Patients affected by monogenic diseases impose a substantial burden on both themselves and their families.The primary preventive measure,i.e.,invasive prenatal diagnosis,carries a risk of miscarriage and cannot be per...Patients affected by monogenic diseases impose a substantial burden on both themselves and their families.The primary preventive measure,i.e.,invasive prenatal diagnosis,carries a risk of miscarriage and cannot be performed early in pregnancy.Hence,there is a need for non-invasive prenatal testing(NIPT)for monogenic diseases.By utilizing enriched cell-free fetal DNA(cffDNA)from maternal plasma,we refine the NIPT method,which combines targeted region capture technology,haplotyping,and analysis of informative site frequency.We apply this method to 93 clinical families at genetic risk for thalassemia,encompassing various genetic variant types,to establish a workflow and evaluate its efficiency.Our approach requires only 3 ng of DNA input to generate 0.1 Gb informative target genomic data and leverages a minimum of 3%cffDNA.This method has a 98.16%success rate and 100%concordance with conventional invasive methods.Furthermore,we demonstrate the ability to analyze fetal genotypes as early as eight weeks of gestation.This study establishes an optimized NIPT method for the early detection of various thalassemia disorders during pregnancy.This technique demonstrates high accuracy and potential for clinical application in prenatal diagnosis.展开更多
The development of non-invasive brain-computer interfaces(BCIs)relies on multidisciplinary integration across neuroscience,artificial intelligence,flexible electronics,and systems engineering.Recent advances in deep l...The development of non-invasive brain-computer interfaces(BCIs)relies on multidisciplinary integration across neuroscience,artificial intelligence,flexible electronics,and systems engineering.Recent advances in deep learning have significantly improved the accuracy and robustness of neural signal decoding.Parallel progress in electrode design—particularly through the use of flexible and stretchable materials like nanostructured conductors and novel fabrication strategies—has enhanced wearability and operational stability.Nevertheless,key challenges persist,including individual variability,biocompatibility limitations,and susceptibility to interference in complex environments.Further validation and optimization are needed to address gaps in generalization capability,long-term reliability,and real-world operational robustness.This review systematically examines the representative progress in neural decoding algorithms and flexible bioelectronic platforms over the past decade,highlighting key design principles,material innovations,and integration strategies that are poised to advance non-invasive BCI capabilities.It also discusses the importance of multimodal data fusion,hardware-software co-optimization,and closed-loop control strategies.Furthermore,the review discusses the application potential and associated engineering challenges of this technology in clinical rehabilitation and industrial translation,aiming to provide a reference for advancing non-invasive BCIs toward practical and scalable deployment.展开更多
Objective: To evaluate the efficacy of noninvasive positive pressure ventilation (NIPPV) in respiratory support for severe pneumonia. Methods: Data were analyzed from 74 patients with severe pneumonia undergoing respi...Objective: To evaluate the efficacy of noninvasive positive pressure ventilation (NIPPV) in respiratory support for severe pneumonia. Methods: Data were analyzed from 74 patients with severe pneumonia undergoing respiratory support at our hospital between May 2024 and April 2025. Patients were randomly assigned using a random number table to two groups (n = 37 each): the experimental group received NIPPV, while the control group underwent conventional invasive mechanical ventilation. Intergroup differences were compared. Results: Compared with the control group, the experimental group demonstrated significantly higher PaO2 and oxygenation index, significantly lower PaCO2, significantly reduced levels of WBC, CRP, and PCT, significantly higher overall efficacy rate, and significantly lower incidence of adverse reactions after treatment (p < 0.05). Pre-treatment PaO2, oxygenation index, PaCO2, WBC, CRP, and PCT levels showed no significant differences between groups (p > 0.05). Conclusion: Non-invasive positive pressure ventilation demonstrates favorable outcomes in respiratory support for severe pneumonia.展开更多
BACKGROUND Despite the developments in the field of kidney transplantation,the already existing diagnostic techniques for patient monitoring are considered insufficient.Protein biomarkers that can be derived from mode...BACKGROUND Despite the developments in the field of kidney transplantation,the already existing diagnostic techniques for patient monitoring are considered insufficient.Protein biomarkers that can be derived from modern approaches of proteomic analysis of liquid biopsies(serum,urine)represent a promising innovation in the monitoring of kidney transplant recipients.AIM To investigate the diagnostic utility of protein biomarkers derived from proteomics approaches in renal allograft assessment.METHODS A systematic review was conducted in accordance with PRISMA guidelines,based on research results from the PubMed and Scopus databases.The primary focus was on evaluating the role of biomarkers in the non-invasive diagnosis of transplant-related com-plications.Eligibility criteria included protein biomarkers and urine and blood samples,while exclusion criteria were language other than English and the use of low resolution and sensitivity methods.The selected research articles,were categorized based on the biological sample,condition and methodology and the significantly and reproducibly differentiated proteins were manually selected and extracted.Functional and network analysis of the selected proteins was performed.RESULTS In 17 included studies,58 proteins were studied,with the cytokine CXCL10 being the most investigated.Biological pathways related to immune response and fibrosis have shown to be enriched.Applications of biomarkers for the assessment of renal damage as well as the prediction of short-term and long-term function of the graft were reported.Overall,all studies have shown satisfactory diagnostic accuracy of proteins alone or in combination with conventional methods,as far as renal graft assessment is concerned.CONCLUSION Our review suggests that protein biomarkers,evaluated in specific biological fluids,can make a significant contribution to the timely,valid and non-invasive assessment of kidney graft.展开更多
Gastric cancer(GC),a multifaceted and highly aggressive malignancy,represents challenging healthcare burdens globally,with a high incidence and mortality rate.Although endoscopy,combined with histological examination,...Gastric cancer(GC),a multifaceted and highly aggressive malignancy,represents challenging healthcare burdens globally,with a high incidence and mortality rate.Although endoscopy,combined with histological examination,is the gold stan-dard for GC diagnosis,its high cost,invasiveness,and specialized requirements hinder widespread use for screening.With the emergence of innovative techno-logies such as advanced imaging,liquid biopsy,and breath tests,the landscape of GC diagnosis is poised for radical transformation,becoming more accessible,less invasive,and more efficient.As the non-invasive diagnostic techniques continue to advance and undergo rigorous clinical validation,they hold the promise of sig-nificantly impacting patient outcomes,ultimately leading to better treatment results and improved quality of life for patients with GC.展开更多
