This study critically analyzes the findings of Geng et al,which investigated the association between continuous glucose monitoring(CGM)metrics and the risk of diabetic foot(DF)in individuals with type 2 diabetes melli...This study critically analyzes the findings of Geng et al,which investigated the association between continuous glucose monitoring(CGM)metrics and the risk of diabetic foot(DF)in individuals with type 2 diabetes mellitus.The study de-monstrated significant associations between lower time in range,higher glycemic risk index,mean blood glucose,and time above range and an increased risk of DF.While acknowledging the study's strengths,such as its large sample size and robust statistical methods,this analysis also highlights its limitations,including its cross-sectional design and reliance on self-reported data.The findings are dis-cussed within the framework of established theories,including the concepts of metabolic memory,the glucocentric paradigm,and the role of inflammation.This analysis emphasizes that a comprehensive approach to glucose management,extending beyond traditional glycated hemoglobin A1c measurements,is crucial for DF risk mitigation.Recognizing the impact of poor adherence and ongoing inflammation,future research should prioritize exploring causal mechanisms,the effectiveness of interventions aimed at improving CGM metrics,and the specific contributions of glucose variability to DF development.In conclusion,these findings strongly support the clinical application of diverse CGM metrics to enhance patient outcomes and effectively manage the risk of DF.展开更多
As containerized environments become increasingly prevalent in cloud-native infrastructures,the need for effective monitoring and detection of malicious behaviors has become critical.Malicious containers pose signific...As containerized environments become increasingly prevalent in cloud-native infrastructures,the need for effective monitoring and detection of malicious behaviors has become critical.Malicious containers pose significant risks by exploiting shared host resources,enabling privilege escalation,or launching large-scale attacks such as cryptomining and botnet activities.Therefore,developing accurate and efficient detection mechanisms is essential for ensuring the security and stability of containerized systems.To this end,we propose a hybrid detection framework that leverages the extended Berkeley Packet Filter(eBPF)to monitor container activities directly within the Linux kernel.The framework simultaneously collects flow-based network metadata and host-based system-call traces,transforms them into machine-learning features,and applies multi-class classification models to distinguish malicious containers from benign ones.Using six malicious and four benign container scenarios,our evaluation shows that runtime detection is feasible with high accuracy:flow-based detection achieved 87.49%,while host-based detection using system-call sequences reached 98.39%.The performance difference is largely due to similar communication patterns exhibited by certain malware families which limit the discriminative power of flow-level features.Host-level monitoring,by contrast,exposes fine-grained behavioral characteristics,such as file-system access patterns,persistence mechanisms,and resource-management calls that do not appear in network metadata.Our results further demonstrate that both monitoring modality and preprocessing strategy directly influence model performance.More importantly,combining flow-based and host-based telemetry in a complementary hybrid approach resolves classification ambiguities that arise when relying on a single data source.These findings underscore the potential of eBPF-based hybrid analysis for achieving accurate,low-overhead,and behavior-aware runtime security in containerized environments,and they establish a practical foundation for developing adaptive and scalable detection mechanisms in modern cloud systems.展开更多
This article examines the study by Lin et al,which explores the effects of night sentry duties on cardiometabolic health in military personnel.The research identifies significant correlations between the frequency of ...This article examines the study by Lin et al,which explores the effects of night sentry duties on cardiometabolic health in military personnel.The research identifies significant correlations between the frequency of night shifts and nega-tive cardiometabolic outcomes,such as elevated resting pulse rates and lowered levels of high-density lipoprotein cholesterol.These outcomes underscore the health risks linked to partial sleep deprivation,a common challenge in military environments.The editorial highlights the clinical significance of these findings,advocating for the implementation of targeted health interventions to mitigate these risks.Strategies such as structured sleep recovery programs and lifestyle modifications are recommended to improve the health management of military personnel engaged in nocturnal duties.By addressing these issues,military health management can better safeguard the well-being and operational readiness of its personnel.展开更多
Bariatric surgery significantly improves glycemic control and can lead to type 2 diabetes remission.However,the reliability of glycated hemoglobin(HbA1c)as a type 2 diabetes biomarker post-surgery can be confounded by...Bariatric surgery significantly improves glycemic control and can lead to type 2 diabetes remission.However,the reliability of glycated hemoglobin(HbA1c)as a type 2 diabetes biomarker post-surgery can be confounded by conditions such as anemia and gastrointestinal complications.Hence,we explored the use of alter-native biomarkers such as glycated albumin(GA),1,5-anhydroglucitol(1,5-AG),and insulin-like growth factor binding protein-1(IGFBP-1)to monitor glycemic control more effectively in post-bariatric surgery patients.Measuring GA and 1,5-AG levels can detect glycemic variability more sensitively than HbA1c,especially under non-fasting conditions.GA shows promise for short-term monitoring post-surgery while 1,5-AG could be useful for real-time glucose monitoring.IGFBP-1 can be used to monitor metabolic improvement and to predict HbA1c normal-ization.However,challenges in assay standardization and cost remain significant barriers to their clinical adoption.Although these biomarkers could offer a more personalized approach to glucose monitoring(thereby addressing the limitations of utilizing HbA1c in this endeavor in post-bariatric surgery patients),this would require overcoming technical,logistical,and cost-related challenges.While using GA,1,5-AG,and IGFBP-1 shows promise for glycemic monitoring,further research and validation are crucial for their routine clinical implementation,espe-cially in the context of diabetes management post-bariatric surgery.展开更多
