Stroke remains a leading cause of death and disability worldwide,and electroacupuncture has a long history of use in stroke treatment.This meta-analysis and systematic review aimed to evaluate the efficacy of electroa...Stroke remains a leading cause of death and disability worldwide,and electroacupuncture has a long history of use in stroke treatment.This meta-analysis and systematic review aimed to evaluate the efficacy of electroacupuncture and explore its potential mechanisms in animal models of ischemic stroke.The PubMed,EMBASE,Web of Science,CENTRAL,and CINAHL databases were comprehensively searched up to May 1,2024.This review included articles on preclinical investigations of the efficacy and mechanisms of electroacupuncture in treating ischemic stroke.Data from 70 eligible studies were analyzed in Stata 18.0,using a random-effects model to calculate the standardized mean difference(Hedge’s g).The risk of bias was assessed using RevMan 5.4 software,and the quality of evidence was rated according to the Grading of Recommendations,Assessment,Development,and Evaluation(GRADE)system.Subgroup analyses were conducted to test the consistency of the results and sensitivity analyses were used to assess their robustness.The quality assessment revealed that most studies adequately handled incomplete data and selective reporting.However,several methodological limitations were identified:only 4 studies demonstrated a low risk of allocation concealment,26 achieved a low risk of outcome assessment bias,and 9 had a high risk of randomization bias.Additionally,there was an unclear risk regarding participant blinding and other methodological aspects.The GRADE assessment rated 12 outcomes as moderate quality and 6 as low quality.The mechanisms of electroacupuncture treatment for ischemic stroke can be categorized as five primary pathways:(1)Electroacupuncture significantly reduced infarct volume and apoptotic cell death(P<0.01)in ischemic stroke models;(2)electroacupuncture significantly decreased the levels of pro-inflammatory factors(P<0.01)while increasing the levels of anti-inflammatory factors(P=0.02);(3)electroacupuncture reduced the levels of oxidative stress indicators(P<0.01)and enhanced the expression of antioxidant enzymes(P<0.01);(4)electroacupuncture significantly promoted nerve regeneration(P<0.01);and(5)electroacupuncture influenced blood flow remodeling(P<0.01)and angiogenesis(P<0.01).Subgroup analyses indicated that electroacupuncture was most effective in the transient middle cerebral artery occlusion model(P<0.01)and in post-middle cerebral artery occlusion intervention(P<0.01).Dispersive waves were found to outperform continuous waves with respect to neuroprotection and anti-inflammatory effects(P<0.01),while scalp acupoints demonstrated greater efficacy than body acupoints(P<0.01).The heterogeneity among the included studies was minimal,and sensitivity analyses indicated stable results.Their methodological quality was generally satisfactory.In conclusion,electroacupuncture is effective in treating cerebral ischemia by modulating cell apoptosis,oxidative stress,inflammation,stroke-induced nerve regeneration,blood flow remodeling,and angiogenesis.The efficacy of electroacupuncture may be influenced by factors such as the middle cerebral artery occlusion model,the timing of intervention onset,waveform,and acupoint selection.Despite the moderate to low quality of evidence,these findings suggest that electroacupuncture has clinical potential for improving outcomes in ischemic stroke.展开更多
Purpose-This paper provides a comprehensive analysis of the Brazilian freight railway system,examining the efficacy of the current concession renewal model in light of persistent structural problems such as market con...Purpose-This paper provides a comprehensive analysis of the Brazilian freight railway system,examining the efficacy of the current concession renewal model in light of persistent structural problems such as market concentration,cargo dependence on export commodities and underutilization of the network.Situating Brazil within the broader international debate on railway reforms,the paper evaluates whether the ongoing early renewal of concessions can deliver a more diversified and competitive freight system.Design/methodology/approach-The study adopts a sequential mixed-methods research design that integrates longitudinal quantitative analysis with qualitative institutional and policy evaluation.The quantitative component examines time-series indicators published by ANTT,DNIT and INFRA S.A.from 1999 to 2023 to identify structural patterns in traffic growth,investment,safety and market concentration.The qualitative component employs a process-tracing logic to reconstruct the evolution of concession renewals and the implementation of Railway Law 14.273/2021,drawing on concepts from regulatory economics,institutional theory and industrial organization.These empirical streams are synthesized through an analytical framework that connects three dimensions-regulatory design,market structure and system performance-allowing for a systematic assessment of how Brazil’s institutional configuration shapes incentives,competitive dynamics and network utilization.Findings-The analysis confirms that the early renewal of concessions has successfully secured substantial private investment for capacity expansion on existing trunk lines.However,it has perpetuated the vertically integrated model,reinforcing the market power of incumbent operators and failing to significantly promote intramodal competition or cargo diversification.The system remains dominated by iron ore and agricultural commodities,with general cargo representing a minuscule share.The new authorization regime and short-line railway policies present a viable pathway for market opening but face significant operational and institutional barriers to implementation.Originality/value-This research offers a timely and critical assessment of a pivotal moment in Brazilian railway policy.It moves beyond a simplistic evaluation of volume growth to a structural analysis of market failures and the interplay between concession renewal and regulatory innovation.The findings provide actionable insights for policymakers in Brazil and other emerging economies seeking to balance private investment with public interest goals in railway infrastructure,highlighting the necessity of complementary,pro-competitive measures alongside financial investment.展开更多
Illicit web ecosystems,encompassing phishing,illegal online gambling,scam platforms,and malicious advertising,have rapidly expanded in scale and complexity,creating severe social,financial,and cybersecurity risks.Trad...Illicit web ecosystems,encompassing phishing,illegal online gambling,scam platforms,and malicious advertising,have rapidly expanded in scale and complexity,creating severe social,financial,and cybersecurity risks.Traditional rule-based and blacklist-driven detection approaches struggle to cope with polymorphic,multilingual,and adversarially manipulated threats,resulting in increasing demand for Artificial Intelligence(AI)-based solutions.This review provides a comprehensive synthesis of research on AI-driven threat detection for illicit web environments.It surveys detection models across multiple modalities,including text-based analysis of Uniform Resource Locator(URL)and HyperText Markup Language(HTML),vision-based recognition of webpage layouts and logos,graphbased modeling of domain and infrastructure relationships,and sequence modeling using transformer architectures.In addition,the paper examines system architectures,data collection and labeling pipelines,real-time detection frameworks,and widely used benchmark datasets,while also discussing their inherent limitations related to imbalance,representativeness,and reproducibility.The review highlights critical challenges such as evasion strategies,cross-lingual detection barriers,deployment latency,and explainability gaps.Furthermore,it identifies emerging research directions,including the use of Generative Adversarial Network(GAN)for threat simulation,few-shot and self-supervised learning for data-scarce environments,Explainable Artificial Intelligence(XAI)for transparency,and predictive AI for proactive threat forecasting.By integrating technical,legal,and societal perspectives,this survey offers a structured foundation for researchers and practitioners to design resilient,adaptive,and trustworthy AI-based defense systems against illicit web threats.展开更多
Background:Artificial intelligence(AI)-assisted threedimensional(3D)surgical platforms,integrated with augmented reality,have the potential to improve intraoperative anatomical recognition and provide surgeons with an...Background:Artificial intelligence(AI)-assisted threedimensional(3D)surgical platforms,integrated with augmented reality,have the potential to improve intraoperative anatomical recognition and provide surgeons with an immersive,dynamic operating environment during urooncological procedures.This review aims to examine the current applications of AI in robotic uro-oncology,with a particular focus on its role in facilitating intraoperative navigation during complex surgeries.Methods:A systematic literature search was performed across PubMed,the National Library of Medicine,MEDLINE,the Cochrane Central Register of Controlled Trials(CENTRAL),ClinicalTrials.gov,and Google Scholar to identify relevant studies published up to July 2025.The search strategy incorporated a predefined set of keywords,including AI,machine learning,radical prostatectomy(RP),robotic-assisted radical prostatectomy(RARP),robotassisted partial nephrectomy(RAPN),and robot-assisted radical cystectomy(RARC).Only clinical trials,full-text peer-reviewed publications,and original research articles were included.Studies were eligible for inclusion if they evaluated or described applications of AI in RARP,RAPN,or RARC.Results:Technological advancements have substantially transformed the field of uro-oncologic surgery.In particular,AI and AI-assisted intraoperative navigation in RARP demonstrate considerable potential to objectively assess surgical performance and predict clinical outcomes.In RAPN,the adoption of preoperative,interactive 3D virtualmodels for surgical planning has influenced surgical decisions,thus,enhanced precision in resection planning correlates with superior nephron-sparing outcomes and optimized selective clamping.AI applications in RARC,techniques such as augmented reality(AR)can overlay critical information on the surgical field,by facilitating navigation through complex anatomical planes and enhancing identification of critical structures.Conclusion:AI appears to enhance robotic uro-oncologic procedures by increasing operative precision and supporting individualised surgical treatment strategies.展开更多
