Postoperative cognitive dysfunction is a seve re complication of the central nervous system that occurs after anesthesia and surgery,and has received attention for its high incidence and effect on the quality of life ...Postoperative cognitive dysfunction is a seve re complication of the central nervous system that occurs after anesthesia and surgery,and has received attention for its high incidence and effect on the quality of life of patients.To date,there are no viable treatment options for postoperative cognitive dysfunction.The identification of postoperative cognitive dysfunction hub genes could provide new research directions and therapeutic targets for future research.To identify the signaling mechanisms contributing to postoperative cognitive dysfunction,we first conducted Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the Gene Expression Omnibus GSE95426 dataset,which consists of mRNAs and long non-coding RNAs differentially expressed in mouse hippocampus3 days after tibial fracture.The dataset was enriched in genes associated with the biological process"regulation of immune cells,"of which Chill was identified as a hub gene.Therefore,we investigated the contribution of chitinase-3-like protein 1 protein expression changes to postoperative cognitive dysfunction in the mouse model of tibial fractu re surgery.Mice were intraperitoneally injected with vehicle or recombinant chitinase-3-like protein 124 hours post-surgery,and the injection groups were compared with untreated control mice for learning and memory capacities using the Y-maze and fear conditioning tests.In addition,protein expression levels of proinflammatory factors(interleukin-1βand inducible nitric oxide synthase),M2-type macrophage markers(CD206 and arginase-1),and cognition-related proteins(brain-derived neurotropic factor and phosphorylated NMDA receptor subunit NR2B)were measured in hippocampus by western blotting.Treatment with recombinant chitinase-3-like protein 1 prevented surgery-induced cognitive impairment,downregulated interleukin-1βand nducible nitric oxide synthase expression,and upregulated CD206,arginase-1,pNR2B,and brain-derived neurotropic factor expression compared with vehicle treatment.Intraperitoneal administration of the specific ERK inhibitor PD98059 diminished the effects of recombinant chitinase-3-like protein 1.Collectively,our findings suggest that recombinant chitinase-3-like protein 1 ameliorates surgery-induced cognitive decline by attenuating neuroinflammation via M2 microglial polarization in the hippocampus.Therefore,recombinant chitinase-3-like protein1 may have therapeutic potential fo r postoperative cognitive dysfunction.展开更多
Humans achieve cognitive development through continuous interaction with their environment,enhancing both perception and behavior.However,current robots lack the capacity for human-like action and evolution,posing a b...Humans achieve cognitive development through continuous interaction with their environment,enhancing both perception and behavior.However,current robots lack the capacity for human-like action and evolution,posing a bottleneck to improving robotic intelligence.Existing research predominantly models robots as one-way,static mappings from observations to actions,neglecting the dynamic processes of perception and behavior.This paper introduces a novel approach to robot cognitive learning by considering physical properties.We propose a theoretical framework wherein a robot is conceptualized as a three-body physical system comprising a perception-body(P-body),a cognition-body(C-body),and a behavior-body(B-body).Each body engages in physical dynamics and operates within a closed-loop interaction.Significantly,three crucial interactions connect these bodies.The C-body relies on the Pbody's extracted states and reciprocally offers long-term rewards,optimizing the P-body's perception policy.In addition,the C-body directs the B-body's actions through sub-goals,and subsequent P-body-derived states facilitate the C-body's cognition dynamics learning.At last,the B-body would follow the sub-goal generated by the C-body and perform actions conditioned on the perceptive state from the P-body,which leads to the next interactive step.These interactions foster the joint evolution of each body,culminating in optimal design.To validate our approach,we employ a navigation task using a four-legged robot,D'Kitty,equipped with a movable global camera.Navigational prowess demands intricate coordination of sensing,planning,and D'Kitty's motion.Leveraging our framework yields superior task performance compared with conventional methodologies.In conclusion,this paper establishes a paradigm shift in robot cognitive learning by integrating physical interactions across the P-body,C-body,and B-body,while considering physical properties.Our framework's successful application to a navigation task underscores its efficacy in enhancing robotic intelligence.展开更多
FedCognis is a secure and scalable federated learning framework designed for continuous anomaly detection in Industrial Internet of Things-enabled Cognitive Cities(IIoTCC).It introduces two key innovations:a Quantum S...FedCognis is a secure and scalable federated learning framework designed for continuous anomaly detection in Industrial Internet of Things-enabled Cognitive Cities(IIoTCC).It introduces two key innovations:a Quantum Secure Authentication(QSA)mechanism for adversarial defense and integrity validation,and a Self-Attention Long Short-Term Memory(SALSTM)model for high-accuracy spatiotemporal anomaly detection.Addressing core challenges in traditional Federated Learning(FL)—such as model poisoning,communication overhead,and concept drift—FedCognis integrates dynamic trust-based aggregation and lightweight cryptographic verification to ensure secure,real-time operation across heterogeneous IIoT domains including utilities,public safety,and traffic systems.Evaluated on the WUSTL-IIoTCC-2021 dataset,FedCognis achieves 94.5%accuracy,0.941 AUC for precision-recall,and 0.896 ROC-AUC,while reducing bandwidth consumption by 72%.The framework demonstrates sublinear computational complexity and a resilience score of 96.56%across six security dimensions.These results confirm FedCognis as a robust and adaptive anomaly detection solution suitable for deployment in large-scale cognitive urban infrastructures.展开更多
The rise of 6G networks and the exponential growth of smart city infrastructures demand intelligent,real-time traffic forecasting solutions that preserve data privacy.This paper introduces NeuroCivitas,a federated dee...The rise of 6G networks and the exponential growth of smart city infrastructures demand intelligent,real-time traffic forecasting solutions that preserve data privacy.This paper introduces NeuroCivitas,a federated deep learning framework that integrates Convolutional Neural Networks(CNNs)for spatial pattern recognition and Long Short-Term Memory(LSTM)networks for temporal sequence modeling.Designed to meet the adaptive intelligence requirements of cognitive cities,NeuroCivitas leverages Federated Averaging(FedAvg)to ensure privacypreserving training while significantly reducing communication overhead—by 98.7%compared to centralized models.The model is evaluated using the Kaggle Traffic Prediction Dataset comprising 48,120 hourly records from four urban junctions.It achieves an RMSE of 2.76,MAE of 2.11,and an R^(2) score of 0.91,outperforming baseline models such as ARIMA,Linear Regression,Random Forest,and non-federated CNN-LSTM in both accuracy and scalability.Junctionwise and time-based performance analyses further validate its robustness,particularly during off-peak hours,while highlighting challenges in peak traffic forecasting.Ablation studies confirm the importance of both CNN and LSTM layers and temporal feature engineering in achieving optimal performance.Moreover,NeuroCivitas demonstrates stable convergence within 25 communication rounds and shows strong adaptability to non-IID data distributions.The framework is built with real-world deployment in mind,offering support for edge environments through lightweight architecture and the potential for enhancement with differential privacy and adversarial defense mechanisms.SHAPbased explainability is integrated to improve interpretability for stakeholders.In sum,NeuroCivitas delivers an accurate,scalable,and privacy-preserving traffic forecasting solution,tailored for 6G cognitive cities.Future extensions will incorporate fairness-aware optimization,real-time anomaly adaptation,multi-city validation,and advanced federated GNN comparisons to further enhance deployment readiness and societal impact.展开更多
College English teaching is a crucial component of higher education.Enhancing college students’English learning outcomes has long been a primary focus for educators.With the continuous evolution of educational philos...College English teaching is a crucial component of higher education.Enhancing college students’English learning outcomes has long been a primary focus for educators.With the continuous evolution of educational philosophies,traditional college English teaching methods no longer meet the learning needs of contemporary students.Situational cognitive learning theory emphasizes learner-centered approaches and highlights the contextual and practical application of knowledge,offering innovative perspectives for reforming college English teaching.When applied effectively,situational cognitive learning theory can optimize teaching methods and significantly improve learning outcomes.This paper explores the connotation and characteristics of situational cognitive learning theory,evaluates its applicability in college English teaching,and discusses its practical implementation in this context.The aim is to provide theoretical and practical references for improving the quality of college English education.展开更多
