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Brain-derived extracellular vesicles:A promising avenue for Parkinson's disease pathogenesis,diagnosis,and treatment 认领 引用 被引量：1
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作者 Shurui Zhang Jingwen Li +7 位作者 Xinyu Hu Hanshu Liu Qinwei Yu Guiying Kuang Long Liu Danfang Yu Zhicheng Lin Nian Xiong 《Neural Regeneration Research》 SCIE CAS CSCD 2026年第4期1447-1467,共21页
The misfolding,aggregation,and deposition of alpha-synuclein into Lewy bodies are pivotal events that trigger pathological changes in Parkinson's disease.Extracellular vesicles are nanosized lipidbilayer vesicles ... The misfolding,aggregation,and deposition of alpha-synuclein into Lewy bodies are pivotal events that trigger pathological changes in Parkinson's disease.Extracellular vesicles are nanosized lipidbilayer vesicles secreted by cells that play a crucial role in intercellular communication due to their diverse cargo.Among these,brain-derived extracellular vesicles,which are secreted by various brain cells such as neurons,glial cells,and Schwann cells,have garnered increasing attention.They serve as a promising tool for elucidating Parkinson's disease pathogenesis and for advancing diagnostic and therapeutic strategies.This review highlights the recent advancements in our understanding of brain-derived extracellular vesicles released into the blood and their role in the pathogenesis of Parkinson's disease,with specific emphasis on their involvement in the aggregation and spread of alpha-synuclein.Brain-derived extracellular vesicles contribute to disease progression through multiple mechanisms,including autophagy-lysosome dysfunction,neuroinflammation,and oxidative stress,collectively driving neurodegeneration in Parkinson's disease.Their application in Parkinson's disease diagnosis is a primary focus of this review.Recent studies have demonstrated that brainderived extracellular vesicles can be isolated from peripheral blood samples,as they carryα-synuclein and other key biomarkers such as DJ-1 and various micro RNAs.These findings highlight the potential of brain-derived extracellular vesicles,not only for the early diagnosis of Parkinson's disease but also for disease progression monitoring and differential diagnosis.Additionally,an overview of explorations into the potential therapeutic applications of brain-derived extracellular vesicles for Parkinson's disease is provided.Therapeutic strategies targeting brain-derived extracellular vesicles involve modulating the release and uptake of pathological alpha-synuclein-containing brain-derived extracellular vesicles to inhibit the spread of the protein.Moreover,brain-derived extracellular vesicles show immense promise as therapeutic delivery vehicles capable of transporting drugs into the central nervous system.Importantly,brain-derived extracellular vesicles also play a crucial role in neural regeneration by promoting neuronal protection,supporting axonal regeneration,and facilitating myelin repair,further enhancing their therapeutic potential in Parkinson's disease and other neurological disorders.Further clarification is needed of the methods for identifying and extracting brain-derived extracellular vesicles,and large-scale cohort studies are necessary to validate the accuracy and specificity of these biomarkers.Future research should focus on systematically elucidating the unique mechanistic roles of brain-derived extracellular vesicles,as well as their distinct advantages in the clinical translation of methods for early detection and therapeutic development. 展开更多
关键词 alpha-synuclein biomarker brain-derived extracellular vesicles diagnosis exosome extracellular vesicles nerve regeneration Parkinson's disease pathogenesis therapeutics
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Cross-domain few-shot learning:A new perspective on overcoming bottlenecks in clinical artificial intelligence tumor diagnosis 认领 引用
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作者 Ziyao Chen Chenglong Fu +4 位作者 Chuang Wang Cheng Chen Xiang Pan Jian Yao Pengjiang Qian 《Chinese Journal of Cancer Research》 SCIE CAS CSCD 2026年第2期204-208,共5页
Introduction One of the leading causes of death globally is cancer.Although early screening and accurate diagnosis may reduce the mortality of patients significantly, the existing clinical practice is confronted by a ... Introduction One of the leading causes of death globally is cancer.Although early screening and accurate diagnosis may reduce the mortality of patients significantly, the existing clinical practice is confronted by a formidable obstacle: a significant discrepancy between the rapidly increasing annual number of suspected cases and the acute lack of specialist physicians. According to the most recent report in the reputable medical publication CA: A Cancer Journal for Clinicians titled Cancer Statistics, 2025, more than 2.04million new cases of cancer are projected in 2025 along in the United States, and the prevalence of cancer among the young population will increase significantly(1).Considering such high number of patients, conventional testing methods are inadequate because they are expensive and slow, in addition to requiring extensive involvement of skilled pathology and radiology specialists, which is a major bottleneck in the clinical setting. 展开更多