The Internet of Vehicles(IoV)is an emerging technology that aims to connect vehicles,infrastructure,and other devices to enable intelligent transportation systems.One of the key challenges in IoV is to ensure safe and...The Internet of Vehicles(IoV)is an emerging technology that aims to connect vehicles,infrastructure,and other devices to enable intelligent transportation systems.One of the key challenges in IoV is to ensure safe and efficient communication among vehicles of different types and capabilities.This paper proposes a data-driven vehicular heterogeneity-based intelligent collision avoidance system for IoV.The system leverages Vehicle-to-Vehicle(V2V)and Vehicle-to-Infrastructure(V2I)communication to collect real-time data about the environment and the vehicles.The data is collected to acknowledge the heterogeneity of vehicles and human behavior.The data is analyzed using machine learning algorithms to identify potential collision risks and recommend appropriate actions to avoid collisions.The system takes into account the heterogeneity of vehicles,such as their size,speed,and maneuverability,to optimize collision avoidance strategies.The proposed system is experimented with real-time datasets and compared with existing collision avoidance systems.The results are shown using the evaluation metrics that show the proposed system can significantly reduce the number of collisions and improve the overall safety and efficiency of IoV with an accuracy of 96.5%using the SVM algorithm.The trial outcomes demonstrated that the new system,incorporating vehicular,weather,and human behavior factors,outperformed previous systems that only considered vehicular and weather aspects.This innovative approach is poised to lead transportation efforts,reducing accident rates and improving the quality of transportation systems in smart cities.By offering predictive capabilities,the proposed model not only helps control accident rates but also prevents them in advance,ensuring road safety.展开更多
Few studies have investigated alterations in the immune cell microenvironment of the dorsal root ganglia following spinal cord injury and whether these modifications facilitate axonal regeneration.In this study,we use...Few studies have investigated alterations in the immune cell microenvironment of the dorsal root ganglia following spinal cord injury and whether these modifications facilitate axonal regeneration.In this study,we used a single-cell RNA sequencing dataset to create a comprehensive profile of the diverse cell types in the dorsal root ganglia and spinal cord of a mid-thoracic contusion injury model in cynomolgus monkeys.Cell communication analysis indicated that specific signaling events among various dorsal root ganglia cell types occur in response to spinal cord injury.Single-cell analysis using dimensionality reduction clustering identified distinct molecular signatures for nine cell types,including macrophage subpopulations,and differential gene expression profiles between dorsal root ganglia cells and spinal cord cells following spinal cord injury.The macrophage subpopulations were categorized into 11 clusters(MC0-MC10)based on differentially expressed genes,with the top 10 genes being ABCA6,RBMS3,EBF1,LAMA4,ANTXR2,LAMA2,SOX5,FOXP2,GHR,and APOD.MC0,MC1,and MC2 constituted the predominant macrophage populations.MC4,MC6,and MC9 were nearly absent in the spinal cord,but exhibited significant increases in the dorsal root ganglia post-spinal cord injury.Notably,these subpopulations possess a strong capacity for regulating axonal regeneration.The developmental progression of dorsal root ganglia macrophages after spinal cord injury was elucidated using cell trajectory and pseudo-time analyses.Genes such as EBF1(MC6 and MC9 marker),RBMS3(MC6 and MC9 marker),and ABCA6(MC6 marker)showed high expression levels in the critical pathways of macrophage function.Through ligand-receptor pair analysis,we determined that the effects of macrophages on microglia are predominantly mediated through interaction pairs(e.g.,SPP1-CD44,LAMC1-CD44,and FN1-CD44),potentially facilitating specific cellular communications within the immune microenvironment.The single-cell RNA sequencing dataset used in this study represents the first comprehensive transcriptional analysis of the dorsal root ganglia after spinal cord injury in cynomolgus monkeys,encompassing nearly all cell types within the dorsal root ganglia region.Using this dataset,we evaluated diverse subtypes of macrophages in the post-spinal cord injury dorsal root ganglia area and examined the signaling pathways that facilitate interactions among immune response-related macrophages in the dorsal root ganglia.Findings from this study provide a theoretical basis for understanding how the immune microenvironment influences the regenerative capacity of dorsal root ganglia neurons after spinal cord injury and offer novel insights into the complex processes underlying the pathobiology of spinal cord injury.展开更多
Objectives This review aimed to systematically synthesize the available research on the disclosure of diagnosis and related issues in childhood cancer from the perspectives of healthcare professionals,with the goal of...Objectives This review aimed to systematically synthesize the available research on the disclosure of diagnosis and related issues in childhood cancer from the perspectives of healthcare professionals,with the goal of informing the optimization of disclosure processes and meeting the communication needs of affected families.Methods In accordance with the Joanna Briggs Institute(JBI)methodology for mixed methods systematic reviews,the convergent segregated approach was used in this review.Articles were retrieved from 11 databases,including PubMed,Web of Science,CINAHL,CENTRAL,Embase,Ovid/Medline,PsycINFO,PsycArticles,Scopus,ERIC,and China National Knowledge Infrastructure(CNKI).The quality of the selected articles was assessed using the Mixed Method Appraisal Tool(MMAT).The review protocol was registered on PROSPERO(CRD42024542746).Results A total of 21 studies from 10 countries were included.Their methodological quality was generally medium to high,with MMAT scores ranging from 60%to 100%.The synthesis yielded three core themes:1)the spectrum of professional and societal attitudes toward disclosure;2)the dynamic practices of navigating disclosure amid uncertainty,including timing and environment,stakeholders,and content of disclosure;and 3)factors influencing disclosure,including children’s,parental,healthcare professionals’,and socio-cultural factors.Conclusions This review synthesized the perspectives and experiences of healthcare professionals regarding disclosure in childhood cancer,highlighting the complexity and multidimensional nature of this process in clinical practice.Future research should further investigate the experiences and needs of children and their parents,explore cultural variations in disclosure practices,develop context-appropriate assessment tools,and construct multidimensional intervention strategies to enhance the humanistic care and professional effectiveness of the disclosure process.展开更多
Objective:Current research highlights periodontal disease as a systemic inflammatory condition that may influence extra-oral diseases such as prostatic diseases,which prompted us to explore the potential association.T...Objective:Current research highlights periodontal disease as a systemic inflammatory condition that may influence extra-oral diseases such as prostatic diseases,which prompted us to explore the potential association.To evaluate whether periodontal disease is associated with an increased risk of prostatic disease,including prostate cancer,benign prostatic hyperplasia(BPH),and prostatitis.Methods:A systematic search of observational studies concerning the relationship between periodontal disease and prostatic disease was performed in online databases PubMed,Embase,Web of Science,Scopus,CENTRAL,CNKI,and WanFang.Searches were conducted from database inception to 31 July 2025.Pooled hazard ratio(HR)or odds ratio(OR)with 95%confidence intervals(CIs)were synthesized.Subgroup analysis was used to detect the origin of heterogeneity,sensitivity analysis was employed to evaluate the robustness of the results,and publication bias analyses were also performed.R software was used to perform statistical analyses.Results:Sixteen studies that met the preset criteria were included in this study.In the pooled analysis,periodontal disease was associated with increased risk of prostate cancer(HR=1.23,95%CI:1.16-1.29,p<0.001)or BPH(OR=1.55,95%CI:1.41-1.70,p<0.001).Sensitivity analysis confirmed the robustness of the results.No obvious publication biaswas found in the meta-analysis.Only one cohort study reported that chronic periodontitis increases the risk of prostatitis(HR=2.521,95%CI:1.685-4.005,p<0.001).The effect of periodontal treatment on prostatic disease is still unclear.Conclusions:The systematic review and meta-analysis identified an observational association between periodontal disease and increased risks of prostate cancer and BPH.Because all included studies were observational,these results indicate association rather than causation,and further prospective and mechanistic studies are required to clarify temporality and causality.展开更多
The reliability of information systems(IS)is a key factor in the sustainable operation of modern digital services.However,existing assessment methods remain fragmented and are often limited to individual indicators or...The reliability of information systems(IS)is a key factor in the sustainable operation of modern digital services.However,existing assessment methods remain fragmented and are often limited to individual indicators or expert judgments.This paper proposes a hybridmethodology for a comprehensive assessment of IS reliability based on the integration of the international standard ISO/IEC 25010:2023,multicriteria analysismethods(ARAS,CoCoSo,and TOPSIS),and theXGBoostmachine learning algorithmfor missing data imputation.Thestructure of the ISO/IEC 25010 standard is used to formalize reliability criteria and subcriteria,while theAHP method allows for the calculation of their weighting coefficients based on expert assessments.The XGBoost algorithm ensures the correct filling of gaps in the source data,increasing the completeness and reliability of the subsequent assessment.The resulting weighted indicators are aggregated using threeMCDMmethods,after which an integral reliability indicator is formed as a percentage.The methodology was tested on six real-world information systems with different architectures.The results demonstrated high consistency between the ARAS,CoCoSo,and TOPSISmethods,as well as the stability of the final rating when the criterion weights vary by±10%.The proposed approach provides a reproducible,transparent,and objective assessment of information system reliability and can be used to identify system bottlenecks,make modernization decisions,and manage the quality of digital infrastructure.展开更多
