Interactivity software tools for teaching in ophthalmology

The use of interactive audience software,such as audience response systems(ARS),in medical education has become increasingly popular in recent years.This technology allows instructors to engage students in real time,encouraging active participation and promoting effective learning.The benefits of interactive audience software in medical education include increased student engagement,promotion of active learning,and enhanced learning outcomes.However,there are also several challenges to its implementation,including technical difficulties,careful planning and preparation,over-reliance on technology,and ethical concerns related to privacy and data security.The cost of implementing interactive audience software may also be a barrier for some institutions.This paper specifically reviews six interactive software platforms,including Socrative,Quizizz,Pear Deck,Slido,Wooclap and ClassPoint.These platforms allow for real-time assessment of student understanding,feedback,and participation.They also enable instructors to adjust their teaching strategies based on student responses and feedback.Overall,interactive audience software has shown great potential to enhance learning and engagement in medical education.It is important for instructors to carefully consider the benefits and challenges of its implementation.While the cost of implementing interactive audience software may be a barrier for some institutions,there are free and low-cost options available.

Research advance of Bacillus velezensis:bioinformatics,characteristics,and applications

Bacillus velezensis is a Gram-positive and spore-forming bacterium.It has potent antimicrobial properties that can be used to promote plant growth and as a pesticide by inhibiting pathogens.B.velezensis has the capability to generate a diverse range of enzymes that have potential applications in various fields,such as enzyme production,fermented food,degradation of pollutants,and bioenergy.In addition,B.velezensis is a promising probiotic.It possesses high bile-salt tolerance characteristics and has a high success rate of colonization in the intestinal mucosa.Besides,the strain can also regulate gut microbiota constitute by increasing the number of beneficial microorganisms and decreasing the number of pathogens.Furthermore,based on its special properties,including high-yield protease production and high salt-tolerance,B.velezensis shows potential for use in marine protein fermentation,opening up new avenues for the development of novel food products and bioactive peptides.In addition,B.velezensis can shorten the fermentation time as well as improve the nutritional value and flavor of fermented food.The safety of B.velezensis for food production was evaluated.This review provides valuable insights into the potential uses and benefits of B.velezensis,particularly in the context of fermented foods.

HV Process Model of Software Development

Software Development Life Cycle (SDLC) is one of the major ingredients for the development of efficient software systems within a time frame and low-cost involvement. From the literature, it is evident that there are various kinds of process models that are used by the software industries for the development of small, medium and long-term software projects, but many of them do not cover risk management. It is quite obvious that the improper selection of the software development process model leads to failure of the software products as it is time bound activity. In the present work, a new software development process model is proposed which covers the risks at any stage of the development of the software product. The model is named a Hemant-Vipin (HV) process model and may be helpful for the software industries for development of the efficient software products and timely delivery at the end of the client. The efficiency of the HV process model is observed by considering various kinds of factors like requirement clarity, user feedback, change agility, predictability, risk identification, practical implementation, customer satisfaction, incremental development, use of ready-made components, quick design, resource organization and many more and found through a case study that the presented approach covers many of parameters in comparison of the existing process models. .

A Hybrid Model for Improving Software Cost Estimation in Global Software Development

Accurate software cost estimation in Global Software Development(GSD)remains challenging due to reliance on historical data and expert judgments.Traditional models,such as the Constructive Cost Model(COCOMO II),rely heavily on historical and accurate data.In addition,expert judgment is required to set many input parameters,which can introduce subjectivity and variability in the estimation process.Consequently,there is a need to improve the current GSD models to mitigate reliance on historical data,subjectivity in expert judgment,inadequate consideration of GSD-based cost drivers and limited integration of modern technologies with cost overruns.This study introduces a novel hybrid model that synergizes the COCOMO II with Artificial Neural Networks(ANN)to address these challenges.The proposed hybrid model integrates additional GSD-based cost drivers identified through a systematic literature review and further vetted by industry experts.This article compares the effectiveness of the proposedmodelwith state-of-the-artmachine learning-basedmodels for software cost estimation.Evaluating the NASA 93 dataset by adopting twenty-six GSD-based cost drivers reveals that our hybrid model achieves superior accuracy,outperforming existing state-of-the-artmodels.The findings indicate the potential of combining COCOMO II,ANN,and additional GSD-based cost drivers to transform cost estimation in GSD.