关键词 accurate diagnosis specialist physicians discrepancy clinical artificial intelligence tumor diagnosis global cancer mortality early screening cross domain few shot learning
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Neuromorphic Computing of Multimodal Temporal Data for Machine Fault Diagnosis:Methodology and Hardware Deployment 认领 引用
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作者 Weipeng Fan Xiang Li +3 位作者 Yaguo Lei Shupeng Yu Naipeng Li Bin Yang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1515-1517,共3页
Dear Editor,Long-term monitoring of different mechanical signals is vital for the health management of modern industrial equipment.Traditional deep learning-based fault diagnosis methods rely much on high-performance ... Dear Editor,Long-term monitoring of different mechanical signals is vital for the health management of modern industrial equipment.Traditional deep learning-based fault diagnosis methods rely much on high-performance computing systems and are difficult to be always-on deployed at the edge.Meanwhile,most of the existing fault diagnosis methods rely on single-sourced sensing data,which only capture limited fault information and cannot well reflect the machine health condition.To address the aforementioned problems,the bio-inspired spiking neural networks(SNNs)offer a promising solution,which simulates the structure and operation of biological neural systems and is more energy and computation-efficient. 展开更多
关键词 computation efficiency fault diagnosis energy efficiency mechanical signals bio inspired spiking neural networks health management multimodal temporal data machine fault diagnosis
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Advances in nanotechnology for the diagnosis and management of autoimmune diseases 认领 引用
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作者 Yongquan Zheng Xiaoyu Cai +6 位作者 Lyu Zhang Weidong Fei Dongxu Qin Xiaoqian Zhang Jimin Zhu Caihong Zheng Yao Yao 《Asian Journal of Pharmaceutical Sciences》 SCIE CAS CSCD 2026年第2期70-107,共38页
Autoimmune diseases(AIDs)are chronic,heterogeneous disorders that are often diagnosed after irreversible tissue damage and treated with broad immunosuppression that fails to deliver durable remission.Nanotechnology of... Autoimmune diseases(AIDs)are chronic,heterogeneous disorders that are often diagnosed after irreversible tissue damage and treated with broad immunosuppression that fails to deliver durable remission.Nanotechnology offers opportunities to sense early immune perturbations and to deliver interventions with molecular,cellular and organ-level precision.Here we synthesize advances from 2015 to 2025 in nano-enabled diagnosis and therapy for rheumatoid arthritis,systemic lupus erythematosus,multiple sclerosis,inflammatory bowel disease,type 1 diabetes,psoriasis and selected rare AIDs.On the diagnostic side,nano-optical and electrochemical biosensors,nano-enhanced imaging probes,and liquid-biopsy platforms for extracellular vesicles and cell-free nucleic acids improve sensitivity,stratification and longitudinal monitoring,while wearable and pointof-care devices extend assessment into home and community settings.We relate these technologies to concrete clinical scenarios and highlight performancemetrics such as limit of detection,sample volume,and clinical sensitivity/specificity.Therapeutically,we review stimuli-responsive and ligand-targeted nanocarriers for small molecules and biologics,tolerogenic nanoparticles and exosomes,mRNA–lipid nanoparticle"inverse vaccines",and microneedles or tissue-nanotransporter systems for local gene and cytokine modulation.We summarize emerging clinical trial data and currently marketed nanomedicines for autoimmune indications,linking formulation design to efficacy and safety readouts.Across platforms,we outline design principles connecting physicochemical properties to biodistribution and immune interactions,and we discuss manufacturability,long-term safety and regulatory hurdles that govern translation.Collectively,these advances,together with emerging AI and digital twin frameworks,illustrate how nanotechnology can support earlier diagnosis,more precise and tolerogenic interventions,and progress toward durable,personalized remission in AIDs. 展开更多
关键词 Nanotechnology Autoimmune diseases Diagnosis Therapy Artificial intelligence
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Updates of CSCO guidelines for diagnosis and treatment of lymphoma:2026 edition 认领 引用
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作者 Weiping Liu Donglu Zhao +2 位作者 Jun Ma Jun Zhu Yuqin Song 《Chinese Journal of Cancer Research》 SCIE CAS CSCD 2026年第3期336-339,共4页
Lymphoma is one of the most common hematological malignancies(1).In China,an estimated 5,840 new cases and 2,232 deaths were attributable to Hodgkin lymphoma,whereas 108,327 new cases and 39,905 deaths were attributab... Lymphoma is one of the most common hematological malignancies(1).In China,an estimated 5,840 new cases and 2,232 deaths were attributable to Hodgkin lymphoma,whereas 108,327 new cases and 39,905 deaths were attributable to non-Hodgkin lymphoma in 2023(2,3).The Chinese Society of Clinical Oncology(CSCO)first issued its guidelines for the diagnosis and treatment of lymphoma in 2018 and has updated them annually thereafter,incorporating evidence from high-quality clinical studies as well as evolving treatment availability(4).This article summarizes the key updates introduced in the 2026 edition relative to the 2025 edition. 展开更多
