Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often stru...Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often struggle with low-contrast MRI images,class imbalance,and suboptimal feature extraction.This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans.Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Enhanced Super-Resolution Generative Adversarial Networks(ESRGAN).A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient(MCC)-based evaluation method into the design.The trained and validated model gives a 98.88%accuracy rate and 0.9614 MCC score.We also performed a 10-fold cross-validation experiment with an average accuracy of 96.52%(±1.51),a loss of 0.1671,and an MCC score of 0.9429 across folds.The proposed framework outperforms the state-of-the-art models with a 98%weighted F1-score while decreasing misdiagnosis results for every AD stage.The model demonstrates apparent separation abilities between AD progression stages according to the results of the confusion matrix analysis.These results validate the effectiveness of hybrid DL models with adaptive preprocessing for early and reliable Alzheimer’s diagnosis,contributing to improved computer-aided diagnosis(CAD)systems in clinical practice.展开更多
High-concentration photovoltaic(HCPV)systems present significant thermal management challenges due to the intense heat fluxes generated under concentrated solar irradiation,especially in arid environments.Effective he...High-concentration photovoltaic(HCPV)systems present significant thermal management challenges due to the intense heat fluxes generated under concentrated solar irradiation,especially in arid environments.Effective heat dissipation is critical to prevent performance degradation and structural failure.This study investigates the thermal performance and design optimization of an enhanced HCPV module,integrating numerical,analytical,and experimental methods.A coupled optical-thermal-electrical model was developed to simulate ray tracing,heat transfer,and temperature-dependent electrical behaviour,with predictions validated under real-world desert conditions.Compared to a baseline commercial module operating at 106℃,the optimized design achieved a peak temperature reduction of 16℃,lowering the cell temperature to 90℃under a concentration ratio of 961×and direct normal irradiance(DNI)of 950 W/m^(2).The total thermal resistance was reduced from 0.25 to 0.15 K/W(a 40%improvement),and the electrical efficiency increased from 37.5%to 38.6%,representing a relative gain of approximately 3.1%.The system consistently maintained a fill factor exceeding 78%,underscoring stable performance under high thermal load.These findings demonstrate that targeted thermal design,informed by integrated modeling,is essential for unlocking the reliability and efficiency of high-flux solar energy systems.展开更多
Malic acid is a crucial determinant of apple(Malus domestica)fruit quality,influencing acidity and flavor.While transcriptional regulation of malic acid metabolism is well-studied,post-transcriptional control and the ...Malic acid is a crucial determinant of apple(Malus domestica)fruit quality,influencing acidity and flavor.While transcriptional regulation of malic acid metabolism is well-studied,post-transcriptional control and the role of jasmonate(JA)remain largely unexplored.We identify a novel regulatory pathway involving JA signaling,a micro RNA(mi RNA),and vacuolar transport regulators that control malic acid accumulation in apple fruit.We show that mdm-mi R858,which increases during fruit maturation,directly targets and cleaves Md MYB73 transcripts.Md MYB73 is a known positive regulator of vacuolar H+-pumping and malate transport,activating genes like Md VHA-A,Md VHP,and Md ALMT9.Overexpression of mdm-mi R858 suppressed Md MYB73,thereby reducing Md VHA-A,Md VHP,and Md ALMT9 expression and malic acid content in apple calli,fruits,and GL-3plantlets,while silencing mdm-mi R858 had opposite effects.Crucially,the JA-responsive transcription factor Md MYC2,the expression of which increases during fruit maturation,directly binds the mdm-mi R858 promoter and activates its expression.Furthermore,the Mediator complex subunit Md MED25 interacts with Md MYC2,enhancing this activation.Manipulating Md MYC2 or Md MED25expression altered mdm-mi R858 levels,Md MYB73expression,and malic acid accumulation,mirroring exogenous methyl jasmonate(Me JA)treatment effects.A mi R858-resistant Md MYB73 variant confirmed the miRNA-target interaction's specificity and functional significance.Our findings reveal a novel JA-Md MYC2/MdMED25-mi R858-Md MYB73regulatory cascade controlling malic acid accumulation in apple,providing a mechanistic link between hormonal signaling and post-transcriptional regulation of fruit acidity.This discovery offers new targets for manipulating fruit quality.展开更多
Digital twin technology,that creates virtual replicas of physical entities using real-time data and simulation models,has emerged as a transformative innovation across multiple healthcare domains.Its application in ph...Digital twin technology,that creates virtual replicas of physical entities using real-time data and simulation models,has emerged as a transformative innovation across multiple healthcare domains.Its application in physiotherapy and rehabilitation represents a paradigm shift from traditional therapeutic approaches to personalized data-driven interventions that optimize patient outcomes.This narrative review examines the current applications,benefits,challenges,and future prospects of digital twin technology in physiotherapy and rehabilitation,providing a comprehensive analysis of the manner in which this technology is reshaping clinical practice and patient care.A narrative review approach was employed,systematically searching PubMed,IEEE Xplore,Scopus,and Web of Science databases.Studies describing digital twin applications,development methodologies,clinical implementations,and theoretical frameworks in physiotherapy and rehabilitation contexts were included.Digital twin technology demonstrates significant potential in personalizing rehabilitation programs,enabling real-time monitoring of patient progress,predicting treatment outcomes,and facilitating remote therapeutic interventions.Current applications span musculoskeletal rehabilitation,neurological recovery,post surgical care,and sports injury management.Key benefits include enhanced treatment precision,improved patient engagement,reduced healthcare costs,and accelerated recovery times.However,implementation faces challenges including technological complexity,data privacy concerns,interoperability issues,and the need for substantial infrastructure investment.Digital twin technology represents a promising frontier in physiotherapy and rehabilitation,offering unprecedented opportunities for personalized,efficient,and effective patient care.Successful integration requires addressing the current limitations while fostering interdisciplinary collaboration between clinicians,engineers,and data scientists.展开更多