Identification and Validation of Vascular-Associated Biomarkers for the Prognosis and Potential Pathogenesis of Hypertension Using Comprehensive Bioinformatics Methods

Background: Hypertension, also known as increased blood pressure, is a phenomenon in which blood flows in blood vessels and causes persistently higher-than-normal pressure on the vessel wall. The identification of novel prognostic and pathogenesis biomarkers plays a key role in the management of hypertension. Methods: The GSE7483 and GSE75815 datasets from the gene expression omnibus (GEO) database were used to identify the genes associated with hypertension that were differentially expressed genes (DEGs). The functional role of the DEGs was elucidated by gene body (GO) enrichment analysis. In addition, we performed an immune infiltration assay and GSEA on the DEGs of hypertensive patients and verified the expression of novel DEGs in the blood of hypertensive patients by RT-qPCR. Results: A total of 267 DEGs were identified from the GEO database. GO analysis revealed that these genes were associated mainly with biological processes such as fibroblast proliferation, cell structural organization, extracellular matrix organization, vasculature development regulation, and angiogenesis. We identified five possible biomarkers, Ecm1, Sparc, Sphk1, Thbsl, and Mecp2, which correlate with vascular development and angiogenesis characteristic of hypertension by bioinformatics, and explored the clinical expression levels of these genes by RT-qPCR, and found that Sparc, Sphk1, and Thbs1 showed significant up-regulation, in agreement with the results of the bioinformatics analysis. Conclusion: Our study suggested that Sparc, Sphk1 and Thbs1 may be potential novel biomarkers for the diagnosis, treatment and prognosis of hypertension and that they are involved in the regulation of vascular development and angiogenesis in hypertension.

Development and Evaluation of 3D Delivery Animation Software Designed to Improve the Mother’s and Spouse’s Satisfaction with Delivery

Purpose: To clarify the effectiveness of 3-D delivery animation software for the mother’s and husband’s satisfaction with delivery. Subjects and Method: We independently developed a software application used to display the pelvic region and explain the labor process. The study involved a collaboration with hospital staff who recruited 18 primiparous and 18 multiparous mothers who were hospitalized for delivery at Facility A. The midwife explained the process of delivery using the “Delivery Animation Software”. A self-administered, anonymous questionnaire was distributed and analyzed separately for primiparous and multiparous mothers and their husbands. Results: 1) For both primiparous and multiparous couples, both mothers and their husbands gained a significantly higher level of understanding after delivery than during pregnancy. 2) The Self-Evaluation Scale for Experience of Delivery results were as follows: “I did my best for the baby even if it was painful” was selected more often for “birth coping skills”;“reliable medical staff” was selected more often for “physiological birth process”;“the birth progressed as I expected” was selected frequently by primiparous mothers;and “the birth progressed smoothly” was selected often by multiparous mothers. 3) In terms of husbands’ satisfaction with the delivery, “I was satisfied with the delivery”, “I was given an easy-to-understand explanation”, and “They explained the process to me” was selected of primiparous and multiparous fathers. 4) All primiparous and multiparous mothers positively evaluated whether the delivery animation was helpful in understanding the process of delivery. Conclusion: The delivery animation was effective in improving the understanding and satisfaction of both the mothers and their husbands.