Background:This study focused on developing and optimizing a self-microemulsifying drug delivery system(SMEDDS)to improve Lafutidine’s solubility and bioavailability,thereby enhancing its effectiveness in treating ga...Background:This study focused on developing and optimizing a self-microemulsifying drug delivery system(SMEDDS)to improve Lafutidine’s solubility and bioavailability,thereby enhancing its effectiveness in treating gastric ulcers.Traditional formulations are less effective due to their limited water solubility and bioavailability.Methods:The study used solubility tests,pseudo-ternary phase diagrams,and central composite design(CCD)to optimize.The formulation was optimized by varying the oil concentration(10–40%)and surfactant/cosurfactant ratio(0.33–3.00),and then tested for droplet size,drug content,emulsification,phase stability,and in vitro dissolution.Results:The study found that the optimized formulation contained 14%Capmul PG 8NF oil,62%Labrasol surfactant,and 24%Tween 80 cosurfactant.This combination generated an average droplet size of 111.02 nm and improved drug release properties.Furthermore,the formulation was stable without phase separation,with a drug content of 88.2–99.8%.Conclusion:SMEDDS significantly improves lafutidine delivery by increasing solubility and absorption,thereby overcoming oral administration challenges.The system quickly formed small droplets in water and released the drug in 15 min.Enhancing lafutidine’s bioavailability may improve its efficacy in treating gastric ulcers,resulting in better patient outcomes and potentially lower dosing frequency.展开更多
Rapid colonization by invasive plants threatened local biodiversity worldwide;however,their distributional hotspots and future habitat shifts remain poorly understood in developing nations such as Nigeria.Using MaxEnt...Rapid colonization by invasive plants threatened local biodiversity worldwide;however,their distributional hotspots and future habitat shifts remain poorly understood in developing nations such as Nigeria.Using MaxEnt model,we investigated present and future habitat suitability for two aggressive invaders,C.odorata and T.diversifolia,across Nigeria's urban landscapes.We used a dataset consisting of 327 and 108 occurrence points for C.odorata and T.diversifolia,respectively,along with twenty-three(23)environmental variables to identify occurrence and areas of concern under current climatic fluctuations.The results revealed that the model performed strongly(AUC>0.85)and identified precipitation seasonality as the dominant predictor for both species.The finding indicates that precipitation seasonality of≤59 CV,isothermality of≥57%and precipitation of wettest month of≥170 mm enhance niche occupancy of C.odorata,while precipitation seasonality of 62-70 CV,precipitation of wettest quarter and maximum temperature of warmest month of≥450 mm and 35℃,respectively enhance that of T.differsifolia.Current predictions place C.odorata primarily along the southern coast,while T.diversifolia is most suitable in the southwest and extends into the northcentral.Future suitable area for C.odorata will slightly expand by 2050 and 2070,encroaching into southeastern and some central states.In contrast,T.diversifolia contracts under CNRM-CM5 but shows a modest expansion under GFDL-CM3.These projections indicate that climate change may reinforce the dominance of C.odorata in southern Nigeria,whereas T.differsifolia may exhibit divergent trajectories from southern to certain northern states in the future.展开更多
Building reliable intent-based,task-oriented dialog systems typically requires substantial manual effort:designers must derive intents,entities,responses,and control logic from raw conversational data,then iterate unt...Building reliable intent-based,task-oriented dialog systems typically requires substantial manual effort:designers must derive intents,entities,responses,and control logic from raw conversational data,then iterate until the assistant behaves consistently.This paper investigates how far large language models(LLMs)can automate this development.In this paper,we use two reference corpora,Let’s Go(English,public transport)and MEDIA(French,hotel booking),to prompt four LLM families(GPT-4o,Claude,Gemini,Mistral Small)and generate the core specifications required by the rasa platform.These include intent sets with example utterances,entity definitions with slot mappings,response templates,and basic dialog flows.To structure this process,we introduce a model-and platform-agnostic pipelinewith two phases.The first normalizes and validates LLM-generated artifacts,enforcing crossfile consistency andmaking slot usage explicit.The second uses a lightweight dialog harness that runs scripted tests and incrementally patches failure points until conversations complete reliably.Across eight projects,all models required some targeted repairs before training.After applying our pipeline,all reached≥70%task completion(many above 84%),while NLU performance ranged from mid-0.6 to 1.0 macro-F1 depending on domain breadth.These results show that,with modest guidance,current LLMs can produce workable end-to-end dialog prototypes directly fromraw transcripts.Our main contributions are:(i)a reusable bootstrap method aligned with industry domain-specific languages(DSLs),(ii)a small set of high-impact corrective patterns,and(iii)a simple but effective harness for closed-loop refinement across conversational platforms.展开更多
Some patients with systemic lupus erythematosus experience neuropsychiatric symptoms.Although magnetic resonance imaging can detect abnormal signals in the white matter of the brain,conventional methods often struggle...Some patients with systemic lupus erythematosus experience neuropsychiatric symptoms.Although magnetic resonance imaging can detect abnormal signals in the white matter of the brain,conventional methods often struggle to accurately capture microstructural changes.Various diffusion models have been used to study white matter in systemic lupus erythematosus;however,comparative analyses of their sensitivity and specificity for detecting microstructural changes remain insufficient.To address this,our team designed a diagnostic trial that used multimodal diffusion imaging techniques to observe white matter microstructural changes in patients with systemic lupus erythematosus who had neuropsychiatric symptoms,with an aim to identify key diagnostic biomarkers for these patients.Patients with active lupus who received treatment at the Department of Rheumatology and Immunology,The First Affiliated Hospital of China Medical University,from September 2023 to March 2024 were recruited.According to the standards of the American College of Rheumatology,patients with systemic lupus erythematosus who had neuropsychiatric symptoms were assigned to the systemic lupus erythematosus group,whereas those without neuropsychiatric symptoms were assigned to the non-systemic lupus erythematosus group.Additionally,healthy volunteers matched by region,sex,and age were recruited as controls.All three groups underwent the same diffusion magnetic resonance imaging examination protocol to compare differences in diffusion parameters.Advanced diffusion imaging models were able to sensitively detect microstructural changes in the white matter fibers of patients with systemic lupus erythematosus who had neuropsychiatric symptoms,with specific diffusion parameters showing significant abnormalities in key brain regions.In the left superior longitudinal fasciculus subregion and the right thalamic radiations of patients with systemic lupus erythematosus who had neuropsychiatric symptoms,we also identified abnormal diffusion characteristics that were clearly correlated with disease activity,suggesting that microstructural changes in these areas may reflect the dynamic process of neuroinflammatory damage.The present study addresses critical challenges in the diagnosis of systemic lupus erythematosus by identifying specific white matter imaging biomarkers and elucidating the association between microstructural damage and clinical manifestations.The main contributions of our study include:1)establishing axial regression probability parameters from mean apparent propagator magnetic resonance imaging as sensitive biomarkers for systemic lupus erythematosus,particularly in the third subregion of the left superior longitudinal fasciculus;2)demonstrating that multimodal diffusion imaging may be superior to conventional diffusion tensor imaging for detecting white matter microstructural abnormalities in patients with systemic lupus erythematosus;and 3)integrating tract-based spatial statistics with clinically relevant analyses to link imaging findings to pathological mechanisms.展开更多
Background:An increasing number of studies have shown that ferroptosis is related to the initiation and development of small cell lung cancer(SCLC).The systematic review aimed to summarize the characteristics of ferro...Background:An increasing number of studies have shown that ferroptosis is related to the initiation and development of small cell lung cancer(SCLC).The systematic review aimed to summarize the characteristics of ferroptosis from its pathogenetic role to translational therapeutic implications in SCLC.Methods:This systematic review,registered in PROSPERO(CRD420251090058),followed PRISMA 2020 guidelines.Comprehensive research of PubMed,Scopus,and Web of Science was performed for studies published between January 2010 and July 2025 investigating ferroptosis mechanisms,genetic or pharmacological modulation,or molecular profiling in SCLC.Two reviewers independently performed data extraction and quality assessment.Results:Nineteen preclinical studies met the inclusion criteria.Key regulators included solute carrier family 7 member 11(SLC7A11),glutathione peroxidase 4(GPX4),ferroptosis suppressor protein 1(FSP1),and acyl-CoA synthetase long chain family member 4(ACSL4).The molecular subtypes of SCLC,achaete-scute homolog 1(ASCL1),neuronal differentiation 1(NEUROD1),POU class 2 homeobox 3(POU2F3),and Yes1 associated transcriptional regulator(YAP1)exhibit differential ferroptosis gene expressions,influencing therapeutic responsiveness.Non-neuroendocrine subtypes are more ferroptosis-prone,whereas neuroendocrine variants display enhanced antioxidant defenses.Ferroptosis induction also promotes immune activation through stimulator of interferon genes(STING)-mediated CD8+T-cell recruitment.Conclusions:Ferroptosis constitutes a promising therapeutic axis in SCLC.Integrating ferroptosis biomarkers into molecular stratification frameworks could refine patient selection and support precision oncology strategies,warranting further translational and clinical validation.展开更多