The rapid proliferation of Internet of Things(IoT)devices in critical healthcare infrastructure has introduced significant security and privacy challenges that demand innovative,distributed architectural solutions.Thi...The rapid proliferation of Internet of Things(IoT)devices in critical healthcare infrastructure has introduced significant security and privacy challenges that demand innovative,distributed architectural solutions.This paper proposes FE-ACS(Fog-Edge Adaptive Cybersecurity System),a novel hierarchical security framework that intelligently distributes AI-powered anomaly detection algorithms across edge,fog,and cloud layers to optimize security efficacy,latency,and privacy.Our comprehensive evaluation demonstrates that FE-ACS achieves superior detection performance with an AUC-ROC of 0.985 and an F1-score of 0.923,while maintaining significantly lower end-to-end latency(18.7 ms)compared to cloud-centric(152.3 ms)and fog-only(34.5 ms)architectures.The system exhibits exceptional scalability,supporting up to 38,000 devices with logarithmic performance degradation—a 67×improvement over conventional cloud-based approaches.By incorporating differential privacy mechanisms with balanced privacy-utility tradeoffs(ε=1.0–1.5),FE-ACS maintains 90%–93%detection accuracy while ensuring strong privacy guarantees for sensitive healthcare data.Computational efficiency analysis reveals that our architecture achieves a detection rate of 12,400 events per second with only 12.3 mJ energy consumption per inference.In healthcare risk assessment,FE-ACS demonstrates robust operational viability with low patient safety risk(14.7%)and high system reliability(94.0%).The proposed framework represents a significant advancement in distributed security architectures,offering a scalable,privacy-preserving,and real-time solution for protecting healthcare IoT ecosystems against evolving cyber threats.展开更多
Male breast cancer(MBC)is rare,representing 0.5%–1%of all breast cancers,but its incidence is increasing due to improved diagnostics and awareness.MBC typically presents in older men,is human epidermal growth factor ...Male breast cancer(MBC)is rare,representing 0.5%–1%of all breast cancers,but its incidence is increasing due to improved diagnostics and awareness.MBC typically presents in older men,is human epidermal growth factor receptor 2(HER2)-negative and estrogen receptor(ER)-positive,and lacks routine screening,leading to delayed diagnosis and advanced disease.Major risk factors include hormonal imbalance,radiation exposure,obesity,alcohol use,and Breast Cancer Gene 1 and 2(BRCA1/2)mutations.Clinically,it may resemble gynecomastia but usually appears as a unilateral,painless mass or nipple discharge.Advances in imaging and liquid biopsy have enhanced early detection.Molecular mechanisms involve hormonal signaling,HER2/epidermal growth factor receptor(EGFR)pathways,tumor suppressor gene alterations,and epigenetic changes.While standard treatments mirror those for female breast cancer,emerging options such as cyclin-dependent kinase 4 and 6(CDK4/6),and poly(ADP-ribose)polymerase(PARP)inhibitors,immunotherapy,and precision medicine are reshaping management.Incorporating artificial intelligence,molecular profiling,and male-specific clinical trials is essential to improve outcomes and bridge current diagnostic and therapeutic gaps.展开更多
Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that ...Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail.It uses the NASA CMAPSS dataset,which has over 200,000 engine cycles from260 engines.The process begins with systematic preprocessing,which includes imputation,outlier removal,scaling,and labelling of the remaining useful life.Dimensionality is reduced using a hybrid selection method that combines variance filtering,recursive elimination,and gradient-boosted importance scores,yielding a stable set of 10 informative sensors.To mitigate class imbalance,minority cases are oversampled,and class-weighted losses are applied during training.Benchmarking is carried out with logistic regression,gradient boosting,and a recurrent design that integrates gated recurrent units with long short-term memory networks.The Long Short-Term Memory–Gated Recurrent Unit(LSTM–GRU)hybrid achieved the strongest performance with an F1 score of 0.92,precision of 0.93,recall of 0.91,ReceiverOperating Characteristic–AreaUnder the Curve(ROC-AUC)of 0.97,andminority recall of 0.75.Interpretability testing using permutation importance and Shapley values indicates that sensors 13,15,and 11 are the most important indicators of engine wear.The proposed system combines imbalance handling,feature reduction,and Interpretability into a practical design suitable for real industrial settings.展开更多
Micro/nanorobots represent a groundbreaking advancement in nanotechnology,with applications spanning medicine,envi-ronmental remediation,and industrial processes.A major challenge in their development is achieving eff...Micro/nanorobots represent a groundbreaking advancement in nanotechnology,with applications spanning medicine,envi-ronmental remediation,and industrial processes.A major challenge in their development is achieving efficient and bio-compatible propulsion.Enzyme-driven propulsion,particularly using catalase,offers a promising solution due to its ability to decompose hydrogen peroxide(H2O2)into water and oxygen,generating thrust for autonomous movement.Compared to metal-based catalysts,catalase-powered systems exhibit superior biocompatibility and lower toxicity,making them ideal for biomedical applications.This review explores the role of catalase in micro/nanorobot propulsion,highlighting self-propulsion mechanisms,different nanorobot types,and their applications in drug delivery,infection treatment,cancer therapy,and biosensing.Additionally,recent advancements in biodegradable enzyme-powered nanorobots and their poten-tial in overcoming biological barriers are discussed.With further research,catalase-driven nanorobots could revolutionize targeted therapy and diagnostic techniques,paving the way for innovative solutions in nanomedicine.展开更多
Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important a...Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.展开更多
Graph neural networks(GNN)have shown strong performance in node classification tasks,yet most existing models rely on uniform or shared weight aggregation,lacking flexibility in modeling the varying strength of relati...Graph neural networks(GNN)have shown strong performance in node classification tasks,yet most existing models rely on uniform or shared weight aggregation,lacking flexibility in modeling the varying strength of relationships among nodes.This paper proposes a novel graph coupling convolutional model that introduces an adaptive weighting mechanism to assign distinct importance to neighboring nodes based on their similarity to the central node.Unlike traditional methods,the proposed coupling strategy enhances the interpretability of node interactions while maintaining competitive classification performance.The model operates in the spatial domain,utilizing adjacency list structures for efficient convolution and addressing the limitations of weight sharing through a coupling-based similarity computation.Extensive experiments are conducted on five graph-structured datasets,including Cora,Citeseer,PubMed,Reddit,and BlogCatalog,as well as a custom topology dataset constructed from the Open University Learning Analytics Dataset(OULAD)educational platform.Results demonstrate that the proposed model achieves good classification accuracy,while significantly reducing training time through direct second-order neighbor fusion and data preprocessing.Moreover,analysis of neighborhood order reveals that considering third-order neighbors offers limited accuracy gains but introduces considerable computational overhead,confirming the efficiency of first-and second-order convolution in practical applications.Overall,the proposed graph coupling model offers a lightweight,interpretable,and effective framework for multi-label node classification in complex networks.展开更多