Deep Learning(DL)offers promising solutions for analyzing wearable signals and gaining valuable insights into cognitive disorders.While previous review studies have explored various aspects of DL in cognitive healthca...Deep Learning(DL)offers promising solutions for analyzing wearable signals and gaining valuable insights into cognitive disorders.While previous review studies have explored various aspects of DL in cognitive healthcare,there remains a lack of comprehensive analysis that integrates wearable signals,data processing techniques,and the broader applications,benefits,and challenges of DL methods.Addressing this limitation,our study provides an extensive review of DL’s role in cognitive healthcare,with a particular emphasis on wearables,data processing,and the inherent challenges in this field.This review also highlights the considerable promise of DL approaches in addressing a broad spectrum of cognitive issues.By enhancing the understanding and analysis of wearable signal modalities,DL models can achieve remarkable accuracy in cognitive healthcare.Convolutional Neural Network(CNN),Recurrent Neural Network(RNN),and Long Short-term Memory(LSTM)networks have demonstrated improved performance and effectiveness in the early diagnosis and progression monitoring of neurological disorders.Beyond cognitive impairment detection,DL has been applied to emotion recognition,sleep analysis,stress monitoring,and neurofeedback.These applications lead to advanced diagnosis,personalized treatment,early intervention,assistive technologies,remote monitoring,and reduced healthcare costs.Nevertheless,the integration of DL and wearable technologies presents several challenges,such as data quality,privacy,interpretability,model generalizability,ethical concerns,and clinical adoption.These challenges emphasize the importance of conducting future research in areas such as multimodal signal analysis and explainable AI.The findings of this review aim to benefit clinicians,healthcare professionals,and society by facilitating better patient outcomes in cognitive healthcare.展开更多
Objective:As an age-related neurodegenerative disease,the prevalence of mild cognitive impairment(MCI)increases with age.Within the framework of traditional Chinese medicine,spleen-kidney deficiency syndrome(SKDS)is r...Objective:As an age-related neurodegenerative disease,the prevalence of mild cognitive impairment(MCI)increases with age.Within the framework of traditional Chinese medicine,spleen-kidney deficiency syndrome(SKDS)is recognized as the most frequent MCI subtype.Due to the covert and gradual onset of MCI,in community settings it poses a significant challenge for patients and their families to discern between typical aging and pathological changes.There exists an urgent need to devise a preliminary diagnostic tool designed for community-residing older adults with MCI attributed to SKDS(MCI-SKDS).Methods:This investigation enrolled 312 elderly individuals diagnosed with MCI,who were randomly distributed into training and test datasets at a 3:1 ratio.Five machine learning methods,including logistic regression(LR),decision tree(DT),naive Bayes(NB),support vector machine(SVM),and gradient boosting(GB),were used to build a diagnostic prediction model for MCI-SKDS.Accuracy,sensitivity,specificity,precision,F1 score,and area under the curve were used to evaluate model performance.Furthermore,the clinical applicability of the model was evaluated through decision curve analysis(DCA).Results:The accuracy,precision,specificity and F1 score of the DT model performed best in the training set(test set),with scores of 0.904(0.845),0.875(0.795),0.973(0.875)and 0.973(0.875).The sensitivity of the training set(test set)of the SVM model performed best among the five models with a score of 0.865(0.821).The area under the curve of all five models was greater than 0.9 for the training dataset and greater than 0.8 for the test dataset.The DCA of all models showed good clinical application value.The study identified ten indicators that were significant predictors of MCI-SKDS.Conclusion:The risk prediction index derived from machine learning for the MCI-SKDS prediction model is simple and practical;the model demonstrates good predictive value and clinical applicability,and the DT model had the best performance.展开更多
The increasing global prevalence of mild cognitive impairment(MCI)necessitates a paradigm shift in early detection strategies.Conventional neuropsychological assessment methods,predominantly paper-and-pencil tests suc...The increasing global prevalence of mild cognitive impairment(MCI)necessitates a paradigm shift in early detection strategies.Conventional neuropsychological assessment methods,predominantly paper-and-pencil tests such as the Mini-Mental State Examination and the Montreal Cognitive Assessment,exhibit inherent limitations with respect to accessibility,administration burden,and sensitivity to subtle cognitive decline,particularly among diverse populations.This commentary critically examines a recent study that champions a novel approach:The integration of gait and handwriting kinematic parameters analyzed via machine learning for MCI screening.The present study positions itself within the broader landscape of MCI detection,with a view to comparing its advantages against established neuropsychological batteries,advanced neuroimaging(e.g.,positron emission tomography,magnetic resonance imaging),and emerging fluid biomarkers(e.g.,cerebrospinal fluid,blood-based assays).While the study demonstrates promising accuracy(74.44%area under the curve 0.74 with gait and graphic handwriting)and addresses key unmet needs in accessibility and objectivity,we highlight its cross-sectional nature,limited sample diversity,and lack of dual-task assessment as areas for future refinement.This commentary posits that kinematic biomarkers offer a distinctive,scalable,and ecologically valid approach to widespread MCI screening,thereby complementing existing methods by providing real-world functional insights.Future research should prioritize longitudinal validation,expansion to diverse cohorts,integration with multimodal data including dual-tasking,and the development of highly portable,artificial intelligence-driven solutions to achieve the democratization of early MCI detection and enable timely interventions.展开更多
The concept of the brain cognitive reserve is derived from the well-acknowledged notion that the degree of brain damage does not always match the severity of clinical symptoms and neurological/cognitive outcomes.It ha...The concept of the brain cognitive reserve is derived from the well-acknowledged notion that the degree of brain damage does not always match the severity of clinical symptoms and neurological/cognitive outcomes.It has been suggested that the size of the brain(brain reserve) and the extent of neural connections acquired through life(neural reserve) set a threshold beyond which noticeable impairments occur.In contrast,cognitive reserve refers to the brain's ability to adapt and reo rganize stru cturally and functionally to resist damage and maintain function,including neural reserve and brain maintenance,resilience,and compensation(Verkhratsky and Zorec,2024).展开更多
Challenges in the prevention and treatment of mild cognitive impairment associated with Alzheimer's disease:Increased life expectancy due to advancements in medical care has given rise to an aging population,accom...Challenges in the prevention and treatment of mild cognitive impairment associated with Alzheimer's disease:Increased life expectancy due to advancements in medical care has given rise to an aging population,accompanied by a surge in the incidence of incurable neurodegenerative diseases(NDDs).These diseases primarily affect the cognitive and behavioral functions of older adults by impacting brain activity.Mild cognitive impairment(MCI)is a neurodegenerative condition that affects a significant portion of the population.展开更多
BACKGROUND The accurate prediction of lymph node metastasis(LNM)is crucial for managing locally advanced(T3/T4)colorectal cancer(CRC).However,both traditional histopathology and standard slide-level deep learning ofte...BACKGROUND The accurate prediction of lymph node metastasis(LNM)is crucial for managing locally advanced(T3/T4)colorectal cancer(CRC).However,both traditional histopathology and standard slide-level deep learning often fail to capture the sparse and diagnostically critical features of metastatic potential.AIM To develop and validate a case-level multiple-instance learning(MIL)framework mimicking a pathologist's comprehensive review and improve T3/T4 CRC LNM prediction.METHODS The whole-slide images of 130 patients with T3/T4 CRC were retrospectively collected.A case-level MIL framework utilising the CONCH v1.5 and UNI2-h deep learning models was trained on features from all haematoxylin and eosinstained primary tumour slides for each patient.These pathological features were subsequently integrated with clinical data,and model performance was evaluated using the area under the curve(AUC).RESULTS The case-level framework demonstrated superior LNM prediction over slide-level training,with the CONCH v1.5 model achieving a mean AUC(±SD)of 0.899±0.033 vs 0.814±0.083,respectively.Integrating pathology features with clinical data further enhanced performance,yielding a top model with a mean AUC of 0.904±0.047,in sharp contrast to a clinical-only model(mean AUC 0.584±0.084).Crucially,a pathologist’s review confirmed that the model-identified high-attention regions correspond to known high-risk histopathological features.CONCLUSION A case-level MIL framework provides a superior approach for predicting LNM in advanced CRC.This method shows promise for risk stratification and therapy decisions,requiring further validation.展开更多