关键词 hodgkin lymphomawhereas treatment av lymphoma diagnosis clinical studies CSCO guidelines edition hematological malignancies
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XJTU-DV:Open-source dynamic vision dataset for non-contact vibration measurement and fault diagnosis of mechanical systems 认领 引用
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作者 Xiang Li Shupeng Yu +3 位作者 Xinrui Chen Yaguo Lei Bin Yang Naipeng Li 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第2期1-13,共13页
Vibration measurement is of great importance for fault diagnosis and prognosis of mechanical systems in the literature.Non-contact vibration measurement methods have been attracting growing attention in recent years.D... Vibration measurement is of great importance for fault diagnosis and prognosis of mechanical systems in the literature.Non-contact vibration measurement methods have been attracting growing attention in recent years.Dynamic vision is an emerging vision technology,which is developed with neuromorphic sensing principles.This paper introduces XJTU-DV,an open-source dynamic vision dataset for non-contact vibration measurement and fault diagnosis of mechanical systems.The XJTU-DV-Beam sub-dataset includes the dynamic vision data on a structural beam system,as well as the corresponding laser vibrometer data as ground truth.The XJTU-DVRotor and XJTU-DV-Pump sub-datasets include the dynamic vision data from a rotor and a pump test bench,respectively.The dynamic vision data are collected under different fault and operating conditions.XJTU-DV provides a data foundation for dynamic vision-based studies on vibration measurement and fault diagnosis,which may promote further development of the emerging vision algorithms for industrial applications.The dataset can be accessed at http://gffzz34a8b68aae444ae4s09qo6qbxwfkv66w9.ffgz.tsg.suse.edu.cn/web/lixiang/xjtu-dv for detailed information. 展开更多
关键词 Dynamic vision Event camera Non-contact measurement Vibration measurement Fault diagnosis
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Expert consensus on the diagnosis and management of tooth developmental anomalies 认领 引用
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作者 Jingxian Zhu Mian Wan +24 位作者 Xiaohong Duan Zhipeng Fan Yao Sun Xudong Wang Shuguo Zheng Liwei Zheng Qinglin Zhu Dong Chen Jiewen Dai Dong Han Miao He Cui Huang Yuegui Jiang Zhonglin Jia Yihuai Pan Yongchu Pan Tiemei Wang Wenmei Wang Baoshan Xu Wei Yin Tingting Zhang Yanli Zhang Zhenjin Zhao Zhuan Bian Yaling Song 《International Journal of Oral Science》 SCIE CAS CSCD 2026年第1期51-65,共15页
Tooth developmental anomalies are a group of disorders caused by unfavorable factors affecting the tooth development process,resulting in abnormalities in tooth number,structure,and morphology.These anomalies typicall... Tooth developmental anomalies are a group of disorders caused by unfavorable factors affecting the tooth development process,resulting in abnormalities in tooth number,structure,and morphology.These anomalies typically manifest during childhood,impairing dental function,maxillofacial development,and facial aesthetics,while also potentially impacting overall physical and mental health.The complex etiology and diverse clinical phenotypes of these anomalies pose significant challenges for prevention,early diagnosis,and treatment.As they usually emerge early in life,long-term management and multidisciplinary collaboration in dental care are essential.However,there is currently a lack of systematic clinical guidelines for the diagnosis and treatment of these conditions,adding to the difficulties in clinical practice.In response to this need,this expert consensus summarizes the classifications,etiology,typical clinical manifestations,and diagnostic criteria of tooth developmental anomalies based on current clinical evidence.It also provides prevention strategies and stage-specific clinical management recommendations to guide clinicians in diagnosis and treatment,promoting early intervention and standardized care for these anomalies. 展开更多
关键词 prevention clinical manifestations diagnosis etiology tooth developmental anomalies diagnostic criteria management clinical guidelines
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A zero-shot across tasks fault diagnosis method of bearings combined simulation model with experimental data 认领 引用
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作者 Yi Wang Qibin Wang +3 位作者 Chenyi Lin Shaohui Du Naining Huang Yanhong Ma 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第3期29-50,共22页
