Software Vulnerability Mining and Analysis Based on Deep Learning

In recent years,the rapid development of computer software has led to numerous security problems,particularly software vulnerabilities.These flaws can cause significant harm to users’privacy and property.Current security defect detection technology relies on manual or professional reasoning,leading to missed detection and high false detection rates.Artificial intelligence technology has led to the development of neural network models based on machine learning or deep learning to intelligently mine holes,reducing missed alarms and false alarms.So,this project aims to study Java source code defect detection methods for defects like null pointer reference exception,XSS(Transform),and Structured Query Language(SQL)injection.Also,the project uses open-source Javalang to translate the Java source code,conducts a deep search on the AST to obtain the empty syntax feature library,and converts the Java source code into a dependency graph.The feature vector is then used as the learning target for the neural network.Four types of Convolutional Neural Networks(CNN),Long Short-Term Memory(LSTM),Bi-directional Long Short-Term Memory(BiLSTM),and Attention Mechanism+Bidirectional LSTM,are used to investigate various code defects,including blank pointer reference exception,XSS,and SQL injection defects.Experimental results show that the attention mechanism in two-dimensional BLSTM is the most effective for object recognition,verifying the correctness of the method.

Static Analysis Techniques for Fixing Software Defects in MPI-Based Parallel Programs

The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed memorysystems.However, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.

Developing Lexicons for Enhanced Sentiment Analysis in Software Engineering:An Innovative Multilingual Approach for Social Media Reviews

Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages other thanEnglish is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existingsentiment analysis systems focus on English, leaving a significant research gap in other languages due to limitedresources and tools. This research aims to address this gap by building a sentiment lexicon for local languages,which is then used with a machine learning algorithm for efficient sentiment analysis. In the first step, a lexiconis developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentimentscores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. Inthe second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis ofRoman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrievalmetrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The resultsshowcase the potential for improving software engineering tasks related to user feedback analysis and productdevelopment.

Cross-Project Software Defect Prediction Based on SMOTE and Deep Canonical Correlation Analysis

Cross-Project Defect Prediction(CPDP)is a method that utilizes historical data from other source projects to train predictive models for defect prediction in the target project.However,existing CPDP methods only consider linear correlations between features(indicators)of the source and target projects.These models are not capable of evaluating non-linear correlations between features when they exist,for example,when there are differences in data distributions between the source and target projects.As a result,the performance of such CPDP models is compromised.In this paper,this paper proposes a novel CPDP method based on Synthetic Minority Oversampling Technique(SMOTE)and Deep Canonical Correlation Analysis(DCCA),referred to as S-DCCA.Canonical Correlation Analysis(CCA)is employed to address the issue of non-linear correlations between features of the source and target projects.S-DCCA extends CCA by incorporating the MlpNet model for feature extraction from the dataset.The redundant features are then eliminated by maximizing the correlated feature subset using the CCA loss function.Finally,cross-project defect prediction is achieved through the application of the SMOTE data sampling technique.Area Under Curve(AUC)and F1 scores(F1)are used as evaluation metrics.This paper conducted experiments on 27 projects from four public datasets to validate the proposed method.The results demonstrate that,on average,our method outperforms all baseline approaches by at least 1.2%in AUC and 5.5%in F1 score.This indicates that the proposed method exhibits favorable performance characteristics.

Agile Development Methods in Software Engineering and Their Efficiency Analysis

This paper delves into Agile Development Methods in Software Engineering,contrasting them with the traditional Waterfall model and analyzing their efficiency.Agile methods,known for their adaptability and customer-centric approach,have gained prominence in the fast-paced software development industry.These methods,including Scrum,Kanban,and Extreme Programming(XP),are characterized by iterative cycles,collaborative efforts,and a focus on rapid delivery and quality improvement.The paper compares these agile methodologies to the sequential and rigid Waterfall model,highlighting agile’s superior flexibility,adaptability,and responsiveness to changing requirements.It emphasizes the importance of customer involvement in agile processes,which leads to higher satisfaction and better alignment with user expectations.The analysis reveals that agile methods not only enhance the speed of delivery but also improve the overall quality of the software product.The paper concludes that agile methodologies are more effective in today's dynamic software development environment,providing a robust framework for managing complex projects and ensuring the delivery of high-quality,relevant software solutions.

