Objective:The increasing global prevalence of mental health disorders highlights the urgent need for the development of innovative diagnostic methods.Conditions such as anxiety,depression,stress,bipolar disorder(BD),a...Objective:The increasing global prevalence of mental health disorders highlights the urgent need for the development of innovative diagnostic methods.Conditions such as anxiety,depression,stress,bipolar disorder(BD),and autism spectrum disorder(ASD)frequently arise from the complex interplay of demographic,biological,and socioeconomic factors,resulting in aggravated symptoms.This review investigates machine intelligence approaches for the early detection and prediction of mental health conditions.Methods:The preferred reporting items for systematic reviews and meta-analyses(PRISMA)framework was employed to conduct a systematic review and analysis covering the period 2018 to 2025.The potential impact of machine intelligence methods was assessed by considering various strategies,hybridization of algorithms,tools,techniques,and datasets,and their applicability.Results:Through a systematic review of studies concentrating on the prediction and evaluation of mental disorders using machine intelligence algorithms,advancements,limitations,and gaps in current methodologies were highlighted.The datasets and tools utilized in these investigations were examined,offering a detailed overview of the status of computational models in understanding and diagnosing mental health disorders.Recent research indicated considerable improvements in diagnostic accuracy and treatment effectiveness,particularly for depression and anxiety,which have shown the greatest methodological diversity and notable advancements in machine intelligence.Conclusions:Despite these improvements,challenges persist,including the need for more diverse datasets,ethical issues surrounding data privacy and algorithmic bias,and obstacles to integrating these technologies into clinical settings.This synthesis emphasizes the transformative potential of machine intelligence in enhancing mental healthcare.展开更多
Male breast cancer(MBC)is rare,representing 0.5%–1%of all breast cancers,but its incidence is increasing due to improved diagnostics and awareness.MBC typically presents in older men,is human epidermal growth factor ...Male breast cancer(MBC)is rare,representing 0.5%–1%of all breast cancers,but its incidence is increasing due to improved diagnostics and awareness.MBC typically presents in older men,is human epidermal growth factor receptor 2(HER2)-negative and estrogen receptor(ER)-positive,and lacks routine screening,leading to delayed diagnosis and advanced disease.Major risk factors include hormonal imbalance,radiation exposure,obesity,alcohol use,and Breast Cancer Gene 1 and 2(BRCA1/2)mutations.Clinically,it may resemble gynecomastia but usually appears as a unilateral,painless mass or nipple discharge.Advances in imaging and liquid biopsy have enhanced early detection.Molecular mechanisms involve hormonal signaling,HER2/epidermal growth factor receptor(EGFR)pathways,tumor suppressor gene alterations,and epigenetic changes.While standard treatments mirror those for female breast cancer,emerging options such as cyclin-dependent kinase 4 and 6(CDK4/6),and poly(ADP-ribose)polymerase(PARP)inhibitors,immunotherapy,and precision medicine are reshaping management.Incorporating artificial intelligence,molecular profiling,and male-specific clinical trials is essential to improve outcomes and bridge current diagnostic and therapeutic gaps.展开更多
This narrative review examines recent advances in salivary biomarkers for oral squamous cell carcinoma(OSCC),a major subtype of oral cancer with persistently low five-year survival rates due to delayed diagnosis.Saliv...This narrative review examines recent advances in salivary biomarkers for oral squamous cell carcinoma(OSCC),a major subtype of oral cancer with persistently low five-year survival rates due to delayed diagnosis.Saliva has emerged as a noninvasive diagnostic medium capable of reflecting both local tumor activity and systemic physiological changes.Various salivary biomarkers,including microRNAs,cytokines,proteins,metabolites,and exosomes,have been linked to oncogenic signaling pathways involved in tumor progression,immune modulation,and therapeutic resistance.Advances in quantitative polymerase chain reaction,mass spectrometry,and next-generation sequencing have enabled comprehensive biomarker profiling,while point-of-care detection systems and saliva-based omics platforms are accelerating clinical translation.Remaining challenges include variability in salivary composition,lack of standardized collection protocols,and insufficient validation across large patient cohorts.This review highlights the mechanistic relevance,diagnostic potential,and translational challenges of salivary biomarkers in OSCC.展开更多
OBJECTIVE:To reach consensus on the diagnostic criteria of syndrome of dampness obstruction in idiopathic membranous nephropathy(IMN)patients by literature research and expert investigation(interviews and a Delphi met...OBJECTIVE:To reach consensus on the diagnostic criteria of syndrome of dampness obstruction in idiopathic membranous nephropathy(IMN)patients by literature research and expert investigation(interviews and a Delphi method).METHODS:Our study was consistent with T/CACM 1336-2020.We searched the monographs and references published in the past 40 years(1983-2022),and established the diagnostic criteria pool of waterdampness syndrome and dampness-turbidity syndrome in Traditional Chinese Medicine(TCM)based on literature by using frequency statistics and correlation analysis.Expert investigation(interview method and two rounds of Delphi method)was used to form the diagnostic criteria of water-dampness syndrome and dampnessturbidity syndrome of idiopathic membranous nephropathy.Clinical diagnostic test research was carried out,and compared with“Diagnostic Criteria for dampness syndrome”(T/CACM 1454-2023)to evaluate the authenticity,reliability and clinical application value of the standard.RESULTS:A total of 122 relevant guides,standards,monographs and documents were included through searching books and Chinese databases.Four experts were interviewed and two rounds of delphi method(75 experts nationwide)were carried out.The experts'opinions are relatively concentrated and the differences are small.Based on the weight of each index,the diagnostic criteria indexes of water-dampness syndrome and dampness-turbidity syndrome were selected.After discussion by the core group members,the diagnostic model of"necessary symptoms and optional symptoms"was established,and the final diagnostic criteria of waterdampness syndrome and dampness-turbidity syndrome were established.One hundred and ninety-one inpatients and outpatients of Guangdong Provincial Hospital of Chinese Medicine from January 2021 to February 2023 were included in Diagnostic test study.There was no statistical difference in gender,age and course of disease(P>0.05).The sensitivity and specificity of the trial standard were 90.34%and 73.33%respectively,while the sensitivity and specificity of T/CACM 1454-2023 were 99.43%and 6.67%,respectively.CONCLUSIONS:The consensus-based diagnostic criteria for IMN can be widely incorporated in TCM.A further clinical study will be conducted to analyze the diagnosis value and cut-off score of our IMN criteria.展开更多
Pancreatic cancer is recognized as one of the leading causes of cancer mortality,representing the second most common source of cancer-related deaths within the gastrointestinal domain.Surgical resection is currently t...Pancreatic cancer is recognized as one of the leading causes of cancer mortality,representing the second most common source of cancer-related deaths within the gastrointestinal domain.Surgical resection is currently the only definitive treatment;however,the subtle emergence of symptoms often leads to a diagnosis at an advanced stage,with merely 10%-15%of patients being eligible for surgical intervention.The primary obstacle to achieving a potential radical resection is the presence of distant metastatic disease or invasion of adjacent major vascular structures.This review aims to highlight the critical role of endoscopic ultrasound in the diagnosis and staging of pancreatic tumors.We systematically searched PubMed,MEDLINE and Web of Science by using‘pancreatic cancer’and‘endoscopic ultrasonography’as keywords.Relevant studies were reviewed and analyzed.Endoscopic ultrasonography(EUS)is efficient in the diagnosis and staging of pancreatic cancer,past studies reported the accuracy of EUS is 63%to 94%for T-staging and 44%to 82%for N-staging but there are still limitations that need to be comprehensively applied with other diagnostic methods to evaluation of distant metastasis for surgical resectability.Our review aims to reveal the value for the staging of pancreatic cancer.展开更多