Zero-shot learning is a hot topic in fault diagnosis in recent years.In practice,it is difficult to collect a complete data set containing all fault categories under the same working condition,especially when the mach... Zero-shot learning is a hot topic in fault diagnosis in recent years.In practice,it is difficult to collect a complete data set containing all fault categories under the same working condition,especially when the machine working condition changes.This study proposed a zero-shot across tasks fault diagnosis method based on simulation and experimental data.Firstly,the domain relationship model is constructed using fault category data and simulation data.Then,the task relationship model between several different working conditions is established by extracting the high order frequency contained in the simulation data.Finally,the new working condition simulation data and conversion data are used to fine-tune the domain relationship model to obtain the missing data of the new working condition,and thereby establish the final fault diagnosis model.The effectiveness of the proposed method is verified on three bearing data sets,and it has good diagnostic performance and can solve the zero-shot problem. 展开更多
关键词 Fault diagnosis Bearing dynamic model Generative adversarial networks Zero-shot Multiple incomplete data
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Enhanced accident diagnosis for molten salt reactors using an adaptive residual CNN with Bayesian optimization 认领 引用
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作者 Chao‑Qun Wang Kai Wang +6 位作者 Qun Yang Rong‑Jian Liang Xin Yue Liang‑Jie Sun Wen‑Tao Jiang Zhao‑Zhong He Na‑Xiu Wang 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2026年第8期280-296,共17页
Safety is of paramount importance in nuclear power plants.Accurate and reliable accident diagnosis is essential for ensuring operational safety in reactor systems.The convergence of Industry 4.0 technologies and deep ... Safety is of paramount importance in nuclear power plants.Accurate and reliable accident diagnosis is essential for ensuring operational safety in reactor systems.The convergence of Industry 4.0 technologies and deep learning methods has emerged as a promising approach for improving the operational safety of nuclear energy systems,particularly in fault detection and diagnosis(FDD)applications.This study proposes a novel adaptive accident diagnosis framework tailored for molten salt reactors(MSRs)based on an enhanced residual convolutional neural network(AM-RCNN).The AM-RCNN incorporates an anti-noise module implemented using the soft thresholding method,together with an attention mechanism,to improve robustness.Datasets representing eight distinct operational scenarios were generated using the RELAP5-TMSR simulation tool.An appropriate subset of input features for MSR accident diagnosis was selected using Pearson correlation analysis and random forest importance ranking.The models were subsequently trained,validated,optimized,and tested.Comparative analyses with conventional RCNN and CNN architectures demonstrate the diagnostic advantages of the proposed approach.In addition,the integration of Bayesian optimization further enhances the performance of the AM-RCNN.As a contribution to intelligent monitoring research for MSRs,the proposed method provides reliable decision support for nuclear system operation,particularly in autonomous scenarios. 展开更多
关键词 Accident diagnosis Feature selection Deep residual CNN Bayesian optimization Molten salt reactors
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Novel bearing fault diagnosis model for wind turbine:PSR-CNN-DLSTM combined with transfer learning 认领 引用
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作者 Yuanhao Du Xiuli Geng +3 位作者 Bowu Zhang Qingchao Zhou Sheng Cheng Hongliu Zhang 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第3期51-67,共17页
Wind turbine bearings operate in harsh environments,which can complicate fault diagnosis due to the strong nonlinearity of vibration signals and imbalance data.To tackle these challenges,we propose a novel fault di-ag... Wind turbine bearings operate in harsh environments,which can complicate fault diagnosis due to the strong nonlinearity of vibration signals and imbalance data.To tackle these challenges,we propose a novel fault di-agnosis model that integrates phase space reconstruction(PSR),convolutional neural networks(CNN),deep long short-term memory(DLSTM),and transfer learning(TL).Firstly,PSR is used innovatively to transform the non-linear vibration signals into chaotic phase diagram,facilitating more accurate feature extraction for the fault diagnosis model.Secondly,to further improve the generalization ability of LSTM,a DLSTM is designed and combined with CNN to form a fault diagnosis model.Subsequently,TL is employed to generalize the model across different working conditions,effectively mitigating the data imbalance issue.The proposed model en-hances fault diagnosis accuracy for wind turbine bearings.Comparative experimental results demonstrate the proposed model's superior accuracy,achieving a significant improvement over existing methods in various working conditions. 展开更多
关键词 Fault diagnosis Chaos theory Transfer learning Phase space reconstruction Deep learning Wind turbine
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Performance of computer-aided diagnosis for colorectal sessile serrated lesions:A systematic review and meta-analysis 认领 引用
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作者 Song Zhang Shi-Hang Wang +11 位作者 You-Dong Zhao Jia-Hui Wei Xiang-Yu Sui Xin Li Huan-Wei Zhang Zhi-YaoHuang Cheng-Long Wang Hao Hu Jing Zhang Zhao-Shen Li Sheng-Bing Zhao Yu Bai 《World Journal of Gastroenterology》 SCIE CAS 2026年第27期160-172,共13页
