Cyber Resilience through Real-Time Threat Analysis in Information Security

This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].

Identification of cell surface markers for acute myeloid leukemia prognosis based on multi-model analysis

Given the extremely high inter-patient heterogeneity of acute myeloid leukemia(AML),the identification of biomarkers for prognostic assessment and therapeutic guidance is critical.Cell surface markers(CSMs)have been shown to play an important role in AML leukemogenesis and progression.In the current study,we evaluated the prognostic potential of all human CSMs in 130 AML patients from The Cancer Genome Atlas(TCGA)based on differential gene expression analysis and univariable Cox proportional hazards regression analysis.By using multi-model analysis,including Adaptive LASSO regression,LASSO regression,and Elastic Net,we constructed a 9-CSMs prognostic model for risk stratification of the AML patients.The predictive value of the 9-CSMs risk score was further validated at the transcriptome and proteome levels.Multivariable Cox regression analysis showed that the risk score was an independent prognostic factor for the AML patients.The AML patients with high 9-CSMs risk scores had a shorter overall and event-free survival time than those with low scores.Notably,single-cell RNA-sequencing analysis indicated that patients with high 9-CSMs risk scores exhibited chemotherapy resistance.Furthermore,PI3K inhibitors were identified as potential treatments for these high-risk patients.In conclusion,we constructed a 9-CSMs prognostic model that served as an independent prognostic factor for the survival of AML patients and held the potential for guiding drug therapy.

Model reduction of fractional impedance spectra for time–frequency analysis of batteries, fuel cells, and supercapacitors

Joint time–frequency analysis is an emerging method for interpreting the underlying physics in fuel cells,batteries,and supercapacitors.To increase the reliability of time–frequency analysis,a theoretical correlation between frequency-domain stationary analysis and time-domain transient analysis is urgently required.The present work formularizes a thorough model reduction of fractional impedance spectra for electrochemical energy devices involving not only the model reduction from fractional-order models to integer-order models and from high-to low-order RC circuits but also insight into the evolution of the characteristic time constants during the whole reduction process.The following work has been carried out:(i)the model-reduction theory is addressed for typical Warburg elements and RC circuits based on the continued fraction expansion theory and the response error minimization technique,respectively;(ii)the order effect on the model reduction of typical Warburg elements is quantitatively evaluated by time–frequency analysis;(iii)the results of time–frequency analysis are confirmed to be useful to determine the reduction order in terms of the kinetic information needed to be captured;and(iv)the results of time–frequency analysis are validated for the model reduction of fractional impedance spectra for lithium-ion batteries,supercapacitors,and solid oxide fuel cells.In turn,the numerical validation has demonstrated the powerful function of the joint time–frequency analysis.The thorough model reduction of fractional impedance spectra addressed in the present work not only clarifies the relationship between time-domain transient analysis and frequency-domain stationary analysis but also enhances the reliability of the joint time–frequency analysis for electrochemical energy devices.

Action of circulating and infiltrating B cells in the immune microenvironment of colorectal cancer by single-cell sequencing analysis

BACKGROUND The complexity of the immune microenvironment has an impact on the treatment of colorectal cancer(CRC),one of the most prevalent malignancies worldwide.In this study,multi-omics and single-cell sequencing techniques were used to investigate the mechanism of action of circulating and infiltrating B cells in CRC.By revealing the heterogeneity and functional differences of B cells in cancer immunity,we aim to deepen our understanding of immune regulation and provide a scientific basis for the development of more effective cancer treatment strategies.AIM To explore the role of circulating and infiltrating B cell subsets in the immune microenvironment of CRC,explore the potential driving mechanism of B cell development,analyze the interaction between B cells and other immune cells in the immune microenvironment and the functions of communication molecules,and search for possible regulatory pathways to promote the anti-tumor effects of B cells.METHODS A total of 69 paracancer(normal),tumor and peripheral blood samples were collected from 23 patients with CRC from The Cancer Genome Atlas database(https://portal.gdc.cancer.gov/).After the immune cells were sorted by multicolor flow cytometry,the single cell transcriptome and B cell receptor group library were sequenced using the 10X Genomics platform,and the data were analyzed using bioinformatics tools such as Seurat.The differences in the number and function of B cell infiltration between tumor and normal tissue,the interaction between B cell subsets and T cells and myeloid cell subsets,and the transcription factor regulatory network of B cell subsets were explored and analyzed.RESULTS Compared with normal tissue,the infiltrating number of CD20+B cell subsets in tumor tissue increased significantly.Among them,germinal center B cells(GCB)played the most prominent role,with positive clone expansion and heavy chain mutation level increasing,and the trend of differentiation into memory B cells increased.However,the number of plasma cells in the tumor microenvironment decreased significantly,and the plasma cells secreting IgA antibodies decreased most obviously.In addition,compared with the immune microenvironment of normal tissues,GCB cells in tumor tissues became more closely connected with other immune cells such as T cells,and communication molecules that positively regulate immune function were significantly enriched.CONCLUSION The role of GCB in CRC tumor microenvironment is greatly enhanced,and its affinity to tumor antigen is enhanced by its significantly increased heavy chain mutation level.Meanwhile,GCB has enhanced its association with immune cells in the microenvironment,which plays a positive anti-tumor effect.