Ultrasound has long been an essential tool in nephrology,traditionally used for procedures like vascular access and kidney biopsies.Point-of-care ultrasonography(POCUS),a rapidly evolving bedside technology,is now gai...Ultrasound has long been an essential tool in nephrology,traditionally used for procedures like vascular access and kidney biopsies.Point-of-care ultrasonography(POCUS),a rapidly evolving bedside technology,is now gaining momentum in nephrology by providing real-time imaging to enhance physical examination findings.Unlike comprehensive radiology-performed ultrasound,POCUS focuses on specific clinical questions,providing immediate and actionable insights.This narrative review examines the philosophy behind POCUS,its expanding applications in nephrology,and its impact on patient care,including its role in diagnosing obstructive uropathy,guiding fluid management,and evaluating hemodynamics in cardiorenal syndrome.Additionally,the review addresses barriers to widespread adoption,such as the need for structured training,competency validation,and interdisciplinary cooperation.By integrating POCUS into routine practice,nephrologists can refine diagnostic accuracy,improve patient outcomes,and strengthen the role of bedside medicine.展开更多
Precise transverse emittance assessment in electron beams is crucial for advancing high-brightness beam injectors.As opposed to intricate methodologies that use specialized devices,quadrupole focusing strength scannin...Precise transverse emittance assessment in electron beams is crucial for advancing high-brightness beam injectors.As opposed to intricate methodologies that use specialized devices,quadrupole focusing strength scanning(Q-scanning)techniques offer notable advantages for various injectors owing to their inherent convenience and cost-effectiveness.However,their stringent approximation conditions lead to inevitable errors in practical operation,thereby limiting their widespread application.This study addressed these challenges by revisiting the analytical derivation procedure and investigating the effects of the underlying approximation conditions.Preliminary corrections were explored through a combination of data processing analysis and numerical simulations.Furthermore,based on theoretical derivations,virtual measurements using beam dynamics calculations were employed to evaluate the correction reliability.Subsequent experimental validations were performed at the Huazhong University of Science and Technology injector to verify the effectiveness of the proposed compensation method.Both the virtual and experimental results confirm the feasibility and reliability of the enhanced Q-scanning-based diagnosis for transverse emittance in typical beam injectors operating under common conditions.Through the integration of these corrections and compensations,enhanced Q-scanning-based techniques emerge as promising alternatives to traditional emittance diagnosis methods.展开更多
Intestinal ischemia-reperfusion injury(IIRI)is a complex and severe pathophysiological process characterized by oxidative stress,inflammation,and apoptosis.In recent years,the critical roles of extracellular matrix(EC...Intestinal ischemia-reperfusion injury(IIRI)is a complex and severe pathophysiological process characterized by oxidative stress,inflammation,and apoptosis.In recent years,the critical roles of extracellular matrix(ECM)genes and microRNAs(miRNAs)in IIRI have garnered widespread attention.This review aims to systematically summarize the diagnostic and therapeutic potential of ECM gene sets and miRNA regulatory networks in IIRI.First,we review the molecular mechanisms of IIRI,focusing on the dual role of the ECM in tissue injury and repair processes.The expression changes and functions of ECM components such as collagen,elastin,and matrix metalloproteinases during IIRI progression are deeply analyzed.Second,we systematically summarize the regulatory roles of miRNAs in IIRI,particularly the mechanisms and functions of miRNAs such as miR-125b and miR-200a in regulating inflammation,apoptosis,and ECM remodeling.Additionally,this review discusses potential diagnostic biomarkers and treatment strategies based on ECM genes and miRNAs.We extensively evaluate the prospects of miRNA-targeted therapy and ECM component modulation in preventing and treating IIRI,emphasizing the clinical translational potential of these emerging therapies.In conclusion,the diagnostic and therapeutic potential of ECM gene sets and miRNA regulatory networks in IIRI provides new directions for further research,necessitating additional clinical and basic studies to validate and expand these findings for improving clinical outcomes in IIRI patients.展开更多
Artificial intelligence(AI)and machine learning(ML)are transforming spine care by addressing diagnostics,treatment planning,and rehabilitation challenges.This study highlights advancements in precision medicine for sp...Artificial intelligence(AI)and machine learning(ML)are transforming spine care by addressing diagnostics,treatment planning,and rehabilitation challenges.This study highlights advancements in precision medicine for spinal pathologies,leveraging AI and ML to enhance diagnostic accuracy through deep learning algorithms,enabling faster and more accurate detection of abnormalities.AIpowered robotics and surgical navigation systems improve implant placement precision and reduce complications in complex spine surgeries.Wearable devices and virtual platforms,designed with AI,offer personalized,adaptive therapies that improve treatment adherence and recovery outcomes.AI also enables preventive interventions by assessing spine condition risks early.Despite progress,challenges remain,including limited healthcare datasets,algorithmic biases,ethical concerns,and integration into existing systems.Interdisciplinary collaboration and explainable AI frameworks are essential to unlock AI’s full potential in spine care.Future developments include multimodal AI systems integrating imaging,clinical,and genetic data for holistic treatment approaches.AI and ML promise significant improvements in diagnostic accuracy,treatment personalization,service accessibility,and cost efficiency,paving the way for more streamlined and effective spine care,ultimately enhancing patient outcomes.展开更多
Bearing is an indispensable key component in mechanical equipment,and its working state is directly related to the stability and safety of the whole equipment.In recent years,with the rapid development of artificial i...Bearing is an indispensable key component in mechanical equipment,and its working state is directly related to the stability and safety of the whole equipment.In recent years,with the rapid development of artificial intelligence technology,especially the breakthrough of deep learning technology,it provides a new idea for bearing fault diagnosis.Deep learning can automatically learn features from a large amount of data,has a strong nonlinear modeling ability,and can effectively solve the problems existing in traditional methods.Aiming at the key problems in bearing fault diagnosis,this paper studies the fault diagnosis method based on deep learning,which not only provides a new solution for bearing fault diagnosis but also provides a reference for the application of deep learning in other mechanical fault diagnosis fields.展开更多
Point-of-care testing(POCT)refers to a category of diagnostic tests that are performed at or near to the site of the patients(also called bedside testing)and is capable of obtaining accurate results in a short time by...Point-of-care testing(POCT)refers to a category of diagnostic tests that are performed at or near to the site of the patients(also called bedside testing)and is capable of obtaining accurate results in a short time by using portable diagnostic devices,avoiding sending samples to the medical laboratories.It has been extensively explored for diagnosing and monitoring patients’diseases and health conditions with the assistance of development in biochemistry and microfluidics.Microfluidic paper-based analytical devices(μPADs)have gained dramatic popularity in POCT because of their simplicity,user-friendly,fast and accurate result reading and low cost.SeveralμPADs have been successfully commercialized and received excellent feedback during the past several decades.This review briefly discusses the main types ofμPADs,preparation methods and their detection principles,followed by a few representative examples.The future perspectives of the development inμPADs are also provided.展开更多