Recent advancements in Zn-halogen batteries have focused on enhancing the adsorptive or catalytic capability of host materials and stabilizing complex intermediates with electrolyte additives,while the halogen-ion ele...Recent advancements in Zn-halogen batteries have focused on enhancing the adsorptive or catalytic capability of host materials and stabilizing complex intermediates with electrolyte additives,while the halogen-ion electrolyte modifications exhibit strong potential for integrated interfacial regulation.Herein,we design an electrically insulating rigid electrolyte container to immobilize a liquid halogen-ion electrolyte for separator-free Zn-halogen batteries with customizable electron transfer.Robust hydrogen bonding of hydroxyl groups in SiO_(2)with fluorinated moieties in PVDF-hfp regulates Zn^(2+)solvation and suppresses H_(2)O activity,while multi-channels formed by microcracks and interparticle gaps not only enhance mass transfer but also buffer interfacial electric field,jointly enabling a durable Zn plating/stripping.Effective confinement of intermediates also ensures the high reversibility across single-(I^(-)/I0),double-(I^(-)/I0/I^(-)),and triple-(I^(-)/I0/I^(-),Cl-/Cl0)electron transfer mechanisms at cathode,as evidenced by the double-electron transfer systems exhibiting a low capacity decay rate of 0.02‰over 4500 cycles at 10 mA cm^(-2)and a high areal capacity of 11.9 mAh cm^(-2)at 2 mA cm^(-2).This work presents a novel“container engineering”approach to halogen-ion electrolyte design and provides fundamental insights into the relationships between redox reversibility and reaction kinetics.展开更多
Artificial intelligence(AI)is increasingly recognized as a transformative force in the field of solid organ transplantation.From enhancing donor-recipient matching to predicting clinical risks and tailoring immunosupp...Artificial intelligence(AI)is increasingly recognized as a transformative force in the field of solid organ transplantation.From enhancing donor-recipient matching to predicting clinical risks and tailoring immunosuppressive therapy,AI has the potential to improve both operational efficiency and patient outcomes.Despite these advancements,the perspectives of transplant professionals-those at the forefront of critical decision-making-remain insufficiently explored.To address this gap,this study utilizes a multi-round electronic Delphi approach to gather and analyses insights from global experts involved in organ transplantation.Participants are invited to complete structured surveys capturing demographic data,professional roles,institutional practices,and prior exposure to AI technologies.The survey also explores perceptions of AI’s potential benefits.Quantitative responses are analyzed using descriptive statistics,while open-ended qualitative responses undergo thematic analysis.Preliminary findings indicate a generally positive outlook on AI’s role in enhancing transplantation processes,particularly in areas such as donor matching and post-operative care.These mixed views reflect both optimism and caution among professionals tasked with integrating new technologies into high-stakes clinical workflows.By capturing a wide range of expert opinions,the findings will inform future policy development,regulatory considerations,and institutional readiness frameworks for the integration of AI into organ transplantation.展开更多
Particle Swarm Optimization(PSO)has been utilized as a useful tool for solving intricate optimization problems for various applications in different fields.This paper attempts to carry out an update on PSO and gives a...Particle Swarm Optimization(PSO)has been utilized as a useful tool for solving intricate optimization problems for various applications in different fields.This paper attempts to carry out an update on PSO and gives a review of its recent developments and applications,but also provides arguments for its efficacy in resolving optimization problems in comparison with other algorithms.Covering six strategic areas,which include Data Mining,Machine Learning,Engineering Design,Energy Systems,Healthcare,and Robotics,the study demonstrates the versatility and effectiveness of the PSO.Experimental results are,however,used to show the strong and weak parts of PSO,and performance results are included in tables for ease of comparison.The results stress PSO’s efficiency in providing optimal solutions but also show that there are aspects that need to be improved through combination with algorithms or tuning to the parameters of the method.The review of the advantages and limitations of PSO is intended to provide academics and practitioners with a well-rounded view of the methods of employing such a tool most effectively and to encourage optimized designs of PSO in solving theoretical and practical problems in the future.展开更多
BACKGROUND An echocardiogram is an essential tool in the evaluation of potential kidney transplant recipients(KTRs).Despite cardiac clearance,potential KTRs still have structural and functional abnormalities.Identifyi...BACKGROUND An echocardiogram is an essential tool in the evaluation of potential kidney transplant recipients(KTRs).Despite cardiac clearance,potential KTRs still have structural and functional abnormalities.Identifying the prevalence of these abnormalities and understanding their predictors is vital for optimizing pretransplant risk stratification and improving post-transplant outcomes.AIM To determine the prevalence of left ventricular hypertrophy(LVH),left ventricular systolic dysfunction(LVSD),diastolic dysfunction(DD),pulmonary hypertension(PH),and their predictors,and to assess their impact on graft function in pre-transplant candidates.METHODS The study included all successful transplant candidates older than 14 who had a baseline echocardiogram.Binary logistic regression models were constructed to identify factors associated with LVH,LVSD,DD,and PH.RESULTS Out of 259 patients,LVH was present in 64%(166),12%(31)had LVSD,27.5%(71)had DD,and 66(25.5%)had PH.Independent predictors of LVH included male gender[odds ratio(OR):2.51;95%CI:1.17-5.41 P=0.02],PH(OR=2.07;95%CI:1.11-3.86;P=0.02),DD(OR:2.47;95%CI:1.29-4.73;P=0.006),and dyslipidemia(OR=1.94;95%CI:1.07-3.53;P=0.03).Predictors for LVSD included patients with DD(OR=3.3,95%CI:1.41-7.81;P=0.006)and a family history of coronary artery disease(OR=4.50,95%CI:1.33-15.20;P=0.015).Peritoneal dialysis was an independent predictor for DD(OR=10.03;95%CI:1.71-58.94,P=0.011).The presence of LVH(OR=3.32,95%CI:1.05-10.55,P=0.04)and mild to moderate or moderate to severe mitral regurgitation(OR=4.63,95%CI:1.45-14.78,P=0.01)were significant factors associated with PH.These abnormalities had no significant impact on estimated glomerular filtration at discharge,6 months,1 year,or 2 years post-transplant.CONCLUSION Significant echocardiographic abnormalities persist in a potential transplant candidate despite cardiac clearance,although they don’t affect future graft function.Understanding the risk factors associated with these abnormalities may help clinicians address these factors pre-and post-transplant to achieve better outcomes.展开更多