Identification and Validation of Novel Biomarkers Related to the Calcium Metabolism Pathway in Hypertension Patients Based on Comprehensive Bioinformatics Methods

Background: Hypertension is a universal risk factor for cardiovascular diseases and is thus the leading cause of death worldwide. The identification of novel prognostic and pathogenesis biomarkers plays a key role in disease management. Methods: The GSE145854 and GSE164494 datasets were downloaded from the Gene Expression Omnibus (GEO) database and used for screening and validating hypertension signature genes, respectively. Gene Ontology (GO) enrichment analysis was performed on the differentially expressed genes (DEGs) related to calcium ion metabolism in patients with hypertension. The core genes related to immune infiltration were analyzed and screened, and the activity of the signature genes and related pathways was quantified using gene set enrichment analysis (GSEA). The infiltration of immune cells in the blood samples was analyzed, and the DEGs that were abnormally expressed in the clinical blood samples of patients with hypertension were verified via RT-qPCR. Results: A total of 176 DEGs were screened. GO showed that DEGs was involved in the regulation of calcium ion metabolism in biological processes (BP), actin mediated cell contraction, negative regulation of cell movement, and calcium ion transmembrane transport, and in the regulation of protease activity in molecular functions (MF). KEGG analysis revealed that the DEGs were involved mainly in the cGMP-PKG signaling pathway, ubiquitin-protein transferase, tight junction-associated proteins, and the regulation of myocardial cells. MF analysis revealed the immune infiltration function of the cells. RT-qPCR revealed that the expression of Cacna1d, Serpine1, Slc8a3, and Trpc4 was up regulated in hypertension, the expression of Myoz2 and Slc25a23 was down regulated. Conclusion: Cacna1d, Serpine1, Slc8a3, Trpc4, Myoz2 and Slc25a23 may be involved in the regulation of calcium metabolism pathways and play key roles in hypertension. These differentially expressed calcium metabolism-related genes may serve as prognostic markers of hypertension.

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

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

Machine learning and bioinformatics to identify biomarkers in response to Burkholderia pseudomallei infection in mice

Objective:In the realm of Class I pathogens,Burkholderia pseudomallei(BP)stands out for its propensity to induce severe pathogenicity.Investigating the intricate interactions between BP and host cells is imperative for comprehending the dynamics of BP infection and discerning biomarkers indicative of the host cell response process.Methods:mRNA extraction from BP-infected mouse macrophages constituted the initial step of our study.Employing gene expression arrays,the extracted RNA underwent conversion into digital signals.The percentile shift method facilitated data processing,with the identification of genes manifesting significant differences accomplished through the application of the t-test.Subsequently,a comprehensive analysis involving Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway was conducted on the differentially expressed genes(DEGs).Leveraging the ESTIMATE algorithm,gene signatures were utilized to compute risk scores for gene expression data.Support vector machine analysis and gene enrichment scores were instrumental in establishing correlations between biomarkers and macrophages,followed by an evaluation of the predictive power of the identified biomarkers.Results:The functional and pathway associations of the DEGs predominantly centered around G protein-coupled receptors.A noteworthy positive correlation emerged between the blue module,consisting of 416 genes,and the StromaScore.FZD4,identified through support vector machine analysis among intersecting genes,indicated a robust interaction with macrophages,suggesting its potential as a robust biomarker.FZD4 exhibited commendable predictive efficacy,with BP infection inducing its expression in both macrophages and mouse lung tissue.Western blotting in macrophages confirmed a significant upregulation of FZD4 expression from 0.5 to 24 h post-infection.In mouse lung tissue,FZD4 manifested higher expression in the cytoplasm of pulmonary epithelial cells in BP-infected lungs than in the control group.Conclusion:Thesefindings underscore the upregulation of FZD4 expression by BP in both macrophages and lung tissue,pointing to its prospective role as a biomarker in the pathogenesis of BP infection.

