Hyperparameter Optimization for Capsule Network Based Modified Hybrid Rice Optimization Algorithm

Hyperparameters play a vital impact in the performance of most machine learning algorithms.It is a challenge for traditional methods to con-figure hyperparameters of the capsule network to obtain high-performance manually.Some swarm intelligence or evolutionary computation algorithms have been effectively employed to seek optimal hyperparameters as a com-binatorial optimization problem.However,these algorithms are prone to get trapped in the local optimal solution as random search strategies are adopted.The inspiration for the hybrid rice optimization(HRO)algorithm is from the breeding technology of three-line hybrid rice in China,which has the advantages of easy implementation,less parameters and fast convergence.In the paper,genetic search is combined with the hybrid rice optimization algorithm(GHRO)and employed to obtain the optimal hyperparameter of the capsule network automatically,that is,a probability search technique and a hybridization strategy belong with the primary HRO.Thirteen benchmark functions are used to evaluate the performance of GHRO.Furthermore,the MNIST,Chest X-Ray(pneumonia),and Chest X-Ray(COVID-19&pneumonia)datasets are also utilized to evaluate the capsule network learnt by GHRO.The experimental results show that GHRO is an effective method for optimizing the hyperparameters of the capsule network,which is able to boost the performance of the capsule network on image classification.

Effective Customer Review Analysis Using Combined Capsule Networks with Matrix Factorization Filtering

Nowadays,commercial transactions and customer reviews are part of human life and various business applications.The technologies create a great impact on online user reviews and activities,affecting the business process.Customer reviews and ratings are more helpful to the new customer to purchase the product,but the fake reviews completely affect the business.The traditional systems consume maximum time and create complexity while analyzing a large volume of customer information.Therefore,in this work optimized recommendation system is developed for analyzing customer reviews with minimum complexity.Here,Amazon Product Kaggle dataset information is utilized for investigating the customer review.The collected information is analyzed and processed by batch normalized capsule networks(NCN).The network explores the user reviews according to product details,time,price purchasing factors,etc.,ensuring product quality and ratings.Then effective recommendation system is developed using a butterfly optimized matrix factorizationfiltering approach.Then the system’s efficiency is evaluated using the Rand Index,Dunn index,accuracy,and error rate.

Rolling Bearing Fault Diagnosis Based On Convolutional Capsule Network

Fault diagnosis technology has been widely applied and is an important part of ensuring the safe operation of mechanical equipment.In response to the problem of frequent faults in rolling bearings,this paper designs a rolling bearing fault diagnosis method based on convolutional capsule network(CCN).More specifically,the original vibration signal is converted into a two-dimensional time–frequency image using continuous wavelet transform(CWT),and the feature extraction is performed on the two-dimensional time–frequency image using the convolution layer at the front end of the network,and the extracted features are input into the capsule network.The capsule network converts the extracted features into vector neurons,and the dynamic routing algorithm is used to achieve feature transfer and output the results of fault diagnosis.Two different datasets are used to compare with other traditional deep learning models to verify the fault diagnosis capability of the method.The results show that the CCN has good diagnostic capability under different working conditions,even in the presence of noise and insufficient samples,compared to other models.This method contributes to the safe and reliable operation of mechanical equipment and is suitable for other rotating scenarios.

Using Capsule Networks for Android Malware Detection Through Orientation-Based Features

