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
Challenges in the diagnosis and treatment of Parkinson’s disease:Parkinson’s disease(PD)is an increasingly prevalent neurodegenerative disease,at first sight primarily characterized by motor symptoms,although non-mo...Challenges in the diagnosis and treatment of Parkinson’s disease:Parkinson’s disease(PD)is an increasingly prevalent neurodegenerative disease,at first sight primarily characterized by motor symptoms,although non-motor symptoms also constitute a major part of the overall phenotype.Clinically,this disease cannot be diagnosed reliably until a large part of the vulnerable dopaminergic neurons has been irretrievably lost,and the disease progresses inexorably.New biological criteria for PD have been proposed recently and might eventually improve early diagnosis,but they require further validation,and their use will initially be restricted to a research environment(Darweesh et al.,2024).展开更多
Traumatic brain injury (TBI) is defined as damage to the brain resulting from an external sudden physical force or shock to the head.It is considered a silent public health epidemic causing significant death and disab...Traumatic brain injury (TBI) is defined as damage to the brain resulting from an external sudden physical force or shock to the head.It is considered a silent public health epidemic causing significant death and disability globally.There were 64,000 TBI related deaths reported in the USA in 2020,with about US$76 billion in direct and indirect medical costs annually.展开更多
Epilepsy is a severe,relapsing,and multifactorial neurological disorder.Studies regarding the accurate diagnosis,prognosis,and in-depth pathogenesis are crucial for the precise and effective treatment of epilepsy.The ...Epilepsy is a severe,relapsing,and multifactorial neurological disorder.Studies regarding the accurate diagnosis,prognosis,and in-depth pathogenesis are crucial for the precise and effective treatment of epilepsy.The pathogenesis of epilepsy is complex and involves alterations in variables such as gene expression,protein expression,ion channel activity,energy metabolites,and gut microbiota composition.Satisfactory results are lacking for conventional treatments for epilepsy.Surgical resection of lesions,drug therapy,and non-drug interventions are mainly used in clinical practice to treat pain associated with epilepsy.Non-pharmacological treatments,such as a ketogenic diet,gene therapy for nerve regeneration,and neural regulation,are currently areas of research focus.This review provides a comprehensive overview of the pathogenesis,diagnostic methods,and treatments of epilepsy.It also elaborates on the theoretical basis,treatment modes,and effects of invasive nerve stimulation in neurotherapy,including percutaneous vagus nerve stimulation,deep brain electrical stimulation,repetitive nerve electrical stimulation,in addition to non-invasive transcranial magnetic stimulation and transcranial direct current stimulation.Numerous studies have shown that electromagnetic stimulation-mediated neuromodulation therapy can markedly improve neurological function and reduce the frequency of epileptic seizures.Additionally,many new technologies for the diagnosis and treatment of epilepsy are being explored.However,current research is mainly focused on analyzing patients’clinical manifestations and exploring relevant diagnostic and treatment methods to study the pathogenesis at a molecular level,which has led to a lack of consensus regarding the mechanisms related to the disease.展开更多
Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained promine...Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.展开更多
Cancer poses a serious threat to human health worldwide and is a leading cause of death1.The analysis of radiological imaging is crucial in early detection,accurate diagnosis,effective treatment planning,and ongoing m...Cancer poses a serious threat to human health worldwide and is a leading cause of death1.The analysis of radiological imaging is crucial in early detection,accurate diagnosis,effective treatment planning,and ongoing monitoring of patients with cancer.However,several challenges impede the effectiveness of cancer imaging analysis in clinical practice.One difficulty is that healthcare professionals’immense clinical workloads can result in time constraints and increase pressure,thereby hindering their ability to maintain high accuracy and thoroughness in image analysis.Additionally,subjective variability among radiologists can lead to inconsistent interpretations and diagnoses.Because this variability is often influenced by personal biases,standardized assessments are often difficult to achieve.Moreover,the inherent complexity of cancer imaging necessitates extensive clinical experience;this aspect can also be a limiting factor,particularly if expertise or resources are limited.The application of artificial intelligence(AI)can alleviate these problems by enhancing the accuracy,objectivity,and efficiency of cancer imaging analysis while assisting physicians.Therefore,the advancement of AI research is crucial for achieving progress in radiology.展开更多
Early correction of childhood malocclusion is timely managing morphological,structural,and functional abnormalities at different dentomaxillofacial developmental stages.The selection of appropriate imaging examination...Early correction of childhood malocclusion is timely managing morphological,structural,and functional abnormalities at different dentomaxillofacial developmental stages.The selection of appropriate imaging examination and comprehensive radiological diagnosis and analysis play an important role in early correction of childhood malocclusion.This expert consensus is a collaborative effort by multidisciplinary experts in dentistry across the nation based on the current clinical evidence,aiming to provide general guidance on appropriate imaging examination selection,comprehensive and accurate imaging assessment for early orthodontic treatment patients.展开更多
INTRODUCTION.Depressive disorders are mental illnesses that seriously affect public health.There are approximately 320 million patients with depression worldwide,accounting for 4.4% of the total disease burden.1Depres...INTRODUCTION.Depressive disorders are mental illnesses that seriously affect public health.There are approximately 320 million patients with depression worldwide,accounting for 4.4% of the total disease burden.1Depression leads to social and occupational impairment,diminished quality of life and an elevated risk of death by suicide.展开更多