Stem cell transplantation in cerebrovascular accidents:A global bibliometric analysis(2000-2023)

BACKGROUND Cerebrovascular accident(CVA)is a major global contributor to death and disability.As part of its medical management,researchers have recognized the importance of promising neuroprotective strategies,where stem cell transplantation(SCT)is thought to confer advantages via trophic and neuroprotective effects.AIM To evaluate the current state of research on SCT in patients with CVA,assess key trends and highlight literature gaps.METHODS PubMed was screened for SCT in CVA-related articles in October 2023,for each country during the period between 2000 and 2023.Using the World Bank data,total population and gross domestic product were collected for comparison.VOSviewer_1.6.19 was used to create the VOS figure using the results of the same query.Graphs and tables were obtained using Microsoft Office Excel.RESULTS A total of 6923 studies were identified on SCT in CVA,making 0.03%of all published studies worldwide.Approximately,68%were conducted in high-income countries,with a significant focus on mesenchymal stem cells.The journal“Stroke”featured the largest share of these articles,with mesenchymal SCT having the highest rate of inclusion,followed by hematopoietic SCT.Over time,there has been a noticeable shift from in vitro studies,which assess stem cell proliferation and neurogenesis,to in vivo studies aimed at evaluating efficacy and safety.Additionally,the number of reviews increased along this approach.CONCLUSION This bibliometric analysis provides a comprehensive guide for physicians and researchers in the field through an objective overview of research activity,and highlights both current trends and gaps.Having a potential therapeutic role in CVA,more research is needed in the future to focus on different aspects of SCT,aiming to reach a better treatment strategy and improve life quality in patients.

Clinical and molecular significance of homologous recombination deficiency positive non-small cell lung cancer in Chinese population:An integrated genomic and transcriptional analysis

Objective:The clinical significance of homologous recombination deficiency(HRD)in breast cancer,ovarian cancer,and prostate cancer has been established,but the value of HRD in non-small cell lung cancer(NSCLC)has not been fully investigated.This study aimed to systematically analyze the HRD status of untreated NSCLC and its relationship with patient prognosis to further guide clinical care.Methods:A total of 355 treatment-naïve NSCLC patients were retrospectively enrolled.HRD status was assessed using the AmoyDx Genomic Scar Score(GSS),with a score of≥50 considered HRD-positive.Genomic,transcriptomic,tumor microenvironmental characteristics and prognosis between HRD-positive and HRDnegative patients were analyzed.Results:Of the patients,25.1%(89/355)were HRD-positive.Compared to HRD-negative patients,HRDpositive patients had more somatic pathogenic homologous recombination repair(HRR)mutations,higher tumor mutation burden(TMB)(P<0.001),and fewer driver gene mutations(P<0.001).Furthermore,HRD-positive NSCLC had more amplifications in PI3K pathway and cell cycle genes,MET and MYC in epidermal growth factor receptor(EGFR)/anaplastic lymphoma kinase(ALK)mutant NSCLC,and more PIK3CA and AURKA in EGFR/ALK wild-type NSCLC.HRD-positive NSCLC displayed higher tumor proliferation and immunosuppression activity.HRD-negative NSCLC showed activated signatures of major histocompatibility complex(MHC)-II,interferon(IFN)-γand effector memory CD8+T cells.HRD-positive patients had a worse prognosis and shorter progressionfree survival(PFS)to targeted therapy(first-and third-generation EGFR-TKIs)(P=0.042).Additionally,HRDpositive,EGFR/ALK wild-type patients showed a numerically lower response to platinum-free immunotherapy regimens.Conclusions:Unique genomic and transcriptional characteristics were found in HRD-positive NSCLC.Poor prognosis and poor response to EGFR-TKIs and immunotherapy were observed in HRD-positive NSCLC.This study highlights potential actionable alterations in HRD-positive NSCLC,suggesting possible combinational therapeutic strategies for these patients.