Mobile phones are an essential part of modern life.The two popular mobile phone platforms,Android and iPhone Operating System(iOS),have an immense impact on the lives of millions of people.Among these two,Android currently boasts more than 84%market share.Thus,any personal data put on it are at great risk if not properly protected.On the other hand,more than a million pieces of malware have been reported on Android in just 2021 till date.Detecting and mitigating all this malware is extremely difficult for any set of human experts.Due to this reason,machine learning-and specifically deep learning-has been utilized in the recent past to resolve this issue.However,deep learning models have primarily been designed for image analysis.While this line of research has shown promising results,it has been difficult to really understand what the features extracted by deep learning models are in the domain of malware.Moreover,due to the translation invariance property of popular models based on ConvolutionalNeural Network(CNN),the true potential of deep learning for malware analysis is yet to be realized.To resolve this issue,we envision the use of Capsule Networks(CapsNets),a state-of-the-art model in deep learning.We argue that since CapsNets are orientation-based in terms of images,they can potentially be used to capture spatial relationships between different features at different locations within a sequence of opcodes.We design a deep learning-based architecture that efficiently and effectively handles very large scale malware datasets to detect Androidmalware without resorting to very deep networks.This leads tomuch faster detection as well as increased accuracy.We achieve state-of-the-art F1 score of 0.987 with an FPR of just 0.002 for three very large,real-world malware datasets.Our code is made available as open source and can be used to further enhance our work with minimal effort.

Brain MRI Patient Identification Based on Capsule Network

In the deep learning field,“Capsule”structure aims to overcome the shortcomings of traditional Convolutional Neural Networks(CNN)which are difficult to mine the relationship between sibling features.Capsule Net(CapsNet)is a new type of classification network structure with“Capsule”as network elements.It uses the“Squashing”algorithm as an activation function and Dynamic Routing as a network optimization method to achieve better classification performance.The main problem of the Brain Magnetic Resonance Imaging(Brain MRI)recognition algorithm is that the difference between Alzheimer’s disease(AD)image,the Mild Cognitive Impairment(MCI)image,and the normal image is not significant.It is difficult to achieve excellent results using a multi-layer CNN.However,CapsNet can be in the case of a shallower network,which can accommodate more useful feature information for identifying brain MRI.In this paper,we designed a shallow CapsNet to identify patients with brain MRI by binary classification.Compared with VGG16,Resnet34,DenseNet121 and ResNeXt50.Experimental results illustrate that CapsNet is superior to CNN network in its accuracy and F1-score.The indicators were 86.67%and 83.33%,respectively.Furthermore,we show that the capsule network shows excellent performance in brain MRI recognition compared with those popular networks.

Detachable string magnetically controlled capsule endoscopy for the noninvasive diagnosis of esophageal diseases:A prospective,blind clinical study

BACKGROUND Traditional esophagogastroduodenoscopy(EGD),an invasive examination method,can cause discomfort and pain in patients.In contrast,magnetically controlled capsule endoscopy(MCE),a noninvasive method,is being applied for the detection of stomach and small intestinal diseases,but its application in treating esophageal diseases is not widespread.AIM To evaluate the safety and efficacy of detachable string MCE(ds-MCE)for the diagnosis of esophageal diseases.METHODS Fifty patients who had been diagnosed with esophageal diseases were pros-pectively recruited for this clinical study and underwent ds-MCE and conven-tional EGD.The primary endpoints included the sensitivity,specificity,positive predictive value,negative predictive value,and diagnostic accuracy of ds-MCE for patients with esophageal diseases.The secondary endpoints consisted of visualizing the esophageal and dentate lines,as well as the subjects'tolerance of the procedure.RESULTS Using EGD as the gold standard,the sensitivity,specificity,positive predictive value,negative predictive value,and diagnostic accuracy of ds-MCE for esophageal disease detection were 85.71%,86.21%,81.82%,89.29%,and 86%,respectively.ds-MCE was more comfortable and convenient than EGD was,with 80%of patients feeling that ds-MCE examination was very comfortable or comfortable and 50%of patients believing that detachable string v examination was very convenient.CONCLUSION This study revealed that ds-MCE has the same diagnostic effects as traditional EGD for esophageal diseases and is more comfortable and convenient than EGD,providing a novel noninvasive method for treating esophageal diseases.