Analysis of Software Development and Operation Measures from the Perspective of Security Technology

Security technology is crucial in software development and operation in the digital age. Secure software can protect user privacy and data security, prevent hacker attacks and data breaches, ensure legitimate business operations, and protect core assets. However, the development process often faces threats such as injection attacks, Cross-Site Scripting (XSS), and Cross-Site Request Forgery (CSRF), mainly due to code vulnerabilities, configuration errors, and risks from third-party components. To meet these challenges, this paper discusses the application of security technology in development and operation, emphasizing security requirements analysis, design principles, coding practices, and testing during the development phase. Along with focusing on environmental configuration, continuous monitoring, emergency response, disaster recovery, and regular auditing and updating during the operation phase. These measures can significantly enhance the security of software systems and protect user and corporate data.

Dynamic Interaction Analysis of Coupled Axial-Torsional-Lateral Mechanical Vibrations in Rotary Drilling Systems

Maintaining the integrity and longevity of structures is essential in many industries,such as aerospace,nuclear,and petroleum.To achieve the cost-effectiveness of large-scale systems in petroleum drilling,a strong emphasis on structural durability and monitoring is required.This study focuses on the mechanical vibrations that occur in rotary drilling systems,which have a substantial impact on the structural integrity of drilling equipment.The study specifically investigates axial,torsional,and lateral vibrations,which might lead to negative consequences such as bit-bounce,chaotic whirling,and high-frequency stick-slip.These events not only hinder the efficiency of drilling but also lead to exhaustion and harm to the system’s components since they are difficult to be detected and controlled in real time.The study investigates the dynamic interactions of these vibrations,specifically in their high-frequency modes,usingfield data obtained from measurement while drilling.Thefindings have demonstrated the effect of strong coupling between the high-frequency modes of these vibrations on drilling sys-tem performance.The obtained results highlight the importance of considering the interconnected impacts of these vibrations when designing and implementing robust control systems.Therefore,integrating these compo-nents can increase the durability of drill bits and drill strings,as well as improve the ability to monitor and detect damage.Moreover,by exploiting thesefindings,the assessment of structural resilience in rotary drilling systems can be enhanced.Furthermore,the study demonstrates the capacity of structural health monitoring to improve the quality,dependability,and efficiency of rotary drilling systems in the petroleum industry.

Research status and prospects of the fractal analysis of metal material surfaces and interfaces

As a mathematical analysis method,fractal analysis can be used to quantitatively describe irregular shapes with self-similar or self-affine properties.Fractal analysis has been used to characterize the shapes of metal materials at various scales and dimensions.Conventional methods make it difficult to quantitatively describe the relationship between the regular characteristics and properties of metal material surfaces and interfaces.However,fractal analysis can be used to quantitatively describe the shape characteristics of metal materials and to establish the quantitative relationships between the shape characteristics and various properties of metal materials.From the perspective of two-dimensional planes and three-dimensional curved surfaces,this paper reviews the current research status of the fractal analysis of metal precipitate interfaces,metal grain boundary interfaces,metal-deposited film surfaces,metal fracture surfaces,metal machined surfaces,and metal wear surfaces.The relationship between the fractal dimensions and properties of metal material surfaces and interfaces is summarized.Starting from three perspectives of fractal analysis,namely,research scope,image acquisition methods,and calculation methods,this paper identifies the direction of research on fractal analysis of metal material surfaces and interfaces that need to be developed.It is believed that revealing the deep influence mechanism between the fractal dimensions and properties of metal material surfaces and interfaces will be the key research direction of the fractal analysis of metal materials in the future.