Multimodal deep learning has emerged as a key paradigm in contemporary medical diagnostics,advancing precision medicine by enabling integration and learning from diverse data sources.The exponential growth of high-dim...Multimodal deep learning has emerged as a key paradigm in contemporary medical diagnostics,advancing precision medicine by enabling integration and learning from diverse data sources.The exponential growth of high-dimensional healthcare data,encompassing genomic,transcriptomic,and other omics profiles,as well as radiological imaging and histopathological slides,makes this approach increasingly important because,when examined separately,these data sources only offer a fragmented picture of intricate disease processes.Multimodal deep learning leverages the complementary properties of multiple data modalities to enable more accurate prognostic modeling,more robust disease characterization,and improved treatment decision-making.This review provides a comprehensive overview of the current state of multimodal deep learning approaches in medical diagnosis.We classify and examine important application domains,such as(1)radiology,where automated report generation and lesion detection are facilitated by image-text integration;(2)histopathology,where fusion models improve tumor classification and grading;and(3)multi-omics,where molecular subtypes and latent biomarkers are revealed through cross-modal learning.We provide an overview of representative research,methodological advancements,and clinical consequences for each domain.Additionally,we critically analyzed the fundamental issues preventing wider adoption,including computational complexity(particularly in training scalable,multi-branch networks),data heterogeneity(resulting from modality-specific noise,resolution variations,and inconsistent annotations),and the challenge of maintaining significant cross-modal correlations during fusion.These problems impede interpretability,which is crucial for clinical trust and use,in addition to performance and generalizability.Lastly,we outline important areas for future research,including the development of standardized protocols for harmonizing data,the creation of lightweight and interpretable fusion architectures,the integration of real-time clinical decision support systems,and the promotion of cooperation for federated multimodal learning.Our goal is to provide researchers and clinicians with a concise overview of the field’s present state,enduring constraints,and exciting directions for further research through this review.展开更多
Histopathological analysis of chronic wounds is crucial for clinicians to accurately assess wound healing progress and detect potential malignancy.However,traditional pathological tissue sections require specific stai...Histopathological analysis of chronic wounds is crucial for clinicians to accurately assess wound healing progress and detect potential malignancy.However,traditional pathological tissue sections require specific staining procedures involving carcinogenic chemicals.This study proposes an interdisciplinary approach merging materials science,medicine,and artificial intelligence(AI)to develop a virtual staining technique and intelligent evaluation model based on deep learning for chronic wound tissue pathology.This innovation aims to enhance clinical diagnosis and treatment by offering personalized AI-driven therapeutic strategies.By establishing a mouse model of chronic wounds and using a series of hydrogel wound dressings,tissue pathology sections were periodically collected for manual staining and healing assessment.We focused on leveraging the pix2pix image translation framework within deep learning networks.Through CNN models implemented in Python using PyTorch,our study involves learning and feature extraction for region segmentation of pathological slides.Comparative analysis between virtual staining and manual staining results,along with healing diagnosis conclusions,aims to optimize AI models.Ultimately,this approach integrates new metrics such as image recognition,quantitative analysis,and digital diagnostics to formulate an intelligent wound assessment model,facilitating smart monitoring and personalized treatment of wounds.In blind evaluation by pathologists,minimal disparities were found between virtual and conventional histologically stained images of murine wound tissue.The evaluation used pathologists’average scores on real stained images as a benchmark.The scores for virtual stained images were 71.1%for cellular features,75.4%for tissue structures,and 77.8%for overall assessment.Metrics such as PSNR(20.265)and SSIM(0.634)demonstrated our algorithms’superior performance over existing networks.Eight pathological features such as epidermis,hair follicles,and granulation tissue can be accurately identified,and the images were found to be more faithful to the actual tissue feature distribution when compared to manually annotated data.展开更多
Helicobacter pylori(H.pylori)infection induces pathological changes via chronic inflammation and virulence factors,thereby increasing the risk of gastric cancer development.Compared with invasive examination methods,H...Helicobacter pylori(H.pylori)infection induces pathological changes via chronic inflammation and virulence factors,thereby increasing the risk of gastric cancer development.Compared with invasive examination methods,H.pylori-related serum indicators are cost-effective and valuable for the early detection of gastric cancer(GC);however,large-scale clinical validation and sufficient understanding of the specific molecular mechanisms involved are lacking.Therefore,a comprehensive review and analysis of recent advances in this field is necessary.In this review,we systematically analyze the relationship between H.pylori and GC and discuss the application of new molecular biomarkers in GC screening.We also summarize the screening potential and application of anti-H.pylori immunoglobulin G and virulence factor-related serum antibodies for identifying GC risk.These indicators provide early warning of infection and enhance screening accuracy.Additionally,we discuss the potential combination of multiple screening indicators for the comprehensive analysis and development of emerging testing methods to improve the accuracy and efficiency of GC screening.Although this review may lack sufficient evidence due to limitations in existing studies,including small sample sizes,regional variations,and inconsistent testing methods,it contributes to advancing personalized precision medicine in high-risk populations and developing GC screening strategies.展开更多
Osteoarthritis(OA)is a degenerative joint disease with significant clinical and societal impact.Traditional diagnostic methods,including subjective clinical assessments and imaging techniques such as X-rays and MRIs,a...Osteoarthritis(OA)is a degenerative joint disease with significant clinical and societal impact.Traditional diagnostic methods,including subjective clinical assessments and imaging techniques such as X-rays and MRIs,are often limited in their ability to detect early-stage OA or capture subtle joint changes.These limitations result in delayed diagnoses and inconsistent outcomes.Additionally,the analysis of omics data is challenged by the complexity and high dimensionality of biological datasets,making it difficult to identify key molecular mechanisms and biomarkers.Recent advancements in artificial intelligence(AI)offer transformative potential to address these challenges.This review systematically explores the integration of AI into OA research,focusing on applications such as AI-driven early screening and risk prediction from electronic health records(EHR),automated grading and morphological analysis of imaging data,and biomarker discovery through multi-omics integration.By consolidating progress across clinical,imaging,and omics domains,this review provides a comprehensive perspective on how AI is reshaping OA research.The findings have the potential to drive innovations in personalized medicine and targeted interventions,addressing longstanding challenges in OA diagnosis and management.展开更多