Helicobacter pylori infection may result in poor gastric cleanliness in magnetically controlled capsule gastroscopy examination: A singlecenter retrospective study

Objective:Magnetically controlled capsule gastroscopy(MCCG)is an effective method for screening gastric diseases;however,its performance may be affected by gastric cleanliness.We aimed to explore the correlation between Helicobacter pylori infection and the degree of gastric cleanliness in the MCCG.Methods:This retrospective study enrolled 297 participants from October 2020 to April 2024 at Sir Run Run Shaw Hospital,Zhejiang University School of Medicine.Participant characteristics,MCCG examination results,and(13)C-urea breath test(C13-UBT)results were collected.The gastric cleanliness in MCCG examinations was assessed using a gastric cleanliness score.Binary logistic regression was used to analyze the relationships among participant characteristics,H.pylori infection,and gastric cleanliness.Chi-square tests and Fisher's exact tests were used to analyze the relationships among gastric lesions,H.pylori infection,and gastric cleanliness.Results:Among the participants,24.2%had H.pylori infection,and 17.5%had poor gastric cleanliness.Hypertension(odds ratio[OR]:2.63;95%confidence interval[CI]:1.36e5.09;p?0.004)was associated with a greater likelihood of H.pylori infection.H.pylori infection(OR:3.76;95%CI:1.99e7.09;p<0.001)was an independent risk factor for poor gastric cleanliness in the MCCG.A significant disparity was noted in the prevalence of focal erosions(p<0.001),gastric ulcers(p?0.001),and positive gastric lesions(p?0.027)between the 2 groups with and without H.pylori infection.The proportion of positive gastric lesions was not significantly different between the good gastric cleanliness group and the poor gastric cleanliness group(25.7%vs.21.2%;p?0.490).Conclusion:The findings of this study revealed that H.pylori infection was associated with hypertension.H.pylori infection may lead to poor gastric cleanliness.Institutions are advised to perform C13-UBT before MCCG,and participants should be informed of the risk of poor gastric cleanliness if the results are positive.The decision to perform H.pylori eradication before MCCG should take into account patient willingness and the benefit-to-risk ratio.

Expression Recognition Method Based on Convolutional Neural Network and Capsule Neural Network

Convolutional neural networks struggle to accurately handle changes in angles and twists in the direction of images,which affects their ability to recognize patterns based on internal feature levels. In contrast, CapsNet overcomesthese limitations by vectorizing information through increased directionality and magnitude, ensuring that spatialinformation is not overlooked. Therefore, this study proposes a novel expression recognition technique calledCAPSULE-VGG, which combines the strengths of CapsNet and convolutional neural networks. By refining andintegrating features extracted by a convolutional neural network before introducing theminto CapsNet, ourmodelenhances facial recognition capabilities. Compared to traditional neural network models, our approach offersfaster training pace, improved convergence speed, and higher accuracy rates approaching stability. Experimentalresults demonstrate that our method achieves recognition rates of 74.14% for the FER2013 expression dataset and99.85% for the CK+ expression dataset. By contrasting these findings with those obtained using conventionalexpression recognition techniques and incorporating CapsNet’s advantages, we effectively address issues associatedwith convolutional neural networks while increasing expression identification accuracy.

Efficacy of Juanbi capsule on ameliorating knee osteoarthritis:a network pharmacology and experimental verification-based study

Background:The purpose of the study was to investigate the active ingredients and potential biochemical mechanisms of Juanbi capsule in knee osteoarthritis based on network pharmacology,molecular docking and animal experiments.Methods:Chemical components for each drug in the Juanbi capsule were obtained from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform,while the target proteins for knee osteoarthritis were retrieved from the Drugbank,GeneCards,and OMIM databases.The study compared information on knee osteoarthritis and the targets of drugs to identify common elements.The data was imported into the STRING platform to generate a protein-protein interaction network diagram.Subsequently,a“component-target”network diagram was created using the screened drug components and target information with Cytoscape software.Common targets were imported into Metascape for GO function and KEGG pathway enrichment analysis.AutoDockTools was utilized to predict the molecular docking of the primary chemical components and core targets.Ultimately,the key targets were validated through animal experiments.Results:Juanbi capsule ameliorated Knee osteoarthritis mainly by affecting tumor necrosis factor,interleukin1β,MMP9,PTGS2,VEGFA,TP53,and other cytokines through quercetin,kaempferol,andβ-sitosterol.The drug also influenced the AGE-RAGE,interleukin-17,tumor necrosis factor,Relaxin,and NF-κB signaling pathways.The network pharmacology analysis results were further validated in animal experiments.The results indicated that Juanbi capsule could decrease the levels of tumor necrosis factor-αand interleukin-1βin the serum and synovial fluid of knee osteoarthritis rats and also down-regulate the expression levels of MMP9 and PTGS2 proteins in the articular cartilage.Conclusion:Juanbi capsule may improve the knee bone microstructure and reduce the expression of inflammatory factors of knee osteoarthritis via multiple targets and multiple signaling pathways.