Mesenchymal stem cells in wound healing:A bibliometric analysis as a powerful research tool

Bibliographic analysis is still very rarely used in experimental basic study papers.The comprehensive bibliometric analysis of scientific literature on research progress and challenges in stem cell therapy for diabetic chronic wounds,which was conducted in the work of Shi et al can be a case study and a source of valuable information for writing reviews and experimental papers in this field.Basic experimental studies on a role of mesenchymal stem cells(MSCs)in wound healing that are published in 2023-2024,such as Zhang et al in 2023,Hu et al in 2023,Wang et al in 2023 are certainly also subjects for applying this powerful tool to analyze current research,challenges and perspectives in this field.This is due to the fact that these studies have addressed a great variety of aspects of the application of MSCs for the treatment of chronic wounds,such as using both the cells themselves and their various products:Sponges,hydrogels,exosomes,and genetic constructions.Such a wide variety of directions in the field of study and biomedical application of MSCs requires a deep understanding of the current state of research in this area,which can be provided by bibliometric analysis.Thus,the use of such elements of bibliographic analysis as publication count by year and analysis of top-10 keywords calculated independently or cited from bibliometric analysis studies can be safely recommended for every basic study manuscripts,primarily for the“Introduction”section,and review.

Mechanisms of Sophora flavescens in the treatment of cervical squamous cell carcinoma based on comprehensive biological analysis,network pharmacology,and experimental verification

Objective:This study used comprehensive bioinformatics analysis and network pharmacology analysis to investigate the potentially relevant mechanisms of Sophora flavescens against cervical squamous cell carcinoma.Methods:Consistently altered genes involved in cervical squamous cell cancerization were analyzed in the GEO database.The chemical ingredients and target genes of Sophora flavescens were explored using the TCMSP database.We obtained the potential therapeutic targets of Sophora flavescens by intersecting the above genesets and validated them in the GEPIA database.The interaction between Sophora flavescens and target genes was predicted by molecular docking.RT-qPCR was used to verify the changes of target genes in HeLa cells treated with Sophora flavescens.Single-gene GSEA functional analysis were performed to determine the molecular mechanisms.Results:Fifteen genes related to the transformation of cervical squamous cell carcinoma were identified,among which AR and ESR1 were confirmed as targets for kaempferol,wighteone,formononetin,and phaseolinon.These compounds are the active ingredients in Sophora flavescens.Low expressions of AR and ESR1 correlate with a poor prognosis,while Sophora flavescens treatment increases the expression of AR and ESR1 in HeLa.GSEA analysis showed that AR and ESR1 mainly participate in the epithelial-mesenchymal transition in cervical squamous cell carcinoma.Conclusion:Sophora flavescens exert anti-tumor effects by targeting AR and ESR1,which may regulate cancer metastasis.

Data network traffic analysis and optimization strategy of real-time power grid dynamic monitoring system for wide-frequency measurements

The application and development of a wide-area measurement system(WAMS)has enabled many applications and led to several requirements based on dynamic measurement data.Such data are transmitted as big data information flow.To ensure effective transmission of wide-frequency electrical information by the communication protocol of a WAMS,this study performs real-time traffic monitoring and analysis of the data network of a power information system,and establishes corresponding network optimization strategies to solve existing transmission problems.This study utilizes the traffic analysis results obtained using the current real-time dynamic monitoring system to design an optimization strategy,covering the optimization in three progressive levels:the underlying communication protocol,source data,and transmission process.Optimization of the system structure and scheduling optimization of data information are validated to be feasible and practical via tests.