The real-time monitoring of fracture propagation during hydraulic fracturing is crucial for obtaining a deeper understanding of fracture morphology and optimizing hydraulic fracture designs.Accurate measurements of ke...The real-time monitoring of fracture propagation during hydraulic fracturing is crucial for obtaining a deeper understanding of fracture morphology and optimizing hydraulic fracture designs.Accurate measurements of key fracture parameters,such as the fracture height and width,are particularly important to ensure efficient oilfield development and precise fracture diagnosis.This study utilized the optical frequency domain reflectometer(OFDR)technique in physical simulation experiments to monitor fractures during indoor true triaxial hydraulic fracturing experiments.The results indicate that the distributed fiber optic strain monitoring technology can efficiently capture the initiation and expansion of fractures.In horizontal well monitoring,the fiber strain waterfall plot can be used to interpret the fracture width,initiation location,and expansion speed.The fiber response can be divided into three stages:strain contraction convergence,strain band formation,and postshutdown strain rate reversal.When the fracture does not contact the fiber,a dual peak strain phenomenon occurs in the fiber and gradually converges as the fracture approaches.During vertical well monitoring in adjacent wells,within the effective monitoring range of the fiber,the axial strain produced by the fiber can represent the fracture height with an accuracy of 95.6%relative to the actual fracture height.This study provides a new perspective on real-time fracture monitoring.The response patterns of fiber-induced strain due to fractures can help us better understand and assess the dynamic fracture behavior,offering significant value for the optimization of oilfield development and fracture diagnostic techniques.展开更多
The principal breast cancer treatment approach has long been surgical removal of the primary breast lesions and regional lymph nodes,particularly the axillary lymph nodes.However,the advent of minimally invasive diagn...The principal breast cancer treatment approach has long been surgical removal of the primary breast lesions and regional lymph nodes,particularly the axillary lymph nodes.However,the advent of minimally invasive diagnostic techniques,such as sentinel lymph node biopsy(SLNB),has markedly diminished the extent of surgery required for regional lymph nodes.展开更多
This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain technology.By combining the strengths of blockchain an...This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain technology.By combining the strengths of blockchain and generative AI,the research team aimed to address the timely challenge of safeguarding visual medical content.The participating researchers conducted a comprehensive analysis,examining the vulnerabilities of medical AI services,personal information protection issues,and overall security weaknesses.This multi faceted exploration led to an indepth evaluation of the model’s performance and security.Notably,the correlation between accuracy,detection rate,and error rate was scrutinized.This analysis revealed insights into the model’s strengths and limitations,while the consideration of standard deviation shed light on the model’s stability and performance variability.The study proposed practical improvements,emphasizing the reduction of false negatives to enhance detection rate and leveraging blockchain technology to ensure visual data integrity in medical applications.Applying blockchain to generative AI-created medical content addresses key personal information protection issues.By utilizing the distributed ledger system of blockchain,the research team aimed to protect the privacy and integrity of medical data especially medical images.This approach not only enhances security but also enables transparent and tamperproof record-keeping.Additionally,the use of generative AI models ensures the creation of novel medical content without compromising personal information,further safeguarding patient privacy.In conclusion,this study showcases the potential of blockchain-based solutions in the medical field,particularly in securing sensitive medical data and protecting patient privacy.The proposed approach,combining blockchain and generative AI,offers a promising direction toward more robust and secure medical content management.Further research and advancements in this area will undoubtedly contribute to the development of robust and privacy-preserving healthcare systems,and visual diagnostic systems.展开更多
In a recent case report in the World Journal of Clinical Cases,emphasized the crucial role of rapidly and accurately identifying pathogens to optimize patient treatment outcomes.Laboratory-on-a-chip(LOC)technology has...In a recent case report in the World Journal of Clinical Cases,emphasized the crucial role of rapidly and accurately identifying pathogens to optimize patient treatment outcomes.Laboratory-on-a-chip(LOC)technology has emerged as a transformative tool in health care,offering rapid,sensitive,and specific identification of microorganisms.This editorial provides a comprehensive overview of LOC technology,highlighting its principles,advantages,applications,challenges,and future directions.Success studies from the field have demonstrated the practical benefits of LOC devices in clinical diagnostics,epidemiology,and food safety.Comparative studies have underscored the superiority of LOC technology over traditional methods,showcasing improvements in speed,accuracy,and portability.The future integration of LOC with biosensors,artificial intelligence,and data analytics promises further innovation and expansion.This call to action emphasizes the importance of continued research,investment,and adoption to realize the full potential of LOC technology in improving healthcare outcomes worldwide.展开更多
BACKGROUND Colorectal cancer(CRC)is a prevalent malignant neoplasm characterized by subtle early manifestations.AIM To investigate the correlation among serum lipid profiles,the triglyceride-glucose(TyG)index,and the ...BACKGROUND Colorectal cancer(CRC)is a prevalent malignant neoplasm characterized by subtle early manifestations.AIM To investigate the correlation among serum lipid profiles,the triglyceride-glucose(TyG)index,and the atherosclerotic index(AI)in patients with CRC.Furthermore,it explored the clinical diagnostic utility of combining serum lipids with cancer antigens in the context of CRC.METHODS A retrospective analysis encompassed 277 patients with CRC and 1034 healthy individuals.RESULTS Following propensity score matching,patients with CRC exhibited significantly reduced levels of serum triglyceride(TG),total cholesterol(TC),high-density lipoprotein cholesterol,and low-density lipoprotein cholesterol(LDL-C),as well as a diminished TyG index.Conversely,they displayed elevated AI levels compared to their healthy counterparts.Patients in advanced stages exhibited lower serum levels of TG,TC,and LDL-C compared to those in early stages.Patients with positive lymph node metastasis demonstrated reduced levels of TG,LDL-C,and the TyG index.Receiver operating characteristic analysis revealed that the combination of the TyG index,carcinoembryonic antigen,and carbohydrate antigen 19-9 yielded the highest positive prediction rate for CRC at 75.3%.CONCLUSION Preoperative serum lipid profiles exhibit a robust association with patients with CRC.The concurrent assessment of multiple serum lipids and cancer antigens effectively enhances the diagnostic accuracy for CRC.展开更多
文摘Objective:The increasing global prevalence of mental health disorders highlights the urgent need for the development of innovative diagnostic methods.Conditions such as anxiety,depression,stress,bipolar disorder(BD),and autism spectrum disorder(ASD)frequently arise from the complex interplay of demographic,biological,and socioeconomic factors,resulting in aggravated symptoms.This review investigates machine intelligence approaches for the early detection and prediction of mental health conditions.Methods:The preferred reporting items for systematic reviews and meta-analyses(PRISMA)framework was employed to conduct a systematic review and analysis covering the period 2018 to 2025.The potential impact of machine intelligence methods was assessed by considering various strategies,hybridization of algorithms,tools,techniques,and datasets,and their applicability.Results:Through a systematic review of studies concentrating on the prediction and evaluation of mental disorders using machine intelligence algorithms,advancements,limitations,and gaps in current methodologies were highlighted.The datasets and tools utilized in these investigations were examined,offering a detailed overview of the status of computational models in understanding and diagnosing mental health disorders.Recent research indicated considerable improvements in diagnostic accuracy and treatment effectiveness,particularly for depression and anxiety,which have shown the greatest methodological diversity and notable advancements in machine intelligence.Conclusions:Despite these improvements,challenges persist,including the need for more diverse datasets,ethical issues surrounding data privacy and algorithmic bias,and obstacles to integrating these technologies into clinical settings.This synthesis emphasizes the transformative potential of machine intelligence in enhancing mental healthcare.