Gallbladder cancer(GBC)is the most common malignant tumor in the biliary system,characterized by high malignancy,aggressiveness,and poor prognosis.Early diagnosis holds paramount importance in ameliorating therapeutic...Gallbladder cancer(GBC)is the most common malignant tumor in the biliary system,characterized by high malignancy,aggressiveness,and poor prognosis.Early diagnosis holds paramount importance in ameliorating therapeutic outcomes.Presently,the clinical diagnosis of GBC primarily relies on clinicalradiological-pathological approach.However,there remains a potential for missed diagnosis and misdiagnose in the realm of clinical practice.We firstly analyzed the blood-based biomarkers,such as carcinoembryonic antigen and carbohydrate antigen 19–9.Subsequently,we evaluated the diagnostic performance of various imaging modalities,including ultrasound(US),endoscopic ultrasound(EUS),computed tomography(CT),magnetic resonance imaging(MRI),positron emission tomography/computed tomography(PET/CT)and pathological examination,emphasizing their strengths and limitations in detecting early-stage GBC.Furthermore,we explored the potential of emerging technologies,particularly artificial intelligence(AI)and liquid biopsy,to revolutionize GBC diagnosis.AI algorithms have demonstrated improved image analysis capabilities,while liquid biopsy offers the promise of non-invasive and real-time monitoring.However,the translation of these advancements into clinical practice necessitates further validation and standardization.The review highlighted the advantages and limitations of current diagnostic approaches and underscored the need for innovative strategies to enhance diagnostic accuracy of GBC.In addition,we emphasized the importance of multidisciplinary collaboration to improve early diagnosis of GBC and ultimately patient outcomes.This review endeavoured to impart fresh perspectives and insights into the early diagnosis of GBC.展开更多
In the process of feeding,broilers are susceptible to leg diseases,which are often caused by factors such as genetics,bacteria,viruses,the growth environment,and diet management.Treating leg disorders/diseases in broi...In the process of feeding,broilers are susceptible to leg diseases,which are often caused by factors such as genetics,bacteria,viruses,the growth environment,and diet management.Treating leg disorders/diseases in broilers is challenging,and once they suffer from such conditions,it generally leads to reduced production performance and affects the quality of meat.It is worth mentioning that with the advancement of intensive management technologies and the accelerated growth rate of broilers,the leg diseases in broilers has increased,resulting in higher culling rates during production.Leg diseases not only cause significant economic losses to the poultry industry,but also severely jeopardize the animal welfare of broilers.Therefore,effective early diagnosis is crucial to mitigate the adverse effects of chicken leg diseases.This study aims to review various diagnostic methods,including clinical diagnosis,autopsy,radiological diagnosis,infrared thermal imagery,biomarkers and emerging diagnostic techniques,to establish a theoretical foundation for the identification or monitoring of leg diseases in poultry industry.展开更多
Gastrointestinal(GI)cancers,which predominantly manifest in the stomach,colorectum,liver,esophagus,and pancreas,accounting for approximately 35%of global cancer-related mortality.The advent of liquid biopsy has introd...Gastrointestinal(GI)cancers,which predominantly manifest in the stomach,colorectum,liver,esophagus,and pancreas,accounting for approximately 35%of global cancer-related mortality.The advent of liquid biopsy has introduced a pivotal diagnostic modality for the early identification of premalignant GI lesions and incipient cancers.This non-invasive technique not only facilitates prompt therapeutic intervention,but also serves as a critical adjunct in prognosticating the likelihood of tumor recurrence.The wealth of circulating exosomes present in body fluids is often enriched with proteins,lipids,microRNAs,and other RNAs derived from tumor cells.These specific cargo components are reflective of processes involved in GI tumorigenesis,tumor progression,and response to treatment.As such,they represent a group of promising biomarkers for aiding in the diagnosis of GI cancer.In this review,we delivered an exhaustive overview of the composition of exosomes and the pathways for cargo sorting within these vesicles.We laid out some of the clinical evidence that supported the utilization of exosomes as diagnostic biomarkers for GI cancers and discussed their potential for clinical application.Furthermore,we addressed the challenges encountered when harnessing exosomes as diagnostic and predictive instruments in the realm of GI cancers.展开更多
BACKGROUND Cholangiocarcinoma(CCA),also known as bile duct cancer,is a devastating malignancy primarily affecting the biliary tract.AIM To assess their performance in clinical diagnosis and monitoring of CCA,plasma me...BACKGROUND Cholangiocarcinoma(CCA),also known as bile duct cancer,is a devastating malignancy primarily affecting the biliary tract.AIM To assess their performance in clinical diagnosis and monitoring of CCA,plasma methylation and circulating tumor cells were detected.METHODS Plasma samples were collected from Hubei Cancer Hospital(n=156).Plasma DNA was tested to detect SHOX2,HOXA9,SEPTIN9,and RASSF1A methylation using TaqMan PCR.Circulating tumor cells(CTCs)were detected in the peripheral blood of patients using the United States Food and Drug Administration-approved cell search system before and after clinical therapy.The CCA diagnostic value was estimated using the area under the curve.The independent prognosis risk factors for patients with CCA were estimated using Cox and logistic regression analyses.RESULTS The sensitivity and specificity of the four DNA plasma methylations exhibited 64.74%sensitivity and 93.88%specificity for detecting CCA.The receiver operating characteristic curve of the combined value for CCA diagnosis in plasma was 0.828±0.032.RASSF1A plasma methylation was related to the prognosis of patients with CCA.We determined the prognostic hazard ratio for CCA using CTC count,tumor stage,methylation,and carbohydrate antigen 19-9 levels as key factors.Our overall survival nomogram achieved a C-index of 0.705(0.605-0.805).CONCLUSION SHOX2,HOXA9,SEPTIN9,and RASSF1A plasma methylation demonstrated increased sensitivity for diagnosing CCA.RASSF1A plasma methylation and CTCs were valuable predictors to assess CCA prognosis and recurrence.展开更多