Performance analysis model for real-time Ethernet-based computer numerical control system

In order to optimize the embedded system implementation for Ethernet-based computer numerical control （CNC） system, it is very necessary to establish the performance analysis model and further adopt the codesign method from the control, communication and computing perspectives. On the basis of analyzing real-time Ethemet, system architecture, time characteristic parameters of control-loop ere, a performance analysis model for real-time Ethemet-based CNC system was proposed, which is able to include the timing effects caused by the implementation platform in the simulation. The key for establishing the model is accomplished by designing the error analysis module and the controller nodes. Under the restraint of CPU resource and communication bandwidth, the experiment with a case study was conducted, and the results show that if the deadline miss ratio of data packets is 0.2%, then the percentage error is 1.105%. The proposed model can be used at several stages of CNC system development.

Real-Time Multimodal Biometric Authentication of Human Using Face Feature Analysis

As multimedia data sharing increases,data security in mobile devices and its mechanism can be seen as critical.Biometrics combines the physiological and behavioral qualities of an individual to validate their character in real-time.Humans incorporate physiological attributes like a fingerprint,face,iris,palm print,finger knuckle print,Deoxyribonucleic Acid(DNA),and behavioral qualities like walk,voice,mark,or keystroke.The main goal of this paper is to design a robust framework for automatic face recognition.Scale Invariant Feature Transform(SIFT)and Speeded-up Robust Features(SURF)are employed for face recognition.Also,we propose a modified Gabor Wavelet Transform for SIFT/SURF(GWT-SIFT/GWT-SURF)to increase the recognition accuracy of human faces.The proposed scheme is composed of three steps.First,the entropy of the image is removed using Discrete Wavelet Transform(DWT).Second,the computational complexity of the SIFT/SURF is reduced.Third,the accuracy is increased for authentication by the proposed GWT-SIFT/GWT-SURF algorithm.A comparative analysis of the proposed scheme is done on real-time Olivetti Research Laboratory(ORL)and Poznan University of Technology(PUT)databases.When compared to the traditional SIFT/SURF methods,we verify that the GWT-SIFT achieves the better accuracy of 99.32%and the better approach is the GWT-SURF as the run time of the GWT-SURF for 100 images is 3.4 seconds when compared to the GWT-SIFT which has a run time of 4.9 seconds for 100 images.

A Mathematical Model of Real-Time Simulation and the Convergence Analysis on Real-Time Runge-Kutta Algorithms

In this paper, a mathematical model of real-time simulation is given, and the problem of convergence on real-time Runge-Kutta algorithms is analysed. At last a theorem on the relation between the order of compensation and the convergent order of real-time algorithm is proved.

Integrated Real-Time Big Data Stream Sentiment Analysis Service

Opinion (sentiment) analysis on big data streams from the constantly generated text streams on social media networks to hundreds of millions of online consumer reviews provides many organizations in every field with opportunities to discover valuable intelligence from the massive user generated text streams. However, the traditional content analysis frameworks are inefficient to handle the unprecedentedly big volume of unstructured text streams and the complexity of text analysis tasks for the real time opinion analysis on the big data streams. In this paper, we propose a parallel real time sentiment analysis system: Social Media Data Stream Sentiment Analysis Service (SMDSSAS) that performs multiple phases of sentiment analysis of social media text streams effectively in real time with two fully analytic opinion mining models to combat the scale of text data streams and the complexity of sentiment analysis processing on unstructured text streams. We propose two aspect based opinion mining models: Deterministic and Probabilistic sentiment models for a real time sentiment analysis on the user given topic related data streams. Experiments on the social media Twitter stream traffic captured during the pre-election weeks of the 2016 Presidential election for real-time analysis of public opinions toward two presidential candidates showed that the proposed system was able to predict correctly Donald Trump as the winner of the 2016 Presidential election. The cross validation results showed that the proposed sentiment models with the real-time streaming components in our proposed framework delivered effectively the analysis of the opinions on two presidential candidates with average 81% accuracy for the Deterministic model and 80% for the Probabilistic model, which are 1% - 22% improvements from the results of the existing literature.