文摘Male breast cancer(MBC)is rare,representing 0.5%–1%of all breast cancers,but its incidence is increasing due to improved diagnostics and awareness.MBC typically presents in older men,is human epidermal growth factor receptor 2(HER2)-negative and estrogen receptor(ER)-positive,and lacks routine screening,leading to delayed diagnosis and advanced disease.Major risk factors include hormonal imbalance,radiation exposure,obesity,alcohol use,and Breast Cancer Gene 1 and 2(BRCA1/2)mutations.Clinically,it may resemble gynecomastia but usually appears as a unilateral,painless mass or nipple discharge.Advances in imaging and liquid biopsy have enhanced early detection.Molecular mechanisms involve hormonal signaling,HER2/epidermal growth factor receptor(EGFR)pathways,tumor suppressor gene alterations,and epigenetic changes.While standard treatments mirror those for female breast cancer,emerging options such as cyclin-dependent kinase 4 and 6(CDK4/6),and poly(ADP-ribose)polymerase(PARP)inhibitors,immunotherapy,and precision medicine are reshaping management.Incorporating artificial intelligence,molecular profiling,and male-specific clinical trials is essential to improve outcomes and bridge current diagnostic and therapeutic gaps.
基金supported by the College of Oral Medicine,Taipei Medical University,Taipei,Taiwan(Grant No.TMUCOM202502)supported by Taipei Medical University Hospital,Taipei,Taiwan(Grant No.114TMUH-NE-05).
文摘This narrative review examines recent advances in salivary biomarkers for oral squamous cell carcinoma(OSCC),a major subtype of oral cancer with persistently low five-year survival rates due to delayed diagnosis.Saliva has emerged as a noninvasive diagnostic medium capable of reflecting both local tumor activity and systemic physiological changes.Various salivary biomarkers,including microRNAs,cytokines,proteins,metabolites,and exosomes,have been linked to oncogenic signaling pathways involved in tumor progression,immune modulation,and therapeutic resistance.Advances in quantitative polymerase chain reaction,mass spectrometry,and next-generation sequencing have enabled comprehensive biomarker profiling,while point-of-care detection systems and saliva-based omics platforms are accelerating clinical translation.Remaining challenges include variability in salivary composition,lack of standardized collection protocols,and insufficient validation across large patient cohorts.This review highlights the mechanistic relevance,diagnostic potential,and translational challenges of salivary biomarkers in OSCC.
基金the Special Project of State Key Laboratory of Dampness Syndrome of Chinese Medicine:Study on Criteria for Diagnosis of Dampness Syndrome of Idiopathic Membranous Nephropathy,Cohort Study on Pathogenesis and Material Basis of Dampness Syndrome of Idiopathic Membranous Nephropathy,Randomized Controlled Clinical Study of Sanqi Qushi Granule in Treatment of Membranous Nephropathy(No.SZ2021ZZ02,SZ2021ZZ09 and SZ2021ZZ36)the 2020 Guangdong Provincial Science and Technology Innovation Strategy Special Fund:Guangdong-Hong Kong-Macao Joint Lab(No.2020B1212030006)+2 种基金the Natural Science Foundation of Guangdong Province:Study on the Mechanism of Sanqi Qushi Prescription Delaying Podocellular Senescence in Membranous Nephropathy based on Cyclic Guanosine Monophosphate-Adenosine Monophosphate Synthase-Stimulator of Interferon Genes-Nuclear Factor Kappa-B Signaling Pathway(No.2022A1515011628)the Guangzhou Science and Technology Plan Project:to Explore the Mechanism of Treating Membranous Nephropathy from the Perspective of Regulating Amino Acid Metabolism Disorder(No.2023A03J0746)Special Funding for Scientific and Technological Research on Traditional Chinese Medicine,Guangdong Provincial Hospital of Chinese Medicine:a Multimodular Machine Learning Prediction Model based on Pathological Image-transcriptomics and Traditional Chinese Medicine Syndromes was Used to Investigate the Prognostic Correlation of Long non-coding RNA Molecules in Nephropathy and the Intervention Mechanism of Sanqi Qushi Formula,to Investigate the Pathogenesis and Microbiological Mechanism of Dampness Syndrome of Membranous Nephropathy based on the Microecological Changes of Tongue Coating(No.YN2023MB02,YN2023MB10)。
文摘OBJECTIVE:To reach consensus on the diagnostic criteria of syndrome of dampness obstruction in idiopathic membranous nephropathy(IMN)patients by literature research and expert investigation(interviews and a Delphi method).METHODS:Our study was consistent with T/CACM 1336-2020.We searched the monographs and references published in the past 40 years(1983-2022),and established the diagnostic criteria pool of waterdampness syndrome and dampness-turbidity syndrome in Traditional Chinese Medicine(TCM)based on literature by using frequency statistics and correlation analysis.Expert investigation(interview method and two rounds of Delphi method)was used to form the diagnostic criteria of water-dampness syndrome and dampnessturbidity syndrome of idiopathic membranous nephropathy.Clinical diagnostic test research was carried out,and compared with“Diagnostic Criteria for dampness syndrome”(T/CACM 1454-2023)to evaluate the authenticity,reliability and clinical application value of the standard.RESULTS:A total of 122 relevant guides,standards,monographs and documents were included through searching books and Chinese databases.Four experts were interviewed and two rounds of delphi method(75 experts nationwide)were carried out.The experts'opinions are relatively concentrated and the differences are small.Based on the weight of each index,the diagnostic criteria indexes of water-dampness syndrome and dampness-turbidity syndrome were selected.After discussion by the core group members,the diagnostic model of"necessary symptoms and optional symptoms"was established,and the final diagnostic criteria of waterdampness syndrome and dampness-turbidity syndrome were established.One hundred and ninety-one inpatients and outpatients of Guangdong Provincial Hospital of Chinese Medicine from January 2021 to February 2023 were included in Diagnostic test study.There was no statistical difference in gender,age and course of disease(P>0.05).The sensitivity and specificity of the trial standard were 90.34%and 73.33%respectively,while the sensitivity and specificity of T/CACM 1454-2023 were 99.43%and 6.67%,respectively.CONCLUSIONS:The consensus-based diagnostic criteria for IMN can be widely incorporated in TCM.A further clinical study will be conducted to analyze the diagnosis value and cut-off score of our IMN criteria.