BACKGROUND Internet gaming disorder(IGD)is a growing concern among adolescents and adults,necessitating effective treatment strategies beyond pharmacological interventions.AIM To evaluated the effectiveness of non-inv...BACKGROUND Internet gaming disorder(IGD)is a growing concern among adolescents and adults,necessitating effective treatment strategies beyond pharmacological interventions.AIM To evaluated the effectiveness of non-invasive interventions for treating IGD among adolescents and adults.METHODS A total of 11 randomized controlled trials published between 2020 and 2025 were included in this meta-analysis,encompassing 1208 participants from diverse geographic and cultural contexts.The interventions examined included cognitive behavioral therapy(CBT),internet-based CBT,neurofeedback,virtual reality therapy,abstinence-based programs,and school-based prevention.The primary outcomes assessed were reductions in gaming time and IGD severity.Secondary outcomes included improvements in mood,anxiety,and psychosocial functioning(e.g.,stronger peer relationships,better academic or work performance,and healthier daily-life role fulfillment).RESULTS The pooled standardized mean difference for IGD symptom reduction significantly favored non-invasive interventions(Hedges’g=0.56,95%CI:0.38-0.74,P<0.001),with moderate heterogeneity observed(I2=47%).Subgroup analyses indicated that CBT-based programs,both in-person and online,yielded the strongest effects,particularly when caregiver involvement or self-monitoring was incorporated.Funnel plot asymmetry was minimal,suggesting a low risk of publication bias.CONCLUSION These findings support the efficacy of scalable,low-risk non-invasive interventions as first-line treatment options for IGD,particularly in youth populations.Future studies should prioritize investigating long-term outcomes,comparing the effectiveness of different non-invasive modalities,and developing culturally adaptive delivery methods.展开更多
This letter critically evaluates the study by Yue et al investigating the association between gray matter volume(GMV)and cognitive/motor function in amnestic mild cognitive impairment(aMCI).Yue et al utilized voxel-ba...This letter critically evaluates the study by Yue et al investigating the association between gray matter volume(GMV)and cognitive/motor function in amnestic mild cognitive impairment(aMCI).Yue et al utilized voxel-based morphometry(VBM)and comprehensive functional assessments,finding significant GMV reductions in aMCI patients compared to controls,notably in temporal,parietal,occipital,and frontal regions.These structural changes correlated significantly with lower cognitive scores(mini-metal state examination,cambridge cognitive examination-Chinese version,activities of daily living)and impaired gait parameters(timed up and go test,dual task timed up and go test,speed).While strengths include the use of VBM and combined cognitive-motor assessment,the study's cross-sectional design precludes causal inferences.The reliance on laboratory-based gait analysis may also limit ecological validity.The findings support the potential role of GMV as an aMCI biomarker and highlight the concept of shared neural substrates for cognitive and motor control.Future longitudinal,multi-modal imaging,and mechanistic studies are crucial to confirm causality,understand underlying pathways,and guide the development of integrated interventions for aMCI.展开更多
Personalized management of inflammatory bowel disease(IBD)is crucial due to the heterogeneity in disease presentation,variable therapeutic response,and the unpredictable nature of disease progression.Although artifici...Personalized management of inflammatory bowel disease(IBD)is crucial due to the heterogeneity in disease presentation,variable therapeutic response,and the unpredictable nature of disease progression.Although artificial intelligence(AI)and machine learning algorithms offer promising solutions by analyzing complex,multidimensional patient data,the“black-box”nature of many AI models limits their clinical adoption.Explainable AI(XAI)addresses this challenge by making data-driven predictions more transparent and clinically actionable.This mini-review focuses on recent advancements and clinical relevance of integrating XAI for personalized IBD management.We explore the importance of XAI in priori-tizing treatment and highlight how XAI techniques,such as feature-attribution explanations and interpretable model architectures,enhance transparency in AI models.In recent years,XAI models have been applied to diagnose IBD anomalies by prioritizing the predictive features for gastrointestinal bleeding and dietary intake patterns.Furthermore,studies have revealed that XAI application enhances IBD risk stratification and improves the prediction of drug efficacy and patient responses with high accuracy.By transforming opaque AI models into inter-pretable tools,XAI fosters clinician trust,supports personalized decision-making,and enables the safe deployment of AI systems in sensitive,individualized IBD care pathways.展开更多