Ensemble of Two-Path Capsule Networks for Diagnosis of Turner Syndrome Using Global-Local Facial Images

Turner syndrome(TS)is a chromosomal disorder disease that only affects the growth of female patients.Prompt diagnosis is of high significance for the patients.However,clinical screening methods are time-consuming and cost-expensive.Some researchers used machine learning-based methods to detect TS,the performance of which needed to be improved.Therefore,we propose an ensemble method of two-path capsule networks(CapsNets)for detecting TS based on global-local facial images.Specifically,the TS facial images are preprocessed and segmented into eight local parts under the direction of physicians;then,nine two-path CapsNets are respectively trained using the complete TS facial images and eight local images,in which the few-shot learning is utilized to solve the problem of limited data;finally,a probability-based ensemble method is exploited to combine nine classifiers for the classification of TS.By studying base classifiers,we find two meaningful facial areas are more related to TS patients,i.e.,the parts of eyes and nose.The results demonstrate that the proposed model is effective for the TS classification task,which achieves the highest accuracy of 0.9241.

Capsule networks embedded with prior known support information for image reconstruction

Compressed sensing(CS)has been successfully applied to realize image reconstruction.Neural networks have been introduced to the CS of images to exploit the prior known support information,which can improve the reconstruction quality.Capsule Network(Caps Net)is the latest achievement in neural networks,and can well represent the instantiation parameters of a specific type of entity or part of an object.This study aims to propose a Caps Net with a novel dynamic routing to embed the information within the CS framework.The output of the network represents the probability that the index of the nonzero entry exists on the support of the signal of interest.To lead the dynamic routing to the most likely index,a group of prediction vectors is designed determined by the information.Furthermore,the results of experiments on imaging signals are taken for a comparation of the performances among different algorithms.It is concluded that the proposed capsule network(Caps Net)creates higher reconstruction quality at nearly the same time with traditional Caps Net.

Deep Capsule Residual Networks for Better Diagnosis Rate in Medical Noisy Images

With the advent of Machine and Deep Learning algorithms,medical image diagnosis has a new perception of diagnosis and clinical treatment.Regret-tably,medical images are more susceptible to capturing noises despite the peak in intelligent imaging techniques.However,the presence of noise images degrades both the diagnosis and clinical treatment processes.The existing intelligent meth-ods suffer from the deficiency in handling the diverse range of noise in the ver-satile medical images.This paper proposes a novel deep learning network which learns from the substantial extent of noise in medical data samples to alle-viate this challenge.The proposed deep learning architecture exploits the advan-tages of the capsule network,which is used to extract correlation features and combine them with redefined residual features.Additionally,thefinal stage of dense learning is replaced with powerful extreme learning machines to achieve a better diagnosis rate,even for noisy and complex images.Extensive experimen-tation has been conducted using different medical images.Various performances such as Peak-Signal-To-Noise Ratio(PSNR)and Structural-Similarity-Index-Metrics(SSIM)are compared with the existing deep learning architectures.Addi-tionally,a comprehensive analysis of individual algorithms is analyzed.The experimental results prove that the proposed model has outperformed the other existing algorithms by a substantial margin and proved its supremacy over the other learning models.