文摘Pancreatic cancer is recognized as one of the leading causes of cancer mortality,representing the second most common source of cancer-related deaths within the gastrointestinal domain.Surgical resection is currently the only definitive treatment;however,the subtle emergence of symptoms often leads to a diagnosis at an advanced stage,with merely 10%-15%of patients being eligible for surgical intervention.The primary obstacle to achieving a potential radical resection is the presence of distant metastatic disease or invasion of adjacent major vascular structures.This review aims to highlight the critical role of endoscopic ultrasound in the diagnosis and staging of pancreatic tumors.We systematically searched PubMed,MEDLINE and Web of Science by using‘pancreatic cancer’and‘endoscopic ultrasonography’as keywords.Relevant studies were reviewed and analyzed.Endoscopic ultrasonography(EUS)is efficient in the diagnosis and staging of pancreatic cancer,past studies reported the accuracy of EUS is 63%to 94%for T-staging and 44%to 82%for N-staging but there are still limitations that need to be comprehensively applied with other diagnostic methods to evaluation of distant metastasis for surgical resectability.Our review aims to reveal the value for the staging of pancreatic cancer.
文摘Ultrasound has long been an essential tool in nephrology,traditionally used for procedures like vascular access and kidney biopsies.Point-of-care ultrasonography(POCUS),a rapidly evolving bedside technology,is now gaining momentum in nephrology by providing real-time imaging to enhance physical examination findings.Unlike comprehensive radiology-performed ultrasound,POCUS focuses on specific clinical questions,providing immediate and actionable insights.This narrative review examines the philosophy behind POCUS,its expanding applications in nephrology,and its impact on patient care,including its role in diagnosing obstructive uropathy,guiding fluid management,and evaluating hemodynamics in cardiorenal syndrome.Additionally,the review addresses barriers to widespread adoption,such as the need for structured training,competency validation,and interdisciplinary cooperation.By integrating POCUS into routine practice,nephrologists can refine diagnostic accuracy,improve patient outcomes,and strengthen the role of bedside medicine.
基金supported by the National Natural Science Foundation of China(Nos.12341501 and 11905074)。
文摘Precise transverse emittance assessment in electron beams is crucial for advancing high-brightness beam injectors.As opposed to intricate methodologies that use specialized devices,quadrupole focusing strength scanning(Q-scanning)techniques offer notable advantages for various injectors owing to their inherent convenience and cost-effectiveness.However,their stringent approximation conditions lead to inevitable errors in practical operation,thereby limiting their widespread application.This study addressed these challenges by revisiting the analytical derivation procedure and investigating the effects of the underlying approximation conditions.Preliminary corrections were explored through a combination of data processing analysis and numerical simulations.Furthermore,based on theoretical derivations,virtual measurements using beam dynamics calculations were employed to evaluate the correction reliability.Subsequent experimental validations were performed at the Huazhong University of Science and Technology injector to verify the effectiveness of the proposed compensation method.Both the virtual and experimental results confirm the feasibility and reliability of the enhanced Q-scanning-based diagnosis for transverse emittance in typical beam injectors operating under common conditions.Through the integration of these corrections and compensations,enhanced Q-scanning-based techniques emerge as promising alternatives to traditional emittance diagnosis methods.
基金Supported by Health Science and Technology Programme of Zhejiang Province,No.2022KY1391.
文摘Intestinal ischemia-reperfusion injury(IIRI)is a complex and severe pathophysiological process characterized by oxidative stress,inflammation,and apoptosis.In recent years,the critical roles of extracellular matrix(ECM)genes and microRNAs(miRNAs)in IIRI have garnered widespread attention.This review aims to systematically summarize the diagnostic and therapeutic potential of ECM gene sets and miRNA regulatory networks in IIRI.First,we review the molecular mechanisms of IIRI,focusing on the dual role of the ECM in tissue injury and repair processes.The expression changes and functions of ECM components such as collagen,elastin,and matrix metalloproteinases during IIRI progression are deeply analyzed.Second,we systematically summarize the regulatory roles of miRNAs in IIRI,particularly the mechanisms and functions of miRNAs such as miR-125b and miR-200a in regulating inflammation,apoptosis,and ECM remodeling.Additionally,this review discusses potential diagnostic biomarkers and treatment strategies based on ECM genes and miRNAs.We extensively evaluate the prospects of miRNA-targeted therapy and ECM component modulation in preventing and treating IIRI,emphasizing the clinical translational potential of these emerging therapies.In conclusion,the diagnostic and therapeutic potential of ECM gene sets and miRNA regulatory networks in IIRI provides new directions for further research,necessitating additional clinical and basic studies to validate and expand these findings for improving clinical outcomes in IIRI patients.
文摘Artificial intelligence(AI)and machine learning(ML)are transforming spine care by addressing diagnostics,treatment planning,and rehabilitation challenges.This study highlights advancements in precision medicine for spinal pathologies,leveraging AI and ML to enhance diagnostic accuracy through deep learning algorithms,enabling faster and more accurate detection of abnormalities.AIpowered robotics and surgical navigation systems improve implant placement precision and reduce complications in complex spine surgeries.Wearable devices and virtual platforms,designed with AI,offer personalized,adaptive therapies that improve treatment adherence and recovery outcomes.AI also enables preventive interventions by assessing spine condition risks early.Despite progress,challenges remain,including limited healthcare datasets,algorithmic biases,ethical concerns,and integration into existing systems.Interdisciplinary collaboration and explainable AI frameworks are essential to unlock AI’s full potential in spine care.Future developments include multimodal AI systems integrating imaging,clinical,and genetic data for holistic treatment approaches.AI and ML promise significant improvements in diagnostic accuracy,treatment personalization,service accessibility,and cost efficiency,paving the way for more streamlined and effective spine care,ultimately enhancing patient outcomes.