BACKGROUND Excessive video game use,recognized as internet gaming disorder in Diagnostic and Statistical Manual of Mental Disorders,Fifth Edition and Gaming Disorder in International Classification of Diseases,11th Re...BACKGROUND Excessive video game use,recognized as internet gaming disorder in Diagnostic and Statistical Manual of Mental Disorders,Fifth Edition and Gaming Disorder in International Classification of Diseases,11th Revision,has raised concerns regarding its effects on individuals’social well-being.AIM To analyze the association between internet gaming disorder and social interaction across Western and Asian populations.METHODS The review examined 14 observational studies published between 2000 and 2025.It assessed the frequency and quality of face-to-face interactions,the shift towards online socialization,and the methodological quality of the included studies.RESULTS The findings generally indicate that gaming addiction is associated with a decrease in the frequency of offline social interaction.Addicted gamers reported spending less time with family and friends and experiencing increased isolation.Furthermore,the quality of social relationships appeared poorer,with addicted gamers reporting higher levels of loneliness,lower social support,and decreased relationship satisfaction.While online social interactions increased,they did not fully compensate for the loss of real-world connections.CONCLUSION This review highlights the potential of gaming addiction to negatively impact overall social lives,emphasizing the necessity for interventions focused on promoting real-world social engagement.展开更多
The study by Lu et al explores the integration of remote family psychological support courses(R-FPSC)with traditional caregiver-mediated interventions(CMI)in the context of autism spectrum disorder(ASD).Conducted as a...The study by Lu et al explores the integration of remote family psychological support courses(R-FPSC)with traditional caregiver-mediated interventions(CMI)in the context of autism spectrum disorder(ASD).Conducted as a singleblinded randomized controlled trial involving 140 parents of children with ASD,the research highlights the crucial role of parental mental health in optimizing therapeutic outcomes.Results indicate that the addition of R-FPSC significantly enhances parental competence and reduces stress more effectively than CMI alone.Despite improvements in parenting stress and competence,no significant differences were noted in anxiety and depression symptoms between the groups,suggesting that while R-FPSC strengthens parenting skills,its impact on mood disorders requires further investigation.The findings advocate for the inclusion of remote psychological support in family interventions as a feasible and costeffective strategy,broadening access to essential resources and improving both parental and child outcomes.The study emphasizes the need for future research to evaluate the long-term impacts of such interventions and to explore the specific mechanisms through which parental mental health improvements affect child development.展开更多
This letter provides a critical appraisal of the comprehensive meta-analysis by Hou et al,which synthesizes the incidence and risk factors for postoperative delirium(POD)in organ transplant recipients.Their work estab...This letter provides a critical appraisal of the comprehensive meta-analysis by Hou et al,which synthesizes the incidence and risk factors for postoperative delirium(POD)in organ transplant recipients.Their work establishes a pooled POD incidence of 20%,with significant variability across organ types(lung 34%,liver 22%,kidney 6%),and identifies key risk factors including primary graft dysfunction,hepatic encephalopathy,and high model for end-stage liver disease/acute physiology and chronic health evaluation Ⅱ scores.This commentary acknowledges the study's strength in providing a robust,trans-organ synthesis of current evidence.However,it critically discusses the substantial heterogeneity,the counterintuitive non-significance of age as a risk factor,and the unavoidable limitation of unmeasured confounders inherent in meta-analyses,such as preoperative cognitive/psychiatric status and anesthetic protocols.While the findings provide an essential evidence base for risk stratification and prevention,this letter argues that the high heterogeneity underscores the need for organ-specific analysis and calls for large-scale,prospective studies with standardized protocols to translate these findings into reliable clinical prediction tools and targeted interventions.展开更多
As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. Ther...As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. There exists a gap in research on the detection and response to attacks on Medium Access Control (MAC) mechanisms themselves, which would lead to service outages between nodes. Classifying exploitation and deceptive jamming attacks on control mechanisms is particularly challengingdue to their resemblance to normal heavy communication patterns. Accordingly, this paper proposes a machine learning-based selective attack mitigation model that detects DoS attacks on wireless networks by monitoring packet log data. Based on the type of detected attack, it implements effective corresponding mitigation techniques to restore performance to nodes whose availability has been compromised. Experimental results reveal that the accuracy of the proposed model is 14% higher than that of a baseline anomaly detection model. Further, the appropriate mitigation techniques selected by the proposed system based on the attack type improve the average throughput by more than 440% compared to the case without a response.展开更多
The Internet of Things(IoT)is extensively applied across various industrial domains,such as smart homes,factories,and intelligent transportation,becoming integral to daily life.Establishing robust policies for managin...The Internet of Things(IoT)is extensively applied across various industrial domains,such as smart homes,factories,and intelligent transportation,becoming integral to daily life.Establishing robust policies for managing and governing IoT devices is imperative.Secure authentication for IoT devices in resource-constrained environments remains challenging due to the limitations of conventional complex protocols.Prior methodologies enhanced mutual authentication through key exchange protocols or complex operations,which are impractical for lightweight devices.To address this,our study introduces the privacy-preserving software-defined range proof(SDRP)model,which achieves secure authentication with low complexity.SDRP minimizes the overhead of confidentiality and authentication processes by utilizing range proof to verify whether the attribute information of a user falls within a specific range.Since authentication is performed using a digital ID sequence generated from indirect personal data,it can avoid the disclosure of actual individual attributes.Experimental results demonstrate that SDRP significantly improves security efficiency,increasing it by an average of 93.02%compared to conventional methods.It mitigates the trade-off between security and efficiency by reducing leakage risk by an average of 98.7%.展开更多
