberculosis;and healthy control.Our method firstly introduced an improved multiple-way data augmentation.Secondly,an n-conv rankbased average pooling module(NRAPM)was proposed in which rank-based pooling—particularly,rank-based average pooling(RAP)—was employed to avoid overfitting.Third,a novel DRAPNet was proposed based on NRAPM and inspired by the VGGnetwork.Grad-CAM was used to generate heatmaps and gave our AI model an explainable analysis.(Results)Our DRAPNet achieved a micro-averaged F1 score of 95.49%by 10 runs over the test set.The sensitivities of the four classes were 95.44%,96.07%,94.41%,and 96.07%,respectively.The precisions of four classes were 96.45%,95.22%,95.05%,and 95.28%,respectively.The F1 scores of the four classes were 95.94%,95.64%,94.73%,and 95.67%,respectively.Besides,the confusion matrix was given.(Conclusions)The DRAPNet is effective in diagnosing COVID-19 and other chest infectious diseases.The RAP gives better results than four other methods:strided convolution,l2-norm pooling,average pooling,and max pooling.
基金was supported by the grants from the Education Department of Liaoning Province(No.LFW201701)Liaoning Provincial Key R&D Project(No.2020JH2/10300114)Key Laboratory of Shenyang Science and Technology Bureau(No.18-007-0-02)。
文摘Since the outbreak of the novel corona virus disease 2019(COVID-19)at the end of 2019,specific antiviral drugs have been lacking.A Chinese patent medicine Toujiequwen granules has been promoted in the treatment of COVID-19.The present study was designed to reveal the molecular mechanism of Toujiequwen granules against COVID-19.A network pharmacological method was applied to screen the main active ingredients of Toujiequwen granules.Network analysis of 149 active ingredients and 330 drug targets showed the most active ingredient interacting with many drug targets is quercetin.Drug targets most ffected by the active ingredients were PTGS2,PTGS1,and DPP4.Drug target disease enrichment analysis showed drug targets were significantly enriched in cardiovascular diseases and digestive tract diseases.An"active ingredient-target-disease"network showed that 57 active ingredients from Toujiequwen granules interacted with 15 key targets of COVID-19.There were 53 ingredients that could act on DPP4,suggesting that DPP4 may become a potential new key target for the treatment of COVID-19.GO analysis results showed that key targets were mainly enriched in the cellular response to lipopolysaccharide,cytokine activity and other functions.KEGG analysis showed they were mainly concentrated in viral protein interaction with cytokine and cytokine receptors and endocrine resistance pathway.The evidence suggests that Toujiequwen granules might play an effective role by improving the symptoms of underlying diseases in patients with COVID-19 and multi-target interventions against mutiple signaling pathways related to the pathogenesis of COVID-19.
基金supported by the National Key Research and Development Program(No.2018YFC1705900)the Emergency Committee of the World Federation of Chinese Medicine Societies and Shanghai Society of Traditional Chinese Medicine Novel Coronavirus Pneumonia Emergency Tackling Key Project(No.SJZLJZ.N01)。
文摘Objective:Severe cases of coronavirus disease 2019(COVID-19)are expected to have a worse prognosis than mild cases.Shenhuang Granule(SHG)has been shown to be a safe and effective treatment for severe COVID-19 in a previous randomized clinical trial,but the active chemical constituents and underlying mechanisms of action remain unknown.The goal of this study is to explore the chemical basis and mechanisms of SHG in the treatment of severe COVID-19,using network pharmacology.Methods:Ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry was employed to screen chemical constituents of SHG.Putative therapeutic targets were predicted by searching traditional Chinese medicine system pharmacology database and analysis platform,Swiss Target Prediction,and Gene Expression Omnibus(GEO)databases.The target protein–protein interaction network and enrichment analysis were performed to investigate the hub genes and presumptive mechanisms.Molecular docking and molecular dynamics simulations were used to verify the stability and interaction between the key chemical constituents of SHG and COVID-19 protein targets.Results:Forty-five chemical constituents of SHG were identified along with 131 corresponding therapeutic targets,including hub genes such as HSP90AA1,MMP9,CXCL8,PTGS2,IFNG,DNMT1,TYMS,MDM2,HDAC3 and ABCB1.Functional enrichment analysis indicated that SHG mainly acted on the neuroactive ligand-receptor interaction,calcium signaling pathway and c AMP signaling pathway.Molecular docking showed that the key constituents had a good affinity with the severe acute respiratory syndrome coronavirus 2 protein targets.Molecular dynamics simulations indicated that ginsenoside Rg4 formed a stable protein-ligand complex with helicase.Conclusion:Multiple components of SHG regulated multiple targets to inhibit virus invasion and cytokine storm through several signaling pathways;this provides a scientific basis for clinical applications and further experiments.