文摘Bearing is an indispensable key component in mechanical equipment,and its working state is directly related to the stability and safety of the whole equipment.In recent years,with the rapid development of artificial intelligence technology,especially the breakthrough of deep learning technology,it provides a new idea for bearing fault diagnosis.Deep learning can automatically learn features from a large amount of data,has a strong nonlinear modeling ability,and can effectively solve the problems existing in traditional methods.Aiming at the key problems in bearing fault diagnosis,this paper studies the fault diagnosis method based on deep learning,which not only provides a new solution for bearing fault diagnosis but also provides a reference for the application of deep learning in other mechanical fault diagnosis fields.
文摘Point-of-care testing(POCT)refers to a category of diagnostic tests that are performed at or near to the site of the patients(also called bedside testing)and is capable of obtaining accurate results in a short time by using portable diagnostic devices,avoiding sending samples to the medical laboratories.It has been extensively explored for diagnosing and monitoring patients’diseases and health conditions with the assistance of development in biochemistry and microfluidics.Microfluidic paper-based analytical devices(μPADs)have gained dramatic popularity in POCT because of their simplicity,user-friendly,fast and accurate result reading and low cost.SeveralμPADs have been successfully commercialized and received excellent feedback during the past several decades.This review briefly discusses the main types ofμPADs,preparation methods and their detection principles,followed by a few representative examples.The future perspectives of the development inμPADs are also provided.
文摘Multimodal deep learning has emerged as a key paradigm in contemporary medical diagnostics,advancing precision medicine by enabling integration and learning from diverse data sources.The exponential growth of high-dimensional healthcare data,encompassing genomic,transcriptomic,and other omics profiles,as well as radiological imaging and histopathological slides,makes this approach increasingly important because,when examined separately,these data sources only offer a fragmented picture of intricate disease processes.Multimodal deep learning leverages the complementary properties of multiple data modalities to enable more accurate prognostic modeling,more robust disease characterization,and improved treatment decision-making.This review provides a comprehensive overview of the current state of multimodal deep learning approaches in medical diagnosis.We classify and examine important application domains,such as(1)radiology,where automated report generation and lesion detection are facilitated by image-text integration;(2)histopathology,where fusion models improve tumor classification and grading;and(3)multi-omics,where molecular subtypes and latent biomarkers are revealed through cross-modal learning.We provide an overview of representative research,methodological advancements,and clinical consequences for each domain.Additionally,we critically analyzed the fundamental issues preventing wider adoption,including computational complexity(particularly in training scalable,multi-branch networks),data heterogeneity(resulting from modality-specific noise,resolution variations,and inconsistent annotations),and the challenge of maintaining significant cross-modal correlations during fusion.These problems impede interpretability,which is crucial for clinical trust and use,in addition to performance and generalizability.Lastly,we outline important areas for future research,including the development of standardized protocols for harmonizing data,the creation of lightweight and interpretable fusion architectures,the integration of real-time clinical decision support systems,and the promotion of cooperation for federated multimodal learning.Our goal is to provide researchers and clinicians with a concise overview of the field’s present state,enduring constraints,and exciting directions for further research through this review.
基金supported by the Fundamental Research Funds for the Central Universities(No.20720230037)the National Natural Science Foundation of China(No.52273305)+2 种基金Natural Science Foundation of Fujian Province of China(No.2023J05012)State Key Laboratory of Vaccines for Infectious Diseases,Xiang An Biomedicine Laboratory(Nos.2023XAKJ0103071,2023XAKJ0102061)Natural Science Foundation of Xiamen,China(No.3502Z20227010).
文摘Histopathological analysis of chronic wounds is crucial for clinicians to accurately assess wound healing progress and detect potential malignancy.However,traditional pathological tissue sections require specific staining procedures involving carcinogenic chemicals.This study proposes an interdisciplinary approach merging materials science,medicine,and artificial intelligence(AI)to develop a virtual staining technique and intelligent evaluation model based on deep learning for chronic wound tissue pathology.This innovation aims to enhance clinical diagnosis and treatment by offering personalized AI-driven therapeutic strategies.By establishing a mouse model of chronic wounds and using a series of hydrogel wound dressings,tissue pathology sections were periodically collected for manual staining and healing assessment.We focused on leveraging the pix2pix image translation framework within deep learning networks.Through CNN models implemented in Python using PyTorch,our study involves learning and feature extraction for region segmentation of pathological slides.Comparative analysis between virtual staining and manual staining results,along with healing diagnosis conclusions,aims to optimize AI models.Ultimately,this approach integrates new metrics such as image recognition,quantitative analysis,and digital diagnostics to formulate an intelligent wound assessment model,facilitating smart monitoring and personalized treatment of wounds.In blind evaluation by pathologists,minimal disparities were found between virtual and conventional histologically stained images of murine wound tissue.The evaluation used pathologists’average scores on real stained images as a benchmark.The scores for virtual stained images were 71.1%for cellular features,75.4%for tissue structures,and 77.8%for overall assessment.Metrics such as PSNR(20.265)and SSIM(0.634)demonstrated our algorithms’superior performance over existing networks.Eight pathological features such as epidermis,hair follicles,and granulation tissue can be accurately identified,and the images were found to be more faithful to the actual tissue feature distribution when compared to manually annotated data.
文摘Helicobacter pylori(H.pylori)infection induces pathological changes via chronic inflammation and virulence factors,thereby increasing the risk of gastric cancer development.Compared with invasive examination methods,H.pylori-related serum indicators are cost-effective and valuable for the early detection of gastric cancer(GC);however,large-scale clinical validation and sufficient understanding of the specific molecular mechanisms involved are lacking.Therefore,a comprehensive review and analysis of recent advances in this field is necessary.In this review,we systematically analyze the relationship between H.pylori and GC and discuss the application of new molecular biomarkers in GC screening.We also summarize the screening potential and application of anti-H.pylori immunoglobulin G and virulence factor-related serum antibodies for identifying GC risk.These indicators provide early warning of infection and enhance screening accuracy.Additionally,we discuss the potential combination of multiple screening indicators for the comprehensive analysis and development of emerging testing methods to improve the accuracy and efficiency of GC screening.Although this review may lack sufficient evidence due to limitations in existing studies,including small sample sizes,regional variations,and inconsistent testing methods,it contributes to advancing personalized precision medicine in high-risk populations and developing GC screening strategies.
基金supported by the National Natural Science Foundation of China(82302757)Shenzhen Science and Technology Program(JCY20240813145204006,SGDX20201103095600002,JCYJ20220818103417037,KJZD20230923115200002)+1 种基金Shenzhen Key Laboratory of Digital Surgical Printing Project(ZDSYS201707311542415)Shenzhen Development and Reform Program(XMHT20220106001).