BACKGROUND The efficacy of computer-aided diagnosis(CADx)systems in identifying sessile serrated lesions(SSLs),which are critical precancerous lesions in colorectal cancer,remains unclear.AIM To comprehensively evalua... BACKGROUND The efficacy of computer-aided diagnosis(CADx)systems in identifying sessile serrated lesions(SSLs),which are critical precancerous lesions in colorectal cancer,remains unclear.AIM To comprehensively evaluate the diagnostic performance of CADx systems in differentiating between SSLs and non-SSLs and hyperplastic polyps(HPs).METHODS MEDLINE,EMBASE,and the Cochrane Library were searched up to 11 June 2025 for studies evaluating the performance of CADx systems in differentiating SSLs.The primary outcomes were the pooled diagnostic accuracy,sensitivity,and specificity of the CADx systems in distinguishing SSLs from non-SSLs or HPs.RESULTS Nine studies encompassing 2915 images and 746 videos on SSL differentiation were included.For SSLs vs non-SSLs,the CADx system demonstrated an overall area under the curve(AUC)of 0.93,66% sensitivity,95% specificity,a positive predictive value(PPV)of 0.56,a negative predictive value(NPV)of 0.96,a positive likelihood ratio(LR+)of 12.3,and a negative likelihood ratio(LR-)of 0.36.For SSLs vs HPs,the overall AUC was 0.64,with 55% sensitivity,64% specificity,a PPV of 0.40,an NPV of 0.80,an LR+of 1.5,and an LR-of 0.70.The sensitivity analysis indicated stable findings,whereas the latest World Health Organization pathological standards,image classification algorithms(ICAs),real-time scenarios,multicenter settings and narrow band imaging(NBI)significantly affected CADx system sensitivity.ICA served as an independent factor influencing the sensitivity of differentiating between SSLs and non-SSLs[odds ratio(OR)=14.13],whereas NBI was an independent factor influencing the sensitivity of differentiating between SSLs and HPs(OR=7.27)according to multivariate meta-regression.CONCLUSION Current CADx systems cannot adequately differentiate SSLs from non-SSLs or HPs.Future development should focus on improving the differentiation capability and sensitivity of SSLs. 展开更多
关键词 Computer-aided diagnosis Sessile serrated lesions Colonoscopy Diagnostic accuracy Sensitivity Specificity
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Decoupling incremental classifier and representation learning based continual learning machinery fault diagnosis framework under long-tailed distribution 认领 引用
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作者 Changqing Shen Yao Liu +3 位作者 Bojian Chen Xuyang Tao Yifan Huangfu Dong Wang 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第1期74-87,共14页
Continual learning fault diagnosis(CLFD)has gained growing interest in mechanical systems for its ability to accumulate and transfer knowledge in dynamic fault diagnosis scenarios.However,existing CLFD methods typical... Continual learning fault diagnosis(CLFD)has gained growing interest in mechanical systems for its ability to accumulate and transfer knowledge in dynamic fault diagnosis scenarios.However,existing CLFD methods typically assume balanced task distributions,neglecting the long-tailed nature of real-world fault occurrences,where certain faults dominate while others are rare.Due to the long-tailed distribution among different me-chanical conditions,excessive attention has been focused on the dominant type,leading to performance de-gradation in rarer types.In this paper,decoupling incremental classifier and representation learning(DICRL)is proposed to address the dual challenges of catastrophic forgetting introduced by incremental tasks and the bias in long-tailed CLFD(LT-CLFD).The core innovation lies in the structural decoupling of incremental classifier learning and representation learning.An instance-balanced sampling strategy is employed to learn more dis-criminative deep representations from the exemplars selected by the herding algorithm and new data.Then,the previous classifiers are frozen to prevent damage to representation learning during backward propagation.Cosine normalization classifier with learnable weight scaling is trained using a class-balanced sampling strategy to enhance classification accuracy.Experimental results demonstrate that DICRL outperforms existing continual learning methods across multiple benchmarks,demonstrating superior performance and robustness in both LT-CLFD and conventional CLFD.DICRL effectively tackles both catastrophic forgetting and long-tailed distribution in CLFD,enabling more reliable fault diagnosis in industrial applications. 展开更多
关键词 Fault diagnosis Continual learning Long-tailed distribution Catastrophic forgetting
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Plasmon-Enhanced NaYF4:Yb/Er/Tm UCNPs Fluorescent Biosensing for Ultrasensitive Detection of GP73 in Clinical Liver Cancer Diagnosis 认领 引用
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作者 Changsheng Shi Wei Cai +8 位作者 Zhixiang Lin Yizhou Xu Liu Xu Bingru Zheng Wen Li Hui Tong Qing Yang Caiping Ding Youju Huang 《Rare Metals》 SCIE EI CAS CSCD 2026年第5期220-230,共11页