The integration of machine learning(ML)technology with Internet of Things(IoT)systems produces essential changes in healthcare operations.Healthcare personnel can track patients around the clock thanks to healthcare I...The integration of machine learning(ML)technology with Internet of Things(IoT)systems produces essential changes in healthcare operations.Healthcare personnel can track patients around the clock thanks to healthcare IoT(H-IoT)technology,which also provides proactive statistical findings and precise medical diagnoses that enhance healthcare performance.This study examines how ML might support IoT-based health care systems,namely in the areas of prognostic systems,disease detection,patient tracking,and healthcare operations control.The study looks at the benefits and drawbacks of several machine learning techniques for H-IoT applications.It also examines the fundamental problems,such as data security and cyberthreats,as well as the high processing demands that these systems face.Alongside this,the essay discusses the advantages of all the technologies,including machine learning,deep learning,and the Internet of Things,as well as the significant difficulties and problems that arise when integrating the technology into healthcare forecasts.展开更多
Reuse of irrigation water after appropriate filtration has emerged as one of the most important strategies for addressing global water scarcity and improving the sustainability of agricultural systems.This study revie...Reuse of irrigation water after appropriate filtration has emerged as one of the most important strategies for addressing global water scarcity and improving the sustainability of agricultural systems.This study reviews the research progress on filtration technologies and the reuse of secondary water through a comprehensive visual and bibliometric analysis of the relevant scientific literature.Using tools such as R Studio,VOSviewer,and the Bibliometrix R‐package,a total of 374 publications published between 2003 and 2024 were retrieved from the Web of Science database and systematically analyzed.The collected literature was examined with respect to publication trends,disciplinary distributions,leading journals,contributing countries,institutions,and authors.Additionally,an in-depth keyword analysis was conducted to explore co-occurrence networks,thematic clustering,and emerging research frontiers.The results indicate three distinct developmental stages in this field:a slow and exploratory phase beginning in 2003,followed by a period of moderate growth around 2013,and a rapid expansion phase that has been evident since 2018.Research outputs primarily span environmental sciences,engineering,water resources management,and agricultural sciences.The findings highlight an increasing global interest in sustainable water reuse and the need for continued innovation in filtration methods to enhance water quality and agricultural productivity.Future scientific efforts should emphasize the development of advanced,cost-effective filtration technologies,the reduction of environmental risks,and the promotion of large-scale water reuse practices to alleviate water shortages and support resilient agricultural systems.展开更多
Objective:Early sepsis can be treated if recognised early,but progression to severe sepsis and septic shock and multiple organ dysfunction syndrome substantially increases mortality.The objectives of our study were to...Objective:Early sepsis can be treated if recognised early,but progression to severe sepsis and septic shock and multiple organ dysfunction syndrome substantially increases mortality.The objectives of our study were to assess morbidity and mortality of patients with sepsis and to compare the effectiveness of a simple bedside satisfiable Quick Sequential Organ Failure Assessment(qSOFA)score with National Early Warning Score(NEWS)in prognosticating sepsis.Methods:This prospective observational study was conducted among patients>18 years old presenting with sepsis at B.J.Medical College.The SOFA,qSOFA and NEWS scores were calculated.The effectiveness in predicting mortality was evaluated using receiver operating characteristic curve analysis.Results:A total of 200 patients were evaluated(56%male)with a mean age of 51.7 years.The mortality rate was 23%.Patients categorized under high risk according to SOFA score>8,qSOFA score of 2-3 and NEWS>7 had a mortality rate of 33.3%,27.5%and 28.4%,respectively.AUC for mortality prediction was 0.695 using SOFA score,0.665 using qSOFA and 0.725 using NEWS.At a cut off of 7.50,NEWS demonstrated a sensitivity of 97.8%with a specificity of 28.0%and outperformed both SOFA and qSOFA which yielded a sensitivity of 43.5%and 91.3%and a specificity of 77.9%and 27.9%,respectively.Conclusions:The NEWS score outperforms SOFA and qSOFA in predicting mortality among sepsis patients.However,qSOFA is more helpful in identifying high risk patients and performs better in intensive care setting.展开更多
Advanced adiabatic compressed air energy storage(AA-CAES),with its dual capability for electricity-heat cogeneration and energy storage,offers significant potential as an energy hub for integrated electricity and heat...Advanced adiabatic compressed air energy storage(AA-CAES),with its dual capability for electricity-heat cogeneration and energy storage,offers significant potential as an energy hub for integrated electricity and heat systems(IEHS).While synergies in the electricity-heat market are known to enhance economic efficiency,it is hard to achieve cooperative operation due to the inherent differences among participants of IEHS and the absence of an incentive-compatible mechanism.To address this challenge,this paper proposes a Nash bargaining-based cooperative operation strategy for IEHS with AA-CAES.First,a cooperative alliance framework based on the Nash bargaining is proposed to optimize energy trading.Second,to overcome computational complexity,the non-convex,nonlinear Nash bargaining problem is decomposed into a two-stage optimization approach.In the first stage,a joint planning model maximizes the total profit of the alliance,determining the optimal energy interaction for each participant.In the second stage,a subsequent model ensures fair profit distribution by optimizing pricing and benefit-sharing mechanisms.Subsequently,a distributed solution strategy based on the self-adaptive alternating direction method of multipliers is utilized to preserve operator privacy and improve computational efficiency.Finally,case studies demonstrate that within the electricity-heat co-supply mode,the daily profit of AA-CAES can improve by approximately 4137.45 CNY.Meanwhile,through the proposed cooperative strategy,participants in the IEHS can obtain greater profits,which validates the effectiveness of this strategy.展开更多
The contemporary smart cities,smart homes,smart buildings,and smart health care systems are the results of the explosive growth of Internet of Things(IoT)devices and deep learning.Yet the centralized training paradigm...The contemporary smart cities,smart homes,smart buildings,and smart health care systems are the results of the explosive growth of Internet of Things(IoT)devices and deep learning.Yet the centralized training paradigms have fundamental issues in data privacy,regulatory compliance,and ownership silo alongside the scaled limitations of the real-life application.The concept of Federated Deep Learning(FDL)is a privacy-by-design method that will enable the distributed training of machine learning models among distributed clients without sharing raw data and is suitable in heterogeneous urban settings.It is an overview of the privacy-preserving developments in FDL as of 2018-2025 with a narrow scope on its usage in smart cities(traffic prediction,environmental monitoring,energy grids),smart homes/buildings/IoT(non-intrusive load monitoring,HVAC optimization,anomaly detection)and the healthcare application(medical imaging,Electronic Health Records(EHR)analysis,remote monitoring).It gives coherent taxonomy,domain pipelines,comparative analyses of privacy mechanisms(differential privacy,secure aggregation,Homomorphic Encryption(HE),Trusted Execution Environments(TEEs),blockchain enhanced and hybrids),system structures,security/robustness defense,deployment/Machine Learning Operation(MLOps)issues,and the longstanding challenges(non-IID heterogeneity,communication efficiency,fairness,and sustainability).Some of the contributions made are structured comparisons of privacy threats,practical design advice on urban areas,recognition of open problems,and a research roadmap into the future up to 2035.The paper brings out the transformational worth of FDL in building credible,scalable,and sustainable intelligent urban ecosystems and the need to do further interdisciplinary research in standardization,real-world testbeds,and ethical governance.展开更多
Objective:Artemether is a semi-synthetic derivative of artemisinin and is widely used in the treatment of Plasmodium(P.)falciparum malaria.This study aimed to characterize the safety profile of artemether based on 15-...Objective:Artemether is a semi-synthetic derivative of artemisinin and is widely used in the treatment of Plasmodium(P.)falciparum malaria.This study aimed to characterize the safety profile of artemether based on 15-year data retrived from FDA adverse event reporting system(FAERS).Methods:This is a retrospective analysis on 15-year data of artemether-related adverse effects(AEs)retrieved from the FAERS.AEs were classified according to System Organ Class(SOC)and Preferred Terms(PT).Signal detection was performed using Reporting Odds Ratios(ROR),Proportional Reporting Ratios(PRR),and Empirical Bayes Geometric Mean(EBGM).Stratified analyses examined the impact of demographic factors such as sex,age,and time-to-onset.Temporal patterns and associated risk factors were also investigated.Results:Haemolytic anaemia and haemolysis emerged as the most frequently reported AEs,exhibiting significantly elevated RORs(males:ROR 381.36,95%CI 247.06-588.60;females:ROR 455.11,95%CI 286.43-723.12).Sex-specific differences were evident,with females showing a higher incidence of reproductive-related AEs,including spontaneous abortion and premature labour.Temporal trend analysis revealed that the majority of AEs occurred within the first 30 days after the initiation of artemether administration,indicating a rapid onset.The most affected SOCs were blood and lymphatic system disorders and hepatobiliary disorders.Conclusions:Artemether is associated with a notable frequency of early-onset AEs,particularly hematological and hepatobiliary disorders.The observed sex-specific vulnerability to reproductive AEs highlights the need for sex-conscious clinical approaches.Enhanced post-treatment monitoring and further investigations into the drug’s pharmacokinetics and mechanistic pathways are recommended.展开更多
基金supported by the National Natural Science Foundation of China,Nos.82174496(to NW),82374574(to NW),82302865(to LL)Shanghai Science and Technology Committee Sailing Program,Nos.23YF1403800(to LL),23YF1405200(to YX)Shanghai Hospital Development Center Foundation-Shanghai Municipal Hospital Rehabilitation Medicine Specialty Alliance,No.SHDC22023304(to YW).