Real-time lane departure warning system based on principal component analysis of grayscale distribution and risk evaluation model

A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames. The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic, dice similarity coefficient and real-time performance. For lane departure detection, a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform. Experimental results indicate that the algorithm generates satisfactory lane detection results under different traffic and lighting conditions, and the proposed warning mechanism sends effective warning signals, avoiding most false warning.

Electricity Price Influence Factors Analysis Using Stochastic Matrix for Real-Time Electricity Price Forecasting

Real-time electricity price( RTEP) influence factor extraction is essential to forecasting accurate power system electricity prices. At present,new electricity price forecasting models have been studied to improve predictive accuracy,ignoring the extraction and analysis of RTEP influence factors. In this study,a correlation analysis method is proposed based on stochastic matrix theory.Firstly, an augmented matrix is formulated, including RTEP influence factor data and RTEP state data. Secondly, data correlation analysis results are obtained given the statistical characteristics of source data based on stochastic matrix theory.Mean spectral radius( MSR) is used as the measure of correlativity.Finally,the proposed method is evaluated in New England electricity markets and compared with the BP neural network forecasting method. Experimental results show that the extracted index system comprehensively generalizes RTEP influence factors,which play a significant role in improving RTEP forecasting accuracy.

Real-Time Computer Analytical Application in Pressure Transient Analysis of Petroleum Reservoirs

Today with certainty, the petroleum industry is fostering sanguinely the fields’ development programs for the optimization of reservoir characterization through worth-full appliances of computer analysis techniques. The time element is of prime importance for optimistic petroleum development projects. Therefore, the frontline of “Real-time Analysis” is added into the applications of computer solving techniques for achieving and sketching up the real-time cost effectiveness in analyzing field development programs. It focuses on the phases of real-time well test data acquisition system, real-time secure access to the well test data either on field or in office and real-time data interpretation unit. This interface will yield the productive results for the field of reservoir’s pressure transient analysis and wells’ systems analysis by following the up-to-date preferred, accurate and effective well test analytical principles with modern real-time computer applications and techniques. It also lays emphasis for the comfort and reliability of data in creating best interpersonal working modes within a reputable and esteemed petroleum development organization.

Real-Time Grouting Monitoring and Visualization Analysis System for Dam Foundation Curtain Grouting

A real-time monitoring and 3D visualization analysis system is proposed for dam foundation curtain grouting. Based on the real-time control technology, the optimization method and the set theory, a mathematical model of the system is established. The real-time collection and transmission technology of the grouting data provides a data foundation for the system. The real-time grouting monitoring and dynamic alarming method helps the system control the grouting quality during the grouting process, thus, the abnormalities of grouting, such as jacking and hydraulic uplift, can be effectively controlled. In addition, the 3D grouting visualization analysis technology is proposed to establish the grouting information model(GIM). The GIM provides a platform to visualize and analyze the grouting process and results. The system has been applied to a hydraulic project of China as a case study, and the application results indicate that the real-time grouting monitoring and 3D visualization analysis for the grouting process can help engineers control the grouting quality more efficiently.

A Novel Three-parameter Flow Cytometric Analysis for Cell Cycle

To set up a three-parameter method for cell cycle analysis by two-laser flowcy-tometer, which can detect two types of cyclin plus DNA content in one measurement, and thatanalyze unscheduled expression of cyclins. Methods: Three-color fluorescence was used for analysisof two types of cyclins and DNA content simultaneously in individual cells by two-laser flowcytometry. MOLT-4 cells were used to study the expression of major cyclins in mammalian cells. ATriton-X100 permeabilization procedure was optimized for detection of two types of cyclins. Onecyclin was stained directly with a FITC-conjugated monoclonal antibody (mAb), and the other,indirectly with RPE-Cy5-conjugated secondary antibody, while DNA was stained with the fluorochromeDAPI. mAMSA and mimosine treated MOLT-4 cells were used to test this three-parameter method.Results: Permeabilization with 0.5% Triton-XlOO in PBS containing 1% BSA for 5 min on ice providedoptimal conditions for the simultaneous labelling of two cyclins plus DNA in single cells. It wasfound that the emission spectrum of the three dyes (DAPI, FITC and RPE-Cy5) could be measured withno compensation. Based on cyclinA/cyclinE/DNA flow cytometric analysis, asynchronously growingMOLT-4 cells could be divided into 6 compartments (G1o, G1e, G1l, S, G2, and M) simultaneously,allowing for analysis of cell cycle phase specific perturbations without the necessity of cellsynchronization. Unscheduled cyclin B1 expression was observed in G1 cells treated with mimosine andcyclin E in G2 cells treated with mAMSA. We found that unscheduled cyclin expression paralleledexpected cyclin expression. Conclusion: Thus, three-color FCM analysis of cells may not only beapplied to measure unscheduled vs. expected cyclin expression but may also be used to estimate thefraction of cycling cells in up to 6 cell populations.