Chronic kidney disease(CKD)is a degenerative disorder that affects millions of people throughout the world,causing considerable morbidity and healthcare burden.Frequent blood sampling is the current gold standard for ...Chronic kidney disease(CKD)is a degenerative disorder that affects millions of people throughout the world,causing considerable morbidity and healthcare burden.Frequent blood sampling is the current gold standard for monitoring CKD to evaluate biochemical and mineral indicators.However,there are draw-backs to frequent blood draws,such as pain for patients,the possibility of infe-ction,and higher medical expenses.Saliva-based diagnostics offer advantages such as ease of collection,reduced invasiveness,and improved patient compli-ance.A comprehensive literature review was conducted to analyze studies eva-luating the diagnostic utility of salivary creatinine,urea,calcium,and parathyroid hormone(PTH)in patients with CKD.Various saliva collection methods,inc-luding stimulated and unstimulated approaches,were investigated for efficiency and reliability,and a correlation was shown between serum and salivary crea-tinine,urea,PTH,and calcium levels,indicating their potential as CKD biomar-kers.Despite these promising findings,challenges such as standardization of collection methods,variability in salivary flow rates,and predictive value in association with blood parameters are addressed to ensure clinical applicability.This review explores the potential and challenges of saliva as a non-invasive alternative for CKD diagnostics.展开更多
Flip-flow screens offer unique advantages in grading fine-grained materials.To address inaccuracies caused by sensor vibra-tions in traditional contact measurement methods,we constructed a non-invasive measurement sys...Flip-flow screens offer unique advantages in grading fine-grained materials.To address inaccuracies caused by sensor vibra-tions in traditional contact measurement methods,we constructed a non-invasive measurement system based on electrical and optical sig-nals.A trajectory tracking algorithm for the screen-body was developed to visually measure the kinematics.Employing the principle oflaser reflection for distance measurement,optical techniques were performed to capture the kinematic information of the screen-plate.Ad-ditionally,by using Wi-Fi and Bluetooth transmission of electrical signals,tracer particle tracking technology was implemented to elec-trically measure the kinematic information of mineral particles.Consequently,intelligent fusion and perception of the kinematic informa-tion for the screen-body,screen-plate,and particles in the screening system have been achieved.展开更多
Accurate real-time monitoring of internal temperature in lithium-ion batteries remains critical for preventing thermal runaway,as conventional approaches sacrifice either computational efficiency or cross-scenario rob...Accurate real-time monitoring of internal temperature in lithium-ion batteries remains critical for preventing thermal runaway,as conventional approaches sacrifice either computational efficiency or cross-scenario robustness.We present a generalized fuzzy physics-informed framework that distills thermally sensitive electrochemical processes while circumventing redundant physical constraints,thereby establishing an explicit mechanism-constrained mapping between frequency-domain signals and internal temperature.This framework facilitates online thermal estimation,with dynamic validations in LiFePO_4/graphite 18650-type cells confirming real-time capability with near-instantaneous acquisition(~6 s per measurement),exceptional accuracy(±0.5℃) within the operational temperature range(30-50℃),and operational resilience across 20 %-80 % state-of-charge.The framework maintains predictive fidelity(±1.0℃ at 30℃ and ±4.0℃ at 60℃,95 % prediction intervals) across 80 %-100 % state-of-health while demonstrating adaptability to cathode materials and structural architectures.This strategy resolves the competing imperatives of physical interpretability,computational efficiency,and crossscenario generalizability,offering a universal paradigm for embedded thermal management in safetycritical applications.展开更多
Hyaline Membrane Disease(HMD)in newborns,also known as neonatal respiratory distress syndrome,is a common critical illness in premature infants,with an incidence inversely correlated with gestational age,posing a seri...Hyaline Membrane Disease(HMD)in newborns,also known as neonatal respiratory distress syndrome,is a common critical illness in premature infants,with an incidence inversely correlated with gestational age,posing a serious threat to the life and health of newborns.This paper systematically reviews the core pathogenesis of HMD,focusing on the abnormal metabolism of pulmonary surfactant(PS),genetic factors,immature lung development,and the synergistic effects of inflammatory oxidative stress.It highlights the advances in non-invasive ventilation(NIV)therapy for HMD,including the mechanisms of action,clinical application effects,and optimization strategies of mainstream modalities such as nasal continuous positive airway pressure ventilation(NCPAP),nasal intermittent positive pressure ventilation(NIPPV),and heated humidified high-flow nasal cannula ventilation(HHHFNC).The aim is to provide references for standardized clinical treatment.展开更多
Metabolic dysfunction-associated steatotic liver disease(MASLD)requires accurate liver fibrosis assessment for management.While liver biopsy remains the gold standard,its invasiveness drives demand for non-invasive bi...Metabolic dysfunction-associated steatotic liver disease(MASLD)requires accurate liver fibrosis assessment for management.While liver biopsy remains the gold standard,its invasiveness drives demand for non-invasive biomarkers.This review evaluates blood biomarkers for MASLD fibrosis staging.Established scores(fibrosis-4,non-alcoholic fatty liver disease fibrosis score)offer accessible screening but exhibit variable performance influenced by age,obesity,and comorbidities.Patented panels(e.g.,enhanced liver fibrosis test,FibroMeter)improve accuracy by integrating extracellular matrix or metabolic markers,though context-specific thresholds are essential.Emerging biomarkers like propeptide of type 3 collagen,Mac-2 binding protein glycosylation isomer,epigenetic markers(proliferator-activated receptor-γmethylation),and angiopoietin-like proteins a family of eight glycoproteins show promise but require large-scale validation.Genetic risk scores and multi-omics approaches face generalizability challenges.Integration strategies,such as combining serum biomarkers with liver stiffness measurement via Agile scores,enhance diagnostic precision and reduce indeterminate classifications.Current tools aid risk stratification,but no single biomarker replicates biopsy-level precision.Future efforts must prioritize MASLD-specific diagnostic frameworks,standardized protocols,and multi-modal integration to enhance clinical utility and address MASLD’s growing burden.展开更多