Bioinformatics Analysis of the Biological Properties of Ewing Sarcoma

Purpose: Bioinformatics-based approach to screen and analyze differentially expressed genes associated with the biological characteristics of Ewing sarcoma. Means: The GSE17674 dataset was selected for analysis, obtained by data retrieval based on the GEO public database. The R language limma toolkit was used to screen DEmRNAs. After the data were normalized, the Metascape online analysis software and the R language clusterProfiler package were used to analyze the GO function and KEGG pathway enrichment of DEmRNAs lines, respectively. The string database was selected for PPI analysis, and the results were imported into Cytoscape software to derive the core modules and predicted core genes. The genes selected above were analyzed for tissue localization specificity. Results: Through the analysis of GSE17674, differentially expressed genes were screened out, and GO and KEGG analyses were performed on the differentially expressed genes. The GO functional enrichment analysis was mainly enriched in the process of muscle system, muscle contraction, myocyte development, contractile fibers, myogenic fibers, myofibers, myofibrillar segments, actin binding, structural composition of muscle, and actin filament binding. KEGG pathway analysis showed that the core pathways associated with the development of ES were the core genes for myocardial contraction, congestive cardiomyopathy, and hypertrophic cardiomyopathy. Five Hub genes were obtained based on Cytoscape prediction. Tissue localization specificity analysis of Hub genes was performed, and a total of 2 Hub genes with tissue specificity were screened;MYH6 was specifically expressed in cardiac cells and MYL1 was specifically expressed in skeletal muscle cells. Conclusions: The differential genes screened will help to understand the molecular mechanisms underlying the highly invasive and metastasis-prone biological characteristics of ES, as well as provide new ideas for clinical drug-targeted treatment of ES.

Identification of Prognosis-Related Genes and Key Target Genes for Pancreatic Cancer: A Bioinformatics Analysis

Objective: The mortality and morbidity rates associated with pancreatic cancer (PaCa) are extremely high. Various studies have demonstrated that pancreatic cancer will be the fourth cancer-related death by 2030, raising more concern for scholars to find effective methods to prevent and treat in order to improve the pancreatic cancer outcome. Using bioinformatic analysis, this study aims to pinpoint key genes that could impact PaCa patients’ prognosis and could be used as therapeutic targets. Methods: The TCGA and GEO datasets were integratively analyzed to identify prognosis-related differentially expressed genes. Next, the STRING database was used to develop PPI networks, and the MCODE and CytoNCA Cytoscape in Cytoscape were used to screen for critical genes. Through CytoNCA, three kinds of topology analysis were considered (degree, betweenness, and eigenvector). Essential genes were confirmed as potential target treatment through Go function and pathways enrichment analysis, a developed predictive risk model based on multivariate analysis, and the establishment of nomograms using the clinical information. Results: Overall, the GSE183795 and TCGA datasets associated 1311 and 2244 genes with pancreatic cancer prognosis, respectively. We identified 132 genes that were present in both datasets. The PPI network analysis using, the centrality analysis approach with the CytoNCA plug-in, showed that CDK2, PLK1, CCNB1, and TOP2A ranked in the top 5% across all three metrics. The independent analysis of a risk model revealed that the four key genes had a Hazard Ratio (HR) > 1. The monogram showed the predictive risk model and individual patient survival predictions were accurate. The results indicate that the effect of the selected vital genes was significant and that they could be used as biomarkers to predict a patient’s outcome and as possible target therapy in patients with pancreatic cancer. GO function and pathway analysis demonstrated that crucial genes might affect the P53 signaling pathway and FoxO signaling pathway, through which Meiotic nuclear division and cell cycle may have a significant function in essential genes affecting the outcome of patients who have pancreatic cancer. Conclusions: This study suggests that CDK2, CCNB1, PLK1 and TOP2A are four key genes that have a significant influence on PaCa migration and proliferation. CDK2, CCNB1, PLK1, and TOP2A can be used as potential PaCa prognostic biomarkers and therapeutic targets. However, experimental validation is necessary to confirm these predictions. Our study comes into contributions to the development of personalized target therapy for pancreatic cancer patients.

SCIRD: Revealing Infection of Malicious Software in Edge Computing-Enabled IoT Networks