Antioxidant and lipoxygenase inhibitory properties of a novel flavonoid from Pistacia chinensis Bunge and its molecular docking analysis

Background:Pistacia chinensis Bunge has been traditionally used to manage various conditions,including asthma,pain,inflammation,hepatoprotection,and diabetes.The study was conducted to investigate the antioxidant and anti-lipoxygenase(LOX)properties of the isolated compound 2-(3,4-dihydroxyphenyl)-5,7-dihydroxy-4H-chromen-4-one from Pistacia chinensis.Methods:LOX assay and antioxidant activity using 2,2-diphenyl-1-picrylhydrazyl(DPPH)assay were performed.Molecular docking studies were conducted using a molecular operating environment.Results:The LOX assay revealed significant inhibitory effects at 0.2µM concentration,with an IC50 value of 37.80µM.The antioxidant effect demonstrated dose-dependency across 5 to 100µg/mL concentrations,reaching 93.09%at 100µg/mL,comparable to ascorbic acid’s 95.43%effect.Molecular docking studies highlighted strong interactions with the lipoxygenase enzyme,presenting an excellent docking score of-10.98 kcal/mol.Conclusion:These findings provide valuable insights into Pistacia chinensis’chemical components and biological effects,reinforcing its traditional medicinal applications.

Prenatal ultrasonography and genetic analysis of fetal cleidocranial dysplasia:A case report

BACKGROUND Cleidocranial dysplasia(CCD)is an infrequent clinical condition with an autosomal dominant inheritance pattern.It is characterized by abnormal clavicles,patent sutures and fontanelles,supernumerary teeth,and short stature.Approximately 60%-70%of patients with CCD have mutations in the RUNX family transcription factor 2 gene.However,prenatal diagnosis of CCD is difficult when the family history is unknown.CASE SUMMARY We report a rare case of fetal CCD with an unknown family history,confirmed by prenatal ultrasonography and genetic testing at a gestational age of 16 weeks.The genetic reports indicated that the fetus carried pathogenic mutations in the RUNX family transcription factor 2 gene(c.674G>A).After careful consideration,the pregnant woman and her family decided to continue the pregnancy.CONCLUSION Definitive prenatal diagnosis of CCD should include family history,ultrasound diagnosis,and genetic analysis,especially if family history is unknown.

Retrospective analysis of pathological types and imaging features in pancreatic cancer: A comprehensive study

BACKGROUND Pancreatic cancer remains one of the most lethal malignancies worldwide,with a poor prognosis often attributed to late diagnosis.Understanding the correlation between pathological type and imaging features is crucial for early detection and appropriate treatment planning.AIM To retrospectively analyze the relationship between different pathological types of pancreatic cancer and their corresponding imaging features.METHODS We retrospectively analyzed the data of 500 patients diagnosed with pancreatic cancer between January 2010 and December 2020 at our institution.Pathological types were determined by histopathological examination of the surgical spe-cimens or biopsy samples.The imaging features were assessed using computed tomography,magnetic resonance imaging,and endoscopic ultrasound.Statistical analyses were performed to identify significant associations between pathological types and specific imaging characteristics.RESULTS There were 320(64%)cases of pancreatic ductal adenocarcinoma,75(15%)of intraductal papillary mucinous neoplasms,50(10%)of neuroendocrine tumors,and 55(11%)of other rare types.Distinct imaging features were identified in each pathological type.Pancreatic ductal adenocarcinoma typically presents as a hypodense mass with poorly defined borders on computed tomography,whereas intraductal papillary mucinous neoplasms present as characteristic cystic lesions with mural nodules.Neuroendocrine tumors often appear as hypervascular lesions in contrast-enhanced imaging.Statistical analysis revealed significant correlations between specific imaging features and pathological types(P<0.001).CONCLUSION This study demonstrated a strong association between the pathological types of pancreatic cancer and imaging features.These findings can enhance the accuracy of noninvasive diagnosis and guide personalized treatment approaches.

Comprehensive bibliometric analysis of pharmacotherapy for bipolar disorders:Present trends and future directions

BACKGROUND Bipolar disorder(BD)is a severe mental illness characterized by significant mood swings.Effective drug treatment modalities are crucial for managing BD.AIM To analyze the current status and future trends of global research on BD drug treatment over the last decade.METHODS The Web of Science Core Collection database spanning from 2015 to 2024 was utilized to retrieve literature related to BD drug treatment.A total of 2624 articles were extracted.Data visualization and analysis were conducted using CiteSpace,VOSviewer,Pajek,Scimago Graphica,and R-studio bibliometrix to identify RESULTS The United States,China,and the United Kingdom have made the most significant contributions to research on BD drug treatment and formed notable research collaboration networks.The University of Pittsburgh,Massachusetts General Hospital,and the University of Michigan have been identified as the major research institutions in this field.The Journal of Affective Disorders is the most influential journal.A keyword analysis revealed research hotspots related to clinical symptoms,drug efficacy,and genetic mechanisms.A citation analysis identified the management guidelines published by Yatham et al in 2018 as the most cited paper.CONCLUSION This study provides a detailed overview of the field of BD drug treatment,highlighting key contributors,research hotspots,and future directions.The study findings can be employed as a reference for future research and policymaking,which may enable further development and optimization of BD pharmacotherapy.