Deep Capsule Residual Networks for Better Diagnosis Rate in Medical Noisy Images

With the advent of Machine and Deep Learning algorithms,medical image diagnosis has a new perception of diagnosis and clinical treatment.Regret-tably,medical images are more susceptible to capturing noises despite the peak in intelligent imaging techniques.However,the presence of noise images degrades both the diagnosis and clinical treatment processes.The existing intelligent meth-ods suffer from the deficiency in handling the diverse range of noise in the ver-satile medical images.This paper proposes a novel deep learning network which learns from the substantial extent of noise in medical data samples to alle-viate this challenge.The proposed deep learning architecture exploits the advan-tages of the capsule network,which is used to extract correlation features and combine them with redefined residual features.Additionally,the final stage of dense learning is replaced with powerful extreme learning machines to achieve a better diagnosis rate,even for noisy and complex images.Extensive experimen-tation has been conducted using different medical images.Various performances such as Peak-Signal-To-Noise Ratio(PSNR)and Structural-Similarity-Index-Metrics(SSIM)are compared with the existing deep learning architectures.Addi-tionally,a comprehensive analysis of individual algorithms is analyzed.The experimental results prove that the proposed model has outperformed the other existing algorithms by a substantial margin and proved its supremacy over the other learning models.

Convolutional neural network-based segmentation network applied to image recognition of angiodysplasias lesion under capsule endoscopy

BACKGROUND Small intestinal vascular malformations(angiodysplasias)are common causes of small intestinal bleeding.While capsule endoscopy has become the primary diagnostic method for angiodysplasia,manual reading of the entire gastrointestinal tract is time-consuming and requires a heavy workload,which affects the accuracy of diagnosis.AIM To evaluate whether artificial intelligence can assist the diagnosis and increase the detection rate of angiodysplasias in the small intestine,achieve automatic disease detection,and shorten the capsule endoscopy(CE)reading time.METHODS A convolutional neural network semantic segmentation model with a feature fusion method,which automatically recognizes the category of vascular dysplasia under CE and draws the lesion contour,thus improving the efficiency and accuracy of identifying small intestinal vascular malformation lesions,was proposed.Resnet-50 was used as the skeleton network to design the fusion mechanism,fuse the shallow and depth features,and classify the images at the pixel level to achieve the segmentation and recognition of vascular dysplasia.The training set and test set were constructed and compared with PSPNet,Deeplab3+,and UperNet.RESULTS The test set constructed in the study achieved satisfactory results,where pixel accuracy was 99%,mean intersection over union was 0.69,negative predictive value was 98.74%,and positive predictive value was 94.27%.The model parameter was 46.38 M,the float calculation was 467.2 G,and the time length to segment and recognize a picture was 0.6 s.CONCLUSION Constructing a segmentation network based on deep learning to segment and recognize angiodysplasias lesions is an effective and feasible method for diagnosing angiodysplasias lesions.

Repositioning of clinically approved drug Bazi Bushen capsule for treatment of Alzheimer′s disease using network pharma⁃cology approach and in vitro experimental validation

OBJECTIVE To explore the new indications and key mechanism of Bazi Bushen capsule(BZBS)by network pharmacology and in vitro experiment.METHODS The potential tar⁃get profiles of the components of BZBS were pre⁃dicted.Subsequently,new indications for BZBS were predicted by disease ontology(DO)enrich⁃ment analysis and initially validated by GO and KEGG pathway enrichment analysis.Further⁃more,the therapeutic target of BZBS acting on AD signaling pathway were identified by intersec⁃tion analysis.Two Alzheimer′s disease(AD)cell models,BV-2 and SH-SY5Y,were used to pre⁃liminarily verify the anti-AD efficacy and mecha⁃nism of BZBS in vitro.RESULTS In total,1499 non-repeated ingredients were obtained from 16 herbs in BZBS formula,and 1320 BZBS targets with high confidence were predicted.Disease enrichment results strongly suggested that BZBS formula has the potential to be used in the treat⁃ment of AD.In vitro experiments showed that BZ⁃BS could significantly reduce the release of TNF-αand IL-6 and the expression of COX-2 and PSEN1 in Aβ25-35-induced BV-2 cells.BZBS reduced the apoptosis rate of Aβ25-35 induced SH-SY5Y cells,significantly increased mitochon⁃drial membrane potential,reduced the expres⁃sion of Caspase3 active fragment and PSEN1,and increased the expression of IDE.CONCLU⁃SIONS BZBS formula has a potential use in the treatment of AD,which is achieved through regu⁃lation of ERK1/2,NF-κB signaling pathways,and GSK-3β/β-catenin signaling pathway.Further⁃more,the network pharmacology technology is a feasible drug repurposing strategy to reposition new clinical use of approved TCM and explore the mechanism of action.The study lays a foun⁃dation for the subsequent in-depth study of BZBS in the treatment of AD and provides a basis for its application in the clinical treatment of AD.