The Internet of Things(IoT)has characteristics such as node mobility,node heterogeneity,link heterogeneity,and topology heterogeneity.In the face of the IoT characteristics and the explosive growth of IoT nodes,which brings about large-scale data processing requirements,edge computing architecture has become an emerging network architecture to support IoT applications due to its ability to provide powerful computing capabilities and good service functions.However,the defense mechanism of Edge Computing-enabled IoT Nodes(ECIoTNs)is still weak due to their limited resources,so that they are susceptible to malicious software spread,which can compromise data confidentiality and network service availability.Facing this situation,we put forward an epidemiology-based susceptible-curb-infectious-removed-dead(SCIRD)model.Then,we analyze the dynamics of ECIoTNs with different infection levels under different initial conditions to obtain the dynamic differential equations.Additionally,we establish the presence of equilibrium states in the SCIRD model.Furthermore,we conduct an analysis of the model’s stability and examine the conditions under which malicious software will either spread or disappear within Edge Computing-enabled IoT(ECIoT)networks.Lastly,we validate the efficacy and superiority of the SCIRD model through MATLAB simulations.These research findings offer a theoretical foundation for suppressing the propagation of malicious software in ECIoT networks.The experimental results indicate that the theoretical SCIRD model has instructive significance,deeply revealing the principles of malicious software propagation in ECIoT networks.This study solves a challenging security problem of ECIoT networks by determining the malicious software propagation threshold,which lays the foundation for buildingmore secure and reliable ECIoT networks.

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

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

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

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

Gene Cloning and Bioinformatics Analysis of phoR Gene from Vibrio alginolyticus HY9901

PhoR is a histidine kinase in a two-component regulatory system that regulates phosphorus metabolic pathways and undertakes the key mission of information transmission in pathogenic bacteria.The full-length phoR gene was successfully cloned from the Vibrio alginolyticus HY9901 strain.A comprehensive analysis of the cloned gene was conducted using bioinformatics.Sequence analysis revealed that the total length of the phoR gene(GenBank accession No.:KJ958404.1)is 1299 bp,with the coding region containing a total of 432 amino acid residues.The phylogenetic tree of PhoR revealed that it belongs to the same subclade as V.diabolicus.The SMART program was employed for the purpose of functional domain prediction,which revealed that PhoR possesses three major functional domains:PAS(amino acids 98-166),HisKA(amino acids 205-272),and HATPase_c(amino acids 317-429).

Effects of OGFOD1 in bladder cancer progression and its prognostic significance:Insights from bioinformatics analysis

Background:Previous studies have established the role of 2-oxoglutarate and Fe(II)-dependent oxygenase domain–containing protein 1(OGFOD1)in oncogenesis.The objective of this investigation was to discern the diagnostic and prognostic relevance of OGFOD1 within the context of bladder cancer(BLCA)using bioinformatics methodologies.Methods:We collected RNA sequencing data from The Cancer Genome Atlas database and verified it using the GSE13507 dataset.Immunohistochemical analysis was based on data from the human protein atlas,and the protein-protein interaction network was constructed using the STRING database.Bioinformatics analysis was performed using the R application,analyzing the correlation between clinical characteristics and OGFOD1 expression,exploring the potential mechanisms of OGFOD1 in BLCA through Kyoto Encyclopedia of Genes and Genomes analysis,and evaluating the diagnostic and prognostic value of OGFOD1 expression in BLCA through receiver operating characteristic curve analysis,Kaplan-Meier analysis,and multivariate Cox analysis.Furthermore,a BLCA prognostic nomogram was constructed.Results:We report higher expression levels of OGFOD1 in BLCA specimens compared with those in noncancerous tissues;this can be used to predict the outcome of the disease.Further,results suggest that OGFOD1 is implicated in the activation of the peroxisome proliferator-activated receptor signaling cascade,potentially interacting with other genes linked to expression in promoting the onset and progression of BLCA.Conclusions:OGFOD1 is a promising candidate as a prognostic indicator in BLCA.

ProTSA: A Testing Process for Automotive Software Domain

This study evaluates the development of a testing process for the automotive software domain, highlighting challenges stemming from the absence of adequate processes. The research demonstrates the application of Design Science Research methodology in developing, an automotive software testing process—ProTSA, using six functional testing modules. Additionally, the study evaluates the benefits of implementing ProTSA in a specific Original Equipment Manufacturer (OEM) using an experimental single-case approach with industry professionals’ participation through a survey. The study concludes that combining testing techniques with effective communication and alignment is crucial for enhancing software quality. Furthermore, survey data indicates that implementing ProTSA leads to productivity gains by initiating tests early, resulting in time savings in the testing program and increased productivity for the testing team. Future work will explore implementing ProTSA in cybersecurity, over-the-air software updates, and autonomous vehicle testing processes. .