文摘The latest studies with radiological imaging techniques indicate that X-ray images provide valuable details on the Coronavirus disease 2019(COVID-19).The usage of sophisticated artificial intelligence technology(AI)and the radiological images can help in diagnosing the disease reliably and addressing the problem of the shortage of trained doctors in remote villages.In this research,the automated diagnosis of Coronavirus disease was performed using a dataset of X-ray images of patients with severe bacterial pneumonia,reported COVID-19 disease,and normal cases.The goal of the study is to analyze the achievements for medical image recognition of state-of-the-art neural networking architectures.Transfer Learning technique has been implemented in this work.Transfer learning is an ambitious task,but it results in impressive outcomes for identifying distinct patterns in tiny datasets of medical images.The findings indicate that deep learning with X-ray imagery could retrieve important biomarkers relevant for COVID-19 disease detection.Since all diagnostic measures show failure levels that pose questions,the scientific profession should determine the probability of integration of X-rays with the clinical treatment,utilizing the results.The proposed model achieved 96.73%accuracy outperforming the ResNet50 and traditional Resnet18 models.Based on our findings,the proposed system can help the specialist doctors in making verdicts for COVID-19 detection.
文摘Many people around the world have lost their lives due to COVID-19.The symptoms of most COVID-19 patients are fever,tiredness and dry cough,and the disease can easily spread to those around them.If the infected people can be detected early,this will help local authorities control the speed of the virus,and the infected can also be treated in time.We proposed a six-layer convolutional neural network combined with max pooling,batch normalization and Adam algorithm to improve the detection effect of COVID-19 patients.In the 10-fold cross-validation methods,our method is superior to several state-of-the-art methods.In addition,we use Grad-CAM technology to realize heat map visualization to observe the process of model training and detection.
基金supported by the National Natural Science Foundation of China(Grant No.12002135)the Natural Science Foundation of Jiangsu Province(Grant No.BK20190836)+2 种基金China Postdoctoral Science Foundation(Grant No.2019M661732)the Natural Science Research of Jiangsu Higher Education Institutions of China(Grant No.19KJB110001)Priority Academic Program Developmentof Jiangsu Higher Education Institutions(Grant No.PAPD-2018-87).
文摘The coronavirus disease 2019(COVID-19)has been widely spread around the world,and the control and behavior dynamics are still one of the important research directions in the world.Based on the characteristics of COVID-19’s spread,a coupled disease-awareness model on multiplex networks is proposed in this paper to study and simulate the interaction between the spreading behavior of COVID-19 and related information.In the layer of epidemic spreading,the nodes can be divided into five categories,where the topology of the network represents the physical contact relationship of the population.The topological structure of the upper network shows the information interaction among the nodes,which can be divided into aware and unaware states.Awareness will make people play a positive role in preventing the epidemic diffusion,influencing the spread of the disease.Based on the above model,we have established the state transition equation,through the microscopic Markov chain approach(MMCA),and proposed the propagation threshold calculation method under the epidemic model.Furthermore,MMCA iteration and the Monte Carlo method are simulated on the static network and dynamic network,respectively.The current results will be beneficial to the study of COVID-19,and propose a more rational and effective model for future research on epidemics.
基金supported by the National Natural Science Foundation of China(61876089,61876185,61902281,61395121)the Opening Project of Jiangsu Key Laboratory of Data Science and Smart Software(No.2019DS301)+1 种基金the Engineering Research Center of Digital Forensics,Ministry of Education,the Key Research and Development Program of Nanjing Jiangbei New Area(ZDYF20200129)the Priority Academic Program Development of Jiangsu Higher Education Institutions。
文摘Coronavirus disease 2019(Covid-19)is a life-threatening infectious disease caused by a newly discovered strain of the coronaviruses.As by the end of 2020,Covid-19 is still not fully understood,but like other similar viruses,the main mode of transmission or spread is believed to be through droplets from coughs and sneezes of infected persons.The accurate detection of Covid-19 cases poses some questions to scientists and physicians.The two main kinds of tests available for Covid-19 are viral tests,which tells you whether you are currently infected and antibody test,which tells if you had been infected previously.Rou-tine Covid-19 test can take up to 2 days to complete;in reducing chances of false negative results,serial testing is used.Medical image processing by means of using Chest X-ray images and Computed Tomography(CT)can help radiologists detect the virus.This imaging approach can detect certain characteristic changes in the lung associated with Covid-19.In this paper,a deep learning model or tech-nique based on the Convolutional Neural Network is proposed to improve the accuracy and precisely detect Covid-19 from Chest Xray scans by identifying structural abnormalities in scans or X-ray images.The entire model proposed is categorized into three stages:dataset,data pre-processing andfinal stage being training and classification.