Neuromodulation techniques effectively intervene in cognitive function,holding considerable scientific and practical value in fields such as aerospace,medicine,life sciences,and brain research.These techniques utilize...Neuromodulation techniques effectively intervene in cognitive function,holding considerable scientific and practical value in fields such as aerospace,medicine,life sciences,and brain research.These techniques utilize electrical stimulation to directly or indirectly target specific brain regions,modulating neural activity and influencing broader brain networks,thereby regulating cognitive function.Regulating cognitive function involves an understanding of aspects such as perception,learning and memory,attention,spatial cognition,and physical function.To enhance the application of cognitive regulation in the general population,this paper reviews recent publications from the Web of Science to assess the advancements and challenges of invasive and non-invasive stimulation methods in modulating cognitive functions.This review covers various neuromodulation techniques for cognitive intervention,including deep brain stimulation,vagus nerve stimulation,and invasive methods using microelectrode arrays.The non-invasive techniques discussed include transcranial magnetic stimulation,transcranial direct current stimulation,transcranial alternating current stimulation,transcutaneous electrical acupoint stimulation,and time interference stimulation for activating deep targets.Invasive stimulation methods,which are ideal for studying the pathogenesis of neurological diseases,tend to cause greater trauma and have been less researched in the context of cognitive function regulation.Non-invasive methods,particularly newer transcranial stimulation techniques,are gentler and more appropriate for regulating cognitive functions in the general population.These include transcutaneous acupoint electrical stimulation using acupoints and time interference methods for activating deep targets.This paper also discusses current technical challenges and potential future breakthroughs in neuromodulation technology.It is recommended that neuromodulation techniques be combined with neural detection methods to better assess their effects and improve the accuracy of non-invasive neuromodulation.Additionally,researching closed-loop feedback neuromodulation methods is identified as a promising direction for future development.展开更多
BACKGROUND Due to the dry and cold climate,the obvious temperature difference between day and night,and the low oxygen content of the air in the plateau area,people are prone to upper respiratory tract diseases,and of...BACKGROUND Due to the dry and cold climate,the obvious temperature difference between day and night,and the low oxygen content of the air in the plateau area,people are prone to upper respiratory tract diseases,and often the condition is prolonged,and the patients are prone to anxiety and uneasiness,which may be related to the harshness of the plateau environment,somatic discomfort due to the lack of oxygen,anxiety about the disease,and other factors.AIM To investigate the effects of cognitive behavioral therapy(CBT)on anxiety,sleep disorders,and hypoxia tolerance in patients with high-altitude respiratory diseases.METHODS A total of 2337 patients with high-altitude-related respiratory diseases treated at our hospital between November 2023 and January 2024 were selected as the study subjects.The subjects’pre-high-altitude residential altitude was approximately 1700 meters.They were divided into two groups.Both groups were given symptomatic treatment,and the control group implemented conventional nursing intervention,while the research group simultaneously conducted CBT intervention;assessed the degree of health knowledge of the two groups,and applied the Hamilton Anxiety Scale and the Pittsburgh Sleep Quality Index to assess the anxiety and sleep quality of the patients before and after the intervention,respectively.It also observed the length and efficiency of sleep,and detected the level of serum hypoxia inducible factor-1α,erythropoietin(EPO)and clinical intervention before and after intervention.EPO levels,and investigated satisfaction with the clinical intervention.RESULTS The rate of excellent health knowledge in the intervention group was 93.64%,which was higher than that in the control group(74.23%;P<0.05).Before the intervention,there was no significant difference in Hamilton Anxiety Scale and Pittsburgh Sleep Quality Index scores between the two groups(P>0.05),and after the intervention,the scores of the study group were significantly lower than those of the control group(P<0.05).There was no significant difference in sleep duration and sleep efficiency between the groups before the intervention(P>0.05),and after the intervention,the scores of the study group were significantly larger than those of the control group(P<0.05).There was no significant difference in serum hypoxia inducible factor-1αand EPO between the two groups before intervention(P>0.05),and both research groups were significantly lower than the control group after intervention(P<0.05).According to the questionnaire survey,the intervention satisfaction of the study group was 95.53%,which was higher than that of the control group(80.14%;P<0.05).CONCLUSION The CBT intervention in the treatment of patients with high-altitude-related respiratory diseases helps improve patients'health knowledge,relieve anxiety,improve sleep quality and hypoxia tolerance,and improve nursing satisfaction.展开更多
BACKGROUND Approximately 30%of patients with head and neck cancer experience adverse effects caused by anxiety and depression.Considering the high prevalence,implementing customized interventions to ease adverse emoti...BACKGROUND Approximately 30%of patients with head and neck cancer experience adverse effects caused by anxiety and depression.Considering the high prevalence,implementing customized interventions to ease adverse emotional states is imperative.AIM To evaluate the efficacy of cognitive behavioral therapy(CBT)-based psychological interventions in improving the psychological well-being and quality of life(QoL)of patients with laryngeal carcinoma.METHODS This study enrolled 120 patients admitted from February 2022 to February 2024.The control group,comprising 50 participants,received standard supportive psychological care,while the research group,consisting 70 participants,underwent CBT-based interventions.Several clinical outcomes were systematically assessed that included postoperative recovery metrics(duration of tracheostomy and nasogastric tube dependence and length of hospitalization),psychological status(Self-Rating Anxiety Scale and Self-Rating Depression Scale),nutritional markers(serum albumin and hemoglobin levels),sleep quality(Self-Rating Scale of Sleep and Athens Insomnia Scale),and QoL(Functional Assessment of Cancer Therapy-Head and Neck).RESULTS The results demonstrated that the research group experienced superior outcomes,with significantly reduced durations of tracheostomy and nasogastric tube dependence,as well as shorter hospital stays,compared with the control group.Additionally,the research group exhibited markedly lower post-intervention Self-Rating Anxiety Scale,Self-Rating Depression Scale,Self-Rating Scale of Sleep,and Athens Insomnia Scale scores,along with minimal but higher change in serum albumin and hemoglobin levels compared with the control group.All five domains of Functional Assessment of Cancer Therapy-Head and Neck showed notable improvements in the research group,exceeding those observed in the control group.CONCLUSION CBT-based psychological support positively affects the mental well-being and QoL of patients with laryngeal carcinoma,highlighting its potential for broader clinical application.展开更多