Objective This study reports the first imported case of Lassa fever(LF)in China.Laboratory detection and molecular epidemiological analysis of the Lassa virus(LASV)from this case offer valuable insights for the preven...Objective This study reports the first imported case of Lassa fever(LF)in China.Laboratory detection and molecular epidemiological analysis of the Lassa virus(LASV)from this case offer valuable insights for the prevention and control of LF.Methods Samples of cerebrospinal fluid(CSF),blood,urine,saliva,and environmental materials were collected from the patient and their close contacts for LASV nucleotide detection.Whole-genome sequencing was performed on positive samples to analyze the genetic characteristics of the virus.Results LASV was detected in the patient’s CSF,blood,and urine,while all samples from close contacts and the environment tested negative.The virus belongs to the lineage IV strain and shares the highest homology with strains from Sierra Leone.The variability in the glycoprotein complex(GPC)among different strains ranged from 3.9%to 15.1%,higher than previously reported for the seven known lineages.Amino acid mutation analysis revealed multiple mutations within the GPC immunogenic epitopes,increasing strain diversity and potentially impacting immune response.Conclusion The case was confirmed through nucleotide detection,with no evidence of secondary transmission or viral spread.The LASV strain identified belongs to lineage IV,with broader GPC variability than previously reported.Mutations in the immune-related sites of GPC may affect immune responses,necessitating heightened vigilance regarding the virus.展开更多
A 28-year-old pregnant woman with no prior obstetric complications had a normal prenatal workup before 24 weeks'gestation.At 24 weeks,ultrasound revealed gastrointestinal malformations,a persistent left superior v...A 28-year-old pregnant woman with no prior obstetric complications had a normal prenatal workup before 24 weeks'gestation.At 24 weeks,ultrasound revealed gastrointestinal malformations,a persistent left superior vena cava,and polyhydramnios.展开更多
BACKGROUND Phelan-McDermid syndrome(PMS)is a rare genetic disorder characterized by intellectual disability,delayed language development,autism spectrum disorders,motor tone abnormalities,and a high risk of psychiatri...BACKGROUND Phelan-McDermid syndrome(PMS)is a rare genetic disorder characterized by intellectual disability,delayed language development,autism spectrum disorders,motor tone abnormalities,and a high risk of psychiatric symptoms,including bipolar disorder.CASE SUMMARY This report presented an 18-year clinical history of a 36-year-old woman with PMS,marked by intellectual disabilities,social withdrawal,and stereotyped behaviors.Diagnosed with bipolar disorder at the age of 18 years old,she encountered significant treatment challenges,including severe adverse reactions to antipsychotic medications in 2022,which led to speech and functional regression.Through rehabilitation and comprehensive therapy,her condition gradually improved.In 2024,after further treatment,her symptoms stabilized,highlighting the complexities and successes of long-term management.CONCLUSION Effective management of PMS requires a thorough clinical history,genetic testing,and long-term supportive care.展开更多
BACKGROUND Esophageal squamous cell carcinoma is a major histological subtype of esophageal cancer.Many molecular genetic changes are associated with its occurrence.Raman spectroscopy has become a new method for the e...BACKGROUND Esophageal squamous cell carcinoma is a major histological subtype of esophageal cancer.Many molecular genetic changes are associated with its occurrence.Raman spectroscopy has become a new method for the early diagnosis of tumors because it can reflect the structures of substances and their changes at the molecular level.AIM To detect alterations in Raman spectral information across different stages of esophageal neoplasia.METHODS Different grades of esophageal lesions were collected,and a total of 360 groups of Raman spectrum data were collected.A 1D-transformer network model was proposed to handle the task of classifying the spectral data of esophageal squamous cell carcinoma.In addition,a deep learning model was applied to visualize the Raman spectral data and interpret their molecular characteristics.RESULTS A comparison among Raman spectral data with different pathological grades and a visual analysis revealed that the Raman peaks with significant differences were concentrated mainly at 1095 cm^(-1)(DNA,symmetric PO,and stretching vibration),1132 cm^(-1)(cytochrome c),1171 cm^(-1)(acetoacetate),1216 cm^(-1)(amide III),and 1315 cm^(-1)(glycerol).A comparison among the training results of different models revealed that the 1Dtransformer network performed best.A 93.30%accuracy value,a 96.65%specificity value,a 93.30%sensitivity value,and a 93.17%F1 score were achieved.CONCLUSION Raman spectroscopy revealed significantly different waveforms for the different stages of esophageal neoplasia.The combination of Raman spectroscopy and deep learning methods could significantly improve the accuracy of classification.展开更多
As batteries become increasingly essential for energy storage technologies,battery prognosis,and diagnosis remain central to ensure reliable operation and effective management,as well as to aid the in-depth investigat...As batteries become increasingly essential for energy storage technologies,battery prognosis,and diagnosis remain central to ensure reliable operation and effective management,as well as to aid the in-depth investigation of degradation mechanisms.However,dynamic operating conditions,cell-to-cell inconsistencies,and limited availability of labeled data have posed significant challenges to accurate and robust prognosis and diagnosis.Herein,we introduce a time-series-decomposition-based ensembled lightweight learning model(TELL-Me),which employs a synergistic dual-module framework to facilitate accurate and reliable forecasting.The feature module formulates features with physical implications and sheds light on battery aging mechanisms,while the gradient module monitors capacity degradation rates and captures aging trend.TELL-Me achieves high accuracy in end-of-life prediction using minimal historical data from a single battery without requiring offline training dataset,and demonstrates impressive generality and robustness across various operating conditions and battery types.Additionally,by correlating feature contributions with degradation mechanisms across different datasets,TELL-Me is endowed with the diagnostic ability that not only enhances prediction reliability but also provides critical insights into the design and optimization of next-generation batteries.展开更多