This paper proposes a deep learning-based 3D LiDAR perception framework designed for applications such as autonomous robots and vehicles.To address the high dependency on large-scale annotated data—an inherent limita...This paper proposes a deep learning-based 3D LiDAR perception framework designed for applications such as autonomous robots and vehicles.To address the high dependency on large-scale annotated data—an inherent limitation of deep learning models—this study introduces a hybrid perception architecture that incorporates expertdriven LiDAR processing techniques into the deep neural network.Traditional 3DLiDAR processingmethods typically remove ground planes and apply distance-or density-based clustering for object detection.In this work,such expert knowledge is encoded as feature-level inputs and fused with the deep network,therebymitigating the data dependency issue of conventional learning-based approaches.Specifically,the proposedmethod combines two expert algorithms—Patchwork++for ground segmentation and DBSCAN for clustering—with a PointPillars-based LiDAR detection network.We design four hybrid versions of the network depending on the stage and method of integrating expert features into the feature map of the deep model.Among these,Version 4 incorporates a modified neck structure in PointPillars and introduces a new Cluster 2D Pseudo-Map Branch that utilizes cluster-level pseudo-images generated from Patchwork++and DBSCAN.This version achieved a+3.88%improvement mean Average Precision(mAP)compared to the baseline PointPillars.The results demonstrate that embedding expert-based perception logic into deep neural architectures can effectively enhance performance and reduce dependency on extensive training datasets,offering a promising direction for robust 3D LiDAR object detection in real-world scenarios.展开更多
Nanohairs, which can be found on the epidermis of Tokay gecko＇s toes, contribute to the adhesion by means of van der Waals force, capillary force, etc. This structure has inspired many researchers to fabricate the at...Nanohairs, which can be found on the epidermis of Tokay gecko＇s toes, contribute to the adhesion by means of van der Waals force, capillary force, etc. This structure has inspired many researchers to fabricate the attachable nano-scale structures. However, the efficiency of artificial nano-scale structures is not reliable sufficiently. Moreover, the mechanical parameters related to the nano-hair attachment are not yet revealed qualitatively. The mechanical parameters which have influence on the ability of adhesive nano-hairs were investigated through numerical simulation in which only van der Waals force was considered. For the numerical analysis, finite element method was utilized and van der Waals force, assumed as 12-6 Lennard-Jones potential, was implemented as the body force term in the finite element formulation.展开更多
As time and space constraints decrease due to the development of wireless communication network technology,the scale and scope of cyber-attacks targeting the Internet of Things(IoT)are increasing.However,it is difficu...As time and space constraints decrease due to the development of wireless communication network technology,the scale and scope of cyber-attacks targeting the Internet of Things(IoT)are increasing.However,it is difficult to apply high-performance security modules to the IoT owing to the limited battery,memory capacity,and data transmission performance depend-ing on the size of the device.Conventional research has mainly reduced power consumption by lightening encryption algorithms.However,it is difficult to defend large-scale information systems and networks against advanced and intelligent attacks because of the problem of deteriorating security perfor-mance.In this study,we propose wake-up security(WuS),a low-power security architecture that can utilize high-performance security algorithms in an IoT environment.By introducing a small logic that performs anomaly detection on the IoT platform and executes the security module only when necessary according to the anomaly detection result,WuS improves security and power efficiency while using a relatively high-complexity security module in a low-power environment compared to the conventional method of periodically exe-cuting a high-performance security module.In this study,a Python simulator based on the UNSW-NB15 dataset is used to evaluate the power consumption,latency,and security of the proposed method.The evaluation results reveal that the power consumption of the proposed WuS mechanism is approxi-mately 51.8%and 27.2%lower than those of conventional high-performance security and lightweight security modules,respectively.Additionally,the laten-cies are approximately 74.8%and 65.9%lower,respectively.Furthermore,the WuS mechanism achieved a high detection accuracy of approximately 96.5%or greater,proving that the detection efficiency performance improved by approximately 33.5%compared to the conventional model.The performance evaluation results for the proposed model varied depending on the applied anomaly-detection model.Therefore,they can be used in various ways by selecting suitable models based on the performance levels required in each industry.展开更多