Cognitive impairment is a particularly severe non-motor symptom of Parkinson's disease that significantly diminishes the quality of life of affected individuals.Identifying reliable biomarkers for cognitive impair...Cognitive impairment is a particularly severe non-motor symptom of Parkinson's disease that significantly diminishes the quality of life of affected individuals.Identifying reliable biomarkers for cognitive impairment in Parkinson's disease is essential for early diagnosis,prognostic assessments,and the development of targeted therapies.This review aims to summarize recent advancements in biofluid biomarkers for cognitive impairment in Parkinson's disease,focusing on the detection of specific proteins,metabolites,and other biomarkers in blood,cerebrospinal fluid,and saliva.These biomarkers can shed light on the multifaceted etiology of cognitive impairment in Parkinson's disease,which includes protein misfolding,neurodegeneration,inflammation,and oxidative stress.The integration of biofluid biomarkers with neuroimaging and clinical data can facilitate the development of predictive models to enhance early diagnosis and monitor the progression of cognitive impairment in patients with Parkinson's disease.This comprehensive approach can improve the existing understanding of the mechanisms driving cognitive decline and support the development of targeted therapeutic strategies aimed at modifying the course of cognitive impairment in Parkinson's disease.Despite the promise of these biomarkers in characterizing the mechanisms underlying cognitive decline in Parkinson's disease,further research is necessary to validate their clinical utility and establish a standardized framework for early detection and monitoring of cognitive impairment in Parkinson's disease.展开更多
Background:Stomach cancer(SC)is one of the most lethal malignancies worldwide due to late-stage diagnosis and limited treatment.The transcriptomic,epigenomic,and proteomic,etc.,omics datasets generated by high-through...Background:Stomach cancer(SC)is one of the most lethal malignancies worldwide due to late-stage diagnosis and limited treatment.The transcriptomic,epigenomic,and proteomic,etc.,omics datasets generated by high-throughput sequencing technology have become prominent in biomedical research,and they reveal molecular aspects of cancer diagnosis and therapy.Despite the development of advanced sequencing technology,the presence of high-dimensionality in multi-omics data makes it challenging to interpret the data.Methods:In this study,we introduce RankXLAN,an explainable ensemble-based multi-omics framework that integrates feature selection(FS),ensemble learning,bioinformatics,and in-silico validation for robust biomarker detection,potential therapeutic drug-repurposing candidates’identification,and classification of SC.To enhance the interpretability of the model,we incorporated explainable artificial intelligence(SHapley Additive exPlanations analysis),as well as accuracy,precision,F1-score,recall,cross-validation,specificity,likelihood ratio(LR)+,LR−,and Youden index results.Results:The experimental results showed that the top four FS algorithms achieved improved results when applied to the ensemble learning classification model.The proposed ensemble model produced an area under the curve(AUC)score of 0.994 for gene expression,0.97 for methylation,and 0.96 for miRNA expression data.Through the integration of bioinformatics and ML approach of the transcriptomic and epigenomic multi-omics dataset,we identified potential marker genes,namely,UBE2D2,HPCAL4,IGHA1,DPT,and FN3K.In-silico molecular docking revealed a strong binding affinity between ANKRD13C and the FDA-approved drug Everolimus(binding affinity−10.1 kcal/mol),identifying ANKRD13C as a potential therapeutic drug-repurposing target for SC.Conclusion:The proposed framework RankXLAN outperforms other existing frameworks for serum biomarker identification,therapeutic target identification,and SC classification with multi-omics datasets.展开更多
Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human-machine interfaces for real-time health monitoring,clinical diagnosis,and robotic applications.Nevertheless,...Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human-machine interfaces for real-time health monitoring,clinical diagnosis,and robotic applications.Nevertheless,it remains a critical challenge to simultaneously achieve desirable mechanical and electrical performance along with biocompatibility,adhesion,self-healing,and environmental robustness with excellent sensing metrics.Herein,we report a multifunctional,anti-freezing,selfadhesive,and self-healable organogel pressure sensor composed of cobalt nanoparticle encapsulated nitrogen-doped carbon nanotubes(CoN CNT)embedded in a polyvinyl alcohol-gelatin(PVA/GLE)matrix.Fabricated using a binary solvent system of water and ethylene glycol(EG),the CoN CNT/PVA/GLE organogel exhibits excellent flexibility,biocompatibility,and temperature tolerance with remarkable environmental stability.Electrochemical impedance spectroscopy confirms near-stable performance across a broad humidity range(40%-95%RH).Freeze-tolerant conductivity under sub-zero conditions(-20℃)is attributed to the synergistic role of CoN CNT and EG,preserving mobility and network integrity.The Co N CNT/PVA/GLE organogel sensor exhibits high sensitivity of 5.75 k Pa^(-1)in the detection range from 0 to 20 k Pa,ideal for subtle biomechanical motion detection.A smart human-machine interface for English letter recognition using deep learning achieved 98%accuracy.The organogel sensor utility was extended to detect human gestures like finger bending,wrist motion,and throat vibration during speech.展开更多
Adult hippocampal neurogenesis is linked to memory formation in the adult brain,with new neurons in the hippocampus exhibiting greater plasticity during their immature stages compared to mature neurons.Abnormal adult ...Adult hippocampal neurogenesis is linked to memory formation in the adult brain,with new neurons in the hippocampus exhibiting greater plasticity during their immature stages compared to mature neurons.Abnormal adult hippocampal neurogenesis is closely associated with cognitive impairment in central nervous system diseases.Targeting and regulating adult hippocampal neurogenesis have been shown to improve cognitive deficits.This review aims to expand the current understanding and prospects of targeting neurogenesis in the treatment of cognitive impairment.Recent research indicates the presence of abnormalities in AHN in several diseases associated with cognitive impairment,including cerebrovascular diseases,Alzheimer's disease,aging-related conditions,and issues related to anesthesia and surgery.The role of these abnormalities in the cognitive deficits caused by these diseases has been widely recognized,and targeting AHN is considered a promising approach for treating cognitive impairment.However,the underlying mechanisms of this role are not yet fully understood,and the effectiveness of targeting abnormal adult hippocampal neurogenesis for treatment remains limited,with a need for further development of treatment methods and detection techniques.By reviewing recent studies,we classify the potential mechanisms of adult hippocampal neurogenesis abnormalities into four categories:immunity,energy metabolism,aging,and pathological states.In immunity-related mechanisms,abnormalities in meningeal,brain,and peripheral immunity can disrupt normal adult hippocampal neurogenesis.Lipid metabolism and mitochondrial function disorders are significant energy metabolism factors that lead to abnormal adult hippocampal neurogenesis.During aging,the inflammatory state of the neurogenic niche and the expression of aging-related microRNAs contribute to reduced adult hippocampal neurogenesis and cognitive impairment in older adult patients.Pathological states of the body and emotional disorders may also result in abnormal adult hippocampal neurogenesis.Among the current strategies used to enhance this form of neurogenesis,physical therapies such as exercise,transcutaneous electrical nerve stimulation,and enriched environments have proven effective.Dietary interventions,including energy intake restriction and nutrient optimization,have shown efficacy in both basic research and clinical trials.However,drug treatments,such as antidepressants and stem cell therapy,are primarily reported in basic research,with limited clinical application.The relationship between abnormal adult hippocampal neurogenesis and cognitive impairment has garnered widespread attention,and targeting the former may be an important strategy for treating the latter.However,the mechanisms underlying abnormal adult hippocampal neurogenesis remain unclear,and treatments are lacking.This highlights the need for greater focus on translating research findings into clinical practice.展开更多
Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing can...Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.展开更多
While reinforcement learning-based underwater acoustic adaptive modulation shows promise for enabling environment-adaptive communication as supported by extensive simulation-based research,its practical performance re...While reinforcement learning-based underwater acoustic adaptive modulation shows promise for enabling environment-adaptive communication as supported by extensive simulation-based research,its practical performance remains underexplored in field investigations.To evaluate the practical applicability of this emerging technique in adverse shallow sea channels,a field experiment was conducted using three communication modes:orthogonal frequency division multiplexing(OFDM),M-ary frequency-shift keying(MFSK),and direct sequence spread spectrum(DSSS)for reinforcement learning-driven adaptive modulation.Specifically,a Q-learning method is used to select the optimal modulation mode according to the channel quality quantified by signal-to-noise ratio,multipath spread length,and Doppler frequency offset.Experimental results demonstrate that the reinforcement learning-based adaptive modulation scheme outperformed fixed threshold detection in terms of total throughput and average bit error rate,surpassing conventional adaptive modulation strategies.展开更多