文摘Osteoarthritis(OA)is a degenerative joint disease with significant clinical and societal impact.Traditional diagnostic methods,including subjective clinical assessments and imaging techniques such as X-rays and MRIs,are often limited in their ability to detect early-stage OA or capture subtle joint changes.These limitations result in delayed diagnoses and inconsistent outcomes.Additionally,the analysis of omics data is challenged by the complexity and high dimensionality of biological datasets,making it difficult to identify key molecular mechanisms and biomarkers.Recent advancements in artificial intelligence(AI)offer transformative potential to address these challenges.This review systematically explores the integration of AI into OA research,focusing on applications such as AI-driven early screening and risk prediction from electronic health records(EHR),automated grading and morphological analysis of imaging data,and biomarker discovery through multi-omics integration.By consolidating progress across clinical,imaging,and omics domains,this review provides a comprehensive perspective on how AI is reshaping OA research.The findings have the potential to drive innovations in personalized medicine and targeted interventions,addressing longstanding challenges in OA diagnosis and management.
基金supported by the National Natural Science Foundation of China(Grant No.52104060)the Natural Science Foundation of Shandong Province,China(Grant No.ZR2021QE015).
文摘The real-time monitoring of fracture propagation during hydraulic fracturing is crucial for obtaining a deeper understanding of fracture morphology and optimizing hydraulic fracture designs.Accurate measurements of key fracture parameters,such as the fracture height and width,are particularly important to ensure efficient oilfield development and precise fracture diagnosis.This study utilized the optical frequency domain reflectometer(OFDR)technique in physical simulation experiments to monitor fractures during indoor true triaxial hydraulic fracturing experiments.The results indicate that the distributed fiber optic strain monitoring technology can efficiently capture the initiation and expansion of fractures.In horizontal well monitoring,the fiber strain waterfall plot can be used to interpret the fracture width,initiation location,and expansion speed.The fiber response can be divided into three stages:strain contraction convergence,strain band formation,and postshutdown strain rate reversal.When the fracture does not contact the fiber,a dual peak strain phenomenon occurs in the fiber and gradually converges as the fracture approaches.During vertical well monitoring in adjacent wells,within the effective monitoring range of the fiber,the axial strain produced by the fiber can represent the fracture height with an accuracy of 95.6%relative to the actual fracture height.This study provides a new perspective on real-time fracture monitoring.The response patterns of fiber-induced strain due to fractures can help us better understand and assess the dynamic fracture behavior,offering significant value for the optimization of oilfield development and fracture diagnostic techniques.
基金supported by grants from the National Natural Science Foundation of China(Grant Nos.81672638 and W2421095)National Natural Science Foundation of Shandong Province(Grant No.ZR2024LMB011)Collaborative Academic Innovation Project of Shandong Cancer Hospital(Grant No.GF003)。
文摘The principal breast cancer treatment approach has long been surgical removal of the primary breast lesions and regional lymph nodes,particularly the axillary lymph nodes.However,the advent of minimally invasive diagnostic techniques,such as sentinel lymph node biopsy(SLNB),has markedly diminished the extent of surgery required for regional lymph nodes.
文摘This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain technology.By combining the strengths of blockchain and generative AI,the research team aimed to address the timely challenge of safeguarding visual medical content.The participating researchers conducted a comprehensive analysis,examining the vulnerabilities of medical AI services,personal information protection issues,and overall security weaknesses.This multi faceted exploration led to an indepth evaluation of the model’s performance and security.Notably,the correlation between accuracy,detection rate,and error rate was scrutinized.This analysis revealed insights into the model’s strengths and limitations,while the consideration of standard deviation shed light on the model’s stability and performance variability.The study proposed practical improvements,emphasizing the reduction of false negatives to enhance detection rate and leveraging blockchain technology to ensure visual data integrity in medical applications.Applying blockchain to generative AI-created medical content addresses key personal information protection issues.By utilizing the distributed ledger system of blockchain,the research team aimed to protect the privacy and integrity of medical data especially medical images.This approach not only enhances security but also enables transparent and tamperproof record-keeping.Additionally,the use of generative AI models ensures the creation of novel medical content without compromising personal information,further safeguarding patient privacy.In conclusion,this study showcases the potential of blockchain-based solutions in the medical field,particularly in securing sensitive medical data and protecting patient privacy.The proposed approach,combining blockchain and generative AI,offers a promising direction toward more robust and secure medical content management.Further research and advancements in this area will undoubtedly contribute to the development of robust and privacy-preserving healthcare systems,and visual diagnostic systems.
文摘In a recent case report in the World Journal of Clinical Cases,emphasized the crucial role of rapidly and accurately identifying pathogens to optimize patient treatment outcomes.Laboratory-on-a-chip(LOC)technology has emerged as a transformative tool in health care,offering rapid,sensitive,and specific identification of microorganisms.This editorial provides a comprehensive overview of LOC technology,highlighting its principles,advantages,applications,challenges,and future directions.Success studies from the field have demonstrated the practical benefits of LOC devices in clinical diagnostics,epidemiology,and food safety.Comparative studies have underscored the superiority of LOC technology over traditional methods,showcasing improvements in speed,accuracy,and portability.The future integration of LOC with biosensors,artificial intelligence,and data analytics promises further innovation and expansion.This call to action emphasizes the importance of continued research,investment,and adoption to realize the full potential of LOC technology in improving healthcare outcomes worldwide.
基金Supported by Pudong New Area Science and Technology Development Fund for Livelihood Research Special Project,No.PKJ2023-Y38.
文摘BACKGROUND Colorectal cancer(CRC)is a prevalent malignant neoplasm characterized by subtle early manifestations.AIM To investigate the correlation among serum lipid profiles,the triglyceride-glucose(TyG)index,and the atherosclerotic index(AI)in patients with CRC.Furthermore,it explored the clinical diagnostic utility of combining serum lipids with cancer antigens in the context of CRC.METHODS A retrospective analysis encompassed 277 patients with CRC and 1034 healthy individuals.RESULTS Following propensity score matching,patients with CRC exhibited significantly reduced levels of serum triglyceride(TG),total cholesterol(TC),high-density lipoprotein cholesterol,and low-density lipoprotein cholesterol(LDL-C),as well as a diminished TyG index.Conversely,they displayed elevated AI levels compared to their healthy counterparts.Patients in advanced stages exhibited lower serum levels of TG,TC,and LDL-C compared to those in early stages.Patients with positive lymph node metastasis demonstrated reduced levels of TG,LDL-C,and the TyG index.Receiver operating characteristic analysis revealed that the combination of the TyG index,carcinoembryonic antigen,and carbohydrate antigen 19-9 yielded the highest positive prediction rate for CRC at 75.3%.CONCLUSION Preoperative serum lipid profiles exhibit a robust association with patients with CRC.The concurrent assessment of multiple serum lipids and cancer antigens effectively enhances the diagnostic accuracy for CRC.