With the introduction of 5G technology,the application of Internet of Things(IoT)devices is expanding to various industrial fields.However,introducing a robust,lightweight,low-cost,and low-power security solution to t...With the introduction of 5G technology,the application of Internet of Things(IoT)devices is expanding to various industrial fields.However,introducing a robust,lightweight,low-cost,and low-power security solution to the IoT environment is challenging.Therefore,this study proposes two methods using a data compression technique to detect malicious traffic efficiently and accurately for a secure IoT environment.The first method,compressed sensing and learning(CSL),compresses an event log in a bitmap format to quickly detect attacks.Then,the attack log is detected using a machine-learning classification model.The second method,precise re-learning after CSL(Ra-CSL),comprises a two-step training.It uses CSL as the 1st step analyzer,and the 2nd step analyzer is applied using the original dataset for a log that is detected as an attack in the 1st step analyzer.In the experiment,the bitmap rule was set based on the boundary value,which was 99.6%true positive on average for the attack and benign data found by analyzing the training data.Experimental results showed that the CSL was effective in reducing the training and detection time,and Ra-CSL was effective in increasing the detection rate.According to the experimental results,the data compression technique reduced the memory size by up to 20%and the training and detection times by 67%when compared with the conventional technique.In addition,the proposed technique improves the detection accuracy;the Naive Bayes model with the highest performance showed a detection rate of approximately 99%.展开更多
With the development of the 5th generation of mobile communi-cation(5G)networks and artificial intelligence(AI)technologies,the use of the Internet of Things(IoT)has expanded throughout industry.Although IoT networks ...With the development of the 5th generation of mobile communi-cation(5G)networks and artificial intelligence(AI)technologies,the use of the Internet of Things(IoT)has expanded throughout industry.Although IoT networks have improved industrial productivity and convenience,they are highly dependent on nonstandard protocol stacks and open-source-based,poorly validated software,resulting in several security vulnerabilities.How-ever,conventional AI-based software vulnerability discovery technologies cannot be applied to IoT because they require excessive memory and com-puting power.This study developed a technique for optimizing training data size to detect software vulnerabilities rapidly while maintaining learning accuracy.Experimental results using a software vulnerability classification dataset showed that different optimal data sizes did not affect the learning performance of the learning models.Moreover,the minimal data size required to train a model without performance degradation could be determined in advance.For example,the random forest model saved 85.18%of memory and improved latency by 97.82%while maintaining a learning accuracy similar to that achieved when using 100%of data,despite using only 1%.展开更多
This study addressed the critical need for an integrated,personalized approach to perimenopausal mental health,addressing both biological and psychosocial fac-tors.Current research highlighted the influence of hormona...This study addressed the critical need for an integrated,personalized approach to perimenopausal mental health,addressing both biological and psychosocial fac-tors.Current research highlighted the influence of hormonal fluctuations,genetic predispositions,and lifestyle factors in shaping perimenopausal mental health outcomes.This transitional period is marked by significant hormonal fluctuations contributing to heightened anxiety,depression,and sleep disturbances,affecting the women’s quality of life.Traditional pharmacological treatments,including selective serotonin reuptake inhibitors and hormone replacement therapy,have limitations due to variable efficacy and side effects,emphasizing the need for precision medicine.Advancements in pharmacogenomics and metabolomics provide new avenues for individualized treatments,with genetic markers(e.g.,Solute carrier organic anion transporter family member 1B1,estrogen receptor 1/estrogen receptor 2,and tachykinin receptor 3)guiding hormone therapy resp-onses.Emerging digital health technologies,such as artificial intelligence-driven diagnostics,wearable monitoring,and telehealth platforms,offer scalable,real-time mental health support,though regulatory and clinical validation challenges remain.Furthermore,integrative treatment models combining hormone-based therapy with non-pharmacological interventions demonstrate significant efficacy in alleviating perimenopausal symptoms.Future directions should prioritize the clinical validation and ethical implementation of digital health solutions,ensuring safety,efficacy,and user accessibility.A multidisciplinary,patient-centric model,incorporating genetics,endocrinology,digital health,and psychosocial interventions,is essential for optimizing perimenopausal mental health outcomes.展开更多
Sleep disorders,particularly insomnia,have emerged as a critical public health challenge,with the situation worsened by the coronavirus disease-2019 pandemic.Insomnia symptoms,which affected up to 45%of the population...Sleep disorders,particularly insomnia,have emerged as a critical public health challenge,with the situation worsened by the coronavirus disease-2019 pandemic.Insomnia symptoms,which affected up to 45%of the population during this period,highlight the urgent need to understand the mechanisms linking sleep disturbances to mental health outcomes.Recent findings suggest that cognitive failures,such as memory lapses and attentional deficits,mediate the relationship between insomnia and emotional disorders such as anxiety and depression.The role of personality traits,particularly neuroticism,adds further complexity,as it may either exacerbate or buffer these effects under specific conditions.This review explores the study by Li et al,which offers valuable insights into the cognitive-emotional pathways influenced by sleep disturbances.The study makes significant contributions by identifying key cognitive mechanisms and proposing the dual role of neuroticism in shaping emotional outcomes.To advance these findings,this letter advocates for future longitudinal research and the integration of targeted interventions,such as cognitive-behavioral therapy for insomnia,into public health frameworks.By addressing insomnia-induced cognitive dysfunction,these strategies can enhance emotional regulation and foster resilience,particularly in vulnerable populations facing the mental health impacts of the pandemic.展开更多