基金supported by the National Natural Science Foundation of China,Nos.81730033,82171193(to XG)the Key Talent Project for Strengthening Health during the 13^(th)Five-Year Plan Period,No.ZDRCA2016069(to XG)+1 种基金the National Key R&D Program of China,No.2018YFC2001901(to XG)Jiangsu Provincial Medical Key Discipline,No.ZDXK202232(to XG)。
文摘Postoperative cognitive dysfunction is a seve re complication of the central nervous system that occurs after anesthesia and surgery,and has received attention for its high incidence and effect on the quality of life of patients.To date,there are no viable treatment options for postoperative cognitive dysfunction.The identification of postoperative cognitive dysfunction hub genes could provide new research directions and therapeutic targets for future research.To identify the signaling mechanisms contributing to postoperative cognitive dysfunction,we first conducted Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the Gene Expression Omnibus GSE95426 dataset,which consists of mRNAs and long non-coding RNAs differentially expressed in mouse hippocampus3 days after tibial fracture.The dataset was enriched in genes associated with the biological process"regulation of immune cells,"of which Chill was identified as a hub gene.Therefore,we investigated the contribution of chitinase-3-like protein 1 protein expression changes to postoperative cognitive dysfunction in the mouse model of tibial fractu re surgery.Mice were intraperitoneally injected with vehicle or recombinant chitinase-3-like protein 124 hours post-surgery,and the injection groups were compared with untreated control mice for learning and memory capacities using the Y-maze and fear conditioning tests.In addition,protein expression levels of proinflammatory factors(interleukin-1βand inducible nitric oxide synthase),M2-type macrophage markers(CD206 and arginase-1),and cognition-related proteins(brain-derived neurotropic factor and phosphorylated NMDA receptor subunit NR2B)were measured in hippocampus by western blotting.Treatment with recombinant chitinase-3-like protein 1 prevented surgery-induced cognitive impairment,downregulated interleukin-1βand nducible nitric oxide synthase expression,and upregulated CD206,arginase-1,pNR2B,and brain-derived neurotropic factor expression compared with vehicle treatment.Intraperitoneal administration of the specific ERK inhibitor PD98059 diminished the effects of recombinant chitinase-3-like protein 1.Collectively,our findings suggest that recombinant chitinase-3-like protein 1 ameliorates surgery-induced cognitive decline by attenuating neuroinflammation via M2 microglial polarization in the hippocampus.Therefore,recombinant chitinase-3-like protein1 may have therapeutic potential fo r postoperative cognitive dysfunction.
基金jointly funded by the National Science and Technology Major Project of the Ministry of Science and Technology of China(2018AAA0102900)the"New Generation Artificial Intelligence"Key Field Research and Development Plan of Guangdong Province(2021B0101410002)。
文摘Humans achieve cognitive development through continuous interaction with their environment,enhancing both perception and behavior.However,current robots lack the capacity for human-like action and evolution,posing a bottleneck to improving robotic intelligence.Existing research predominantly models robots as one-way,static mappings from observations to actions,neglecting the dynamic processes of perception and behavior.This paper introduces a novel approach to robot cognitive learning by considering physical properties.We propose a theoretical framework wherein a robot is conceptualized as a three-body physical system comprising a perception-body(P-body),a cognition-body(C-body),and a behavior-body(B-body).Each body engages in physical dynamics and operates within a closed-loop interaction.Significantly,three crucial interactions connect these bodies.The C-body relies on the Pbody's extracted states and reciprocally offers long-term rewards,optimizing the P-body's perception policy.In addition,the C-body directs the B-body's actions through sub-goals,and subsequent P-body-derived states facilitate the C-body's cognition dynamics learning.At last,the B-body would follow the sub-goal generated by the C-body and perform actions conditioned on the perceptive state from the P-body,which leads to the next interactive step.These interactions foster the joint evolution of each body,culminating in optimal design.To validate our approach,we employ a navigation task using a four-legged robot,D'Kitty,equipped with a movable global camera.Navigational prowess demands intricate coordination of sensing,planning,and D'Kitty's motion.Leveraging our framework yields superior task performance compared with conventional methodologies.In conclusion,this paper establishes a paradigm shift in robot cognitive learning by integrating physical interactions across the P-body,C-body,and B-body,while considering physical properties.Our framework's successful application to a navigation task underscores its efficacy in enhancing robotic intelligence.
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025).
文摘FedCognis is a secure and scalable federated learning framework designed for continuous anomaly detection in Industrial Internet of Things-enabled Cognitive Cities(IIoTCC).It introduces two key innovations:a Quantum Secure Authentication(QSA)mechanism for adversarial defense and integrity validation,and a Self-Attention Long Short-Term Memory(SALSTM)model for high-accuracy spatiotemporal anomaly detection.Addressing core challenges in traditional Federated Learning(FL)—such as model poisoning,communication overhead,and concept drift—FedCognis integrates dynamic trust-based aggregation and lightweight cryptographic verification to ensure secure,real-time operation across heterogeneous IIoT domains including utilities,public safety,and traffic systems.Evaluated on the WUSTL-IIoTCC-2021 dataset,FedCognis achieves 94.5%accuracy,0.941 AUC for precision-recall,and 0.896 ROC-AUC,while reducing bandwidth consumption by 72%.The framework demonstrates sublinear computational complexity and a resilience score of 96.56%across six security dimensions.These results confirm FedCognis as a robust and adaptive anomaly detection solution suitable for deployment in large-scale cognitive urban infrastructures.
文摘The rise of 6G networks and the exponential growth of smart city infrastructures demand intelligent,real-time traffic forecasting solutions that preserve data privacy.This paper introduces NeuroCivitas,a federated deep learning framework that integrates Convolutional Neural Networks(CNNs)for spatial pattern recognition and Long Short-Term Memory(LSTM)networks for temporal sequence modeling.Designed to meet the adaptive intelligence requirements of cognitive cities,NeuroCivitas leverages Federated Averaging(FedAvg)to ensure privacypreserving training while significantly reducing communication overhead—by 98.7%compared to centralized models.The model is evaluated using the Kaggle Traffic Prediction Dataset comprising 48,120 hourly records from four urban junctions.It achieves an RMSE of 2.76,MAE of 2.11,and an R^(2) score of 0.91,outperforming baseline models such as ARIMA,Linear Regression,Random Forest,and non-federated CNN-LSTM in both accuracy and scalability.Junctionwise and time-based performance analyses further validate its robustness,particularly during off-peak hours,while highlighting challenges in peak traffic forecasting.Ablation studies confirm the importance of both CNN and LSTM layers and temporal feature engineering in achieving optimal performance.Moreover,NeuroCivitas demonstrates stable convergence within 25 communication rounds and shows strong adaptability to non-IID data distributions.The framework is built with real-world deployment in mind,offering support for edge environments through lightweight architecture and the potential for enhancement with differential privacy and adversarial defense mechanisms.SHAPbased explainability is integrated to improve interpretability for stakeholders.In sum,NeuroCivitas delivers an accurate,scalable,and privacy-preserving traffic forecasting solution,tailored for 6G cognitive cities.Future extensions will incorporate fairness-aware optimization,real-time anomaly adaptation,multi-city validation,and advanced federated GNN comparisons to further enhance deployment readiness and societal impact.
文摘College English teaching is a crucial component of higher education.Enhancing college students’English learning outcomes has long been a primary focus for educators.With the continuous evolution of educational philosophies,traditional college English teaching methods no longer meet the learning needs of contemporary students.Situational cognitive learning theory emphasizes learner-centered approaches and highlights the contextual and practical application of knowledge,offering innovative perspectives for reforming college English teaching.When applied effectively,situational cognitive learning theory can optimize teaching methods and significantly improve learning outcomes.This paper explores the connotation and characteristics of situational cognitive learning theory,evaluates its applicability in college English teaching,and discusses its practical implementation in this context.The aim is to provide theoretical and practical references for improving the quality of college English education.