Concrete-filled steel tubes(CFST)are widely utilized in civil engineering due to their superior load-bearing capacity,ductility,and seismic resistance.However,existing design codes,such as AISC and Eurocode 4,tend to ...Concrete-filled steel tubes(CFST)are widely utilized in civil engineering due to their superior load-bearing capacity,ductility,and seismic resistance.However,existing design codes,such as AISC and Eurocode 4,tend to be excessively conservative as they fail to account for the composite action between the steel tube and the concrete core.To address this limitation,this study proposes a hybrid model that integrates XGBoost with the Pied Kingfisher Optimizer(PKO),a nature-inspired algorithm,to enhance the accuracy of shear strength prediction for CFST columns.Additionally,quantile regression is employed to construct prediction intervals for the ultimate shear force,while the Asymmetric Squared Error Loss(ASEL)function is incorporated to mitigate overestimation errors.The computational results demonstrate that the PKO-XGBoost model delivers superior predictive accuracy,achieving a Mean Absolute Percentage Error(MAPE)of 4.431%and R2 of 0.9925 on the test set.Furthermore,the ASEL-PKO-XGBoost model substantially reduces overestimation errors to 28.26%,with negligible impact on predictive performance.Additionally,based on the Genetic Algorithm(GA)and existing equation models,a strength equation model is developed,achieving markedly higher accuracy than existing models(R^(2)=0.934).Lastly,web-based Graphical User Interfaces(GUIs)were developed to enable real-time prediction.展开更多
Developing effective and long-term treatment strategies for rare and complex neurodegenerative diseases is challenging. One of the major roadblocks is the extensive heterogeneity among patients. This hinders understan...Developing effective and long-term treatment strategies for rare and complex neurodegenerative diseases is challenging. One of the major roadblocks is the extensive heterogeneity among patients. This hinders understanding the underlying disease-causing mechanisms and building solutions that have implications for a broad spectrum of patients. One potential solution is to develop personalized medicine approaches based on strategies that target the most prevalent cellular events that are perturbed in patients. Especially in patients with a known genetic mutation, it may be possible to understand how these mutations contribute to problems that lead to neurodegeneration. Protein–protein interaction analyses offer great advantages for revealing how proteins interact, which cellular events are primarily involved in these interactions, and how they become affected when key genes are mutated in patients. This line of investigation also suggests novel druggable targets for patients with different mutations. Here, we focus on alsin and spastin, two proteins that are identified as “causative” for amyotrophic lateral sclerosis and hereditary spastic paraplegia, respectively, when mutated. Our review analyzes the protein interactome for alsin and spastin, the canonical pathways that are primarily important for each protein domain, as well as compounds that are either Food and Drug Administration–approved or are in active clinical trials concerning the affected cellular pathways. This line of research begins to pave the way for personalized medicine approaches that are desperately needed for rare neurodegenerative diseases that are complex and heterogeneous.展开更多
This article discusses the innovative use of computed tomography radiomics combined with clinical factors to predict treatment response to first-line transarterial chemoembolization in hepatocellular carcinoma.Zhao et...This article discusses the innovative use of computed tomography radiomics combined with clinical factors to predict treatment response to first-line transarterial chemoembolization in hepatocellular carcinoma.Zhao et al developed a robust predictive model demonstrating high accuracy(area under the curve 0.92 in the training cohort)by integrating venous phase radiomic features with alphafetoprotein levels.This noninvasive approach enables early identification of patients unlikely to benefit from transarterial chemoembolization,allowing a timely transition to alternative therapies such as targeted agents or immunotherapy.Such precision strategies may improve clinical outcomes,optimize resource utilization,and increase survival in advanced hepatocellular carcinoma management.Future studies should emphasize external validation and broader clinical adoption.展开更多
BACKGROUND Gastric antral vascular ectasia(GAVE)accounts for up to 4%of nonvariceal upper gastrointestinal bleeding.Argon plasma coagulation and radiofrequency ablation have been primary treatment modalities for patie...BACKGROUND Gastric antral vascular ectasia(GAVE)accounts for up to 4%of nonvariceal upper gastrointestinal bleeding.Argon plasma coagulation and radiofrequency ablation have been primary treatment modalities for patients with linear and punctate subtypes,with a newer trend of utilization of endoscopic band ligation(EBL).This study evaluates the outcomes of patients undergoing treatment for nodular GAVE.We hypothesize that patients treated initially with EBL will achieve higher rates of clinical remission with fewer endoscopic treatments and a shorter treatment interval.AIM To investigate the effects of EBL as an initial treatment therapy on outcomes associated with nodular GAVE.METHODS A total of 37 patients at a tertiary medical center with nodular GAVE were included in this retrospective study.The study population was divided between those treated initially with EBL(initial EBL)and initial endoscopic thermal therapy.Pretreatment and post-treatment hemoglobin values,the model for end-stage liver disease scores,hospitalization rates,and other outcomes.Additionally,endoscopic treatment modality type and frequency were recorded,including radiofrequency ablation,argon plasma coagulation,and EBL.Continuous variables were compared using a t-test,while categorical variables were compared using Fisher’s exact.RESULTS Linear regression analysis displayed a positive relationship between the time interval from initial therapeutic esophagogastroduodenoscopy to first EBL treatment and overall treatment interval(t=7.39,P<0.001),as well as between the number of endoscopic treatments(t=8.09,P<0.001).Hemoglobin levels increased in both the initial EBL group(8.7 vs 11.4,P<0.001)and the initial endoscopic thermal therapy group(8.6 vs 10.4,P=0.042).Clinical remission rates were higher in the initial EBL group(90%vs 69%P=0.041),with a non-significant trend of higher endoscopic remission rates(57.1%vs 37.5%,P=0.270).CONCLUSION The observed trend favoring EBL,combined with its association with improved clinical remission and reduced treatment burden,supports its consideration as a preferred initial treatment approach.展开更多
BACKGROUND Congenital hallux varus(CHV)is a rare form of hallux varus deformity,characterized by medial deviation of the first toe at the metatarsophalangeal joint.It may be primary or secondary and presents clinicall...BACKGROUND Congenital hallux varus(CHV)is a rare form of hallux varus deformity,characterized by medial deviation of the first toe at the metatarsophalangeal joint.It may be primary or secondary and presents clinically with pain and asymmetry with footwear.CASE SUMMARY We documented a case of a 6-year-old girl with bilateral CHV,accompanied by adduction of the toes in the left foot.Clinical diagnosis was made by physical examination and X-ray imaging based on Bleck’s classification.Conservative treatment did not show any noticeable improvement,so the child underwent corrective surgeries on both feet.CONCLUSION The patient’s family history is positive,which requires us to take into account the importance of checking for a family history with any complaint of CHV,and both feet must be evaluated to confirm whether the deformity is unilateral or bilateral.展开更多
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01295).