Robustness against measurement uncertainties is crucial for gas turbine engine diagnosis.While current research focuses mainly on measurement noise,measurement bias remains challenging.This study proposes a novel perf...Robustness against measurement uncertainties is crucial for gas turbine engine diagnosis.While current research focuses mainly on measurement noise,measurement bias remains challenging.This study proposes a novel performance-based fault detection and identification(FDI)strategy for twin-shaft turbofan gas turbine engines and addresses these uncertainties through a first-order Takagi-Sugeno-Kang fuzzy inference system.To handle ambient condition changes,we use parameter correction to preprocess the raw measurement data,which reduces the FDI’s system complexity.Additionally,the power-level angle is set as a scheduling parameter to reduce the number of rules in the TSK-based FDI system.The data for designing,training,and testing the proposed FDI strategy are generated using a component-level turbofan engine model.The antecedent and consequent parameters of the TSK-based FDI system are optimized using the particle swarm optimization algorithm and ridge regression.A robust structure combining a specialized fuzzy inference system with the TSK-based FDI system is proposed to handle measurement biases.The performance of the first-order TSK-based FDI system and robust FDI structure are evaluated through comprehensive simulation studies.Comparative studies confirm the superior accuracy of the first-order TSK-based FDI system in fault detection,isolation,and identification.The robust structure demonstrates a 2%-8%improvement in the success rate index under relatively large measurement bias conditions,thereby indicating excellent robustness.Accuracy against significant bias values and computation time are also evaluated,suggesting that the proposed robust structure has desirable online performance.This study proposes a novel FDI strategy that effectively addresses measurement uncertainties.展开更多
BACKGROUND Early detection of esophageal squamous neoplasms(ESN)is essential for improving patient prognosis.Optical diagnosis of ESN remains challenging.Probebased confocal laser endomicroscopy(pCLE)enables accurate ...BACKGROUND Early detection of esophageal squamous neoplasms(ESN)is essential for improving patient prognosis.Optical diagnosis of ESN remains challenging.Probebased confocal laser endomicroscopy(pCLE)enables accurate in vivo histological observation and optical biopsy of ESN.However,interpretation of pCLE images requires histopathological expertise and extensive training.Artificial intelligence(AI)has been widely applied in digestive endoscopy;however,AI for pCLE diagnosis of ESN has not been reported.AIM To develop a pCLE computer-aided diagnostic system for ESN and assess its diagnostic performance and assistant efficiency for nonexpert endoscopists.METHODS The intelligent confocal laser endomicroscopy(iCLE)system consists of image recognition(based on inception-ResNet V2),video diagnosis,and quality judgment modules.This system was developed using pCLE images and videos and evaluated through image and prospective video recognition tests.Patients between June 2020 and January 2023 were prospectively enrolled.Expert and nonexpert endoscopists and the iCLE independently performed diagnoses for pCLE videos,with histopathology as the gold standard.Thereafter,the non-expert endoscopists performed a second assessment with iCLE assistance.RESULTS A total of 25056 images from 2803 patients were selected for iCLE training and validation.Another 2442 images from 226 patients were used for testing.iCLE achieved a high accuracy of 98.3%,sensitivity of 95.3%and specificity of 98.8%for diagnosing ESN images.A total of 2581 patients underwent upper gastrointestinal pCLE examination and were prospectively screened;54 patients with suspected ESN were enrolled.Overall,187 videos from 67 lesions were assessed by iCLE,three nonexpert and three expert endoscopists.iCLE achieved a high accuracy,sensitivity and specificity of 90.9%,92.0%,and 90.2%,respectively.Compared to experts,iCLE showed significantly higher sensitivity(92.0%vs 80.4%;P<0.001)and negative predictive value(94.4%vs 87.7%;P=0.003).With iCLE assistance,nonexpert endoscopists showed significant improvements in accuracy(from 83.6%to 88.6%)and sensitivity(from 76.0%to 89.8%).CONCLUSION iCLE system demonstrated high diagnostic performance for ESN.It can assist nonexpert endoscopists in improving the diagnostic efficiency of pCLE for ESN and has the potential for reducing unnecessary biopsies.展开更多
Colon cancer is one of the malignant tumors with high morbidity and mortality worldwide[1],and its early diagnosis is crucial for improving patient survival.However,due to the lack of obvious early symptoms of colon c...Colon cancer is one of the malignant tumors with high morbidity and mortality worldwide[1],and its early diagnosis is crucial for improving patient survival.However,due to the lack of obvious early symptoms of colon cancer,many patients are in the middle to late stage when diagnosed and miss the best time for treatment.Therefore,developing an efficient and accurate diagnostic method for colon cancer is of great clinical significance and scientific value.Currently,the current colon cancer biomarkers carcinoembryonic antigen and carbohydrate antigen 19-9[2]have low sensitivity and specificity,the emerging markers circulating tumor DNA(ctDNA)and miRNA face high cost and standardization challenges,and the existing methods lack spatial resolution,prompting the incorporation of spatial metabolomics technologies to enhance diagnostic capabilities.展开更多
基金supported by the National Natural Science Foundation of China,No.822712782019 Wuhan Huanghe Talents Program+3 种基金2020 Wuhan Medical Research Project,No.20200206010123032021 Hubei Youth Top-notch Talent Training Program2022 Outstanding Youth Project of Natural Science Foundation of Hubei Province,No.2022CFA106Medical Research Program of Huatongguokang,No.2023HT036(all to NX)。
文摘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.