基金supported by grants from the National Natural Science Foundation of China(No.81903786)the Shaanxi Natural Science Foundation of China(No.2021JQ-731)Subject Innovation Team of Shaanxi University of Chinese Medicine(No.2019-YL10)。
文摘Traditional Chinese medicine,as a complementary and alternative medicine,has been practiced for thousands of years in China and possesses remarkable clinical efficacy.Thus,systematic analysis and examination of the mechanistic links between Chinese herbal medicine(CHM)and the complex human body can benefit contemporary understandings by carrying out qualitative and quantitative analysis.With increasing attention,the approach of network pharmacology has begun to unveil the mystery of CHM by constructing the heterogeneous network relationship of“herb-compound-target-pathway,”which corresponds to the holistic mechanisms of CHM.By integrating computational techniques into network pharmacology,the efficiency and accuracy of active compound screening and target fishing have been improved at an unprecedented pace.This review dissects the core innovations to the network pharmacology approach that were developed in the years since 2015 and highlights how this tool has been applied to understanding the coronavirus disease 2019 and refining the clinical use of CHM to combat it.
基金This study was supported by the Foundation of Health Commission of Hebei Province(20190123)the Natural Science Foundation of Hebei Province of China(H2018201179).
文摘Background:To evaluate the mechanism of Chinese patent drug Xuebijing(XBJ)injection in the treatment of a new coronavirus disease 2019(COVID-19)based on network pharmacology and molecular docking technology.Methods:The TCMSP database was employed to collect and screen the active ingredients of the Chinese herb contained in the XBJ injection.The GeneCards database and STRING database were applied to collect and expand the targets of COVID-19 and compare and screen the related targets of COVID-19 by XBJ injection.Cytoscape was employed to build a network connecting Chinese medicine,compounds,targets,disease,and topology analysis was performed via the Network Analyzer to screen the key ingredients and targets.The software of Schrödinger molecular docking was used to verify the binding activity of the key ingredients of XBJ injection and the key targets of COVID-19.Metascape platform and DAVID database were utilized to conduct Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes analysis on the key targets of COVID-19 treated by XBJ injection.Results:Eight key compounds and 15 key targets were screened and verified by molecular docking;these key compounds included luteolin,quercetin,baicalein,and kaempferol.The key targets included DPP4,AR,ESR1,CALM1,and protein kinase 1.Gene Ontology analysis involved an apoptosis and hypoxia reaction and the changes in blood vessel morphology.Kyoto Encyclopedia of Genes and Genomes analysis involved signaling pathways of hypoxia inducible factor-1,VEGF,and PI3K/AKT/NF-κB.Conclusion:The mechanism of XBJ injection when used to treat COVID-19 should be further investigated as the key compounds in XBJ regulated the expression of key targets such as protein kinase 1,VEGF-A,B-cell lymphoma-2,and TNF,which affected the COVID-19 receptors such as angiotensin-converting enzyme 2 and signaling pathways like hypoxia inducible factor-1,PI3K-Akt,and NF-κB,which alleviated the inflammation,respiratory distress,and hypoxia caused by COVID-19 infection.
文摘The COVID-19 pandemic poses an additional serious public health threat due to little or no pre-existing human immunity,and developing a system to identify COVID-19 in its early stages will save millions of lives.This study applied support vector machine(SVM),k-nearest neighbor(K-NN)and deep learning convolutional neural network(CNN)algorithms to classify and detect COVID-19 using chest X-ray radiographs.To test the proposed system,chest X-ray radiographs and CT images were collected from different standard databases,which contained 95 normal images,140 COVID-19 images and 10 SARS images.Two scenarios were considered to develop a system for predicting COVID-19.In the first scenario,the Gaussian filter was applied to remove noise from the chest X-ray radiograph images,and then the adaptive region growing technique was used to segment the region of interest from the chest X-ray radiographs.After segmentation,a hybrid feature extraction composed of 2D-DWT and gray level co-occurrence matrix was utilized to extract the features significant for detecting COVID-19.These features were processed using SVM and K-NN.In the second scenario,a CNN transfer model(ResNet 50)was used to detect COVID-19.The system was examined and evaluated through multiclass statistical analysis,and the empirical results of the analysis found significant values of 97.14%,99.34%,99.26%,99.26%and 99.40%for accuracy,specificity,sensitivity,recall and AUC,respectively.Thus,the CNN model showed significant success;it achieved optimal accuracy,effectiveness and robustness for detecting COVID-19.