Early diagnosis of liver cancer demands high sensitivity,high specificity,convenience and real-time monitoring capabilities.However,it encounters challenges such as low concentration of markers,numerous interfering fa... Early diagnosis of liver cancer demands high sensitivity,high specificity,convenience and real-time monitoring capabilities.However,it encounters challenges such as low concentration of markers,numerous interfering factors,and limitations of detection technologies.This study investigates the potential of plasmonic gold nanorods(AuNRs)and rare-earth-based upconversion nanoparticles(NaYF4:Yb/Er/Tm UCNPs)in liver cancer surveillance and detection.A significant enhancement in Near-infrared(NIR)fluorescence of the UCNPs was observed when the assay was used for detecting the serum biomarker Golgi protein 73(GP73).The method enables ultra-sensitive,quantitative detection of GP73 at concentrations as low as 5 ng mL-1in a short period of time,using only 10μL of serum samples.It demonstrated a sensitivity of 88.90%and a specificity of100%,surpassing conventional immunoassays and showing strong agreement with Magnetic resonance imaging(MRI)and computed tomography(CT)in prognostic performance.Compared to existing advanced detection methods,this approach exhibited superior performance in diagnostic accuracy,cost-effectiveness,and time efficiency,highlighting its high practical value and transformative potential.Based on the findings of this study,the rare-earth-plasmonic hybrid nanomaterials in the GP73 detection method show promise for future liver cancer diagnostics.Despite its simplicity,it offers potential applications in community screening,intraoperative assessment,and postoperative monitoring. 展开更多
关键词 GP73 liver cancer NaYF4:Yb/Er/Tm UCNPs plasmonic fluorescence enhancement ultrasensitive early diagnosis
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Fault Diagnosis of Wind Turbine Blades Based on Multi-Sensor Weighted Alignment Fusion in Noisy Environments 认领 引用
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作者 Lifu He Zhongchu Huang +4 位作者 Haidong Shao Zhangbo Hu Yuting Wang Jie Mei Xiaofei Zhang 《Computers, Materials & Continua》 SCIE EI 2026年第3期1401-1422,共22页
Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction.However,several challenges remain.First,signal noise collected during blade operati... Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction.However,several challenges remain.First,signal noise collected during blade operation masks fault features,severely impairing the fault diagnosis performance of deep learning models.Second,current blade fault diagnosis often relies on single-sensor data,resulting in limited monitoring dimensions and ability to comprehensively capture complex fault states.To address these issues,a multi-sensor fusion-based wind turbine blade fault diagnosis method is proposed.Specifically,a CNN-Transformer Coupled Feature Learning Architecture is constructed to enhance the ability to learn complex features under noisy conditions,while a Weight-Aligned Data Fusion Module is designed to comprehensively and effectively utilize multi-sensor fault information.Experimental results of wind turbine blade fault diagnosis under different noise interferences show that higher accuracy is achieved by the proposed method compared to models with single-source data input,enabling comprehensive and effective fault diagnosis. 展开更多
关键词 Wind turbine blade multi-sensor fusion fault diagnosis CNN-transformer coupled architecture
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Towards generalizable fault diagnosis:Learning invariant features across varying fault severities 认领 引用
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作者 Yiming Zhang Hongbo Shi +1 位作者 Bing Song Yang Tao 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2026年第4期23-36,共14页
Ensuring the generalizability of fault diagnosis models is critical for maintaining the long-term safety of industrial systems operating under diverse conditions.This study presents a novel method,termed the Generaliz... Ensuring the generalizability of fault diagnosis models is critical for maintaining the long-term safety of industrial systems operating under diverse conditions.This study presents a novel method,termed the Generalizable Class-Consistent Network(GCCNet),designed to enhance diagnostic robustness under previously unseen operating conditions.Speciffiifically,GCCNet incorporates a mutual information based feature disentanglement mechanism to extract task-relevant representations.To further promote feature invariance,auxiliary samples are constructed using same-class fault data under different excitation intensities,and a class-consistency regularization is applied during training to enforce consistent predictions.This guides the network to purify task-relevant features into transferable and robust representations.Extensive experiments conducted on the Tennessee Eastman process and industrial dataset validate the effectiveness and generalization ability of the proposed method. 展开更多
关键词 Fault diagnosis Domain generalization Class-consistent learning
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miR-449a:A Novel Biomarker for Diagnosis,Prognosis,and Treatment Response in Locally Advanced Laryngeal Squamous Cell Carcinoma 认领 引用