文摘Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often struggle with low-contrast MRI images,class imbalance,and suboptimal feature extraction.This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans.Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Enhanced Super-Resolution Generative Adversarial Networks(ESRGAN).A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient(MCC)-based evaluation method into the design.The trained and validated model gives a 98.88%accuracy rate and 0.9614 MCC score.We also performed a 10-fold cross-validation experiment with an average accuracy of 96.52%(±1.51),a loss of 0.1671,and an MCC score of 0.9429 across folds.The proposed framework outperforms the state-of-the-art models with a 98%weighted F1-score while decreasing misdiagnosis results for every AD stage.The model demonstrates apparent separation abilities between AD progression stages according to the results of the confusion matrix analysis.These results validate the effectiveness of hybrid DL models with adaptive preprocessing for early and reliable Alzheimer’s diagnosis,contributing to improved computer-aided diagnosis(CAD)systems in clinical practice.
基金funded by King Abdullah City for Atomic and Renewable Energy(KACARE),grant number“PC-2020-1”.
文摘High-concentration photovoltaic(HCPV)systems present significant thermal management challenges due to the intense heat fluxes generated under concentrated solar irradiation,especially in arid environments.Effective heat dissipation is critical to prevent performance degradation and structural failure.This study investigates the thermal performance and design optimization of an enhanced HCPV module,integrating numerical,analytical,and experimental methods.A coupled optical-thermal-electrical model was developed to simulate ray tracing,heat transfer,and temperature-dependent electrical behaviour,with predictions validated under real-world desert conditions.Compared to a baseline commercial module operating at 106℃,the optimized design achieved a peak temperature reduction of 16℃,lowering the cell temperature to 90℃under a concentration ratio of 961×and direct normal irradiance(DNI)of 950 W/m^(2).The total thermal resistance was reduced from 0.25 to 0.15 K/W(a 40%improvement),and the electrical efficiency increased from 37.5%to 38.6%,representing a relative gain of approximately 3.1%.The system consistently maintained a fill factor exceeding 78%,underscoring stable performance under high thermal load.These findings demonstrate that targeted thermal design,informed by integrated modeling,is essential for unlocking the reliability and efficiency of high-flux solar energy systems.
基金supported by the Earmarked Fund for Modern Agro-industry Technology Research System,China(CARS27)the National Natural Science Foundation of China(32072555)+1 种基金the Nationally Funded Postdoctoral Researcher Program(GZC20230863)the China Postdoctoral Science Foundation(2024M760968)。
文摘Malic acid is a crucial determinant of apple(Malus domestica)fruit quality,influencing acidity and flavor.While transcriptional regulation of malic acid metabolism is well-studied,post-transcriptional control and the role of jasmonate(JA)remain largely unexplored.We identify a novel regulatory pathway involving JA signaling,a micro RNA(mi RNA),and vacuolar transport regulators that control malic acid accumulation in apple fruit.We show that mdm-mi R858,which increases during fruit maturation,directly targets and cleaves Md MYB73 transcripts.Md MYB73 is a known positive regulator of vacuolar H+-pumping and malate transport,activating genes like Md VHA-A,Md VHP,and Md ALMT9.Overexpression of mdm-mi R858 suppressed Md MYB73,thereby reducing Md VHA-A,Md VHP,and Md ALMT9 expression and malic acid content in apple calli,fruits,and GL-3plantlets,while silencing mdm-mi R858 had opposite effects.Crucially,the JA-responsive transcription factor Md MYC2,the expression of which increases during fruit maturation,directly binds the mdm-mi R858 promoter and activates its expression.Furthermore,the Mediator complex subunit Md MED25 interacts with Md MYC2,enhancing this activation.Manipulating Md MYC2 or Md MED25expression altered mdm-mi R858 levels,Md MYB73expression,and malic acid accumulation,mirroring exogenous methyl jasmonate(Me JA)treatment effects.A mi R858-resistant Md MYB73 variant confirmed the miRNA-target interaction's specificity and functional significance.Our findings reveal a novel JA-Md MYC2/MdMED25-mi R858-Md MYB73regulatory cascade controlling malic acid accumulation in apple,providing a mechanistic link between hormonal signaling and post-transcriptional regulation of fruit acidity.This discovery offers new targets for manipulating fruit quality.
文摘Digital twin technology,that creates virtual replicas of physical entities using real-time data and simulation models,has emerged as a transformative innovation across multiple healthcare domains.Its application in physiotherapy and rehabilitation represents a paradigm shift from traditional therapeutic approaches to personalized data-driven interventions that optimize patient outcomes.This narrative review examines the current applications,benefits,challenges,and future prospects of digital twin technology in physiotherapy and rehabilitation,providing a comprehensive analysis of the manner in which this technology is reshaping clinical practice and patient care.A narrative review approach was employed,systematically searching PubMed,IEEE Xplore,Scopus,and Web of Science databases.Studies describing digital twin applications,development methodologies,clinical implementations,and theoretical frameworks in physiotherapy and rehabilitation contexts were included.Digital twin technology demonstrates significant potential in personalizing rehabilitation programs,enabling real-time monitoring of patient progress,predicting treatment outcomes,and facilitating remote therapeutic interventions.Current applications span musculoskeletal rehabilitation,neurological recovery,post surgical care,and sports injury management.Key benefits include enhanced treatment precision,improved patient engagement,reduced healthcare costs,and accelerated recovery times.However,implementation faces challenges including technological complexity,data privacy concerns,interoperability issues,and the need for substantial infrastructure investment.Digital twin technology represents a promising frontier in physiotherapy and rehabilitation,offering unprecedented opportunities for personalized,efficient,and effective patient care.Successful integration requires addressing the current limitations while fostering interdisciplinary collaboration between clinicians,engineers,and data scientists.