BACKGROUND Internet gaming disorder(IGD)is a growing concern among adolescents and adults,necessitating effective treatment strategies beyond pharmacological interventions.AIM To evaluated the effectiveness of non-inv...BACKGROUND Internet gaming disorder(IGD)is a growing concern among adolescents and adults,necessitating effective treatment strategies beyond pharmacological interventions.AIM To evaluated the effectiveness of non-invasive interventions for treating IGD among adolescents and adults.METHODS A total of 11 randomized controlled trials published between 2020 and 2025 were included in this meta-analysis,encompassing 1208 participants from diverse geographic and cultural contexts.The interventions examined included cognitive behavioral therapy(CBT),internet-based CBT,neurofeedback,virtual reality therapy,abstinence-based programs,and school-based prevention.The primary outcomes assessed were reductions in gaming time and IGD severity.Secondary outcomes included improvements in mood,anxiety,and psychosocial functioning(e.g.,stronger peer relationships,better academic or work performance,and healthier daily-life role fulfillment).RESULTS The pooled standardized mean difference for IGD symptom reduction significantly favored non-invasive interventions(Hedges’g=0.56,95%CI:0.38-0.74,P<0.001),with moderate heterogeneity observed(I2=47%).Subgroup analyses indicated that CBT-based programs,both in-person and online,yielded the strongest effects,particularly when caregiver involvement or self-monitoring was incorporated.Funnel plot asymmetry was minimal,suggesting a low risk of publication bias.CONCLUSION These findings support the efficacy of scalable,low-risk non-invasive interventions as first-line treatment options for IGD,particularly in youth populations.Future studies should prioritize investigating long-term outcomes,comparing the effectiveness of different non-invasive modalities,and developing culturally adaptive delivery methods.展开更多
In this article,we comment on the article by Peta et al.This study evaluates the diagnostic performance of FibroTest-Actitest,transient elastography,and the fibrosis-4 index against a histological reference.Using the ...In this article,we comment on the article by Peta et al.This study evaluates the diagnostic performance of FibroTest-Actitest,transient elastography,and the fibrosis-4 index against a histological reference.Using the Obuchowski measure,the authors demonstrate that FibroTest and vibration-controlled transient elastography outperform the fibrosis-4 index in detecting fibrosis.Additionally,Actitest offers superior estimation of inflammatory activity compared to conventional biomarkers.Assessing liver fibrosis is crucial for managing autoimmune hepatitis(AIH),yet reliance on invasive liver biopsy remains higher than in other liver diseases.This is partly due to more complex diagnostic criteria for AIH,the lack of standardized scoring for non-invasive testing,and the presence of inflammation,which can lead to falsely elevated results with non-invasive tests.A Bayesian latent class model further supports the reliability of these non-invasive tests,highlighting their potential to complement biopsy,particularly for longterm disease monitoring.These findings underscore the importance of noninvasive diagnostics in optimizing AIH management.展开更多
Patients with acute exacerbation of chronic obstructive pulmonary disease(COPD)often suffer from respiratory failure and require respiratory support therapy.High-flow nasal cannula oxygen therapy(HFNC)and non-invasive...Patients with acute exacerbation of chronic obstructive pulmonary disease(COPD)often suffer from respiratory failure and require respiratory support therapy.High-flow nasal cannula oxygen therapy(HFNC)and non-invasive positive pressure ventilation(NIPPV)are commonly used non-invasive respiratory support methods.HFNC can provide precisely heated and humidified high-flow oxygen,reducing dead space and increasing alveolar ventilation.NIPPV can supply stable high-concentration oxygen and improve gas exchange.This article reviews the application of HFNC and NIPPV in the acute exacerbation stage of COPD,aiming to provide references for reasonable clinical selection.展开更多
In federated learning,backdoor attacks have become an important research topic with their wide application in processing sensitive datasets.Since federated learning detects or modifies local models through defense mec...In federated learning,backdoor attacks have become an important research topic with their wide application in processing sensitive datasets.Since federated learning detects or modifies local models through defense mechanisms during aggregation,it is difficult to conduct effective backdoor attacks.In addition,existing backdoor attack methods are faced with challenges,such as low backdoor accuracy,poor ability to evade anomaly detection,and unstable model training.To address these challenges,a method called adaptive simulation backdoor attack(ASBA)is proposed.Specifically,ASBA improves the stability of model training by manipulating the local training process and using an adaptive mechanism,the ability of the malicious model to evade anomaly detection by combing large simulation training and clipping,and the backdoor accuracy by introducing a stimulus model to amplify the impact of the backdoor in the global model.Extensive comparative experiments under five advanced defense scenarios show that ASBA can effectively evade anomaly detection and achieve high backdoor accuracy in the global model.Furthermore,it exhibits excellent stability and effectiveness after multiple rounds of attacks,outperforming state-of-the-art backdoor attack methods.展开更多
Federated Learning(FL)protects data privacy through a distributed training mechanism,yet its decentralized nature also introduces new security vulnerabilities.Backdoor attacks inject malicious triggers into the global...Federated Learning(FL)protects data privacy through a distributed training mechanism,yet its decentralized nature also introduces new security vulnerabilities.Backdoor attacks inject malicious triggers into the global model through compromised updates,posing significant threats to model integrity and becoming a key focus in FL security.Existing backdoor attack methods typically embed triggers directly into original images and consider only data heterogeneity,resulting in limited stealth and adaptability.To address the heterogeneity of malicious client devices,this paper proposes a novel backdoor attack method named Capability-Adaptive Shadow Backdoor Attack(CASBA).By incorporating measurements of clients’computational and communication capabilities,CASBA employs a dynamic hierarchical attack strategy that adaptively aligns attack intensity with available resources.Furthermore,an improved deep convolutional generative adversarial network(DCGAN)is integrated into the attack pipeline to embed triggers without modifying original data,significantly enhancing stealthiness.Comparative experiments with Shadow Backdoor Attack(SBA)across multiple scenarios demonstrate that CASBA dynamically adjusts resource consumption based on device capabilities,reducing average memory usage per iteration by 5.8%.CASBA improves resource efficiency while keeping the drop in attack success rate within 3%.Additionally,the effectiveness of CASBA against three robust FL algorithms is also validated.展开更多
The surge in smishing attacks underscores the urgent need for robust,real-time detection systems powered by advanced deep learning models.This paper introduces PhishNet,a novel ensemble learning framework that integra...The surge in smishing attacks underscores the urgent need for robust,real-time detection systems powered by advanced deep learning models.This paper introduces PhishNet,a novel ensemble learning framework that integrates transformer-based models(RoBERTa)and large language models(LLMs)(GPT-OSS 120B,LLaMA3.370B,and Qwen332B)to enhance smishing detection performance significantly.To mitigate class imbalance,we apply synthetic data augmentation using T5 and leverage various text preprocessing techniques.Our system employs a duallayer voting mechanism:weighted majority voting among LLMs and a final ensemble vote to classify messages as ham,spam,or smishing.Experimental results show an average accuracy improvement from 96%to 98.5%compared to the best standalone transformer,and from 93%to 98.5%when compared to LLMs across datasets.Furthermore,we present a real-time,user-friendly application to operationalize our detection model for practical use.PhishNet demonstrates superior scalability,usability,and detection accuracy,filling critical gaps in current smishing detection methodologies.展开更多