Yu et al's study has advanced the understanding of the neural mechanisms underlying major depressive disorder(MDD)in adolescents,emphasizing the significant role of the amygdala.While traditional diagnostic method...Yu et al's study has advanced the understanding of the neural mechanisms underlying major depressive disorder(MDD)in adolescents,emphasizing the significant role of the amygdala.While traditional diagnostic methods have limitations in objectivity and accuracy,this research demonstrates a notable advancement through the integration of machine learning techniques with neuroimaging data.Utilizing resting-state functional magnetic resonance imaging(fMRI),the study investigated functional connectivity(FC)in adolescents with MDD,identifying notable reductions in regions such as the left inferior temporal gyrus and right lingual gyrus,alongside increased connectivity in Vermis-10.The application of support vector machines(SVM)to resting-state fMRI(rs-fMRI)data achieved an accuracy of 83.91%,sensitivity of 79.55%,and specificity of 88.37%,with an area under the curve of 0.6765.These results demonstrate how SVM analysis of rs-fMRI data represents a significant improvement in diagnostic precision,with reduced FC in the right lingual gyrus emerging as a particularly critical marker.These findings underscore the critical role of the amygdala in MDD pathophysiology and highlight the potential of rs-fMRI and SVM as tools for identifying reliable neuroimaging biomarkers.展开更多
基金Supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education,No.NRF-RS-2023-00237287。
文摘This study critically analyzes the findings of Geng et al,which investigated the association between continuous glucose monitoring(CGM)metrics and the risk of diabetic foot(DF)in individuals with type 2 diabetes mellitus.The study de-monstrated significant associations between lower time in range,higher glycemic risk index,mean blood glucose,and time above range and an increased risk of DF.While acknowledging the study's strengths,such as its large sample size and robust statistical methods,this analysis also highlights its limitations,including its cross-sectional design and reliance on self-reported data.The findings are dis-cussed within the framework of established theories,including the concepts of metabolic memory,the glucocentric paradigm,and the role of inflammation.This analysis emphasizes that a comprehensive approach to glucose management,extending beyond traditional glycated hemoglobin A1c measurements,is crucial for DF risk mitigation.Recognizing the impact of poor adherence and ongoing inflammation,future research should prioritize exploring causal mechanisms,the effectiveness of interventions aimed at improving CGM metrics,and the specific contributions of glucose variability to DF development.In conclusion,these findings strongly support the clinical application of diverse CGM metrics to enhance patient outcomes and effectively manage the risk of DF.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2024-00351898 and No.RS-2025-02263915)the MOTIE under Training Industrial Security Specialist forHigh-Tech Industry(RS-2024-00415520)supervised by theKorea Institute for Advancement of Technology(KIAT)+1 种基金the MSIT under the ICAN(ICT Challenge and Advanced Network of HRD)program(No.IITP-2022-RS-2022-00156310)supervised by the Institute of Information&Communication Technology Planning&Evaluation(IITP).
文摘As containerized environments become increasingly prevalent in cloud-native infrastructures,the need for effective monitoring and detection of malicious behaviors has become critical.Malicious containers pose significant risks by exploiting shared host resources,enabling privilege escalation,or launching large-scale attacks such as cryptomining and botnet activities.Therefore,developing accurate and efficient detection mechanisms is essential for ensuring the security and stability of containerized systems.To this end,we propose a hybrid detection framework that leverages the extended Berkeley Packet Filter(eBPF)to monitor container activities directly within the Linux kernel.The framework simultaneously collects flow-based network metadata and host-based system-call traces,transforms them into machine-learning features,and applies multi-class classification models to distinguish malicious containers from benign ones.Using six malicious and four benign container scenarios,our evaluation shows that runtime detection is feasible with high accuracy:flow-based detection achieved 87.49%,while host-based detection using system-call sequences reached 98.39%.The performance difference is largely due to similar communication patterns exhibited by certain malware families which limit the discriminative power of flow-level features.Host-level monitoring,by contrast,exposes fine-grained behavioral characteristics,such as file-system access patterns,persistence mechanisms,and resource-management calls that do not appear in network metadata.Our results further demonstrate that both monitoring modality and preprocessing strategy directly influence model performance.More importantly,combining flow-based and host-based telemetry in a complementary hybrid approach resolves classification ambiguities that arise when relying on a single data source.These findings underscore the potential of eBPF-based hybrid analysis for achieving accurate,low-overhead,and behavior-aware runtime security in containerized environments,and they establish a practical foundation for developing adaptive and scalable detection mechanisms in modern cloud systems.
基金Supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education,No.NRF-RS-2023-00237287.
文摘This article examines the study by Lin et al,which explores the effects of night sentry duties on cardiometabolic health in military personnel.The research identifies significant correlations between the frequency of night shifts and nega-tive cardiometabolic outcomes,such as elevated resting pulse rates and lowered levels of high-density lipoprotein cholesterol.These outcomes underscore the health risks linked to partial sleep deprivation,a common challenge in military environments.The editorial highlights the clinical significance of these findings,advocating for the implementation of targeted health interventions to mitigate these risks.Strategies such as structured sleep recovery programs and lifestyle modifications are recommended to improve the health management of military personnel engaged in nocturnal duties.By addressing these issues,military health management can better safeguard the well-being and operational readiness of its personnel.