基金supported by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01276).
文摘The rapid proliferation of Internet of Things(IoT)devices in critical healthcare infrastructure has introduced significant security and privacy challenges that demand innovative,distributed architectural solutions.This paper proposes FE-ACS(Fog-Edge Adaptive Cybersecurity System),a novel hierarchical security framework that intelligently distributes AI-powered anomaly detection algorithms across edge,fog,and cloud layers to optimize security efficacy,latency,and privacy.Our comprehensive evaluation demonstrates that FE-ACS achieves superior detection performance with an AUC-ROC of 0.985 and an F1-score of 0.923,while maintaining significantly lower end-to-end latency(18.7 ms)compared to cloud-centric(152.3 ms)and fog-only(34.5 ms)architectures.The system exhibits exceptional scalability,supporting up to 38,000 devices with logarithmic performance degradation—a 67×improvement over conventional cloud-based approaches.By incorporating differential privacy mechanisms with balanced privacy-utility tradeoffs(ε=1.0–1.5),FE-ACS maintains 90%–93%detection accuracy while ensuring strong privacy guarantees for sensitive healthcare data.Computational efficiency analysis reveals that our architecture achieves a detection rate of 12,400 events per second with only 12.3 mJ energy consumption per inference.In healthcare risk assessment,FE-ACS demonstrates robust operational viability with low patient safety risk(14.7%)and high system reliability(94.0%).The proposed framework represents a significant advancement in distributed security architectures,offering a scalable,privacy-preserving,and real-time solution for protecting healthcare IoT ecosystems against evolving cyber threats.
文摘Male breast cancer(MBC)is rare,representing 0.5%–1%of all breast cancers,but its incidence is increasing due to improved diagnostics and awareness.MBC typically presents in older men,is human epidermal growth factor receptor 2(HER2)-negative and estrogen receptor(ER)-positive,and lacks routine screening,leading to delayed diagnosis and advanced disease.Major risk factors include hormonal imbalance,radiation exposure,obesity,alcohol use,and Breast Cancer Gene 1 and 2(BRCA1/2)mutations.Clinically,it may resemble gynecomastia but usually appears as a unilateral,painless mass or nipple discharge.Advances in imaging and liquid biopsy have enhanced early detection.Molecular mechanisms involve hormonal signaling,HER2/epidermal growth factor receptor(EGFR)pathways,tumor suppressor gene alterations,and epigenetic changes.While standard treatments mirror those for female breast cancer,emerging options such as cyclin-dependent kinase 4 and 6(CDK4/6),and poly(ADP-ribose)polymerase(PARP)inhibitors,immunotherapy,and precision medicine are reshaping management.Incorporating artificial intelligence,molecular profiling,and male-specific clinical trials is essential to improve outcomes and bridge current diagnostic and therapeutic gaps.
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia Grant No.KFU253765.
文摘Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail.It uses the NASA CMAPSS dataset,which has over 200,000 engine cycles from260 engines.The process begins with systematic preprocessing,which includes imputation,outlier removal,scaling,and labelling of the remaining useful life.Dimensionality is reduced using a hybrid selection method that combines variance filtering,recursive elimination,and gradient-boosted importance scores,yielding a stable set of 10 informative sensors.To mitigate class imbalance,minority cases are oversampled,and class-weighted losses are applied during training.Benchmarking is carried out with logistic regression,gradient boosting,and a recurrent design that integrates gated recurrent units with long short-term memory networks.The Long Short-Term Memory–Gated Recurrent Unit(LSTM–GRU)hybrid achieved the strongest performance with an F1 score of 0.92,precision of 0.93,recall of 0.91,ReceiverOperating Characteristic–AreaUnder the Curve(ROC-AUC)of 0.97,andminority recall of 0.75.Interpretability testing using permutation importance and Shapley values indicates that sensors 13,15,and 11 are the most important indicators of engine wear.The proposed system combines imbalance handling,feature reduction,and Interpretability into a practical design suitable for real industrial settings.
基金The Large Research Group Project under grant number RGP.02/516/45.
文摘Micro/nanorobots represent a groundbreaking advancement in nanotechnology,with applications spanning medicine,envi-ronmental remediation,and industrial processes.A major challenge in their development is achieving efficient and bio-compatible propulsion.Enzyme-driven propulsion,particularly using catalase,offers a promising solution due to its ability to decompose hydrogen peroxide(H2O2)into water and oxygen,generating thrust for autonomous movement.Compared to metal-based catalysts,catalase-powered systems exhibit superior biocompatibility and lower toxicity,making them ideal for biomedical applications.This review explores the role of catalase in micro/nanorobot propulsion,highlighting self-propulsion mechanisms,different nanorobot types,and their applications in drug delivery,infection treatment,cancer therapy,and biosensing.Additionally,recent advancements in biodegradable enzyme-powered nanorobots and their poten-tial in overcoming biological barriers are discussed.With further research,catalase-driven nanorobots could revolutionize targeted therapy and diagnostic techniques,paving the way for innovative solutions in nanomedicine.
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2024-9/1).
文摘Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.
基金Support by Sichuan Science and Technology Program[2023YFSY0026,2023YFH0004]Guangzhou Huashang University[2024HSZD01,HS2023JYSZH01].
文摘Graph neural networks(GNN)have shown strong performance in node classification tasks,yet most existing models rely on uniform or shared weight aggregation,lacking flexibility in modeling the varying strength of relationships among nodes.This paper proposes a novel graph coupling convolutional model that introduces an adaptive weighting mechanism to assign distinct importance to neighboring nodes based on their similarity to the central node.Unlike traditional methods,the proposed coupling strategy enhances the interpretability of node interactions while maintaining competitive classification performance.The model operates in the spatial domain,utilizing adjacency list structures for efficient convolution and addressing the limitations of weight sharing through a coupling-based similarity computation.Extensive experiments are conducted on five graph-structured datasets,including Cora,Citeseer,PubMed,Reddit,and BlogCatalog,as well as a custom topology dataset constructed from the Open University Learning Analytics Dataset(OULAD)educational platform.Results demonstrate that the proposed model achieves good classification accuracy,while significantly reducing training time through direct second-order neighbor fusion and data preprocessing.Moreover,analysis of neighborhood order reveals that considering third-order neighbors offers limited accuracy gains but introduces considerable computational overhead,confirming the efficiency of first-and second-order convolution in practical applications.Overall,the proposed graph coupling model offers a lightweight,interpretable,and effective framework for multi-label node classification in complex networks.