Zero-click attacks represent an advanced cybersecurity threat,capable of compromising devices without user interaction.High-profile examples such as Pegasus,Simjacker,Bluebugging,and Bluesnarfing exploit hidden vulner...Zero-click attacks represent an advanced cybersecurity threat,capable of compromising devices without user interaction.High-profile examples such as Pegasus,Simjacker,Bluebugging,and Bluesnarfing exploit hidden vulnerabilities in software and communication protocols to silently gain access,exfiltrate data,and enable long-term surveillance.Their stealth and ability to evade traditional defenses make detection and mitigation highly challenging.This paper addresses these threats by systematically mapping the tactics and techniques of zero-click attacks using the MITRE ATT&CK framework,a widely adopted standard for modeling adversarial behavior.Through this mapping,we categorize real-world attack vectors and better understand how such attacks operate across the cyber-kill chain.To support threat detection efforts,we propose an Active Learning-based method to efficiently label the Pegasus spyware dataset in alignment with the MITRE ATT&CK framework.This approach reduces the effort of manually annotating data while improving the quality of the labeled data,which is essential to train robust cybersecurity models.In addition,our analysis highlights the structured execution paths of zero-click attacks and reveals gaps in current defense strategies.The findings emphasize the importance of forward-looking strategies such as continuous surveillance,dynamic threat profiling,and security education.By bridging zero-click attack analysis with the MITRE ATT&CK framework and leveraging machine learning for dataset annotation,this work provides a foundation for more accurate threat detection and the development of more resilient and structured cybersecurity frameworks.展开更多
Dear Editor,This letter studies the problem of stealthy attacks targeting stochastic event-based estimation,alongside proposing measures for their mitigation.A general attack framework is introduced,and the correspond...Dear Editor,This letter studies the problem of stealthy attacks targeting stochastic event-based estimation,alongside proposing measures for their mitigation.A general attack framework is introduced,and the corresponding stealthiness condition is analyzed.To enhance system security,we advocate for a single-dimensional encryption method,showing that securing a singular data element is sufficient to shield the system from the perils of stealthy attacks.展开更多
Graph Neural Networks(GNNs)have proven highly effective for graph classification across diverse fields such as social networks,bioinformatics,and finance,due to their capability to learn complex graph structures.Howev...Graph Neural Networks(GNNs)have proven highly effective for graph classification across diverse fields such as social networks,bioinformatics,and finance,due to their capability to learn complex graph structures.However,despite their success,GNNs remain vulnerable to adversarial attacks that can significantly degrade their classification accuracy.Existing adversarial attack strategies primarily rely on label information to guide the attacks,which limits their applicability in scenarios where such information is scarce or unavailable.This paper introduces an innovative unsupervised attack method for graph classification,which operates without relying on label information,thereby enhancing its applicability in a broad range of scenarios.Specifically,our method first leverages a graph contrastive learning loss to learn high-quality graph embeddings by comparing different stochastic augmented views of the graphs.To effectively perturb the graphs,we then introduce an implicit estimator that measures the impact of various modifications on graph structures.The proposed strategy identifies and flips edges with the top-K highest scores,determined by the estimator,to maximize the degradation of the model’s performance.In addition,to defend against such attack,we propose a lightweight regularization-based defense mechanism that is specifically tailored to mitigate the structural perturbations introduced by our attack strategy.It enhances model robustness by enforcing embedding consistency and edge-level smoothness during training.We conduct experiments on six public TU graph classification datasets:NCI1,NCI109,Mutagenicity,ENZYMES,COLLAB,and DBLP_v1,to evaluate the effectiveness of our attack and defense strategies.Under an attack budget of 3,the maximum reduction in model accuracy reaches 6.67%on the Graph Convolutional Network(GCN)and 11.67%on the Graph Attention Network(GAT)across different datasets,indicating that our unsupervised method induces degradation comparable to state-of-the-art supervised attacks.Meanwhile,our defense achieves the highest accuracy recovery of 3.89%(GCN)and 5.00%(GAT),demonstrating improved robustness against structural perturbations.展开更多
In recent years,with the rapid advancement of artificial intelligence,object detection algorithms have made significant strides in accuracy and computational efficiency.Notably,research and applications of Anchor-Free...In recent years,with the rapid advancement of artificial intelligence,object detection algorithms have made significant strides in accuracy and computational efficiency.Notably,research and applications of Anchor-Free models have opened new avenues for real-time target detection in optical remote sensing images(ORSIs).However,in the realmof adversarial attacks,developing adversarial techniques tailored to Anchor-Freemodels remains challenging.Adversarial examples generated based on Anchor-Based models often exhibit poor transferability to these new model architectures.Furthermore,the growing diversity of Anchor-Free models poses additional hurdles to achieving robust transferability of adversarial attacks.This study presents an improved cross-conv-block feature fusion You Only Look Once(YOLO)architecture,meticulously engineered to facilitate the extraction ofmore comprehensive semantic features during the backpropagation process.To address the asymmetry between densely distributed objects in ORSIs and the corresponding detector outputs,a novel dense bounding box attack strategy is proposed.This approach leverages dense target bounding boxes loss in the calculation of adversarial loss functions.Furthermore,by integrating translation-invariant(TI)and momentum-iteration(MI)adversarial methodologies,the proposed framework significantly improves the transferability of adversarial attacks.Experimental results demonstrate that our method achieves superior adversarial attack performance,with adversarial transferability rates(ATR)of 67.53%on the NWPU VHR-10 dataset and 90.71%on the HRSC2016 dataset.Compared to ensemble adversarial attack and cascaded adversarial attack approaches,our method generates adversarial examples in an average of 0.64 s,representing an approximately 14.5%improvement in efficiency under equivalent conditions.展开更多
基金supported by the National Key R&D Program of China(2024YFA1802300)the Major Science and Technology Program of Hainan Province(ZDKJ2021037)+4 种基金the Regional Innovation and Development Joint Fund of the National Natural Science Foundation of China(U24A20677)Hainan Province Science and Technology Special Fund(ZDYF2020117,ZDY2024SHFZ143)Hainan Province Science and TechnologyProject(LCXY202102,LCYX202203,LCYX202301,LCYx202502)Innovative research project for postgraduate students in Hainan Medical University(HYYB2021A05)the Hainan Province Clinical Medical Center,and the specific research fund of The Innovation Platform for Academicians of Hainan Province(YSPTZX202310).