文摘Challenges in the diagnosis and treatment of Parkinson’s disease:Parkinson’s disease(PD)is an increasingly prevalent neurodegenerative disease,at first sight primarily characterized by motor symptoms,although non-motor symptoms also constitute a major part of the overall phenotype.Clinically,this disease cannot be diagnosed reliably until a large part of the vulnerable dopaminergic neurons has been irretrievably lost,and the disease progresses inexorably.New biological criteria for PD have been proposed recently and might eventually improve early diagnosis,but they require further validation,and their use will initially be restricted to a research environment(Darweesh et al.,2024).
文摘Traumatic brain injury (TBI) is defined as damage to the brain resulting from an external sudden physical force or shock to the head.It is considered a silent public health epidemic causing significant death and disability globally.There were 64,000 TBI related deaths reported in the USA in 2020,with about US$76 billion in direct and indirect medical costs annually.
基金supported by the National Natural Science Foundation of China,No.32130060(to XG).
文摘Epilepsy is a severe,relapsing,and multifactorial neurological disorder.Studies regarding the accurate diagnosis,prognosis,and in-depth pathogenesis are crucial for the precise and effective treatment of epilepsy.The pathogenesis of epilepsy is complex and involves alterations in variables such as gene expression,protein expression,ion channel activity,energy metabolites,and gut microbiota composition.Satisfactory results are lacking for conventional treatments for epilepsy.Surgical resection of lesions,drug therapy,and non-drug interventions are mainly used in clinical practice to treat pain associated with epilepsy.Non-pharmacological treatments,such as a ketogenic diet,gene therapy for nerve regeneration,and neural regulation,are currently areas of research focus.This review provides a comprehensive overview of the pathogenesis,diagnostic methods,and treatments of epilepsy.It also elaborates on the theoretical basis,treatment modes,and effects of invasive nerve stimulation in neurotherapy,including percutaneous vagus nerve stimulation,deep brain electrical stimulation,repetitive nerve electrical stimulation,in addition to non-invasive transcranial magnetic stimulation and transcranial direct current stimulation.Numerous studies have shown that electromagnetic stimulation-mediated neuromodulation therapy can markedly improve neurological function and reduce the frequency of epileptic seizures.Additionally,many new technologies for the diagnosis and treatment of epilepsy are being explored.However,current research is mainly focused on analyzing patients’clinical manifestations and exploring relevant diagnostic and treatment methods to study the pathogenesis at a molecular level,which has led to a lack of consensus regarding the mechanisms related to the disease.
基金supported by the National Natural Science Foundation of China(No.52277055).
文摘Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.
基金funded by grants from the National Natural Science Foundation of China(Grant Nos.82171932 and 82302180)the Ministry of Science and Technology of China(Grant No.2024ZD0520002)+3 种基金the Chinese National Key Research and Development Project(Grant Nos.2021YFC2500402 and 2021YFC2500400)the National Health Commission Capacity Building and Continuing Education Center(Grant No.YXFSC2022JJSJ011)the Tianjin Key Medical Discipline(Specialty)Construction Project(Grant No.TJYXZDXK-010A)the Scientific Developing Foundation of Tianjin Education Commission(Grant No.2024KJ182).
文摘Cancer poses a serious threat to human health worldwide and is a leading cause of death1.The analysis of radiological imaging is crucial in early detection,accurate diagnosis,effective treatment planning,and ongoing monitoring of patients with cancer.However,several challenges impede the effectiveness of cancer imaging analysis in clinical practice.One difficulty is that healthcare professionals’immense clinical workloads can result in time constraints and increase pressure,thereby hindering their ability to maintain high accuracy and thoroughness in image analysis.Additionally,subjective variability among radiologists can lead to inconsistent interpretations and diagnoses.Because this variability is often influenced by personal biases,standardized assessments are often difficult to achieve.Moreover,the inherent complexity of cancer imaging necessitates extensive clinical experience;this aspect can also be a limiting factor,particularly if expertise or resources are limited.The application of artificial intelligence(AI)can alleviate these problems by enhancing the accuracy,objectivity,and efficiency of cancer imaging analysis while assisting physicians.Therefore,the advancement of AI research is crucial for achieving progress in radiology.
基金supports by the National Natural Science Foundation of China(Nos.82201135)"2015"Cultivation Program for Reserve Talents for Academic Leaders of Nanjing Stomatological School,Medical School of Nanjing University(No.0223A204).
文摘Early correction of childhood malocclusion is timely managing morphological,structural,and functional abnormalities at different dentomaxillofacial developmental stages.The selection of appropriate imaging examination and comprehensive radiological diagnosis and analysis play an important role in early correction of childhood malocclusion.This expert consensus is a collaborative effort by multidisciplinary experts in dentistry across the nation based on the current clinical evidence,aiming to provide general guidance on appropriate imaging examination selection,comprehensive and accurate imaging assessment for early orthodontic treatment patients.
基金funded by the Construction Project of the"Flagship"Department of Chinese and Western Medicine Coordination(LiuL/2024-221)the 2024 Medical Service and Security Capacity Improvement Project(National Clinical Key Specialty Construction)(LiuL/Huwei Medical/2024-65)+5 种基金the Shanghai Traditional Chinese Medicine Standardization Project(LiuL/No.2023JSP03)the Shanghai Key Discipline Construction Project of Traditional Chinese Medicine(Clinical)(LiuL/2024-No.3)the Shanghai Technical Standardization Management and Promotion Project(LiuL/No.SHDC22023212)the Shanghai Municipal Health Commission Traditional Chinese Medicine Research Project(2022)(LiuL/No.2022Cx004)Clinical research project of Shanghai Health Commission-Youth Project(LW/No.20214Y0056)Shanghai Institute of Traditional Chinese Medicine for Mental Health(LW/No.SZB2023201).