Network pharmacological and molecular docking study of the effect of Liu-Wei-Bu-Qi capsule on lung cancer

BACKGROUND Although Liu-Wei-Bu-Qi capsule(LBC)inhibits tumor progression by improving the physical condition and immunity of patients with lung cancer(LC),its exact mechanism of action is unknown.AIM To through compound multi-dimensional network of chemical ingredient-targetdisease-target-protein-protein interaction(PPI)network,the principle of action of Chinese medicine prescription was explained from molecular level.METHODS Network pharmacology and molecular docking simulations were used to analyze the relationship among the main components,targets,and signaling pathways of LBC in treatment of LC.RESULTS From the analysis,360 LBC active ingredient-related targets and 908 LC-related targets were identified.PPI network analysis of the LBC and LC overlapping targets identified 16 hub genes.Kyoto Encyclopedia of Genes and Genomes analysis suggested that LBC can target the vascular endothelial growth factor signaling pathway,Toll-like receptor signaling pathway,prolactin signaling pathway,FoxO signaling pathway,PI3K-Akt signaling pathway and HIF-1 signaling pathway in the treatment of LC.Molecular docking simulations showed that quercetin had the best affinity for MAPK3,suggesting that quercetin in LBC may play an important role in the treatment of LC.CONCLUSION The results showed that the active ingredients in LBC can play a crucial role in the treatment of LC by regulating multiple signaling pathways.These results provide insights into further studies on the mechanism of action of LBC in the treatment of LC.

Capsule endoscopy and panendoscopy:A journey to the future of gastrointestinal endoscopy

In 2000,the small bowel capsule revolutionized the management of patients with small bowel disorders.Currently,the technological development achieved by the new models of double-headed endoscopic capsules,as miniaturized devices to evaluate the small bowel and colon[pan-intestinal capsule endoscopy(PCE)],makes this non-invasive procedure a disruptive concept for the management of patients with digestive disorders.This technology is expected to identify which patients will require conventional invasive endoscopic procedures(colonoscopy or balloon-assisted enteroscopy),based on the lesions detected by the capsule,i.e.,those with an indication for biopsies or endoscopic treatment.The use of PCE in patients with inflammatory bowel diseases,namely Crohn’s disease,as well as in patients with iron deficiency anaemia and/or overt gastrointestinal(GI)bleeding,after a non-diagnostic upper endoscopy(esophagogastroduodenoscopy),enables an effective,safe and comfortable way to identify patients with relevant lesions,who should undergo subsequent invasive endoscopic procedures.The recent development of magnetically controlled capsule endoscopy to evaluate the upper GI tract,is a further step towards the possibility of an entirely non-invasive assessment of all the segments of the digestive tract,from mouth-to-anus,meeting the expectations of the early developers of capsule endoscopy.