基金Supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education,No.NRF-RS 2023-00237287.
文摘Bariatric surgery significantly improves glycemic control and can lead to type 2 diabetes remission.However,the reliability of glycated hemoglobin(HbA1c)as a type 2 diabetes biomarker post-surgery can be confounded by conditions such as anemia and gastrointestinal complications.Hence,we explored the use of alter-native biomarkers such as glycated albumin(GA),1,5-anhydroglucitol(1,5-AG),and insulin-like growth factor binding protein-1(IGFBP-1)to monitor glycemic control more effectively in post-bariatric surgery patients.Measuring GA and 1,5-AG levels can detect glycemic variability more sensitively than HbA1c,especially under non-fasting conditions.GA shows promise for short-term monitoring post-surgery while 1,5-AG could be useful for real-time glucose monitoring.IGFBP-1 can be used to monitor metabolic improvement and to predict HbA1c normal-ization.However,challenges in assay standardization and cost remain significant barriers to their clinical adoption.Although these biomarkers could offer a more personalized approach to glucose monitoring(thereby addressing the limitations of utilizing HbA1c in this endeavor in post-bariatric surgery patients),this would require overcoming technical,logistical,and cost-related challenges.While using GA,1,5-AG,and IGFBP-1 shows promise for glycemic monitoring,further research and validation are crucial for their routine clinical implementation,espe-cially in the context of diabetes management post-bariatric surgery.
基金Supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)Funded by the Ministry of Education,No.NRF-RS-2023-00237287.
文摘BACKGROUND Internet gaming disorder(IGD)is a growing concern among adolescents and adults,necessitating effective treatment strategies beyond pharmacological interventions.AIM To evaluated the effectiveness of non-invasive interventions for treating IGD among adolescents and adults.METHODS A total of 11 randomized controlled trials published between 2020 and 2025 were included in this meta-analysis,encompassing 1208 participants from diverse geographic and cultural contexts.The interventions examined included cognitive behavioral therapy(CBT),internet-based CBT,neurofeedback,virtual reality therapy,abstinence-based programs,and school-based prevention.The primary outcomes assessed were reductions in gaming time and IGD severity.Secondary outcomes included improvements in mood,anxiety,and psychosocial functioning(e.g.,stronger peer relationships,better academic or work performance,and healthier daily-life role fulfillment).RESULTS The pooled standardized mean difference for IGD symptom reduction significantly favored non-invasive interventions(Hedges’g=0.56,95%CI:0.38-0.74,P<0.001),with moderate heterogeneity observed(I2=47%).Subgroup analyses indicated that CBT-based programs,both in-person and online,yielded the strongest effects,particularly when caregiver involvement or self-monitoring was incorporated.Funnel plot asymmetry was minimal,suggesting a low risk of publication bias.CONCLUSION These findings support the efficacy of scalable,low-risk non-invasive interventions as first-line treatment options for IGD,particularly in youth populations.Future studies should prioritize investigating long-term outcomes,comparing the effectiveness of different non-invasive modalities,and developing culturally adaptive delivery methods.
基金Supported by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education,No.NRF-RS-2023-00237287New Professor Research Program of KOREATECH in 2025.
文摘This letter critically evaluates the study by Yue et al investigating the association between gray matter volume(GMV)and cognitive/motor function in amnestic mild cognitive impairment(aMCI).Yue et al utilized voxel-based morphometry(VBM)and comprehensive functional assessments,finding significant GMV reductions in aMCI patients compared to controls,notably in temporal,parietal,occipital,and frontal regions.These structural changes correlated significantly with lower cognitive scores(mini-metal state examination,cambridge cognitive examination-Chinese version,activities of daily living)and impaired gait parameters(timed up and go test,dual task timed up and go test,speed).While strengths include the use of VBM and combined cognitive-motor assessment,the study's cross-sectional design precludes causal inferences.The reliance on laboratory-based gait analysis may also limit ecological validity.The findings support the potential role of GMV as an aMCI biomarker and highlight the concept of shared neural substrates for cognitive and motor control.Future longitudinal,multi-modal imaging,and mechanistic studies are crucial to confirm causality,understand underlying pathways,and guide the development of integrated interventions for aMCI.
基金Supported by National Research Foundation of Korea,No.RS-2023-00237287。
文摘Personalized management of inflammatory bowel disease(IBD)is crucial due to the heterogeneity in disease presentation,variable therapeutic response,and the unpredictable nature of disease progression.Although artificial intelligence(AI)and machine learning algorithms offer promising solutions by analyzing complex,multidimensional patient data,the“black-box”nature of many AI models limits their clinical adoption.Explainable AI(XAI)addresses this challenge by making data-driven predictions more transparent and clinically actionable.This mini-review focuses on recent advancements and clinical relevance of integrating XAI for personalized IBD management.We explore the importance of XAI in priori-tizing treatment and highlight how XAI techniques,such as feature-attribution explanations and interpretable model architectures,enhance transparency in AI models.In recent years,XAI models have been applied to diagnose IBD anomalies by prioritizing the predictive features for gastrointestinal bleeding and dietary intake patterns.Furthermore,studies have revealed that XAI application enhances IBD risk stratification and improves the prediction of drug efficacy and patient responses with high accuracy.By transforming opaque AI models into inter-pretable tools,XAI fosters clinician trust,supports personalized decision-making,and enables the safe deployment of AI systems in sensitive,individualized IBD care pathways.