文摘In this study,we have proposed an artificial neural network(ANN)model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17,2020.The proposed model is based on the existing data(training data)published in the Saudi Arabia Coronavirus disease(COVID-19)situation—Demographics.The Prey-Predator algorithm is employed for the training.Multilayer perceptron neural network(MLPNN)is used in this study.To improve the performance of MLPNN,we determined the parameters of MLPNN using the prey-predator algorithm(PPA).The proposed model is called the MLPNN–PPA.The performance of the proposed model has been analyzed by the root mean squared error(RMSE)function,and correlation coefficient(R).Furthermore,we tested the proposed model using other existing data recorded in Saudi Arabia(testing data).It is demonstrated that the MLPNN-PPA model has the highest performance in predicting the number of infected and recovering in Saudi Arabia.The results reveal that the number of infected persons will increase in the coming days and become a minimum of 9789.The number of recoveries will be 2000 to 4000 per day.
文摘Currently,COVID-19 is spreading all over the world and profoundly impacting people’s lives and economic activities.In this paper,a novel approach called the COVID-19 Quantum Neural Network(CQNN)for predicting the severity of COVID-19 in patients is proposed.It consists of two phases:In the first,the most distinct subset of features in a dataset is identified using a Quick Reduct Feature Selection(QRFS)method to improve its classification performance;and,in the second,machine learning is used to train the quantum neural network to classify the risk.It is found that patients’serial blood counts(their numbers of lymphocytes from days 1 to 15 after admission to hospital)are associated with relapse rates and evaluations of COVID-19 infections.Accordingly,the severity of COVID-19 is classified in two categories,serious and non-serious.The experimental results indicate that the proposed CQNN’s prediction approach outperforms those of other classification algorithms and its high accuracy confirms its effectiveness.
文摘The paper is proposed to explore the potential effects of Shufeng Jiedu Capsule against COVID-19. The ingredients and targets of Shufeng Jiedu Capsule were collected by the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and the gene names of potential targets were extracted by UniProtKB. Then we did protein-protein interaction networks functional enrichment analysis by the STRING platform, reconstructed drug-target pathways and networks to predict the likely protein targets of the capsule against COVID-19 with software Cytoscape 3.6.1, and carried out GO enrichment analysis and KEGG analysis with R 5.3.2 software. At last we validated our predictions on molecular docking. The results suggested that Shufeng Jiedu Capsule contained 155 ingredients and 237 targets, including 26 main active ingredients and 45 key targets. There were 2334 biological processes (BP), 103 cell composition (CC) and 198 molecular functions (MF) in GO Enrichment Analysis, and 177 pathways in the KEGG analysis. The molecular docking analysis showed that binding energy for 26 main active ingredients ranged from -32.21 to -25.94 kJ·mol-1, and the main targets bind to SARS-CoV-2 3CL hydrolase by acting on CASP9, PRKCA, RELA and others. Our study suggested that Shufeng Jiedu Capsule has potential therapeutic effects on COVID-19.
文摘Network pharmacology is an emerging technology based on systems biology and computer information technology,with the help of databases and related auxiliary software,to carry out new drug development and the screening analysis of drug active ingredients and targets.At present,the network pharmacology has been used widely in the research of prevention and treatment drugs for coronavirus disease 2019(COVID-19).This paper reviews the research methods of network pharmacology in the field of prevention and treatment of COVID-19 by traditional Chinese medicine(TCM)and the development of its specific drugs and further explores the concrete application ideas of this technology.The necessary databases and tools of necessary for screening the active components and targets to molecular docking are summarized.In addition,the practical application of network pharmacology in the study of several potential TCM and active components against COVID-19 is reviewed,mainly including the screening of active components,the discovery of target,and the elucidation of action mechanism.The diversification of research ideas of network pharmacology in the field of TCM was realized,in particular,with two specific ideas in the study of active ingredients of TCM.Finally,the difference of control effect among several TCM and Western medicines on COVID-19 and the limitation and challenge of network pharmacology in TCM,i.e.,the insufficient integrity and accuracy of the database,the uncertain complexity of components analysis,the unclear mechanism of component-target action,and some new challenges due to the characteristics of TCM,are discussed.In view of the importance of TCM in the field of control of COVID-19,the combination of TCM and network pharmacology will continue to play an important role in the development of specific drugs of COVID-19 in the future,in particular,to save time and reduce the workload of drug developers,which is also a direction of TCM development.This study provides theoretical reference and methodological basis for the prevention and treatment of COVID-19 by TCM.