基金the Asian Institute of Technology,Khlong Nueng,Thailand for their support in carrying out this study。
文摘Deep Learning(DL)offers promising solutions for analyzing wearable signals and gaining valuable insights into cognitive disorders.While previous review studies have explored various aspects of DL in cognitive healthcare,there remains a lack of comprehensive analysis that integrates wearable signals,data processing techniques,and the broader applications,benefits,and challenges of DL methods.Addressing this limitation,our study provides an extensive review of DL’s role in cognitive healthcare,with a particular emphasis on wearables,data processing,and the inherent challenges in this field.This review also highlights the considerable promise of DL approaches in addressing a broad spectrum of cognitive issues.By enhancing the understanding and analysis of wearable signal modalities,DL models can achieve remarkable accuracy in cognitive healthcare.Convolutional Neural Network(CNN),Recurrent Neural Network(RNN),and Long Short-term Memory(LSTM)networks have demonstrated improved performance and effectiveness in the early diagnosis and progression monitoring of neurological disorders.Beyond cognitive impairment detection,DL has been applied to emotion recognition,sleep analysis,stress monitoring,and neurofeedback.These applications lead to advanced diagnosis,personalized treatment,early intervention,assistive technologies,remote monitoring,and reduced healthcare costs.Nevertheless,the integration of DL and wearable technologies presents several challenges,such as data quality,privacy,interpretability,model generalizability,ethical concerns,and clinical adoption.These challenges emphasize the importance of conducting future research in areas such as multimodal signal analysis and explainable AI.The findings of this review aim to benefit clinicians,healthcare professionals,and society by facilitating better patient outcomes in cognitive healthcare.
基金funded by the National Natural Science Foundation of China(No.82405530,81973921 and 72374068)the Science and Technology Research Project of Hubei Provincial Department of Education(No.B2023098)。
文摘Objective:As an age-related neurodegenerative disease,the prevalence of mild cognitive impairment(MCI)increases with age.Within the framework of traditional Chinese medicine,spleen-kidney deficiency syndrome(SKDS)is recognized as the most frequent MCI subtype.Due to the covert and gradual onset of MCI,in community settings it poses a significant challenge for patients and their families to discern between typical aging and pathological changes.There exists an urgent need to devise a preliminary diagnostic tool designed for community-residing older adults with MCI attributed to SKDS(MCI-SKDS).Methods:This investigation enrolled 312 elderly individuals diagnosed with MCI,who were randomly distributed into training and test datasets at a 3:1 ratio.Five machine learning methods,including logistic regression(LR),decision tree(DT),naive Bayes(NB),support vector machine(SVM),and gradient boosting(GB),were used to build a diagnostic prediction model for MCI-SKDS.Accuracy,sensitivity,specificity,precision,F1 score,and area under the curve were used to evaluate model performance.Furthermore,the clinical applicability of the model was evaluated through decision curve analysis(DCA).Results:The accuracy,precision,specificity and F1 score of the DT model performed best in the training set(test set),with scores of 0.904(0.845),0.875(0.795),0.973(0.875)and 0.973(0.875).The sensitivity of the training set(test set)of the SVM model performed best among the five models with a score of 0.865(0.821).The area under the curve of all five models was greater than 0.9 for the training dataset and greater than 0.8 for the test dataset.The DCA of all models showed good clinical application value.The study identified ten indicators that were significant predictors of MCI-SKDS.Conclusion:The risk prediction index derived from machine learning for the MCI-SKDS prediction model is simple and practical;the model demonstrates good predictive value and clinical applicability,and the DT model had the best performance.
文摘The increasing global prevalence of mild cognitive impairment(MCI)necessitates a paradigm shift in early detection strategies.Conventional neuropsychological assessment methods,predominantly paper-and-pencil tests such as the Mini-Mental State Examination and the Montreal Cognitive Assessment,exhibit inherent limitations with respect to accessibility,administration burden,and sensitivity to subtle cognitive decline,particularly among diverse populations.This commentary critically examines a recent study that champions a novel approach:The integration of gait and handwriting kinematic parameters analyzed via machine learning for MCI screening.The present study positions itself within the broader landscape of MCI detection,with a view to comparing its advantages against established neuropsychological batteries,advanced neuroimaging(e.g.,positron emission tomography,magnetic resonance imaging),and emerging fluid biomarkers(e.g.,cerebrospinal fluid,blood-based assays).While the study demonstrates promising accuracy(74.44%area under the curve 0.74 with gait and graphic handwriting)and addresses key unmet needs in accessibility and objectivity,we highlight its cross-sectional nature,limited sample diversity,and lack of dual-task assessment as areas for future refinement.This commentary posits that kinematic biomarkers offer a distinctive,scalable,and ecologically valid approach to widespread MCI screening,thereby complementing existing methods by providing real-world functional insights.Future research should prioritize longitudinal validation,expansion to diverse cohorts,integration with multimodal data including dual-tasking,and the development of highly portable,artificial intelligence-driven solutions to achieve the democratization of early MCI detection and enable timely interventions.
文摘The concept of the brain cognitive reserve is derived from the well-acknowledged notion that the degree of brain damage does not always match the severity of clinical symptoms and neurological/cognitive outcomes.It has been suggested that the size of the brain(brain reserve) and the extent of neural connections acquired through life(neural reserve) set a threshold beyond which noticeable impairments occur.In contrast,cognitive reserve refers to the brain's ability to adapt and reo rganize stru cturally and functionally to resist damage and maintain function,including neural reserve and brain maintenance,resilience,and compensation(Verkhratsky and Zorec,2024).
基金supported by The Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2023-00244901)(to RB)。
文摘Challenges in the prevention and treatment of mild cognitive impairment associated with Alzheimer's disease:Increased life expectancy due to advancements in medical care has given rise to an aging population,accompanied by a surge in the incidence of incurable neurodegenerative diseases(NDDs).These diseases primarily affect the cognitive and behavioral functions of older adults by impacting brain activity.Mild cognitive impairment(MCI)is a neurodegenerative condition that affects a significant portion of the population.
基金Supported by Chongqing Medical Scientific Research Project(Joint Project of Chongqing Health Commission and Science and Technology Bureau),No.2023MSXM060.
文摘BACKGROUND The accurate prediction of lymph node metastasis(LNM)is crucial for managing locally advanced(T3/T4)colorectal cancer(CRC).However,both traditional histopathology and standard slide-level deep learning often fail to capture the sparse and diagnostically critical features of metastatic potential.AIM To develop and validate a case-level multiple-instance learning(MIL)framework mimicking a pathologist's comprehensive review and improve T3/T4 CRC LNM prediction.METHODS The whole-slide images of 130 patients with T3/T4 CRC were retrospectively collected.A case-level MIL framework utilising the CONCH v1.5 and UNI2-h deep learning models was trained on features from all haematoxylin and eosinstained primary tumour slides for each patient.These pathological features were subsequently integrated with clinical data,and model performance was evaluated using the area under the curve(AUC).RESULTS The case-level framework demonstrated superior LNM prediction over slide-level training,with the CONCH v1.5 model achieving a mean AUC(±SD)of 0.899±0.033 vs 0.814±0.083,respectively.Integrating pathology features with clinical data further enhanced performance,yielding a top model with a mean AUC of 0.904±0.047,in sharp contrast to a clinical-only model(mean AUC 0.584±0.084).Crucially,a pathologist’s review confirmed that the model-identified high-attention regions correspond to known high-risk histopathological features.CONCLUSION A case-level MIL framework provides a superior approach for predicting LNM in advanced CRC.This method shows promise for risk stratification and therapy decisions,requiring further validation.