文摘Stroke remains a leading cause of death and disability worldwide,and electroacupuncture has a long history of use in stroke treatment.This meta-analysis and systematic review aimed to evaluate the efficacy of electroacupuncture and explore its potential mechanisms in animal models of ischemic stroke.The PubMed,EMBASE,Web of Science,CENTRAL,and CINAHL databases were comprehensively searched up to May 1,2024.This review included articles on preclinical investigations of the efficacy and mechanisms of electroacupuncture in treating ischemic stroke.Data from 70 eligible studies were analyzed in Stata 18.0,using a random-effects model to calculate the standardized mean difference(Hedge’s g).The risk of bias was assessed using RevMan 5.4 software,and the quality of evidence was rated according to the Grading of Recommendations,Assessment,Development,and Evaluation(GRADE)system.Subgroup analyses were conducted to test the consistency of the results and sensitivity analyses were used to assess their robustness.The quality assessment revealed that most studies adequately handled incomplete data and selective reporting.However,several methodological limitations were identified:only 4 studies demonstrated a low risk of allocation concealment,26 achieved a low risk of outcome assessment bias,and 9 had a high risk of randomization bias.Additionally,there was an unclear risk regarding participant blinding and other methodological aspects.The GRADE assessment rated 12 outcomes as moderate quality and 6 as low quality.The mechanisms of electroacupuncture treatment for ischemic stroke can be categorized as five primary pathways:(1)Electroacupuncture significantly reduced infarct volume and apoptotic cell death(P<0.01)in ischemic stroke models;(2)electroacupuncture significantly decreased the levels of pro-inflammatory factors(P<0.01)while increasing the levels of anti-inflammatory factors(P=0.02);(3)electroacupuncture reduced the levels of oxidative stress indicators(P<0.01)and enhanced the expression of antioxidant enzymes(P<0.01);(4)electroacupuncture significantly promoted nerve regeneration(P<0.01);and(5)electroacupuncture influenced blood flow remodeling(P<0.01)and angiogenesis(P<0.01).Subgroup analyses indicated that electroacupuncture was most effective in the transient middle cerebral artery occlusion model(P<0.01)and in post-middle cerebral artery occlusion intervention(P<0.01).Dispersive waves were found to outperform continuous waves with respect to neuroprotection and anti-inflammatory effects(P<0.01),while scalp acupoints demonstrated greater efficacy than body acupoints(P<0.01).The heterogeneity among the included studies was minimal,and sensitivity analyses indicated stable results.Their methodological quality was generally satisfactory.In conclusion,electroacupuncture is effective in treating cerebral ischemia by modulating cell apoptosis,oxidative stress,inflammation,stroke-induced nerve regeneration,blood flow remodeling,and angiogenesis.The efficacy of electroacupuncture may be influenced by factors such as the middle cerebral artery occlusion model,the timing of intervention onset,waveform,and acupoint selection.Despite the moderate to low quality of evidence,these findings suggest that electroacupuncture has clinical potential for improving outcomes in ischemic stroke.
文摘Purpose-This paper provides a comprehensive analysis of the Brazilian freight railway system,examining the efficacy of the current concession renewal model in light of persistent structural problems such as market concentration,cargo dependence on export commodities and underutilization of the network.Situating Brazil within the broader international debate on railway reforms,the paper evaluates whether the ongoing early renewal of concessions can deliver a more diversified and competitive freight system.Design/methodology/approach-The study adopts a sequential mixed-methods research design that integrates longitudinal quantitative analysis with qualitative institutional and policy evaluation.The quantitative component examines time-series indicators published by ANTT,DNIT and INFRA S.A.from 1999 to 2023 to identify structural patterns in traffic growth,investment,safety and market concentration.The qualitative component employs a process-tracing logic to reconstruct the evolution of concession renewals and the implementation of Railway Law 14.273/2021,drawing on concepts from regulatory economics,institutional theory and industrial organization.These empirical streams are synthesized through an analytical framework that connects three dimensions-regulatory design,market structure and system performance-allowing for a systematic assessment of how Brazil’s institutional configuration shapes incentives,competitive dynamics and network utilization.Findings-The analysis confirms that the early renewal of concessions has successfully secured substantial private investment for capacity expansion on existing trunk lines.However,it has perpetuated the vertically integrated model,reinforcing the market power of incumbent operators and failing to significantly promote intramodal competition or cargo diversification.The system remains dominated by iron ore and agricultural commodities,with general cargo representing a minuscule share.The new authorization regime and short-line railway policies present a viable pathway for market opening but face significant operational and institutional barriers to implementation.Originality/value-This research offers a timely and critical assessment of a pivotal moment in Brazilian railway policy.It moves beyond a simplistic evaluation of volume growth to a structural analysis of market failures and the interplay between concession renewal and regulatory innovation.The findings provide actionable insights for policymakers in Brazil and other emerging economies seeking to balance private investment with public interest goals in railway infrastructure,highlighting the necessity of complementary,pro-competitive measures alongside financial investment.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea goverment(MSIT)(No.RS-2024-00439139,Development of a Cyber Crisis Response and Resilience Test Evaluation Systems)this research was supported by the MSIT(Ministry of Science and ICT),Korea,under the Graduate School of Virtual Convergence support program(IITP-2026-RS-2023-00254129)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation)+1 种基金supported by the“Regional Innovation System&Education(RISE)”through the Seoul RISE Center,funded by the Ministry of Education(MOE)and the Seoul Metropolitan Government(2026-RISE-01-018-05)supported by QuadMiners Corp.
文摘Illicit web ecosystems,encompassing phishing,illegal online gambling,scam platforms,and malicious advertising,have rapidly expanded in scale and complexity,creating severe social,financial,and cybersecurity risks.Traditional rule-based and blacklist-driven detection approaches struggle to cope with polymorphic,multilingual,and adversarially manipulated threats,resulting in increasing demand for Artificial Intelligence(AI)-based solutions.This review provides a comprehensive synthesis of research on AI-driven threat detection for illicit web environments.It surveys detection models across multiple modalities,including text-based analysis of Uniform Resource Locator(URL)and HyperText Markup Language(HTML),vision-based recognition of webpage layouts and logos,graphbased modeling of domain and infrastructure relationships,and sequence modeling using transformer architectures.In addition,the paper examines system architectures,data collection and labeling pipelines,real-time detection frameworks,and widely used benchmark datasets,while also discussing their inherent limitations related to imbalance,representativeness,and reproducibility.The review highlights critical challenges such as evasion strategies,cross-lingual detection barriers,deployment latency,and explainability gaps.Furthermore,it identifies emerging research directions,including the use of Generative Adversarial Network(GAN)for threat simulation,few-shot and self-supervised learning for data-scarce environments,Explainable Artificial Intelligence(XAI)for transparency,and predictive AI for proactive threat forecasting.By integrating technical,legal,and societal perspectives,this survey offers a structured foundation for researchers and practitioners to design resilient,adaptive,and trustworthy AI-based defense systems against illicit web threats.
文摘Background:Artificial intelligence(AI)-assisted threedimensional(3D)surgical platforms,integrated with augmented reality,have the potential to improve intraoperative anatomical recognition and provide surgeons with an immersive,dynamic operating environment during urooncological procedures.This review aims to examine the current applications of AI in robotic uro-oncology,with a particular focus on its role in facilitating intraoperative navigation during complex surgeries.Methods:A systematic literature search was performed across PubMed,the National Library of Medicine,MEDLINE,the Cochrane Central Register of Controlled Trials(CENTRAL),ClinicalTrials.gov,and Google Scholar to identify relevant studies published up to July 2025.The search strategy incorporated a predefined set of keywords,including AI,machine learning,radical prostatectomy(RP),robotic-assisted radical prostatectomy(RARP),robotassisted partial nephrectomy(RAPN),and robot-assisted radical cystectomy(RARC).Only clinical trials,full-text peer-reviewed publications,and original research articles were included.Studies were eligible for inclusion if they evaluated or described applications of AI in RARP,RAPN,or RARC.Results:Technological advancements have substantially transformed the field of uro-oncologic surgery.In particular,AI and AI-assisted intraoperative navigation in RARP demonstrate considerable potential to objectively assess surgical performance and predict clinical outcomes.In RAPN,the adoption of preoperative,interactive 3D virtualmodels for surgical planning has influenced surgical decisions,thus,enhanced precision in resection planning correlates with superior nephron-sparing outcomes and optimized selective clamping.AI applications in RARC,techniques such as augmented reality(AR)can overlay critical information on the surgical field,by facilitating navigation through complex anatomical planes and enhancing identification of critical structures.Conclusion:AI appears to enhance robotic uro-oncologic procedures by increasing operative precision and supporting individualised surgical treatment strategies.