Altered synaptic currents,mitophagy,mitochondrial dynamics in Alzheimer's disease models and therapeutic potential of Dengzhan Shengmai capsules intervention

Emerging research suggests a potential association of progression of Alzheimer's disease(AD)with alterations in synaptic currents and mitochondrial dynamics.However,the specific associations between these pathological changes remain unclear.In this study,we utilized Aβ42-induced AD rats and primary neural cells as in vivo and in vitro models.The investigations included behavioural tests,brain magnetic resonance imaging(MRI),liquid chromatography-tandem mass spectrometry(UPLC-MS/MS)analysis,Nissl staining,thioflavin-S staining,enzyme-linked immunosorbent assay,Golgi-Cox staining,transmission electron microscopy(TEM),immunofluorescence staining,proteomics,adenosine triphosphate(ATP)detection,mitochondrial membrane potential(MMP)and reactive oxygen species(ROS)assessment,mitochondrial morphology analysis,electrophysiological studies,Western blotting,and molecular docking.The results revealed changes in synaptic currents,mitophagy,and mitochondrial dynamics in the AD models.Remarkably,intervention with Dengzhan Shengmai(DZSM)capsules emerged as a pivotal element in this investigation.Aβ42-induced synaptic dysfunction was significantly mitigated by DZSM intervention,which notably amplified the frequency and amplitude of synaptic transmission.The cognitive impairment observed in AD rats was ameliorated and accompanied by robust protection against structural damage in key brain regions,including the hippocampal CA3,primary cingular cortex,prelimbic system,and dysgranular insular cortex.DZSM intervention led to increased IDE levels,augmented long-term potential(LTP)amplitude,and enhanced dendritic spine density and length.Moreover,DZSM intervention led to favourable changes in mitochondrial parameters,including ROS expression,MMP and ATP contents,and mitochondrial morphology.In conclusion,our findings delved into the realm of altered synaptic currents,mitophagy,and mitochondrial dynamics in AD,concurrently highlighting the therapeutic potential of DZSM intervention.

Exploring the mechanism of Bazi Bushen capsule in delaying the senescence of mesenchymal stem cells by network pharmacology method and verifying it in vitro

OBJECTIVE To explore the key mechanism of Bazi Bushen capsule(BZBS)in delaying the senescence of mesenchymal stem cells(MSCs)through network pharmacology and in vitro experiments.METHODS Network pharmacology was used to predict the mechanism targets of BZBS in delaying MSCs senescence.A MSCs senescence model induced by D-galactose(D-gal)was used to investigate the effect and mechanism of BZBS on MSCs senescence in vitro.RESULTS Network pharmacology analy⁃sis showed that BZSB could delay MSCs senes⁃cence.The experiment showed that BZBS could significantly improve the survival activity of the aged MSCs.It significantly reduced the positive rate ofβ-galactosidase staining and p16,p21 expression in aged MSCs,enhanced the ability of adipogenic differentiation and osteogenic differentiation,and increased expression of Nanog,OCT4 and SOX2 in senescent MSCs.CONCLU⁃SIONS Network pharmacology and in vitro cell experiments verified that BZBS could delay MSCs senescence.

Material Basis of Ningmitai Capsule for the Treatment of Urinary Tract Infections Based on Network Pharmacology

[Objectives]To study the main active components,targets and related pathways of Ningmitai capsule for the treatment of urinary tract infections(UTIs)based on network pharmacology.[Methods]The chemical components of Ningmitai capsule were collected through literature search,and the relevant target information of the components was sorted out.The UTIs-associated targets were also screened out using DisGeNET database and GeneCards database.Cytoscape 3.6.1 software and STRING platform were used to construct the protein-protein interaction(PPI)network,and MCODE plug-in in this software was used to analyze the action pathway and key targets of Ningmitai capsule for the treatment of UTIs.GO and KEGG pathway enrichment analysis of key targets was conducted using David database,and the component-target-pathway network diagram of Ningmitai capsule for the treatment of UTIs was established.[Results]A total of 37 active compounds,including salicylate,ferulic acid,baicalin,quercetin,apigenin and ellagic acid were screened from seven TCM components of Ningmitai capsule.There were 26 possible targets related to the treatment of UTIs,such as NFKB1,JUN,CTNNB1 and STAT3,which play an important role for the treatment of UTIs through prostate cancer,bladder cancer,pancreatic cancer and other signaling pathways.[Conclusions]The study provides a theoretical basis for the study of the mechanism of Ningmitai capsule in the treatment of UTIs.