文摘INTRODUCTION.Depressive disorders are mental illnesses that seriously affect public health.There are approximately 320 million patients with depression worldwide,accounting for 4.4% of the total disease burden.1Depression leads to social and occupational impairment,diminished quality of life and an elevated risk of death by suicide.
基金supported by grants from the National Natu-ral Science Foundation of China(82027803,81971623,82202151 and 82171937)National Natural Science Foundation of Zhejiang Province(Y24H180007 and 2024C03092)。
文摘Gallbladder cancer(GBC)is the most common malignant tumor in the biliary system,characterized by high malignancy,aggressiveness,and poor prognosis.Early diagnosis holds paramount importance in ameliorating therapeutic outcomes.Presently,the clinical diagnosis of GBC primarily relies on clinicalradiological-pathological approach.However,there remains a potential for missed diagnosis and misdiagnose in the realm of clinical practice.We firstly analyzed the blood-based biomarkers,such as carcinoembryonic antigen and carbohydrate antigen 19–9.Subsequently,we evaluated the diagnostic performance of various imaging modalities,including ultrasound(US),endoscopic ultrasound(EUS),computed tomography(CT),magnetic resonance imaging(MRI),positron emission tomography/computed tomography(PET/CT)and pathological examination,emphasizing their strengths and limitations in detecting early-stage GBC.Furthermore,we explored the potential of emerging technologies,particularly artificial intelligence(AI)and liquid biopsy,to revolutionize GBC diagnosis.AI algorithms have demonstrated improved image analysis capabilities,while liquid biopsy offers the promise of non-invasive and real-time monitoring.However,the translation of these advancements into clinical practice necessitates further validation and standardization.The review highlighted the advantages and limitations of current diagnostic approaches and underscored the need for innovative strategies to enhance diagnostic accuracy of GBC.In addition,we emphasized the importance of multidisciplinary collaboration to improve early diagnosis of GBC and ultimately patient outcomes.This review endeavoured to impart fresh perspectives and insights into the early diagnosis of GBC.
基金supported by the National Natural Science Foundation of China(32202876)the China Postdoctoral Science Foundation(2023T160198)the Key Scientific and Technological Project of Henan Province Department of China(232102111046)。
文摘In the process of feeding,broilers are susceptible to leg diseases,which are often caused by factors such as genetics,bacteria,viruses,the growth environment,and diet management.Treating leg disorders/diseases in broilers is challenging,and once they suffer from such conditions,it generally leads to reduced production performance and affects the quality of meat.It is worth mentioning that with the advancement of intensive management technologies and the accelerated growth rate of broilers,the leg diseases in broilers has increased,resulting in higher culling rates during production.Leg diseases not only cause significant economic losses to the poultry industry,but also severely jeopardize the animal welfare of broilers.Therefore,effective early diagnosis is crucial to mitigate the adverse effects of chicken leg diseases.This study aims to review various diagnostic methods,including clinical diagnosis,autopsy,radiological diagnosis,infrared thermal imagery,biomarkers and emerging diagnostic techniques,to establish a theoretical foundation for the identification or monitoring of leg diseases in poultry industry.
文摘Gastrointestinal(GI)cancers,which predominantly manifest in the stomach,colorectum,liver,esophagus,and pancreas,accounting for approximately 35%of global cancer-related mortality.The advent of liquid biopsy has introduced a pivotal diagnostic modality for the early identification of premalignant GI lesions and incipient cancers.This non-invasive technique not only facilitates prompt therapeutic intervention,but also serves as a critical adjunct in prognosticating the likelihood of tumor recurrence.The wealth of circulating exosomes present in body fluids is often enriched with proteins,lipids,microRNAs,and other RNAs derived from tumor cells.These specific cargo components are reflective of processes involved in GI tumorigenesis,tumor progression,and response to treatment.As such,they represent a group of promising biomarkers for aiding in the diagnosis of GI cancer.In this review,we delivered an exhaustive overview of the composition of exosomes and the pathways for cargo sorting within these vesicles.We laid out some of the clinical evidence that supported the utilization of exosomes as diagnostic biomarkers for GI cancers and discussed their potential for clinical application.Furthermore,we addressed the challenges encountered when harnessing exosomes as diagnostic and predictive instruments in the realm of GI cancers.
基金Supported by the Medical Talents of Wuhan Health and Family Planning Commission,No.2017[51](to Yu J)the Medical Talents of Wuhan Hospital of Traditional Chinese and Western Medicine(to Yu J)+1 种基金the Hubei Natural Science Foundation,No.2023AFB1091Wuhan Medical Research Project,No.WX23A36(to Yu J).