Targeted gene sequencing and bioinformatics analysis of patients with gallbladder neuroendocrine carcinoma:A case report

BACKGROUND Gallbladder neuroendocrine carcinoma(NEC)represents a subtype of gallbladder malignancies characterized by a low incidence,aggressive nature,and poor prognosis.Despite its clinical severity,the genetic alterations,mechanisms,and signaling pathways underlying gallbladder NEC remain unclear.CASE SUMMARY This case study presents a rare instance of primary gallbladder NEC in a 73-year-old female patient,who underwent a radical cholecystectomy with hepatic hilar lymphadenectomy and resection of liver segments IV-B and V.Targeted gene sequencing and bioinformatics analysis tools,including STRING,GeneMANIA,Metascape,TRRUST,Sangerbox,cBioPortal and GSCA,were used to analyze the biological functions and features of mutated genes in gallbladder NEC.Twelve mutations(APC,ARID2,IFNA6,KEAP1,RB1,SMAD4,TP53,BTK,GATA1,GNAS,and PRDM3)were identified,and the tumor mutation burden was determined to be 9.52 muts/Mb via targeted gene sequencing.A protein-protein interaction network showed significant interactions among the twelve mutated genes.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were used to assess mutation functions and pathways.The results revealed 40 tumor-related pathways.A key regulatory factor for gallbladder NEC-related genes was identified,and its biological functions and features were compared with those of gallbladder carcinoma.CONCLUSION Gallbladder NEC requires standardized treatment.Comparisons with other gallbladder carcinomas revealed clinical phenotypes,molecular alterations,functional characteristics,and enriched pathways.

Fatigue Strength Analysis of Dissimilar Aluminum Alloy TIG Welds

The constant amplitude loading fatigue tests were carried out on the 6061/7075 aluminum alloy TIG fillet welded lap specimens in this study,and the weld seam cross-section hardness was measured.The experimental results show that most specimens mainly failed at the 7075 side weld toes even though the base material tensile strength of 7075 is higher than that of 6061.The maximum stress-strain concentration in the two finite element models is located at the 7075 side weld toe,which is basically consistent with the actual fracture location.The weld zone on the 7075 side experiences severe material softening,with a large gradient.However,the Vickers hardness value on the 6061 side negligibly changes and fluctuates around 70 HV.No obvious defects are found on the fatigue fracture,but a large number of secondary cracks appear.Cracks germinate from the weld toe and propagate in the direction of the plate thickness.Weld reinforcement has a serious impact on fatigue life.Fatigue life will decrease exponentially as the weld reinforcement increases under low stress.It is found that the notch stress method can give a better fatigue life prediction for TIG weldments,and the errors of the predicted results are within the range of two factors,while the prediction accuracy decreases under low stress.The equivalent structural stress method can also be used for fatigue life prediction of TIG weldments,but the errors of prediction results are within the range of three factors,and the accuracy decreases under high stress.

Machine learning based damage state identification:A novel perspective on fragility analysis for nuclear power plants considering structural uncertainties

Seismic fragility analysis(SFA)is known as an effective probabilistic-based approach used to evaluate seismic fragility.There are various sources of uncertainties associated with this approach.A nuclear power plant(NPP)system is an extremely important infrastructure and contains many structural uncertainties due to construction issues or structural deterioration during service.Simulation of structural uncertainties effects is a costly and time-consuming endeavor.A novel approach to SFA for the NPP considering structural uncertainties based on the damage state is proposed and examined.The results suggest that considering the structural uncertainties is essential in assessing the fragility of the NPP structure,and the impact of structural uncertainties tends to increase with the state of damage.Subsequently,machine learning(ML)is found to be superior in high-precision damage state identification of the NPP for reducing the time of nonlinear time-history analysis(NLTHA)and could be applied in the damage state-based SFA.Also,the impact of various sources of uncertainties is investigated through sensitivity analysis.The Sobol and Shapley additive explanations(SHAP)method can be complementary to each other and able to solve the problem of quantifying seismic and structural uncertainties simultaneously and the interaction effect of each parameter.