文摘Patients affected by monogenic diseases impose a substantial burden on both themselves and their families.The primary preventive measure,i.e.,invasive prenatal diagnosis,carries a risk of miscarriage and cannot be performed early in pregnancy.Hence,there is a need for non-invasive prenatal testing(NIPT)for monogenic diseases.By utilizing enriched cell-free fetal DNA(cffDNA)from maternal plasma,we refine the NIPT method,which combines targeted region capture technology,haplotyping,and analysis of informative site frequency.We apply this method to 93 clinical families at genetic risk for thalassemia,encompassing various genetic variant types,to establish a workflow and evaluate its efficiency.Our approach requires only 3 ng of DNA input to generate 0.1 Gb informative target genomic data and leverages a minimum of 3%cffDNA.This method has a 98.16%success rate and 100%concordance with conventional invasive methods.Furthermore,we demonstrate the ability to analyze fetal genotypes as early as eight weeks of gestation.This study establishes an optimized NIPT method for the early detection of various thalassemia disorders during pregnancy.This technique demonstrates high accuracy and potential for clinical application in prenatal diagnosis.
基金the National Natural Science Foundation of China for Distinguished Young Scholars(62325403)the National Natural Science Foundation of China(62504103 and 82002454)+4 种基金the Basic Research Program of Jiangsu(BK20251214)the Natural Science Foundation of Jiangsu Province(BK20230498)the China Postdoctoral Science Foundation under Grant Number 2025T180143 and 2025M770547the Medical Scientific Research Project of Jiangsu Health Commission(ZD2021011)the Jiangsu Funding Program for Excellent Postdoctoral Talent(2024ZB427)。
文摘The development of non-invasive brain-computer interfaces(BCIs)relies on multidisciplinary integration across neuroscience,artificial intelligence,flexible electronics,and systems engineering.Recent advances in deep learning have significantly improved the accuracy and robustness of neural signal decoding.Parallel progress in electrode design—particularly through the use of flexible and stretchable materials like nanostructured conductors and novel fabrication strategies—has enhanced wearability and operational stability.Nevertheless,key challenges persist,including individual variability,biocompatibility limitations,and susceptibility to interference in complex environments.Further validation and optimization are needed to address gaps in generalization capability,long-term reliability,and real-world operational robustness.This review systematically examines the representative progress in neural decoding algorithms and flexible bioelectronic platforms over the past decade,highlighting key design principles,material innovations,and integration strategies that are poised to advance non-invasive BCI capabilities.It also discusses the importance of multimodal data fusion,hardware-software co-optimization,and closed-loop control strategies.Furthermore,the review discusses the application potential and associated engineering challenges of this technology in clinical rehabilitation and industrial translation,aiming to provide a reference for advancing non-invasive BCIs toward practical and scalable deployment.
文摘Objective: To evaluate the efficacy of noninvasive positive pressure ventilation (NIPPV) in respiratory support for severe pneumonia. Methods: Data were analyzed from 74 patients with severe pneumonia undergoing respiratory support at our hospital between May 2024 and April 2025. Patients were randomly assigned using a random number table to two groups (n = 37 each): the experimental group received NIPPV, while the control group underwent conventional invasive mechanical ventilation. Intergroup differences were compared. Results: Compared with the control group, the experimental group demonstrated significantly higher PaO2 and oxygenation index, significantly lower PaCO2, significantly reduced levels of WBC, CRP, and PCT, significantly higher overall efficacy rate, and significantly lower incidence of adverse reactions after treatment (p < 0.05). Pre-treatment PaO2, oxygenation index, PaCO2, WBC, CRP, and PCT levels showed no significant differences between groups (p > 0.05). Conclusion: Non-invasive positive pressure ventilation demonstrates favorable outcomes in respiratory support for severe pneumonia.
文摘BACKGROUND Despite the developments in the field of kidney transplantation,the already existing diagnostic techniques for patient monitoring are considered insufficient.Protein biomarkers that can be derived from modern approaches of proteomic analysis of liquid biopsies(serum,urine)represent a promising innovation in the monitoring of kidney transplant recipients.AIM To investigate the diagnostic utility of protein biomarkers derived from proteomics approaches in renal allograft assessment.METHODS A systematic review was conducted in accordance with PRISMA guidelines,based on research results from the PubMed and Scopus databases.The primary focus was on evaluating the role of biomarkers in the non-invasive diagnosis of transplant-related com-plications.Eligibility criteria included protein biomarkers and urine and blood samples,while exclusion criteria were language other than English and the use of low resolution and sensitivity methods.The selected research articles,were categorized based on the biological sample,condition and methodology and the significantly and reproducibly differentiated proteins were manually selected and extracted.Functional and network analysis of the selected proteins was performed.RESULTS In 17 included studies,58 proteins were studied,with the cytokine CXCL10 being the most investigated.Biological pathways related to immune response and fibrosis have shown to be enriched.Applications of biomarkers for the assessment of renal damage as well as the prediction of short-term and long-term function of the graft were reported.Overall,all studies have shown satisfactory diagnostic accuracy of proteins alone or in combination with conventional methods,as far as renal graft assessment is concerned.CONCLUSION Our review suggests that protein biomarkers,evaluated in specific biological fluids,can make a significant contribution to the timely,valid and non-invasive assessment of kidney graft.