基金supported by the Science Fund for Distinguished Young Scholars of Hunan Province(2023JJ10060)the National Natural Science Foundation of China(22575269)Young Elite Scientists Sponsorship Program by CAST(2023QNRC001)。
文摘Recent advancements in Zn-halogen batteries have focused on enhancing the adsorptive or catalytic capability of host materials and stabilizing complex intermediates with electrolyte additives,while the halogen-ion electrolyte modifications exhibit strong potential for integrated interfacial regulation.Herein,we design an electrically insulating rigid electrolyte container to immobilize a liquid halogen-ion electrolyte for separator-free Zn-halogen batteries with customizable electron transfer.Robust hydrogen bonding of hydroxyl groups in SiO_(2)with fluorinated moieties in PVDF-hfp regulates Zn^(2+)solvation and suppresses H_(2)O activity,while multi-channels formed by microcracks and interparticle gaps not only enhance mass transfer but also buffer interfacial electric field,jointly enabling a durable Zn plating/stripping.Effective confinement of intermediates also ensures the high reversibility across single-(I^(-)/I0),double-(I^(-)/I0/I^(-)),and triple-(I^(-)/I0/I^(-),Cl-/Cl0)electron transfer mechanisms at cathode,as evidenced by the double-electron transfer systems exhibiting a low capacity decay rate of 0.02‰over 4500 cycles at 10 mA cm^(-2)and a high areal capacity of 11.9 mAh cm^(-2)at 2 mA cm^(-2).This work presents a novel“container engineering”approach to halogen-ion electrolyte design and provides fundamental insights into the relationships between redox reversibility and reaction kinetics.
文摘Artificial intelligence(AI)is increasingly recognized as a transformative force in the field of solid organ transplantation.From enhancing donor-recipient matching to predicting clinical risks and tailoring immunosuppressive therapy,AI has the potential to improve both operational efficiency and patient outcomes.Despite these advancements,the perspectives of transplant professionals-those at the forefront of critical decision-making-remain insufficiently explored.To address this gap,this study utilizes a multi-round electronic Delphi approach to gather and analyses insights from global experts involved in organ transplantation.Participants are invited to complete structured surveys capturing demographic data,professional roles,institutional practices,and prior exposure to AI technologies.The survey also explores perceptions of AI’s potential benefits.Quantitative responses are analyzed using descriptive statistics,while open-ended qualitative responses undergo thematic analysis.Preliminary findings indicate a generally positive outlook on AI’s role in enhancing transplantation processes,particularly in areas such as donor matching and post-operative care.These mixed views reflect both optimism and caution among professionals tasked with integrating new technologies into high-stakes clinical workflows.By capturing a wide range of expert opinions,the findings will inform future policy development,regulatory considerations,and institutional readiness frameworks for the integration of AI into organ transplantation.
文摘Particle Swarm Optimization(PSO)has been utilized as a useful tool for solving intricate optimization problems for various applications in different fields.This paper attempts to carry out an update on PSO and gives a review of its recent developments and applications,but also provides arguments for its efficacy in resolving optimization problems in comparison with other algorithms.Covering six strategic areas,which include Data Mining,Machine Learning,Engineering Design,Energy Systems,Healthcare,and Robotics,the study demonstrates the versatility and effectiveness of the PSO.Experimental results are,however,used to show the strong and weak parts of PSO,and performance results are included in tables for ease of comparison.The results stress PSO’s efficiency in providing optimal solutions but also show that there are aspects that need to be improved through combination with algorithms or tuning to the parameters of the method.The review of the advantages and limitations of PSO is intended to provide academics and practitioners with a well-rounded view of the methods of employing such a tool most effectively and to encourage optimized designs of PSO in solving theoretical and practical problems in the future.
文摘BACKGROUND An echocardiogram is an essential tool in the evaluation of potential kidney transplant recipients(KTRs).Despite cardiac clearance,potential KTRs still have structural and functional abnormalities.Identifying the prevalence of these abnormalities and understanding their predictors is vital for optimizing pretransplant risk stratification and improving post-transplant outcomes.AIM To determine the prevalence of left ventricular hypertrophy(LVH),left ventricular systolic dysfunction(LVSD),diastolic dysfunction(DD),pulmonary hypertension(PH),and their predictors,and to assess their impact on graft function in pre-transplant candidates.METHODS The study included all successful transplant candidates older than 14 who had a baseline echocardiogram.Binary logistic regression models were constructed to identify factors associated with LVH,LVSD,DD,and PH.RESULTS Out of 259 patients,LVH was present in 64%(166),12%(31)had LVSD,27.5%(71)had DD,and 66(25.5%)had PH.Independent predictors of LVH included male gender[odds ratio(OR):2.51;95%CI:1.17-5.41 P=0.02],PH(OR=2.07;95%CI:1.11-3.86;P=0.02),DD(OR:2.47;95%CI:1.29-4.73;P=0.006),and dyslipidemia(OR=1.94;95%CI:1.07-3.53;P=0.03).Predictors for LVSD included patients with DD(OR=3.3,95%CI:1.41-7.81;P=0.006)and a family history of coronary artery disease(OR=4.50,95%CI:1.33-15.20;P=0.015).Peritoneal dialysis was an independent predictor for DD(OR=10.03;95%CI:1.71-58.94,P=0.011).The presence of LVH(OR=3.32,95%CI:1.05-10.55,P=0.04)and mild to moderate or moderate to severe mitral regurgitation(OR=4.63,95%CI:1.45-14.78,P=0.01)were significant factors associated with PH.These abnormalities had no significant impact on estimated glomerular filtration at discharge,6 months,1 year,or 2 years post-transplant.CONCLUSION Significant echocardiographic abnormalities persist in a potential transplant candidate despite cardiac clearance,although they don’t affect future graft function.Understanding the risk factors associated with these abnormalities may help clinicians address these factors pre-and post-transplant to achieve better outcomes.