基金supported by STI 2030-Major Projects,No.2021ZD0201603(to JL)the Joint Foundation Program of the Chinese Academy of Sciences,No.8091A170201(to JL)+1 种基金the National Natural Science Foundation of China,Nos.T2293730(to XC),T2293731(to XC),T2293734(to XC),62471291(to YW),62121003(to XC),61960206012(to XC),62333020(to XC),and 62171434(to XC)the National Key Research and Development Program of China,Nos.2022YFC2402501(to XC),2022YFB3205602(to XC).
文摘Neuromodulation techniques effectively intervene in cognitive function,holding considerable scientific and practical value in fields such as aerospace,medicine,life sciences,and brain research.These techniques utilize electrical stimulation to directly or indirectly target specific brain regions,modulating neural activity and influencing broader brain networks,thereby regulating cognitive function.Regulating cognitive function involves an understanding of aspects such as perception,learning and memory,attention,spatial cognition,and physical function.To enhance the application of cognitive regulation in the general population,this paper reviews recent publications from the Web of Science to assess the advancements and challenges of invasive and non-invasive stimulation methods in modulating cognitive functions.This review covers various neuromodulation techniques for cognitive intervention,including deep brain stimulation,vagus nerve stimulation,and invasive methods using microelectrode arrays.The non-invasive techniques discussed include transcranial magnetic stimulation,transcranial direct current stimulation,transcranial alternating current stimulation,transcutaneous electrical acupoint stimulation,and time interference stimulation for activating deep targets.Invasive stimulation methods,which are ideal for studying the pathogenesis of neurological diseases,tend to cause greater trauma and have been less researched in the context of cognitive function regulation.Non-invasive methods,particularly newer transcranial stimulation techniques,are gentler and more appropriate for regulating cognitive functions in the general population.These include transcutaneous acupoint electrical stimulation using acupoints and time interference methods for activating deep targets.This paper also discusses current technical challenges and potential future breakthroughs in neuromodulation technology.It is recommended that neuromodulation techniques be combined with neural detection methods to better assess their effects and improve the accuracy of non-invasive neuromodulation.Additionally,researching closed-loop feedback neuromodulation methods is identified as a promising direction for future development.
基金Supported by Army Logistics Department Health Bureau Project,No.QJGYXYJZX-012.
文摘BACKGROUND Due to the dry and cold climate,the obvious temperature difference between day and night,and the low oxygen content of the air in the plateau area,people are prone to upper respiratory tract diseases,and often the condition is prolonged,and the patients are prone to anxiety and uneasiness,which may be related to the harshness of the plateau environment,somatic discomfort due to the lack of oxygen,anxiety about the disease,and other factors.AIM To investigate the effects of cognitive behavioral therapy(CBT)on anxiety,sleep disorders,and hypoxia tolerance in patients with high-altitude respiratory diseases.METHODS A total of 2337 patients with high-altitude-related respiratory diseases treated at our hospital between November 2023 and January 2024 were selected as the study subjects.The subjects’pre-high-altitude residential altitude was approximately 1700 meters.They were divided into two groups.Both groups were given symptomatic treatment,and the control group implemented conventional nursing intervention,while the research group simultaneously conducted CBT intervention;assessed the degree of health knowledge of the two groups,and applied the Hamilton Anxiety Scale and the Pittsburgh Sleep Quality Index to assess the anxiety and sleep quality of the patients before and after the intervention,respectively.It also observed the length and efficiency of sleep,and detected the level of serum hypoxia inducible factor-1α,erythropoietin(EPO)and clinical intervention before and after intervention.EPO levels,and investigated satisfaction with the clinical intervention.RESULTS The rate of excellent health knowledge in the intervention group was 93.64%,which was higher than that in the control group(74.23%;P<0.05).Before the intervention,there was no significant difference in Hamilton Anxiety Scale and Pittsburgh Sleep Quality Index scores between the two groups(P>0.05),and after the intervention,the scores of the study group were significantly lower than those of the control group(P<0.05).There was no significant difference in sleep duration and sleep efficiency between the groups before the intervention(P>0.05),and after the intervention,the scores of the study group were significantly larger than those of the control group(P<0.05).There was no significant difference in serum hypoxia inducible factor-1αand EPO between the two groups before intervention(P>0.05),and both research groups were significantly lower than the control group after intervention(P<0.05).According to the questionnaire survey,the intervention satisfaction of the study group was 95.53%,which was higher than that of the control group(80.14%;P<0.05).CONCLUSION The CBT intervention in the treatment of patients with high-altitude-related respiratory diseases helps improve patients'health knowledge,relieve anxiety,improve sleep quality and hypoxia tolerance,and improve nursing satisfaction.
文摘BACKGROUND Approximately 30%of patients with head and neck cancer experience adverse effects caused by anxiety and depression.Considering the high prevalence,implementing customized interventions to ease adverse emotional states is imperative.AIM To evaluate the efficacy of cognitive behavioral therapy(CBT)-based psychological interventions in improving the psychological well-being and quality of life(QoL)of patients with laryngeal carcinoma.METHODS This study enrolled 120 patients admitted from February 2022 to February 2024.The control group,comprising 50 participants,received standard supportive psychological care,while the research group,consisting 70 participants,underwent CBT-based interventions.Several clinical outcomes were systematically assessed that included postoperative recovery metrics(duration of tracheostomy and nasogastric tube dependence and length of hospitalization),psychological status(Self-Rating Anxiety Scale and Self-Rating Depression Scale),nutritional markers(serum albumin and hemoglobin levels),sleep quality(Self-Rating Scale of Sleep and Athens Insomnia Scale),and QoL(Functional Assessment of Cancer Therapy-Head and Neck).RESULTS The results demonstrated that the research group experienced superior outcomes,with significantly reduced durations of tracheostomy and nasogastric tube dependence,as well as shorter hospital stays,compared with the control group.Additionally,the research group exhibited markedly lower post-intervention Self-Rating Anxiety Scale,Self-Rating Depression Scale,Self-Rating Scale of Sleep,and Athens Insomnia Scale scores,along with minimal but higher change in serum albumin and hemoglobin levels compared with the control group.All five domains of Functional Assessment of Cancer Therapy-Head and Neck showed notable improvements in the research group,exceeding those observed in the control group.CONCLUSION CBT-based psychological support positively affects the mental well-being and QoL of patients with laryngeal carcinoma,highlighting its potential for broader clinical application.
基金supported by Applied Basic Research Foundation of Yunnan Province,Nos.202301AS070045,202101AY070001-115(to XY and BL)National Natural Science Foundation of China,No.81960242(to XY)。
文摘Cognitive impairment is a particularly severe non-motor symptom of Parkinson's disease that significantly diminishes the quality of life of affected individuals.Identifying reliable biomarkers for cognitive impairment in Parkinson's disease is essential for early diagnosis,prognostic assessments,and the development of targeted therapies.This review aims to summarize recent advancements in biofluid biomarkers for cognitive impairment in Parkinson's disease,focusing on the detection of specific proteins,metabolites,and other biomarkers in blood,cerebrospinal fluid,and saliva.These biomarkers can shed light on the multifaceted etiology of cognitive impairment in Parkinson's disease,which includes protein misfolding,neurodegeneration,inflammation,and oxidative stress.The integration of biofluid biomarkers with neuroimaging and clinical data can facilitate the development of predictive models to enhance early diagnosis and monitor the progression of cognitive impairment in patients with Parkinson's disease.This comprehensive approach can improve the existing understanding of the mechanisms driving cognitive decline and support the development of targeted therapeutic strategies aimed at modifying the course of cognitive impairment in Parkinson's disease.Despite the promise of these biomarkers in characterizing the mechanisms underlying cognitive decline in Parkinson's disease,further research is necessary to validate their clinical utility and establish a standardized framework for early detection and monitoring of cognitive impairment in Parkinson's disease.