基金Supported by National Natural Science Foundation of China,No.82300451Research Foundation of Wuhan Union Hospital,No.2022xhyn050.
文摘Gastric cancer(GC),a multifaceted and highly aggressive malignancy,represents challenging healthcare burdens globally,with a high incidence and mortality rate.Although endoscopy,combined with histological examination,is the gold stan-dard for GC diagnosis,its high cost,invasiveness,and specialized requirements hinder widespread use for screening.With the emergence of innovative techno-logies such as advanced imaging,liquid biopsy,and breath tests,the landscape of GC diagnosis is poised for radical transformation,becoming more accessible,less invasive,and more efficient.As the non-invasive diagnostic techniques continue to advance and undergo rigorous clinical validation,they hold the promise of sig-nificantly impacting patient outcomes,ultimately leading to better treatment results and improved quality of life for patients with GC.
文摘Chronic kidney disease(CKD)is a degenerative disorder that affects millions of people throughout the world,causing considerable morbidity and healthcare burden.Frequent blood sampling is the current gold standard for monitoring CKD to evaluate biochemical and mineral indicators.However,there are draw-backs to frequent blood draws,such as pain for patients,the possibility of infe-ction,and higher medical expenses.Saliva-based diagnostics offer advantages such as ease of collection,reduced invasiveness,and improved patient compli-ance.A comprehensive literature review was conducted to analyze studies eva-luating the diagnostic utility of salivary creatinine,urea,calcium,and parathyroid hormone(PTH)in patients with CKD.Various saliva collection methods,inc-luding stimulated and unstimulated approaches,were investigated for efficiency and reliability,and a correlation was shown between serum and salivary crea-tinine,urea,PTH,and calcium levels,indicating their potential as CKD biomar-kers.Despite these promising findings,challenges such as standardization of collection methods,variability in salivary flow rates,and predictive value in association with blood parameters are addressed to ensure clinical applicability.This review explores the potential and challenges of saliva as a non-invasive alternative for CKD diagnostics.
基金financially supported by ChinaNational Funds for Distinguished Young Scientists(No.52125403)National Natural Science Foundation of China(Nos.52261135540 and 52404303)Science and Tech-nology Plan Special Fund Project of Jiangsu Province,China(No.BZ2024046)。
文摘Flip-flow screens offer unique advantages in grading fine-grained materials.To address inaccuracies caused by sensor vibra-tions in traditional contact measurement methods,we constructed a non-invasive measurement system based on electrical and optical sig-nals.A trajectory tracking algorithm for the screen-body was developed to visually measure the kinematics.Employing the principle oflaser reflection for distance measurement,optical techniques were performed to capture the kinematic information of the screen-plate.Ad-ditionally,by using Wi-Fi and Bluetooth transmission of electrical signals,tracer particle tracking technology was implemented to elec-trically measure the kinematic information of mineral particles.Consequently,intelligent fusion and perception of the kinematic informa-tion for the screen-body,screen-plate,and particles in the screening system have been achieved.
基金supported by the National Natural Science Foundation of China (Grant No.22109008)。
文摘Accurate real-time monitoring of internal temperature in lithium-ion batteries remains critical for preventing thermal runaway,as conventional approaches sacrifice either computational efficiency or cross-scenario robustness.We present a generalized fuzzy physics-informed framework that distills thermally sensitive electrochemical processes while circumventing redundant physical constraints,thereby establishing an explicit mechanism-constrained mapping between frequency-domain signals and internal temperature.This framework facilitates online thermal estimation,with dynamic validations in LiFePO_4/graphite 18650-type cells confirming real-time capability with near-instantaneous acquisition(~6 s per measurement),exceptional accuracy(±0.5℃) within the operational temperature range(30-50℃),and operational resilience across 20 %-80 % state-of-charge.The framework maintains predictive fidelity(±1.0℃ at 30℃ and ±4.0℃ at 60℃,95 % prediction intervals) across 80 %-100 % state-of-health while demonstrating adaptability to cathode materials and structural architectures.This strategy resolves the competing imperatives of physical interpretability,computational efficiency,and crossscenario generalizability,offering a universal paradigm for embedded thermal management in safetycritical applications.
文摘Hyaline Membrane Disease(HMD)in newborns,also known as neonatal respiratory distress syndrome,is a common critical illness in premature infants,with an incidence inversely correlated with gestational age,posing a serious threat to the life and health of newborns.This paper systematically reviews the core pathogenesis of HMD,focusing on the abnormal metabolism of pulmonary surfactant(PS),genetic factors,immature lung development,and the synergistic effects of inflammatory oxidative stress.It highlights the advances in non-invasive ventilation(NIV)therapy for HMD,including the mechanisms of action,clinical application effects,and optimization strategies of mainstream modalities such as nasal continuous positive airway pressure ventilation(NCPAP),nasal intermittent positive pressure ventilation(NIPPV),and heated humidified high-flow nasal cannula ventilation(HHHFNC).The aim is to provide references for standardized clinical treatment.
基金Supported by the National Natural Science Foundation of China,No.82402719Sichuan Science and Technology Program,No.2025ZNSFSC1553.
文摘Metabolic dysfunction-associated steatotic liver disease(MASLD)requires accurate liver fibrosis assessment for management.While liver biopsy remains the gold standard,its invasiveness drives demand for non-invasive biomarkers.This review evaluates blood biomarkers for MASLD fibrosis staging.Established scores(fibrosis-4,non-alcoholic fatty liver disease fibrosis score)offer accessible screening but exhibit variable performance influenced by age,obesity,and comorbidities.Patented panels(e.g.,enhanced liver fibrosis test,FibroMeter)improve accuracy by integrating extracellular matrix or metabolic markers,though context-specific thresholds are essential.Emerging biomarkers like propeptide of type 3 collagen,Mac-2 binding protein glycosylation isomer,epigenetic markers(proliferator-activated receptor-γmethylation),and angiopoietin-like proteins a family of eight glycoproteins show promise but require large-scale validation.Genetic risk scores and multi-omics approaches face generalizability challenges.Integration strategies,such as combining serum biomarkers with liver stiffness measurement via Agile scores,enhance diagnostic precision and reduce indeterminate classifications.Current tools aid risk stratification,but no single biomarker replicates biopsy-level precision.Future efforts must prioritize MASLD-specific diagnostic frameworks,standardized protocols,and multi-modal integration to enhance clinical utility and address MASLD’s growing burden.