基金funded by United Arab Emirates University(UAEU)under the UAEU-AUA grant number G00004577(12N145)with the corresponding grant at Universiti Malaya(UM)under grant number IF019-2024.
文摘Concrete-filled steel tubes(CFST)are widely utilized in civil engineering due to their superior load-bearing capacity,ductility,and seismic resistance.However,existing design codes,such as AISC and Eurocode 4,tend to be excessively conservative as they fail to account for the composite action between the steel tube and the concrete core.To address this limitation,this study proposes a hybrid model that integrates XGBoost with the Pied Kingfisher Optimizer(PKO),a nature-inspired algorithm,to enhance the accuracy of shear strength prediction for CFST columns.Additionally,quantile regression is employed to construct prediction intervals for the ultimate shear force,while the Asymmetric Squared Error Loss(ASEL)function is incorporated to mitigate overestimation errors.The computational results demonstrate that the PKO-XGBoost model delivers superior predictive accuracy,achieving a Mean Absolute Percentage Error(MAPE)of 4.431%and R2 of 0.9925 on the test set.Furthermore,the ASEL-PKO-XGBoost model substantially reduces overestimation errors to 28.26%,with negligible impact on predictive performance.Additionally,based on the Genetic Algorithm(GA)and existing equation models,a strength equation model is developed,achieving markedly higher accuracy than existing models(R^(2)=0.934).Lastly,web-based Graphical User Interfaces(GUIs)were developed to enable real-time prediction.
基金funded by NIH-NIA R01AG061708 (to PHO)Patrick Grange Memorial Foundation (to PHO)+1 种基金A Long Swim (to PHO)CureSPG4 Foundation (to PHO)。
文摘Developing effective and long-term treatment strategies for rare and complex neurodegenerative diseases is challenging. One of the major roadblocks is the extensive heterogeneity among patients. This hinders understanding the underlying disease-causing mechanisms and building solutions that have implications for a broad spectrum of patients. One potential solution is to develop personalized medicine approaches based on strategies that target the most prevalent cellular events that are perturbed in patients. Especially in patients with a known genetic mutation, it may be possible to understand how these mutations contribute to problems that lead to neurodegeneration. Protein–protein interaction analyses offer great advantages for revealing how proteins interact, which cellular events are primarily involved in these interactions, and how they become affected when key genes are mutated in patients. This line of investigation also suggests novel druggable targets for patients with different mutations. Here, we focus on alsin and spastin, two proteins that are identified as “causative” for amyotrophic lateral sclerosis and hereditary spastic paraplegia, respectively, when mutated. Our review analyzes the protein interactome for alsin and spastin, the canonical pathways that are primarily important for each protein domain, as well as compounds that are either Food and Drug Administration–approved or are in active clinical trials concerning the affected cellular pathways. This line of research begins to pave the way for personalized medicine approaches that are desperately needed for rare neurodegenerative diseases that are complex and heterogeneous.
文摘This article discusses the innovative use of computed tomography radiomics combined with clinical factors to predict treatment response to first-line transarterial chemoembolization in hepatocellular carcinoma.Zhao et al developed a robust predictive model demonstrating high accuracy(area under the curve 0.92 in the training cohort)by integrating venous phase radiomic features with alphafetoprotein levels.This noninvasive approach enables early identification of patients unlikely to benefit from transarterial chemoembolization,allowing a timely transition to alternative therapies such as targeted agents or immunotherapy.Such precision strategies may improve clinical outcomes,optimize resource utilization,and increase survival in advanced hepatocellular carcinoma management.Future studies should emphasize external validation and broader clinical adoption.
文摘BACKGROUND Gastric antral vascular ectasia(GAVE)accounts for up to 4%of nonvariceal upper gastrointestinal bleeding.Argon plasma coagulation and radiofrequency ablation have been primary treatment modalities for patients with linear and punctate subtypes,with a newer trend of utilization of endoscopic band ligation(EBL).This study evaluates the outcomes of patients undergoing treatment for nodular GAVE.We hypothesize that patients treated initially with EBL will achieve higher rates of clinical remission with fewer endoscopic treatments and a shorter treatment interval.AIM To investigate the effects of EBL as an initial treatment therapy on outcomes associated with nodular GAVE.METHODS A total of 37 patients at a tertiary medical center with nodular GAVE were included in this retrospective study.The study population was divided between those treated initially with EBL(initial EBL)and initial endoscopic thermal therapy.Pretreatment and post-treatment hemoglobin values,the model for end-stage liver disease scores,hospitalization rates,and other outcomes.Additionally,endoscopic treatment modality type and frequency were recorded,including radiofrequency ablation,argon plasma coagulation,and EBL.Continuous variables were compared using a t-test,while categorical variables were compared using Fisher’s exact.RESULTS Linear regression analysis displayed a positive relationship between the time interval from initial therapeutic esophagogastroduodenoscopy to first EBL treatment and overall treatment interval(t=7.39,P<0.001),as well as between the number of endoscopic treatments(t=8.09,P<0.001).Hemoglobin levels increased in both the initial EBL group(8.7 vs 11.4,P<0.001)and the initial endoscopic thermal therapy group(8.6 vs 10.4,P=0.042).Clinical remission rates were higher in the initial EBL group(90%vs 69%P=0.041),with a non-significant trend of higher endoscopic remission rates(57.1%vs 37.5%,P=0.270).CONCLUSION The observed trend favoring EBL,combined with its association with improved clinical remission and reduced treatment burden,supports its consideration as a preferred initial treatment approach.
文摘BACKGROUND Congenital hallux varus(CHV)is a rare form of hallux varus deformity,characterized by medial deviation of the first toe at the metatarsophalangeal joint.It may be primary or secondary and presents clinically with pain and asymmetry with footwear.CASE SUMMARY We documented a case of a 6-year-old girl with bilateral CHV,accompanied by adduction of the toes in the left foot.Clinical diagnosis was made by physical examination and X-ray imaging based on Bleck’s classification.Conservative treatment did not show any noticeable improvement,so the child underwent corrective surgeries on both feet.CONCLUSION The patient’s family history is positive,which requires us to take into account the importance of checking for a family history with any complaint of CHV,and both feet must be evaluated to confirm whether the deformity is unilateral or bilateral.