文摘BACKGROUND Cholangiocarcinoma(CCA),also known as bile duct cancer,is a devastating malignancy primarily affecting the biliary tract.AIM To assess their performance in clinical diagnosis and monitoring of CCA,plasma methylation and circulating tumor cells were detected.METHODS Plasma samples were collected from Hubei Cancer Hospital(n=156).Plasma DNA was tested to detect SHOX2,HOXA9,SEPTIN9,and RASSF1A methylation using TaqMan PCR.Circulating tumor cells(CTCs)were detected in the peripheral blood of patients using the United States Food and Drug Administration-approved cell search system before and after clinical therapy.The CCA diagnostic value was estimated using the area under the curve.The independent prognosis risk factors for patients with CCA were estimated using Cox and logistic regression analyses.RESULTS The sensitivity and specificity of the four DNA plasma methylations exhibited 64.74%sensitivity and 93.88%specificity for detecting CCA.The receiver operating characteristic curve of the combined value for CCA diagnosis in plasma was 0.828±0.032.RASSF1A plasma methylation was related to the prognosis of patients with CCA.We determined the prognostic hazard ratio for CCA using CTC count,tumor stage,methylation,and carbohydrate antigen 19-9 levels as key factors.Our overall survival nomogram achieved a C-index of 0.705(0.605-0.805).CONCLUSION SHOX2,HOXA9,SEPTIN9,and RASSF1A plasma methylation demonstrated increased sensitivity for diagnosing CCA.RASSF1A plasma methylation and CTCs were valuable predictors to assess CCA prognosis and recurrence.
基金supported by Public Health Talent Training and Surport Plan(National Administration of Disease Prevention and Control)Research and application of new technology for rapid monitoring and tracing of emergent infectious diseases among entry-exit population(2024YFFK0056)Monitoring,Early warning and Response of Major Infectious Diseases(2022ZDZX0017).
文摘Objective This study reports the first imported case of Lassa fever(LF)in China.Laboratory detection and molecular epidemiological analysis of the Lassa virus(LASV)from this case offer valuable insights for the prevention and control of LF.Methods Samples of cerebrospinal fluid(CSF),blood,urine,saliva,and environmental materials were collected from the patient and their close contacts for LASV nucleotide detection.Whole-genome sequencing was performed on positive samples to analyze the genetic characteristics of the virus.Results LASV was detected in the patient’s CSF,blood,and urine,while all samples from close contacts and the environment tested negative.The virus belongs to the lineage IV strain and shares the highest homology with strains from Sierra Leone.The variability in the glycoprotein complex(GPC)among different strains ranged from 3.9%to 15.1%,higher than previously reported for the seven known lineages.Amino acid mutation analysis revealed multiple mutations within the GPC immunogenic epitopes,increasing strain diversity and potentially impacting immune response.Conclusion The case was confirmed through nucleotide detection,with no evidence of secondary transmission or viral spread.The LASV strain identified belongs to lineage IV,with broader GPC variability than previously reported.Mutations in the immune-related sites of GPC may affect immune responses,necessitating heightened vigilance regarding the virus.
基金supported by the Natural Science Foundation of Zhejiang Province(Grant number:LGF22H040003).
文摘A 28-year-old pregnant woman with no prior obstetric complications had a normal prenatal workup before 24 weeks'gestation.At 24 weeks,ultrasound revealed gastrointestinal malformations,a persistent left superior vena cava,and polyhydramnios.
基金Supported by the Zhejiang Province Medicine and Health Science and Technology Program,No.2023KY980Hangzhou Municipal Health and Family Planning Commission,No.A20220133.
文摘BACKGROUND Phelan-McDermid syndrome(PMS)is a rare genetic disorder characterized by intellectual disability,delayed language development,autism spectrum disorders,motor tone abnormalities,and a high risk of psychiatric symptoms,including bipolar disorder.CASE SUMMARY This report presented an 18-year clinical history of a 36-year-old woman with PMS,marked by intellectual disabilities,social withdrawal,and stereotyped behaviors.Diagnosed with bipolar disorder at the age of 18 years old,she encountered significant treatment challenges,including severe adverse reactions to antipsychotic medications in 2022,which led to speech and functional regression.Through rehabilitation and comprehensive therapy,her condition gradually improved.In 2024,after further treatment,her symptoms stabilized,highlighting the complexities and successes of long-term management.CONCLUSION Effective management of PMS requires a thorough clinical history,genetic testing,and long-term supportive care.
基金Supported by Beijing Hospitals Authority Youth Programme,No.QML20200505.
文摘BACKGROUND Esophageal squamous cell carcinoma is a major histological subtype of esophageal cancer.Many molecular genetic changes are associated with its occurrence.Raman spectroscopy has become a new method for the early diagnosis of tumors because it can reflect the structures of substances and their changes at the molecular level.AIM To detect alterations in Raman spectral information across different stages of esophageal neoplasia.METHODS Different grades of esophageal lesions were collected,and a total of 360 groups of Raman spectrum data were collected.A 1D-transformer network model was proposed to handle the task of classifying the spectral data of esophageal squamous cell carcinoma.In addition,a deep learning model was applied to visualize the Raman spectral data and interpret their molecular characteristics.RESULTS A comparison among Raman spectral data with different pathological grades and a visual analysis revealed that the Raman peaks with significant differences were concentrated mainly at 1095 cm^(-1)(DNA,symmetric PO,and stretching vibration),1132 cm^(-1)(cytochrome c),1171 cm^(-1)(acetoacetate),1216 cm^(-1)(amide III),and 1315 cm^(-1)(glycerol).A comparison among the training results of different models revealed that the 1Dtransformer network performed best.A 93.30%accuracy value,a 96.65%specificity value,a 93.30%sensitivity value,and a 93.17%F1 score were achieved.CONCLUSION Raman spectroscopy revealed significantly different waveforms for the different stages of esophageal neoplasia.The combination of Raman spectroscopy and deep learning methods could significantly improve the accuracy of classification.