Multilevel analysis of the central-peripheral-target organ pathway:contributing to recovery after peripheral nerve injury

Peripheral nerve injury is a common neurological condition that often leads to severe functional limitations and disabilities.Research on the pathogenesis of peripheral nerve injury has focused on pathological changes at individual injury sites,neglecting multilevel pathological analysis of the overall nervous system and target organs.This has led to restrictions on current therapeutic approaches.In this paper,we first summarize the potential mechanisms of peripheral nerve injury from a holistic perspective,covering the central nervous system,peripheral nervous system,and target organs.After peripheral nerve injury,the cortical plasticity of the brain is altered due to damage to and regeneration of peripheral nerves;changes such as neuronal apoptosis and axonal demyelination occur in the spinal cord.The nerve will undergo axonal regeneration,activation of Schwann cells,inflammatory response,and vascular system regeneration at the injury site.Corresponding damage to target organs can occur,including skeletal muscle atrophy and sensory receptor disruption.We then provide a brief review of the research advances in therapeutic approaches to peripheral nerve injury.The main current treatments are conducted passively and include physical factor rehabilitation,pharmacological treatments,cell-based therapies,and physical exercise.However,most treatments only partially address the problem and cannot complete the systematic recovery of the entire central nervous system-peripheral nervous system-target organ pathway.Therefore,we should further explore multilevel treatment options that produce effective,long-lasting results,perhaps requiring a combination of passive(traditional)and active(novel)treatment methods to stimulate rehabilitation at the central-peripheral-target organ levels to achieve better functional recovery.

Comparative transcriptome analysis identifies key regulators of nitrogen use efficiency in chrysanthemum

Nitrogen(N)is a limiting factor that determines the yield and quality of chrysanthemum.Genetic variation in N use efficiency(NUE)has been reported among chrysanthemum genotypes.We performed a transcriptome analysis of two chrysanthemum genotypes,'Nannonglihuang'(LH,N-efficient genotype)and'Nannongxuefeng"(XF,N-inefficient genotype),under low N(0.4 mmol L^(-1)N)and normal N(8 mmol L^(-1)N)treatments for 15 d and an N recovery treatment for 12 h(low N treatment for 15 d and then normal N treatment for 12 h)to understand the genetic factors impacting NUE in chrysanthemum.The two genotypes exhibited contrasting responses to the different N treatments.The N-efficient genotype LH had significant superiority in agronomic traits,N accumulation and glutamine synthase activity under both normal N and low N treatments.Low N treatment promoted root growth in LH,but inhibited root growth in XF.Transcriptome analysis revealed that the low N treatment increased the expression of some N metabolism genes,genes related to auxin and abscisic acid signal transduction in the roots of both genotypes,as well as genes related to gibberellin signal transduction in roots of LH.The N recovery treatment just increased the expression of genes related to cytokinin signal transduction in roots of LH.The expression levels of the NRT2.1,AMT1.1,and Gln1 genes related to gibberellin and cytokinin signal transduction were higher in roots of LH than in XF under different N treatments,suggesting that the genes related to N metabolism and hormone(auxin,abscisic acid,gibberellin,and cytokinin)signal transduction in roots of LH are more sensitive to different N treatments than those of XF.Co-expression network analysis(WGCNA)also identified hub genes like bZIP43,bHLH93,NPF6.3,IBR10,MYB62,PP2C,PP2C06 and NLP7,which may be the key regulators of N-mediated responses in chrysanthemum and play crucial roles in enhancing NUE and resistance to low N stress in the N-efficient chrysanthemum genotype.These results revealed the key factors involved in regulating NUE in chrysanthemum at the genetic level,which provides new insights into the complex mechanism of efficient nitrogen utilization in chrysanthemum,and can be useful for the improvement and breeding of high NUE chrysanthemum genotypes.