文摘The Internet of Vehicles(IoV)is an emerging technology that aims to connect vehicles,infrastructure,and other devices to enable intelligent transportation systems.One of the key challenges in IoV is to ensure safe and efficient communication among vehicles of different types and capabilities.This paper proposes a data-driven vehicular heterogeneity-based intelligent collision avoidance system for IoV.The system leverages Vehicle-to-Vehicle(V2V)and Vehicle-to-Infrastructure(V2I)communication to collect real-time data about the environment and the vehicles.The data is collected to acknowledge the heterogeneity of vehicles and human behavior.The data is analyzed using machine learning algorithms to identify potential collision risks and recommend appropriate actions to avoid collisions.The system takes into account the heterogeneity of vehicles,such as their size,speed,and maneuverability,to optimize collision avoidance strategies.The proposed system is experimented with real-time datasets and compared with existing collision avoidance systems.The results are shown using the evaluation metrics that show the proposed system can significantly reduce the number of collisions and improve the overall safety and efficiency of IoV with an accuracy of 96.5%using the SVM algorithm.The trial outcomes demonstrated that the new system,incorporating vehicular,weather,and human behavior factors,outperformed previous systems that only considered vehicular and weather aspects.This innovative approach is poised to lead transportation efforts,reducing accident rates and improving the quality of transportation systems in smart cities.By offering predictive capabilities,the proposed model not only helps control accident rates but also prevents them in advance,ensuring road safety.
基金supported by the Tianjin Key Medical Discipline(Specialty)Construct Project,No.TJYXZDXK-027A(to SF)the National Key Research andDevelopment Project of Stem Cell and Transformation Research,No.2019YFA0112100(to SF)+2 种基金Tianjin Natural Science Foundation’s Youth Project for DiverseInvestments,No.21JCQNJC01300(to BF)the National Natural Science Foundation of China(Youth Program),No.82102563(to BF)Tianjin Major Science andTechnology Special Projects and Engineering Projects,No.21ZXJBSY00080(to YR).
文摘Few studies have investigated alterations in the immune cell microenvironment of the dorsal root ganglia following spinal cord injury and whether these modifications facilitate axonal regeneration.In this study,we used a single-cell RNA sequencing dataset to create a comprehensive profile of the diverse cell types in the dorsal root ganglia and spinal cord of a mid-thoracic contusion injury model in cynomolgus monkeys.Cell communication analysis indicated that specific signaling events among various dorsal root ganglia cell types occur in response to spinal cord injury.Single-cell analysis using dimensionality reduction clustering identified distinct molecular signatures for nine cell types,including macrophage subpopulations,and differential gene expression profiles between dorsal root ganglia cells and spinal cord cells following spinal cord injury.The macrophage subpopulations were categorized into 11 clusters(MC0-MC10)based on differentially expressed genes,with the top 10 genes being ABCA6,RBMS3,EBF1,LAMA4,ANTXR2,LAMA2,SOX5,FOXP2,GHR,and APOD.MC0,MC1,and MC2 constituted the predominant macrophage populations.MC4,MC6,and MC9 were nearly absent in the spinal cord,but exhibited significant increases in the dorsal root ganglia post-spinal cord injury.Notably,these subpopulations possess a strong capacity for regulating axonal regeneration.The developmental progression of dorsal root ganglia macrophages after spinal cord injury was elucidated using cell trajectory and pseudo-time analyses.Genes such as EBF1(MC6 and MC9 marker),RBMS3(MC6 and MC9 marker),and ABCA6(MC6 marker)showed high expression levels in the critical pathways of macrophage function.Through ligand-receptor pair analysis,we determined that the effects of macrophages on microglia are predominantly mediated through interaction pairs(e.g.,SPP1-CD44,LAMC1-CD44,and FN1-CD44),potentially facilitating specific cellular communications within the immune microenvironment.The single-cell RNA sequencing dataset used in this study represents the first comprehensive transcriptional analysis of the dorsal root ganglia after spinal cord injury in cynomolgus monkeys,encompassing nearly all cell types within the dorsal root ganglia region.Using this dataset,we evaluated diverse subtypes of macrophages in the post-spinal cord injury dorsal root ganglia area and examined the signaling pathways that facilitate interactions among immune response-related macrophages in the dorsal root ganglia.Findings from this study provide a theoretical basis for understanding how the immune microenvironment influences the regenerative capacity of dorsal root ganglia neurons after spinal cord injury and offer novel insights into the complex processes underlying the pathobiology of spinal cord injury.
基金supported by the Fuxing Nursing Research Foundation of Fudan University[FNF202352].
文摘Objectives This review aimed to systematically synthesize the available research on the disclosure of diagnosis and related issues in childhood cancer from the perspectives of healthcare professionals,with the goal of informing the optimization of disclosure processes and meeting the communication needs of affected families.Methods In accordance with the Joanna Briggs Institute(JBI)methodology for mixed methods systematic reviews,the convergent segregated approach was used in this review.Articles were retrieved from 11 databases,including PubMed,Web of Science,CINAHL,CENTRAL,Embase,Ovid/Medline,PsycINFO,PsycArticles,Scopus,ERIC,and China National Knowledge Infrastructure(CNKI).The quality of the selected articles was assessed using the Mixed Method Appraisal Tool(MMAT).The review protocol was registered on PROSPERO(CRD42024542746).Results A total of 21 studies from 10 countries were included.Their methodological quality was generally medium to high,with MMAT scores ranging from 60%to 100%.The synthesis yielded three core themes:1)the spectrum of professional and societal attitudes toward disclosure;2)the dynamic practices of navigating disclosure amid uncertainty,including timing and environment,stakeholders,and content of disclosure;and 3)factors influencing disclosure,including children’s,parental,healthcare professionals’,and socio-cultural factors.Conclusions This review synthesized the perspectives and experiences of healthcare professionals regarding disclosure in childhood cancer,highlighting the complexity and multidimensional nature of this process in clinical practice.Future research should further investigate the experiences and needs of children and their parents,explore cultural variations in disclosure practices,develop context-appropriate assessment tools,and construct multidimensional intervention strategies to enhance the humanistic care and professional effectiveness of the disclosure process.
文摘Objective:Current research highlights periodontal disease as a systemic inflammatory condition that may influence extra-oral diseases such as prostatic diseases,which prompted us to explore the potential association.To evaluate whether periodontal disease is associated with an increased risk of prostatic disease,including prostate cancer,benign prostatic hyperplasia(BPH),and prostatitis.Methods:A systematic search of observational studies concerning the relationship between periodontal disease and prostatic disease was performed in online databases PubMed,Embase,Web of Science,Scopus,CENTRAL,CNKI,and WanFang.Searches were conducted from database inception to 31 July 2025.Pooled hazard ratio(HR)or odds ratio(OR)with 95%confidence intervals(CIs)were synthesized.Subgroup analysis was used to detect the origin of heterogeneity,sensitivity analysis was employed to evaluate the robustness of the results,and publication bias analyses were also performed.R software was used to perform statistical analyses.Results:Sixteen studies that met the preset criteria were included in this study.In the pooled analysis,periodontal disease was associated with increased risk of prostate cancer(HR=1.23,95%CI:1.16-1.29,p<0.001)or BPH(OR=1.55,95%CI:1.41-1.70,p<0.001).Sensitivity analysis confirmed the robustness of the results.No obvious publication biaswas found in the meta-analysis.Only one cohort study reported that chronic periodontitis increases the risk of prostatitis(HR=2.521,95%CI:1.685-4.005,p<0.001).The effect of periodontal treatment on prostatic disease is still unclear.Conclusions:The systematic review and meta-analysis identified an observational association between periodontal disease and increased risks of prostate cancer and BPH.Because all included studies were observational,these results indicate association rather than causation,and further prospective and mechanistic studies are required to clarify temporality and causality.
基金the financial support of the Ministry of Science and Higher Education of the Republic of Kazakhstan,grant No.AP25793823.
文摘The reliability of information systems(IS)is a key factor in the sustainable operation of modern digital services.However,existing assessment methods remain fragmented and are often limited to individual indicators or expert judgments.This paper proposes a hybridmethodology for a comprehensive assessment of IS reliability based on the integration of the international standard ISO/IEC 25010:2023,multicriteria analysismethods(ARAS,CoCoSo,and TOPSIS),and theXGBoostmachine learning algorithmfor missing data imputation.Thestructure of the ISO/IEC 25010 standard is used to formalize reliability criteria and subcriteria,while theAHP method allows for the calculation of their weighting coefficients based on expert assessments.The XGBoost algorithm ensures the correct filling of gaps in the source data,increasing the completeness and reliability of the subsequent assessment.The resulting weighted indicators are aggregated using threeMCDMmethods,after which an integral reliability indicator is formed as a percentage.The methodology was tested on six real-world information systems with different architectures.The results demonstrated high consistency between the ARAS,CoCoSo,and TOPSISmethods,as well as the stability of the final rating when the criterion weights vary by±10%.The proposed approach provides a reproducible,transparent,and objective assessment of information system reliability and can be used to identify system bottlenecks,make modernization decisions,and manage the quality of digital infrastructure.