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作者 Amal F.Gharib Ohud Alsalmi +5 位作者 Hayaa M.Alhuthali Afaf Alharthi Saud Ayed Alharthi Shaimaa A.Alharthi Rasha L.Etewa Wael H.Elsawy 《Oncology Research》 SCIE 2026年第3期558-574,共17页
Background:Locally advanced laryngeal squamous cell carcinoma(LA-LSCC)presents clinical challenges due to the lack of reliable non-invasive biomarkers.This study aimed to evaluate miR-449a as a diagnostic and prognost... Background:Locally advanced laryngeal squamous cell carcinoma(LA-LSCC)presents clinical challenges due to the lack of reliable non-invasive biomarkers.This study aimed to evaluate miR-449a as a diagnostic and prognostic biomarker in LA-LSCC.Methods:miR-449a expression was analyzed in tumor tissues,adjacent normal tissues,and serum from 81 LA-LSCC patients and 50 controls using quantitative real-time reverse transcription polymerase chain reaction(qRT-PCR).We assessed the diagnostic accuracy by Receiver Operating Characteristic curve(ROC curves),clinicopathological associations,survival outcomes(Kaplan-Meier),and treatment response dynamics.Results:miR-449a was significantly downregulated in LA-LSCC tissues(p<0.0001)and serum(p<0.0001),with a strong tissue-serum correlation(R2=0.988).Tissue miR-449a demonstrated a diagnostic accuracy(Area Under the Curve,AUC=0.857),while serum showed moderate accuracy(AUC=0.734).High miR-449a expression correlated with favorable clinicopathological features and improved survival(median overall survival:67.82 vs.23.74 months;p=0.0012).Multivariate analysis confirmed miR-449a as an independent prognostic factor(p<0.001).miR-449a levels increased post-treatment,particularly in responders to chemotherapyadiation(p<0.0001).Conclusion:miR-449a serves as a non-invasive biomarker for LA-LSCC diagnosis,prognosis,and treatment monitoring.Its dynamic expression highlights potential for risk stratification and therapy response prediction,warranting further validation in larger cohorts. 展开更多
关键词 Locally advanced laryngeal squamous cell carcinoma(LA-LSCC) microRNA-449a(miR-449a) biomarker prognosis diagnosis treatment response serum biomarker chemoradiotherapy organ preservation
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A Review on Fault Diagnosis Methods of Gas Turbine 认领 引用
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作者 Tao Zhang Hailun Wang +1 位作者 Tianyue Wang Tian Tian 《Computers, Materials & Continua》 SCIE EI 2026年第3期88-116,共29页
The critical components of gas turbines suffer from prolonged exposure to factors such as thermal oxidation,mechanical wear,and airflow disturbances during prolonged operation.These conditions can lead to a series of ... The critical components of gas turbines suffer from prolonged exposure to factors such as thermal oxidation,mechanical wear,and airflow disturbances during prolonged operation.These conditions can lead to a series of issues,including mechanical faults,air path malfunctions,and combustion irregularities.Traditional modelbased approaches face inherent limitations due to their inability to handle nonlinear problems,natural factors,measurement uncertainties,fault coupling,and implementation challenges.The development of artificial intelligence algorithms has provided an effective solution to these issues,sparking extensive research into data-driven fault diagnosis methodologies.The review mechanism involved searching IEEE Xplore,ScienceDirect,and Web of Science for peerreviewed articles published between 2019 and 2025,focusing on multi-fault diagnosis techniques.A total of 220 papers were identified,with 123 meeting the inclusion criteria.This paper provides a comprehensive review of diagnostic methodologies,detailing their operational principles and distinctive features.It analyzes current research hotspots and challenges while forecasting future trends.The study systematically evaluates the strengths and limitations of various fault diagnosis techniques,revealing their practical applicability and constraints through comparative analysis.Furthermore,this paper looks forward to the future development direction of this field and provides a valuable reference for the optimization and development of gas turbine fault diagnosis technology in the future. 展开更多
关键词 Fault diagnosis machine learning gas turbine artificial intelligence deep learning
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A similarity-guided dynamic adjustment federated learning framework for multicenter keratitis diagnosis 认领 引用
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作者 Jiang Jiewei Cui Yiwei +3 位作者 Yao Qihai Wang Ning Li Kuan Li Zhongwen 《High Technology Letters》 EI CAS 2026年第1期1-10,共10页