Global trends of purinergic receptors and depression:A bibliometric analysis from 2003 to 2023

BACKGROUND Depression significantly threatens human health.Purinergic receptors are reported to be associated with depression.However,there is no bibliometric research in this field have been published.AIM To provide some reference for the further research in the field of purinergic receptors and depression utilizing bibliometric analysis.METHODS Relevant researches were retrieved from the Web of Science Core Collection database.The period of the search was from January 1,2003 to December 31,2023.The CiteSpace(6.2.R7)and VOSviewer(1.6.19)were applied to identify the main contributors of countries,authors,institutions,references and journals.Besides,we evaluate keywords to assess the hotspots and trends over the previous 2 decades.RESULTS Totally,247 articles were identified,showing an increasing trend over time.The most productive country,institution,and journal in this field are China,Harvard University,and Biological Psychiatry,respectively.Liang SD and Rodrigues,Ana Lucia S were the most prolific authors.Burnstock G ranked first among the cited authors.The cooperation among countries and disciplines is crucial.The P2X7 receptor provides promising prospects for treating depression and further studies are warranted to validate the scope and significance of depression therapeutic strategies.CONCLUSION This study provides an overview of the worldwide research status and future trends in purinergic receptors and depression.P2X7 receptor is considered an appropriate target for the treatment of depression,as well as neurological diseases.It is implied that based on purinergic system,the future prospects for interventions aimed at depression treatment are promising,showing the way for both augmentation strategies and new drug treatments in the context of the pharmacology of depression.

Transcriptome Analysis of Derris fordii and Derris elliptica to Identify Potential Genes Involved in Rotenoid Biosynthesis

Derris fordii and Derris elliptica belong to the Derris genus of the Fabaceae family, distinguished by their high isoflavonoid content, particularly rotenoids, which hold significance in pharmaceuticals and agriculture. Rotenone, as a prominent rotenoid, has a longstanding history of use in pesticides, veterinary applications, medicine, and medical research. The accumulation of rotenoids within Derris plants adheres to species-specific and tissue-specific patterns and is also influenced by environmental factors. Current research predominantly addresses extraction techniques, pharmacological applications, and pesticide formulations, whereas investigations into the biosynthesis pathway and regulatory mechanism of rotenoids remain relatively scarce. In this study, we observed notable differences in rotenone content across the roots, stems, and leaves of D. fordii, as well as within the roots of D. elliptica. Utilizing RNA sequencing (RNA-seq), we analyzed the transcriptomes and expression profiles of unigenes from these four tissues, identifying a total of 121,576 unigenes. Differentially expressed genes (DEGs) across four comparison groups demonstrated significant enrichment in the phenylpropanoid and flavonoid biosynthesis pathways. Key unigenes implicated in the rotenoid biosynthesis pathway were identified, with PAL, C4H, CHS, CHI, IFS, and HI4OMT playing critical roles in D. fordii, while IFS and HI4OMT were determined to be essential for rotenoid biosynthesis in D. elliptica. These findings enhance our understanding of the biosynthesis mechanism of rotenoids in Derris species. The unigenes identified in this study represent promising candidates for future investigations aimed at validating their roles in rotenoid biosynthesis.

Interdisciplinary perspectives on diabetes and microcirculatory dysfunction:A global bibliometric analysis

BACKGROUND The prevalence of diabetes and its association with microcirculatory dysfunction presents a significant challenge in contemporary global health.Addressing this nexus is crucial for developing targeted therapeutic interventions.AIM To trace the progression and delineate the current state of interdisciplinary research concerning diabetes and microcirculation.METHODS Employing a bibliometric approach,this study scrutinizes 12886 peer-reviewed publications retrieved from the PubMed and Web of Science databases.The focus is on elucidating the research trajectory and thematic concentrations at the confluence of diabetes and microcirculation.RESULTS Research outputs have surged since 2011,with the United States,China,and the United Kingdom leading in the quantity and quality of publications.This analysis revealed that journals such as Diabetes Care and The New England Journal of Medicine,along with top research institutions,have significantly contributed to advancing the understanding of microvascular processes affected by diabetes.The central themes identified include inflammation,oxidative stress,and endothelial dysfunction,which are critical in mediating the microvascular complications of diabetes.CONCLUSION This bibliometric evaluation reveals an evolving landscape focusing on diabetes and microcirculatory dysfunction.The complexity of diabetic microvascular issues encouraged multidisciplinary research strategies that are imperative for global health outcomes.