文摘Background:This study focused on developing and optimizing a self-microemulsifying drug delivery system(SMEDDS)to improve Lafutidine’s solubility and bioavailability,thereby enhancing its effectiveness in treating gastric ulcers.Traditional formulations are less effective due to their limited water solubility and bioavailability.Methods:The study used solubility tests,pseudo-ternary phase diagrams,and central composite design(CCD)to optimize.The formulation was optimized by varying the oil concentration(10–40%)and surfactant/cosurfactant ratio(0.33–3.00),and then tested for droplet size,drug content,emulsification,phase stability,and in vitro dissolution.Results:The study found that the optimized formulation contained 14%Capmul PG 8NF oil,62%Labrasol surfactant,and 24%Tween 80 cosurfactant.This combination generated an average droplet size of 111.02 nm and improved drug release properties.Furthermore,the formulation was stable without phase separation,with a drug content of 88.2–99.8%.Conclusion:SMEDDS significantly improves lafutidine delivery by increasing solubility and absorption,thereby overcoming oral administration challenges.The system quickly formed small droplets in water and released the drug in 15 min.Enhancing lafutidine’s bioavailability may improve its efficacy in treating gastric ulcers,resulting in better patient outcomes and potentially lower dosing frequency.
文摘Rapid colonization by invasive plants threatened local biodiversity worldwide;however,their distributional hotspots and future habitat shifts remain poorly understood in developing nations such as Nigeria.Using MaxEnt model,we investigated present and future habitat suitability for two aggressive invaders,C.odorata and T.diversifolia,across Nigeria's urban landscapes.We used a dataset consisting of 327 and 108 occurrence points for C.odorata and T.diversifolia,respectively,along with twenty-three(23)environmental variables to identify occurrence and areas of concern under current climatic fluctuations.The results revealed that the model performed strongly(AUC>0.85)and identified precipitation seasonality as the dominant predictor for both species.The finding indicates that precipitation seasonality of≤59 CV,isothermality of≥57%and precipitation of wettest month of≥170 mm enhance niche occupancy of C.odorata,while precipitation seasonality of 62-70 CV,precipitation of wettest quarter and maximum temperature of warmest month of≥450 mm and 35℃,respectively enhance that of T.differsifolia.Current predictions place C.odorata primarily along the southern coast,while T.diversifolia is most suitable in the southwest and extends into the northcentral.Future suitable area for C.odorata will slightly expand by 2050 and 2070,encroaching into southeastern and some central states.In contrast,T.diversifolia contracts under CNRM-CM5 but shows a modest expansion under GFDL-CM3.These projections indicate that climate change may reinforce the dominance of C.odorata in southern Nigeria,whereas T.differsifolia may exhibit divergent trajectories from southern to certain northern states in the future.
基金This publication is part of the TrustBoost project,that has received funding from MICIU/AEI/10.13039/501100011033,from FEDER,UEIt is a coordinated project by a multidisciplinary team from the Universidad Politécnica de Madrid(UPM)and University of Granada(UGR),with two subprojects that address TrustBoost’s objectives:“Enhancing Trustworthiness in Conversational AI through Multimodal Affective Awareness”(Trust Boost-UPM,ref.PID2023-150584OB-C21)“Breaking the Duality of Conversational AI:Going beyond Guided Conversations While Ensuring Compliance with Domain Rules and Constraints”(Trust Boost-UGR,ref.PID2023-150584OB-C22).
文摘Building reliable intent-based,task-oriented dialog systems typically requires substantial manual effort:designers must derive intents,entities,responses,and control logic from raw conversational data,then iterate until the assistant behaves consistently.This paper investigates how far large language models(LLMs)can automate this development.In this paper,we use two reference corpora,Let’s Go(English,public transport)and MEDIA(French,hotel booking),to prompt four LLM families(GPT-4o,Claude,Gemini,Mistral Small)and generate the core specifications required by the rasa platform.These include intent sets with example utterances,entity definitions with slot mappings,response templates,and basic dialog flows.To structure this process,we introduce a model-and platform-agnostic pipelinewith two phases.The first normalizes and validates LLM-generated artifacts,enforcing crossfile consistency andmaking slot usage explicit.The second uses a lightweight dialog harness that runs scripted tests and incrementally patches failure points until conversations complete reliably.Across eight projects,all models required some targeted repairs before training.After applying our pipeline,all reached≥70%task completion(many above 84%),while NLU performance ranged from mid-0.6 to 1.0 macro-F1 depending on domain breadth.These results show that,with modest guidance,current LLMs can produce workable end-to-end dialog prototypes directly fromraw transcripts.Our main contributions are:(i)a reusable bootstrap method aligned with industry domain-specific languages(DSLs),(ii)a small set of high-impact corrective patterns,and(iii)a simple but effective harness for closed-loop refinement across conversational platforms.
基金supported by the National Natural Science Foundation Joint Fund,No.U22A20309(to PY)the Natural Science Foundation of LiaoningProvince,No.2023-MS-07(to HuL)the Unveiling Key Scientific and Technological Projects of Liaoning Province,No.2021JH1/10400051(to HuL).
文摘Some patients with systemic lupus erythematosus experience neuropsychiatric symptoms.Although magnetic resonance imaging can detect abnormal signals in the white matter of the brain,conventional methods often struggle to accurately capture microstructural changes.Various diffusion models have been used to study white matter in systemic lupus erythematosus;however,comparative analyses of their sensitivity and specificity for detecting microstructural changes remain insufficient.To address this,our team designed a diagnostic trial that used multimodal diffusion imaging techniques to observe white matter microstructural changes in patients with systemic lupus erythematosus who had neuropsychiatric symptoms,with an aim to identify key diagnostic biomarkers for these patients.Patients with active lupus who received treatment at the Department of Rheumatology and Immunology,The First Affiliated Hospital of China Medical University,from September 2023 to March 2024 were recruited.According to the standards of the American College of Rheumatology,patients with systemic lupus erythematosus who had neuropsychiatric symptoms were assigned to the systemic lupus erythematosus group,whereas those without neuropsychiatric symptoms were assigned to the non-systemic lupus erythematosus group.Additionally,healthy volunteers matched by region,sex,and age were recruited as controls.All three groups underwent the same diffusion magnetic resonance imaging examination protocol to compare differences in diffusion parameters.Advanced diffusion imaging models were able to sensitively detect microstructural changes in the white matter fibers of patients with systemic lupus erythematosus who had neuropsychiatric symptoms,with specific diffusion parameters showing significant abnormalities in key brain regions.In the left superior longitudinal fasciculus subregion and the right thalamic radiations of patients with systemic lupus erythematosus who had neuropsychiatric symptoms,we also identified abnormal diffusion characteristics that were clearly correlated with disease activity,suggesting that microstructural changes in these areas may reflect the dynamic process of neuroinflammatory damage.The present study addresses critical challenges in the diagnosis of systemic lupus erythematosus by identifying specific white matter imaging biomarkers and elucidating the association between microstructural damage and clinical manifestations.The main contributions of our study include:1)establishing axial regression probability parameters from mean apparent propagator magnetic resonance imaging as sensitive biomarkers for systemic lupus erythematosus,particularly in the third subregion of the left superior longitudinal fasciculus;2)demonstrating that multimodal diffusion imaging may be superior to conventional diffusion tensor imaging for detecting white matter microstructural abnormalities in patients with systemic lupus erythematosus;and 3)integrating tract-based spatial statistics with clinically relevant analyses to link imaging findings to pathological mechanisms.
基金supported by Regione Autonoma della Sardegna,pursuant to Regional Law 07 August 2007,n.7“Promotion of Scientific Research and Technological Innovation in Sardinia—UGOV Project RAS_CRP2023 CARRULilt Nazionale—5 per mille Program for the year 2022,LILT 2023 scientific-health research call,Number:LILT—Protocol number 2024U0001294 of 29.03.2024。
文摘Background:An increasing number of studies have shown that ferroptosis is related to the initiation and development of small cell lung cancer(SCLC).The systematic review aimed to summarize the characteristics of ferroptosis from its pathogenetic role to translational therapeutic implications in SCLC.Methods:This systematic review,registered in PROSPERO(CRD420251090058),followed PRISMA 2020 guidelines.Comprehensive research of PubMed,Scopus,and Web of Science was performed for studies published between January 2010 and July 2025 investigating ferroptosis mechanisms,genetic or pharmacological modulation,or molecular profiling in SCLC.Two reviewers independently performed data extraction and quality assessment.Results:Nineteen preclinical studies met the inclusion criteria.Key regulators included solute carrier family 7 member 11(SLC7A11),glutathione peroxidase 4(GPX4),ferroptosis suppressor protein 1(FSP1),and acyl-CoA synthetase long chain family member 4(ACSL4).The molecular subtypes of SCLC,achaete-scute homolog 1(ASCL1),neuronal differentiation 1(NEUROD1),POU class 2 homeobox 3(POU2F3),and Yes1 associated transcriptional regulator(YAP1)exhibit differential ferroptosis gene expressions,influencing therapeutic responsiveness.Non-neuroendocrine subtypes are more ferroptosis-prone,whereas neuroendocrine variants display enhanced antioxidant defenses.Ferroptosis induction also promotes immune activation through stimulator of interferon genes(STING)-mediated CD8+T-cell recruitment.Conclusions:Ferroptosis constitutes a promising therapeutic axis in SCLC.Integrating ferroptosis biomarkers into molecular stratification frameworks could refine patient selection and support precision oncology strategies,warranting further translational and clinical validation.