基金supported by the National Natural Science Foundation of China(22379021 and 22479021)。
文摘As batteries become increasingly essential for energy storage technologies,battery prognosis,and diagnosis remain central to ensure reliable operation and effective management,as well as to aid the in-depth investigation of degradation mechanisms.However,dynamic operating conditions,cell-to-cell inconsistencies,and limited availability of labeled data have posed significant challenges to accurate and robust prognosis and diagnosis.Herein,we introduce a time-series-decomposition-based ensembled lightweight learning model(TELL-Me),which employs a synergistic dual-module framework to facilitate accurate and reliable forecasting.The feature module formulates features with physical implications and sheds light on battery aging mechanisms,while the gradient module monitors capacity degradation rates and captures aging trend.TELL-Me achieves high accuracy in end-of-life prediction using minimal historical data from a single battery without requiring offline training dataset,and demonstrates impressive generality and robustness across various operating conditions and battery types.Additionally,by correlating feature contributions with degradation mechanisms across different datasets,TELL-Me is endowed with the diagnostic ability that not only enhances prediction reliability but also provides critical insights into the design and optimization of next-generation batteries.
文摘Robustness against measurement uncertainties is crucial for gas turbine engine diagnosis.While current research focuses mainly on measurement noise,measurement bias remains challenging.This study proposes a novel performance-based fault detection and identification(FDI)strategy for twin-shaft turbofan gas turbine engines and addresses these uncertainties through a first-order Takagi-Sugeno-Kang fuzzy inference system.To handle ambient condition changes,we use parameter correction to preprocess the raw measurement data,which reduces the FDI’s system complexity.Additionally,the power-level angle is set as a scheduling parameter to reduce the number of rules in the TSK-based FDI system.The data for designing,training,and testing the proposed FDI strategy are generated using a component-level turbofan engine model.The antecedent and consequent parameters of the TSK-based FDI system are optimized using the particle swarm optimization algorithm and ridge regression.A robust structure combining a specialized fuzzy inference system with the TSK-based FDI system is proposed to handle measurement biases.The performance of the first-order TSK-based FDI system and robust FDI structure are evaluated through comprehensive simulation studies.Comparative studies confirm the superior accuracy of the first-order TSK-based FDI system in fault detection,isolation,and identification.The robust structure demonstrates a 2%-8%improvement in the success rate index under relatively large measurement bias conditions,thereby indicating excellent robustness.Accuracy against significant bias values and computation time are also evaluated,suggesting that the proposed robust structure has desirable online performance.This study proposes a novel FDI strategy that effectively addresses measurement uncertainties.
基金Supported by the National Key Research and Development Program of China,No.2023YFC2413800the Taishan Scholars Program of Shandong Province,No.tsqn202306344the National Natural Science Foundation of China,No.82270580 and No.82070552.
文摘BACKGROUND Early detection of esophageal squamous neoplasms(ESN)is essential for improving patient prognosis.Optical diagnosis of ESN remains challenging.Probebased confocal laser endomicroscopy(pCLE)enables accurate in vivo histological observation and optical biopsy of ESN.However,interpretation of pCLE images requires histopathological expertise and extensive training.Artificial intelligence(AI)has been widely applied in digestive endoscopy;however,AI for pCLE diagnosis of ESN has not been reported.AIM To develop a pCLE computer-aided diagnostic system for ESN and assess its diagnostic performance and assistant efficiency for nonexpert endoscopists.METHODS The intelligent confocal laser endomicroscopy(iCLE)system consists of image recognition(based on inception-ResNet V2),video diagnosis,and quality judgment modules.This system was developed using pCLE images and videos and evaluated through image and prospective video recognition tests.Patients between June 2020 and January 2023 were prospectively enrolled.Expert and nonexpert endoscopists and the iCLE independently performed diagnoses for pCLE videos,with histopathology as the gold standard.Thereafter,the non-expert endoscopists performed a second assessment with iCLE assistance.RESULTS A total of 25056 images from 2803 patients were selected for iCLE training and validation.Another 2442 images from 226 patients were used for testing.iCLE achieved a high accuracy of 98.3%,sensitivity of 95.3%and specificity of 98.8%for diagnosing ESN images.A total of 2581 patients underwent upper gastrointestinal pCLE examination and were prospectively screened;54 patients with suspected ESN were enrolled.Overall,187 videos from 67 lesions were assessed by iCLE,three nonexpert and three expert endoscopists.iCLE achieved a high accuracy,sensitivity and specificity of 90.9%,92.0%,and 90.2%,respectively.Compared to experts,iCLE showed significantly higher sensitivity(92.0%vs 80.4%;P<0.001)and negative predictive value(94.4%vs 87.7%;P=0.003).With iCLE assistance,nonexpert endoscopists showed significant improvements in accuracy(from 83.6%to 88.6%)and sensitivity(from 76.0%to 89.8%).CONCLUSION iCLE system demonstrated high diagnostic performance for ESN.It can assist nonexpert endoscopists in improving the diagnostic efficiency of pCLE for ESN and has the potential for reducing unnecessary biopsies.
文摘Colon cancer is one of the malignant tumors with high morbidity and mortality worldwide[1],and its early diagnosis is crucial for improving patient survival.However,due to the lack of obvious early symptoms of colon cancer,many patients are in the middle to late stage when diagnosed and miss the best time for treatment.Therefore,developing an efficient and accurate diagnostic method for colon cancer is of great clinical significance and scientific value.Currently,the current colon cancer biomarkers carcinoembryonic antigen and carbohydrate antigen 19-9[2]have low sensitivity and specificity,the emerging markers circulating tumor DNA(ctDNA)and miRNA face high cost and standardization challenges,and the existing methods lack spatial resolution,prompting the incorporation of spatial metabolomics technologies to enhance diagnostic capabilities.