基金the Deanship of Research and Graduate Studies at King Khalid University,KSA,for funding this work through the Large Research Project under grant number RGP2/164/46.
文摘Background:Stomach cancer(SC)is one of the most lethal malignancies worldwide due to late-stage diagnosis and limited treatment.The transcriptomic,epigenomic,and proteomic,etc.,omics datasets generated by high-throughput sequencing technology have become prominent in biomedical research,and they reveal molecular aspects of cancer diagnosis and therapy.Despite the development of advanced sequencing technology,the presence of high-dimensionality in multi-omics data makes it challenging to interpret the data.Methods:In this study,we introduce RankXLAN,an explainable ensemble-based multi-omics framework that integrates feature selection(FS),ensemble learning,bioinformatics,and in-silico validation for robust biomarker detection,potential therapeutic drug-repurposing candidates’identification,and classification of SC.To enhance the interpretability of the model,we incorporated explainable artificial intelligence(SHapley Additive exPlanations analysis),as well as accuracy,precision,F1-score,recall,cross-validation,specificity,likelihood ratio(LR)+,LR−,and Youden index results.Results:The experimental results showed that the top four FS algorithms achieved improved results when applied to the ensemble learning classification model.The proposed ensemble model produced an area under the curve(AUC)score of 0.994 for gene expression,0.97 for methylation,and 0.96 for miRNA expression data.Through the integration of bioinformatics and ML approach of the transcriptomic and epigenomic multi-omics dataset,we identified potential marker genes,namely,UBE2D2,HPCAL4,IGHA1,DPT,and FN3K.In-silico molecular docking revealed a strong binding affinity between ANKRD13C and the FDA-approved drug Everolimus(binding affinity−10.1 kcal/mol),identifying ANKRD13C as a potential therapeutic drug-repurposing target for SC.Conclusion:The proposed framework RankXLAN outperforms other existing frameworks for serum biomarker identification,therapeutic target identification,and SC classification with multi-omics datasets.
基金supported by the Basic Science Research Program(2023R1A2C3004336,RS-202300243807)&Regional Leading Research Center(RS-202400405278)through the National Research Foundation of Korea(NRF)grant funded by the Korea Government(MSIT)。
文摘Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human-machine interfaces for real-time health monitoring,clinical diagnosis,and robotic applications.Nevertheless,it remains a critical challenge to simultaneously achieve desirable mechanical and electrical performance along with biocompatibility,adhesion,self-healing,and environmental robustness with excellent sensing metrics.Herein,we report a multifunctional,anti-freezing,selfadhesive,and self-healable organogel pressure sensor composed of cobalt nanoparticle encapsulated nitrogen-doped carbon nanotubes(CoN CNT)embedded in a polyvinyl alcohol-gelatin(PVA/GLE)matrix.Fabricated using a binary solvent system of water and ethylene glycol(EG),the CoN CNT/PVA/GLE organogel exhibits excellent flexibility,biocompatibility,and temperature tolerance with remarkable environmental stability.Electrochemical impedance spectroscopy confirms near-stable performance across a broad humidity range(40%-95%RH).Freeze-tolerant conductivity under sub-zero conditions(-20℃)is attributed to the synergistic role of CoN CNT and EG,preserving mobility and network integrity.The Co N CNT/PVA/GLE organogel sensor exhibits high sensitivity of 5.75 k Pa^(-1)in the detection range from 0 to 20 k Pa,ideal for subtle biomechanical motion detection.A smart human-machine interface for English letter recognition using deep learning achieved 98%accuracy.The organogel sensor utility was extended to detect human gestures like finger bending,wrist motion,and throat vibration during speech.
基金supported by Technological Innovation 2030-Major Projects of“Brain Science and Brain-like Research,”No.2022ZD0206200(to XG)the National Natural Science Foundation of China,No.82371245(to SJ),82102246(to XD),81701092(to XG)+2 种基金the Natural Science Foundation of Shandong Province,No.ZR2020MH129(to SJ)Shanghai Municipal Key Clinical Specialty,No.shslczdzk03601Shanghai Engineering Research Center of Peri-operative Organ Support and Function Preservation,No.20DZ2254200。
文摘Adult hippocampal neurogenesis is linked to memory formation in the adult brain,with new neurons in the hippocampus exhibiting greater plasticity during their immature stages compared to mature neurons.Abnormal adult hippocampal neurogenesis is closely associated with cognitive impairment in central nervous system diseases.Targeting and regulating adult hippocampal neurogenesis have been shown to improve cognitive deficits.This review aims to expand the current understanding and prospects of targeting neurogenesis in the treatment of cognitive impairment.Recent research indicates the presence of abnormalities in AHN in several diseases associated with cognitive impairment,including cerebrovascular diseases,Alzheimer's disease,aging-related conditions,and issues related to anesthesia and surgery.The role of these abnormalities in the cognitive deficits caused by these diseases has been widely recognized,and targeting AHN is considered a promising approach for treating cognitive impairment.However,the underlying mechanisms of this role are not yet fully understood,and the effectiveness of targeting abnormal adult hippocampal neurogenesis for treatment remains limited,with a need for further development of treatment methods and detection techniques.By reviewing recent studies,we classify the potential mechanisms of adult hippocampal neurogenesis abnormalities into four categories:immunity,energy metabolism,aging,and pathological states.In immunity-related mechanisms,abnormalities in meningeal,brain,and peripheral immunity can disrupt normal adult hippocampal neurogenesis.Lipid metabolism and mitochondrial function disorders are significant energy metabolism factors that lead to abnormal adult hippocampal neurogenesis.During aging,the inflammatory state of the neurogenic niche and the expression of aging-related microRNAs contribute to reduced adult hippocampal neurogenesis and cognitive impairment in older adult patients.Pathological states of the body and emotional disorders may also result in abnormal adult hippocampal neurogenesis.Among the current strategies used to enhance this form of neurogenesis,physical therapies such as exercise,transcutaneous electrical nerve stimulation,and enriched environments have proven effective.Dietary interventions,including energy intake restriction and nutrient optimization,have shown efficacy in both basic research and clinical trials.However,drug treatments,such as antidepressants and stem cell therapy,are primarily reported in basic research,with limited clinical application.The relationship between abnormal adult hippocampal neurogenesis and cognitive impairment has garnered widespread attention,and targeting the former may be an important strategy for treating the latter.However,the mechanisms underlying abnormal adult hippocampal neurogenesis remain unclear,and treatments are lacking.This highlights the need for greater focus on translating research findings into clinical practice.
文摘Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.
基金funding from the National Key Research and Development Program of China(No.2018YFE0110000)the National Natural Science Foundation of China(No.11274259,No.11574258)the Science and Technology Commission Foundation of Shanghai(21DZ1205500)in support of the present research.
文摘While reinforcement learning-based underwater acoustic adaptive modulation shows promise for enabling environment-adaptive communication as supported by extensive simulation-based research,its practical performance remains underexplored in field investigations.To evaluate the practical applicability of this emerging technique in adverse shallow sea channels,a field experiment was conducted using three communication modes:orthogonal frequency division multiplexing(OFDM),M-ary frequency-shift keying(MFSK),and direct sequence spread spectrum(DSSS)for reinforcement learning-driven adaptive modulation.Specifically,a Q-learning method is used to select the optimal modulation mode according to the channel quality quantified by signal-to-noise ratio,multipath spread length,and Doppler frequency offset.Experimental results demonstrate that the reinforcement learning-based adaptive modulation scheme outperformed fixed threshold detection in terms of total throughput and average bit error rate,surpassing conventional adaptive modulation strategies.