基金Supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)Funded by the Ministry of Education,No.NRF-RS-2023-00237287.
文摘BACKGROUND Internet gaming disorder(IGD)is a growing concern among adolescents and adults,necessitating effective treatment strategies beyond pharmacological interventions.AIM To evaluated the effectiveness of non-invasive interventions for treating IGD among adolescents and adults.METHODS A total of 11 randomized controlled trials published between 2020 and 2025 were included in this meta-analysis,encompassing 1208 participants from diverse geographic and cultural contexts.The interventions examined included cognitive behavioral therapy(CBT),internet-based CBT,neurofeedback,virtual reality therapy,abstinence-based programs,and school-based prevention.The primary outcomes assessed were reductions in gaming time and IGD severity.Secondary outcomes included improvements in mood,anxiety,and psychosocial functioning(e.g.,stronger peer relationships,better academic or work performance,and healthier daily-life role fulfillment).RESULTS The pooled standardized mean difference for IGD symptom reduction significantly favored non-invasive interventions(Hedges’g=0.56,95%CI:0.38-0.74,P<0.001),with moderate heterogeneity observed(I2=47%).Subgroup analyses indicated that CBT-based programs,both in-person and online,yielded the strongest effects,particularly when caregiver involvement or self-monitoring was incorporated.Funnel plot asymmetry was minimal,suggesting a low risk of publication bias.CONCLUSION These findings support the efficacy of scalable,low-risk non-invasive interventions as first-line treatment options for IGD,particularly in youth populations.Future studies should prioritize investigating long-term outcomes,comparing the effectiveness of different non-invasive modalities,and developing culturally adaptive delivery methods.
文摘In this article,we comment on the article by Peta et al.This study evaluates the diagnostic performance of FibroTest-Actitest,transient elastography,and the fibrosis-4 index against a histological reference.Using the Obuchowski measure,the authors demonstrate that FibroTest and vibration-controlled transient elastography outperform the fibrosis-4 index in detecting fibrosis.Additionally,Actitest offers superior estimation of inflammatory activity compared to conventional biomarkers.Assessing liver fibrosis is crucial for managing autoimmune hepatitis(AIH),yet reliance on invasive liver biopsy remains higher than in other liver diseases.This is partly due to more complex diagnostic criteria for AIH,the lack of standardized scoring for non-invasive testing,and the presence of inflammation,which can lead to falsely elevated results with non-invasive tests.A Bayesian latent class model further supports the reliability of these non-invasive tests,highlighting their potential to complement biopsy,particularly for longterm disease monitoring.These findings underscore the importance of noninvasive diagnostics in optimizing AIH management.
基金2022 Inner Mongolia Autonomous Region Health and Wellness Science and Technology Program Project(Project No.:202201533)。
文摘Patients with acute exacerbation of chronic obstructive pulmonary disease(COPD)often suffer from respiratory failure and require respiratory support therapy.High-flow nasal cannula oxygen therapy(HFNC)and non-invasive positive pressure ventilation(NIPPV)are commonly used non-invasive respiratory support methods.HFNC can provide precisely heated and humidified high-flow oxygen,reducing dead space and increasing alveolar ventilation.NIPPV can supply stable high-concentration oxygen and improve gas exchange.This article reviews the application of HFNC and NIPPV in the acute exacerbation stage of COPD,aiming to provide references for reasonable clinical selection.
文摘In federated learning,backdoor attacks have become an important research topic with their wide application in processing sensitive datasets.Since federated learning detects or modifies local models through defense mechanisms during aggregation,it is difficult to conduct effective backdoor attacks.In addition,existing backdoor attack methods are faced with challenges,such as low backdoor accuracy,poor ability to evade anomaly detection,and unstable model training.To address these challenges,a method called adaptive simulation backdoor attack(ASBA)is proposed.Specifically,ASBA improves the stability of model training by manipulating the local training process and using an adaptive mechanism,the ability of the malicious model to evade anomaly detection by combing large simulation training and clipping,and the backdoor accuracy by introducing a stimulus model to amplify the impact of the backdoor in the global model.Extensive comparative experiments under five advanced defense scenarios show that ASBA can effectively evade anomaly detection and achieve high backdoor accuracy in the global model.Furthermore,it exhibits excellent stability and effectiveness after multiple rounds of attacks,outperforming state-of-the-art backdoor attack methods.
基金supported by the National Natural Science Foundation of China(Grant No.62172123)the Key Research and Development Program of Heilongjiang Province,China(GrantNo.2022ZX01A36).
文摘Federated Learning(FL)protects data privacy through a distributed training mechanism,yet its decentralized nature also introduces new security vulnerabilities.Backdoor attacks inject malicious triggers into the global model through compromised updates,posing significant threats to model integrity and becoming a key focus in FL security.Existing backdoor attack methods typically embed triggers directly into original images and consider only data heterogeneity,resulting in limited stealth and adaptability.To address the heterogeneity of malicious client devices,this paper proposes a novel backdoor attack method named Capability-Adaptive Shadow Backdoor Attack(CASBA).By incorporating measurements of clients’computational and communication capabilities,CASBA employs a dynamic hierarchical attack strategy that adaptively aligns attack intensity with available resources.Furthermore,an improved deep convolutional generative adversarial network(DCGAN)is integrated into the attack pipeline to embed triggers without modifying original data,significantly enhancing stealthiness.Comparative experiments with Shadow Backdoor Attack(SBA)across multiple scenarios demonstrate that CASBA dynamically adjusts resource consumption based on device capabilities,reducing average memory usage per iteration by 5.8%.CASBA improves resource efficiency while keeping the drop in attack success rate within 3%.Additionally,the effectiveness of CASBA against three robust FL algorithms is also validated.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under Grant No.(GPIP:1074-612-2024).