Identification of microRNAs and messenger RNAs involved in human umbilical cord mesenchymal stem cell treatment of ischemic cerebral infarction using integrated bioinformatics analysis

In recent years,a large number of differentially expressed genes have been identified in human umbilical cord mesenchymal stem cell(hUMSC)transplants for the treatment of ischemic cerebral infarction.These genes are involved in various biochemical processes,but the role of microRNAs(miRNAs)in this process is still unclear.From the Gene Expression Omnibus(GEO)database,we downloaded two microarray datasets for GSE78731(messenger RNA(mRNA)profile)and GSE97532(miRNA profile).The differentially expressed genes screened were compared between the hUMSC group and the middle cerebral artery occlusion group.Gene ontology enrichment and pathway enrichment analyses were subsequently conducted using the online Database for Annotation,Visualization,and Integrated Discovery.Identified genes were applied to perform weighted gene co-suppression analyses,to establish a weighted co-expression network model.Furthermore,the protein-protein interaction network for differentially expressed genes from turquoise modules was built using Cytoscape(version 3.40)and the most highly correlated subnetwork was extracted from the protein-protein interaction network using the MCODE plugin.The predicted target genes for differentially expressed miRNAs were also identified using the online database starBase v3.0.A total of 3698 differentially expressed genes were identified.Gene ontology analysis demonstrated that differentially expressed genes that are related to hUMSC treatment of ischemic cerebral infarction are involved in endocytosis and inflammatory responses.We identified 12 differentially expressed miRNAs in middle cerebral artery occlusion rats after hUMSC treatment,and these differentially expressed miRNAs were mainly involved in signaling in inflammatory pathways,such as in the regulation of neutrophil migration.In conclusion,we have identified a number of differentially expressed genes and differentially expressed mRNAs,miRNA-mRNAs,and signaling pathways involved in the hUMSC treatment of ischemic cerebral infarction.Bioinformatics and interaction analyses can provide novel clues for further research into hUMSC treatment of ischemic cerebral infarction.

Mapping theme trends and knowledge structures for human neural stem cells:a quantitative and co-word biclustering analysis for the 2013-2018 period

Neural stem cells,which are capable of multi-potential differentiation and self-renewal,have recently been shown to have clinical potential for repairing central nervous system tissue damage.However,the theme trends and knowledge structures for human neural stem cells have not yet been studied bibliometrically.In this study,we retrieved 2742 articles from the PubMed database from 2013 to 2018 using "Neural Stem Cells" as the retrieval word.Co-word analysis was conducted to statistically quantify the characteristics and popular themes of human neural stem cell-related studies.Bibliographic data matrices were generated with the Bibliographic Item Co-Occurrence Matrix Builder.We identified 78 high-frequency Medical Subject Heading(MeSH)terms.A visual matrix was built with the repeated bisection method in gCLUTO software.A social network analysis network was generated with Ucinet 6.0 software and GraphPad Prism 5 software.The analyses demonstrated that in the 6-year period,hot topics were clustered into five categories.As suggested by the constructed strategic diagram,studies related to cytology and physiology were well-developed,whereas those related to neural stem cell applications,tissue engineering,metabolism and cell signaling,and neural stem cell pathology and virology remained immature.Neural stem cell therapy for stroke and Parkinson’s disease,the genetics of microRNAs and brain neoplasms,as well as neuroprotective agents,Zika virus,Notch receptor,neural crest and embryonic stem cells were identified as emerging hot spots.These undeveloped themes and popular topics are potential points of focus for new studies on human neural stem cells.