Cloning and Bioinformatics Analysis of hcp Gene in Aeromonas hydrophila

[Objectives]To explore the function of hcp gene in Aeromonas hydrophila.[Methods]A pair of specific primers was designed referring to the hcp gene sequence of A.hydrophila.The hcp gene was amplified by PCR,and performed bioinformatics analysis.[Results]The hcp gene had a total length of 1650 bp and encoded 549 amino acids.The theoretical molecular weight of the protein predicted was about 59476.44 kDa.After predicting the N-terminal signal peptide structure of the amino acid sequence,neither obvious signal peptide cleavage site nor signal peptide was found,and the protein had no transmembrane region.The amino acid sequence had a N-glycosylation site,4 protein kinase C phosphorylation sites,7 casein kinase II phosphorylation sites,9 N-myristoylation sites,4 isoprene binding sites,10 microbody C-terminal target signal sites,and an ATP/GTP binding site motif A(P-ring).The amino acid sequence of hcp gene of A.hydrophila was performed homology analysis with other Aeromonas strains,and it showed higher homology with A.veronii.In the secondary structure,theα-helix,β-sheet,random coil and extended strand accounted for 45.36%,6.01%,37.52%and 11.11%,respectively.The tertiary structure model consisted of 18α-helix and 22β-sheet.Analysis of protein-protein network interaction demonstrated that the proteins interacting with Hcp protein were AHA_3407,nrfA,nirB-1,nirB-2 and AHA_1112.[Conclusions]Through the bioinformatics prediction results,the basic information of hcp gene of A.hydrophila is preliminarily understood,and the possible function of this protein is predicted,in order to provide guidance for subsequent vaccine research.

Computational and bioinformatics tools for understanding disease mechanisms

Computational methods have significantly transformed biomedical research,offering a comprehensive exploration of disease mechanisms and molecular protein functions.This article reviews a spectrum of computational tools and network analysis databases that play a crucial role in identifying potential interactions and signaling networks contributing to the onset of disease states.The utilization of protein/gene interaction and genetic variation databases,coupled with pathway analysis can facilitate the identification of potential drug targets.By bridging the gap between molecular-level information and disease understanding,this review contributes insights into the impactful utilization of computational methods,paving the way for targeted interventions and therapeutic advancements in biomedical research.

Study on the effect and mechanism of Swertia mussotii Franch. in the treatment of primary biliary cholangitis based on bioinformatics and in vitro experiments

Background:Primary biliary cholangitis(PBC)is a chronic biliary autoimmune liver disease characterized by intrahepatic cholestasis.Swertia mussotii Franch.(SMF)is a Tibetan medicine with hepatoprotective and anti-inflammatory activities.In this study,the therapeutic effect and potential mechanisms of SMF on PBC were investigated by bioinformatics analysis and in vitro experimental validation,with the aim of promoting the progress of SMF and PBC research.Methods:We first explored the therapeutic effects and key targets of SMF on PBC using a network pharmacology approach,further screened the core targets using the GSE79850 dataset,and finally validated the results using molecular docking techniques and in vitro experiments.Results:By bioinformatics analysis,we identified core targets of SMF for PBC treatment(STAT3,JAK2,TNF-α,and IL-1β)and important signaling pathways:JAK-STAT,TNF,and PI3K-AKT.The molecular docking results showed that the significant components of SMF had good binding properties to the core targets.In vitro experiments showed that SMF extracts improved the extent of epithelial-mesenchymal transition in human intrahepatic biliary epithelial cells and had a significant reversal effect on epithelial-mesenchymal transition process markers and potential targets in PBC.Conclusion:SMF may exert its therapeutic effects on PBC by acting on important targets such as STAT3,JAK2,TNF-α,IL-1β,Vimentin,and E-cadherin and the pathways in which they are involved.