Keratitis is a common ophthalmic disease associated with a high risk of blindness.Although deep learning(DL) based on slit-lamp images has shown great promise for automatic keratitis diagnosis,data heterogeneity and p... Keratitis is a common ophthalmic disease associated with a high risk of blindness.Although deep learning(DL) based on slit-lamp images has shown great promise for automatic keratitis diagnosis,data heterogeneity and privacy constraints hinder data sharing,limiting model generalization across multiple medical centers.To address these challenges,we propose a similarity-guided dynamic adjustment federated learning algorithm for automated keratitis diagnosis(SDAFL_AKD).SDAFL_AKD introduces a similarity-based regularization term during local model updates to alleviate catastrophic forgetting and employs a performance-driven dynamic aggregation mechanism on the server-side to adaptively weight client contributions,thereby enhancing global model robustness under non-independent and identically distributed(Non-IID) conditions.The framework is evaluated on slit-lamp images collected from four independent data sources encompassing keratitis,normal cornea,and other cornea abnormalities,and compared with Fed Avg,model-contrastive federated learning(MOON),stochastic controlled averaging for federated learning(SCAFFOLD) and single-center baseline models.Experimental results demonstrate that SDAFL_AKD consistently outperforms conventional methods,achieving average accuracies of 97.95% on a balanced dataset and 86.05% on an imbalanced smart phone-acquired dataset.Ablation studies further confirm the synergistic benefits of the similarity(SIM) and dynamic aggregation(DA) modules in improving multi-category recognition and generalization.These findings indicate the effectiveness of SDAFL_AKD for keratitis diagnosis under data heterogeneous and privacy-constrained conditions,providing a scalable solution for collaborative ophthalmic image analysis across institutions. 展开更多
关键词 federated learning keratitis diagnosis deep learning data heterogeneity dynamic aggregation
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Recent advances in nonlinear optical techniques for metastatic melanoma diagnosis and mechanistic studies 认领 引用
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作者 Yihui Zhou Wei Lu Hyeon Jeong Lee 《Journal of Innovative Optical Health Sciences》 SCIE EI CSCD 2026年第4期95-113,共19页
Melanoma,a highly aggressive form of skin cancer,is characterized by complex metabolic reprogramming that drives its metastatic potential and therapy resistance.While localized melanoma can be treated effectively,meta... Melanoma,a highly aggressive form of skin cancer,is characterized by complex metabolic reprogramming that drives its metastatic potential and therapy resistance.While localized melanoma can be treated effectively,metastatic melanoma remains largely incurable,creating an urgent need for methods enabling early diagnosis and prognosis.In this review,we summarize recent efforts toward addressing these clinical challenges by nonlinear optical techniques,particularly pump-probe microscopy and vibrational spectroscopy,developed for in vivo investigation of key molecular changes in melanoma progression.These techniques enable label-free,real-time imaging of two critical components in melanoma progression:melanin and lipid droplets,respectively,providing unique insights into molecular mechanisms of melanoma progression.Future research directions focus on transforming these imaging techniques to enhance their clinical applicability and further investigation of the metabolic vulnerabilities of melanoma. 展开更多
关键词 Melanoma nonlinear optical techniques pump-probe microscopy vibrational spectroscopy molecular diagnosis
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A precise identification-based mode decomposition and its application in mechanical fault diagnosis 认领 引用
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作者 Bi Li Zhinong Li +1 位作者 Fengtao Wang Deqiang He 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第1期88-101,共14页
Current improved Empirical Mode Decomposition(EMD)methods enhance the accurate identification of peak and valley points in mechanical signals through noise-assisted filtering techniques,thereby improving the mode deco... Current improved Empirical Mode Decomposition(EMD)methods enhance the accurate identification of peak and valley points in mechanical signals through noise-assisted filtering techniques,thereby improving the mode decomposition performance,which is of great significance in extracting fault features from mechanical signals.However,noise-assisted filtering leads to the loss of critical features in mechanical signals and introduces a large amount of residual noise into Intrinsic Mode Functions(IMFs)that obscure signal features.To address these issues,a Precise Identification-based Mode Decomposition(PIMD)method is proposed.This method directly enhances the ability of EMD to precisely identify peak and valley points by using a proposed precise identifi-cation approach,which improves mode decomposition performance and avoids the negative impacts of noise-assisted filtering,thus benefiting the extraction of more mechanical fault features.Simulation results show that the proposed PIMD method can precisely identify peak and valley points of signals with noise of different signal-tonoise ratios and perform a highly rigorous high-low frequency decomposition,significantly outperforming EMD.Finally,mechanical fault diagnostic experiments on four bearing cases and two gear cases demonstrate that,compared to four mainstream methods,the PIMD method exhibits the best mode decomposition perfor-mance and can extract more and clearer mechanical fault features. 展开更多
关键词 Mechanical fault diagnosis Precise identification-based mode decomposition Peak and valley point identification Mode decomposition performance
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