文摘The surge in smishing attacks underscores the urgent need for robust,real-time detection systems powered by advanced deep learning models.This paper introduces PhishNet,a novel ensemble learning framework that integrates transformer-based models(RoBERTa)and large language models(LLMs)(GPT-OSS 120B,LLaMA3.370B,and Qwen332B)to enhance smishing detection performance significantly.To mitigate class imbalance,we apply synthetic data augmentation using T5 and leverage various text preprocessing techniques.Our system employs a duallayer voting mechanism:weighted majority voting among LLMs and a final ensemble vote to classify messages as ham,spam,or smishing.Experimental results show an average accuracy improvement from 96%to 98.5%compared to the best standalone transformer,and from 93%to 98.5%when compared to LLMs across datasets.Furthermore,we present a real-time,user-friendly application to operationalize our detection model for practical use.PhishNet demonstrates superior scalability,usability,and detection accuracy,filling critical gaps in current smishing detection methodologies.
文摘Zero-click attacks represent an advanced cybersecurity threat,capable of compromising devices without user interaction.High-profile examples such as Pegasus,Simjacker,Bluebugging,and Bluesnarfing exploit hidden vulnerabilities in software and communication protocols to silently gain access,exfiltrate data,and enable long-term surveillance.Their stealth and ability to evade traditional defenses make detection and mitigation highly challenging.This paper addresses these threats by systematically mapping the tactics and techniques of zero-click attacks using the MITRE ATT&CK framework,a widely adopted standard for modeling adversarial behavior.Through this mapping,we categorize real-world attack vectors and better understand how such attacks operate across the cyber-kill chain.To support threat detection efforts,we propose an Active Learning-based method to efficiently label the Pegasus spyware dataset in alignment with the MITRE ATT&CK framework.This approach reduces the effort of manually annotating data while improving the quality of the labeled data,which is essential to train robust cybersecurity models.In addition,our analysis highlights the structured execution paths of zero-click attacks and reveals gaps in current defense strategies.The findings emphasize the importance of forward-looking strategies such as continuous surveillance,dynamic threat profiling,and security education.By bridging zero-click attack analysis with the MITRE ATT&CK framework and leveraging machine learning for dataset annotation,this work provides a foundation for more accurate threat detection and the development of more resilient and structured cybersecurity frameworks.
基金supported by the National Natural Science Foundation of China(62303353,62273030,62573320)。
文摘Dear Editor,This letter studies the problem of stealthy attacks targeting stochastic event-based estimation,alongside proposing measures for their mitigation.A general attack framework is introduced,and the corresponding stealthiness condition is analyzed.To enhance system security,we advocate for a single-dimensional encryption method,showing that securing a singular data element is sufficient to shield the system from the perils of stealthy attacks.
基金funded by the National Key Research and Development Program of China(Grant No.2024YFE0209000)the NSFC(Grant No.U23B2019).
文摘Graph Neural Networks(GNNs)have proven highly effective for graph classification across diverse fields such as social networks,bioinformatics,and finance,due to their capability to learn complex graph structures.However,despite their success,GNNs remain vulnerable to adversarial attacks that can significantly degrade their classification accuracy.Existing adversarial attack strategies primarily rely on label information to guide the attacks,which limits their applicability in scenarios where such information is scarce or unavailable.This paper introduces an innovative unsupervised attack method for graph classification,which operates without relying on label information,thereby enhancing its applicability in a broad range of scenarios.Specifically,our method first leverages a graph contrastive learning loss to learn high-quality graph embeddings by comparing different stochastic augmented views of the graphs.To effectively perturb the graphs,we then introduce an implicit estimator that measures the impact of various modifications on graph structures.The proposed strategy identifies and flips edges with the top-K highest scores,determined by the estimator,to maximize the degradation of the model’s performance.In addition,to defend against such attack,we propose a lightweight regularization-based defense mechanism that is specifically tailored to mitigate the structural perturbations introduced by our attack strategy.It enhances model robustness by enforcing embedding consistency and edge-level smoothness during training.We conduct experiments on six public TU graph classification datasets:NCI1,NCI109,Mutagenicity,ENZYMES,COLLAB,and DBLP_v1,to evaluate the effectiveness of our attack and defense strategies.Under an attack budget of 3,the maximum reduction in model accuracy reaches 6.67%on the Graph Convolutional Network(GCN)and 11.67%on the Graph Attention Network(GAT)across different datasets,indicating that our unsupervised method induces degradation comparable to state-of-the-art supervised attacks.Meanwhile,our defense achieves the highest accuracy recovery of 3.89%(GCN)and 5.00%(GAT),demonstrating improved robustness against structural perturbations.
文摘In recent years,with the rapid advancement of artificial intelligence,object detection algorithms have made significant strides in accuracy and computational efficiency.Notably,research and applications of Anchor-Free models have opened new avenues for real-time target detection in optical remote sensing images(ORSIs).However,in the realmof adversarial attacks,developing adversarial techniques tailored to Anchor-Freemodels remains challenging.Adversarial examples generated based on Anchor-Based models often exhibit poor transferability to these new model architectures.Furthermore,the growing diversity of Anchor-Free models poses additional hurdles to achieving robust transferability of adversarial attacks.This study presents an improved cross-conv-block feature fusion You Only Look Once(YOLO)architecture,meticulously engineered to facilitate the extraction ofmore comprehensive semantic features during the backpropagation process.To address the asymmetry between densely distributed objects in ORSIs and the corresponding detector outputs,a novel dense bounding box attack strategy is proposed.This approach leverages dense target bounding boxes loss in the calculation of adversarial loss functions.Furthermore,by integrating translation-invariant(TI)and momentum-iteration(MI)adversarial methodologies,the proposed framework significantly improves the transferability of adversarial attacks.Experimental results demonstrate that our method achieves superior adversarial attack performance,with adversarial transferability rates(ATR)of 67.53%on the NWPU VHR-10 dataset and 90.71%on the HRSC2016 dataset.Compared to ensemble adversarial attack and cascaded adversarial attack approaches,our method generates adversarial examples in an average of 0.64 s,representing an approximately 14.5%improvement in efficiency under equivalent conditions.