文摘The integration of machine learning(ML)technology with Internet of Things(IoT)systems produces essential changes in healthcare operations.Healthcare personnel can track patients around the clock thanks to healthcare IoT(H-IoT)technology,which also provides proactive statistical findings and precise medical diagnoses that enhance healthcare performance.This study examines how ML might support IoT-based health care systems,namely in the areas of prognostic systems,disease detection,patient tracking,and healthcare operations control.The study looks at the benefits and drawbacks of several machine learning techniques for H-IoT applications.It also examines the fundamental problems,such as data security and cyberthreats,as well as the high processing demands that these systems face.Alongside this,the essay discusses the advantages of all the technologies,including machine learning,deep learning,and the Internet of Things,as well as the significant difficulties and problems that arise when integrating the technology into healthcare forecasts.
文摘Reuse of irrigation water after appropriate filtration has emerged as one of the most important strategies for addressing global water scarcity and improving the sustainability of agricultural systems.This study reviews the research progress on filtration technologies and the reuse of secondary water through a comprehensive visual and bibliometric analysis of the relevant scientific literature.Using tools such as R Studio,VOSviewer,and the Bibliometrix R‐package,a total of 374 publications published between 2003 and 2024 were retrieved from the Web of Science database and systematically analyzed.The collected literature was examined with respect to publication trends,disciplinary distributions,leading journals,contributing countries,institutions,and authors.Additionally,an in-depth keyword analysis was conducted to explore co-occurrence networks,thematic clustering,and emerging research frontiers.The results indicate three distinct developmental stages in this field:a slow and exploratory phase beginning in 2003,followed by a period of moderate growth around 2013,and a rapid expansion phase that has been evident since 2018.Research outputs primarily span environmental sciences,engineering,water resources management,and agricultural sciences.The findings highlight an increasing global interest in sustainable water reuse and the need for continued innovation in filtration methods to enhance water quality and agricultural productivity.Future scientific efforts should emphasize the development of advanced,cost-effective filtration technologies,the reduction of environmental risks,and the promotion of large-scale water reuse practices to alleviate water shortages and support resilient agricultural systems.
文摘Objective:Early sepsis can be treated if recognised early,but progression to severe sepsis and septic shock and multiple organ dysfunction syndrome substantially increases mortality.The objectives of our study were to assess morbidity and mortality of patients with sepsis and to compare the effectiveness of a simple bedside satisfiable Quick Sequential Organ Failure Assessment(qSOFA)score with National Early Warning Score(NEWS)in prognosticating sepsis.Methods:This prospective observational study was conducted among patients>18 years old presenting with sepsis at B.J.Medical College.The SOFA,qSOFA and NEWS scores were calculated.The effectiveness in predicting mortality was evaluated using receiver operating characteristic curve analysis.Results:A total of 200 patients were evaluated(56%male)with a mean age of 51.7 years.The mortality rate was 23%.Patients categorized under high risk according to SOFA score>8,qSOFA score of 2-3 and NEWS>7 had a mortality rate of 33.3%,27.5%and 28.4%,respectively.AUC for mortality prediction was 0.695 using SOFA score,0.665 using qSOFA and 0.725 using NEWS.At a cut off of 7.50,NEWS demonstrated a sensitivity of 97.8%with a specificity of 28.0%and outperformed both SOFA and qSOFA which yielded a sensitivity of 43.5%and 91.3%and a specificity of 77.9%and 27.9%,respectively.Conclusions:The NEWS score outperforms SOFA and qSOFA in predicting mortality among sepsis patients.However,qSOFA is more helpful in identifying high risk patients and performs better in intensive care setting.
基金supported in part by the National Natural Science Foundation of China(No.52407115)State Key Laboratory of Power System Operation and Control(61011000223).
文摘Advanced adiabatic compressed air energy storage(AA-CAES),with its dual capability for electricity-heat cogeneration and energy storage,offers significant potential as an energy hub for integrated electricity and heat systems(IEHS).While synergies in the electricity-heat market are known to enhance economic efficiency,it is hard to achieve cooperative operation due to the inherent differences among participants of IEHS and the absence of an incentive-compatible mechanism.To address this challenge,this paper proposes a Nash bargaining-based cooperative operation strategy for IEHS with AA-CAES.First,a cooperative alliance framework based on the Nash bargaining is proposed to optimize energy trading.Second,to overcome computational complexity,the non-convex,nonlinear Nash bargaining problem is decomposed into a two-stage optimization approach.In the first stage,a joint planning model maximizes the total profit of the alliance,determining the optimal energy interaction for each participant.In the second stage,a subsequent model ensures fair profit distribution by optimizing pricing and benefit-sharing mechanisms.Subsequently,a distributed solution strategy based on the self-adaptive alternating direction method of multipliers is utilized to preserve operator privacy and improve computational efficiency.Finally,case studies demonstrate that within the electricity-heat co-supply mode,the daily profit of AA-CAES can improve by approximately 4137.45 CNY.Meanwhile,through the proposed cooperative strategy,participants in the IEHS can obtain greater profits,which validates the effectiveness of this strategy.
文摘The contemporary smart cities,smart homes,smart buildings,and smart health care systems are the results of the explosive growth of Internet of Things(IoT)devices and deep learning.Yet the centralized training paradigms have fundamental issues in data privacy,regulatory compliance,and ownership silo alongside the scaled limitations of the real-life application.The concept of Federated Deep Learning(FDL)is a privacy-by-design method that will enable the distributed training of machine learning models among distributed clients without sharing raw data and is suitable in heterogeneous urban settings.It is an overview of the privacy-preserving developments in FDL as of 2018-2025 with a narrow scope on its usage in smart cities(traffic prediction,environmental monitoring,energy grids),smart homes/buildings/IoT(non-intrusive load monitoring,HVAC optimization,anomaly detection)and the healthcare application(medical imaging,Electronic Health Records(EHR)analysis,remote monitoring).It gives coherent taxonomy,domain pipelines,comparative analyses of privacy mechanisms(differential privacy,secure aggregation,Homomorphic Encryption(HE),Trusted Execution Environments(TEEs),blockchain enhanced and hybrids),system structures,security/robustness defense,deployment/Machine Learning Operation(MLOps)issues,and the longstanding challenges(non-IID heterogeneity,communication efficiency,fairness,and sustainability).Some of the contributions made are structured comparisons of privacy threats,practical design advice on urban areas,recognition of open problems,and a research roadmap into the future up to 2035.The paper brings out the transformational worth of FDL in building credible,scalable,and sustainable intelligent urban ecosystems and the need to do further interdisciplinary research in standardization,real-world testbeds,and ethical governance.
文摘Objective:Artemether is a semi-synthetic derivative of artemisinin and is widely used in the treatment of Plasmodium(P.)falciparum malaria.This study aimed to characterize the safety profile of artemether based on 15-year data retrived from FDA adverse event reporting system(FAERS).Methods:This is a retrospective analysis on 15-year data of artemether-related adverse effects(AEs)retrieved from the FAERS.AEs were classified according to System Organ Class(SOC)and Preferred Terms(PT).Signal detection was performed using Reporting Odds Ratios(ROR),Proportional Reporting Ratios(PRR),and Empirical Bayes Geometric Mean(EBGM).Stratified analyses examined the impact of demographic factors such as sex,age,and time-to-onset.Temporal patterns and associated risk factors were also investigated.Results:Haemolytic anaemia and haemolysis emerged as the most frequently reported AEs,exhibiting significantly elevated RORs(males:ROR 381.36,95%CI 247.06-588.60;females:ROR 455.11,95%CI 286.43-723.12).Sex-specific differences were evident,with females showing a higher incidence of reproductive-related AEs,including spontaneous abortion and premature labour.Temporal trend analysis revealed that the majority of AEs occurred within the first 30 days after the initiation of artemether administration,indicating a rapid onset.The most affected SOCs were blood and lymphatic system disorders and hepatobiliary disorders.Conclusions:Artemether is associated with a notable frequency of early-onset AEs,particularly hematological and hepatobiliary disorders.The observed sex-specific vulnerability to reproductive AEs highlights the need for sex-conscious clinical approaches.Enhanced post-treatment monitoring and further investigations into the drug’s pharmacokinetics and mechanistic pathways are recommended.