基金Supported by the Basic Science Research Program through the National Research Foundation of Korea Funded by the Ministry of Education,No.NRF-RS-2023-00237287。
文摘BACKGROUND Excessive video game use,recognized as internet gaming disorder in Diagnostic and Statistical Manual of Mental Disorders,Fifth Edition and Gaming Disorder in International Classification of Diseases,11th Revision,has raised concerns regarding its effects on individuals’social well-being.AIM To analyze the association between internet gaming disorder and social interaction across Western and Asian populations.METHODS The review examined 14 observational studies published between 2000 and 2025.It assessed the frequency and quality of face-to-face interactions,the shift towards online socialization,and the methodological quality of the included studies.RESULTS The findings generally indicate that gaming addiction is associated with a decrease in the frequency of offline social interaction.Addicted gamers reported spending less time with family and friends and experiencing increased isolation.Furthermore,the quality of social relationships appeared poorer,with addicted gamers reporting higher levels of loneliness,lower social support,and decreased relationship satisfaction.While online social interactions increased,they did not fully compensate for the loss of real-world connections.CONCLUSION This review highlights the potential of gaming addiction to negatively impact overall social lives,emphasizing the necessity for interventions focused on promoting real-world social engagement.
基金Supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education,No.NRF-RS-2023-00237287.
文摘The study by Lu et al explores the integration of remote family psychological support courses(R-FPSC)with traditional caregiver-mediated interventions(CMI)in the context of autism spectrum disorder(ASD).Conducted as a singleblinded randomized controlled trial involving 140 parents of children with ASD,the research highlights the crucial role of parental mental health in optimizing therapeutic outcomes.Results indicate that the addition of R-FPSC significantly enhances parental competence and reduces stress more effectively than CMI alone.Despite improvements in parenting stress and competence,no significant differences were noted in anxiety and depression symptoms between the groups,suggesting that while R-FPSC strengthens parenting skills,its impact on mood disorders requires further investigation.The findings advocate for the inclusion of remote psychological support in family interventions as a feasible and costeffective strategy,broadening access to essential resources and improving both parental and child outcomes.The study emphasizes the need for future research to evaluate the long-term impacts of such interventions and to explore the specific mechanisms through which parental mental health improvements affect child development.
基金Supported by National Research Foundation of Korea,No.RS-2023-00237287.
文摘This letter provides a critical appraisal of the comprehensive meta-analysis by Hou et al,which synthesizes the incidence and risk factors for postoperative delirium(POD)in organ transplant recipients.Their work establishes a pooled POD incidence of 20%,with significant variability across organ types(lung 34%,liver 22%,kidney 6%),and identifies key risk factors including primary graft dysfunction,hepatic encephalopathy,and high model for end-stage liver disease/acute physiology and chronic health evaluation Ⅱ scores.This commentary acknowledges the study's strength in providing a robust,trans-organ synthesis of current evidence.However,it critically discusses the substantial heterogeneity,the counterintuitive non-significance of age as a risk factor,and the unavoidable limitation of unmeasured confounders inherent in meta-analyses,such as preoperative cognitive/psychiatric status and anesthetic protocols.While the findings provide an essential evidence base for risk stratification and prevention,this letter argues that the high heterogeneity underscores the need for organ-specific analysis and calls for large-scale,prospective studies with standardized protocols to translate these findings into reliable clinical prediction tools and targeted interventions.
基金supported by the Ministry of Trade,Industry and Energy(MOTIE)under Training Industrial Security Specialist for High-Tech Industry(RS-2024-00415520)supervised by the Korea Institute for Advancement of Technology(KIAT)the Ministry of Science and ICT(MSIT)under the ICT Challenge and Advanced Network of HRD(ICAN)Program(No.IITP-2022-RS-2022-00156310)supervised by the Institute of Information&Communication Technology Planning&Evaluation(IITP).
文摘As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. There exists a gap in research on the detection and response to attacks on Medium Access Control (MAC) mechanisms themselves, which would lead to service outages between nodes. Classifying exploitation and deceptive jamming attacks on control mechanisms is particularly challengingdue to their resemblance to normal heavy communication patterns. Accordingly, this paper proposes a machine learning-based selective attack mitigation model that detects DoS attacks on wireless networks by monitoring packet log data. Based on the type of detected attack, it implements effective corresponding mitigation techniques to restore performance to nodes whose availability has been compromised. Experimental results reveal that the accuracy of the proposed model is 14% higher than that of a baseline anomaly detection model. Further, the appropriate mitigation techniques selected by the proposed system based on the attack type improve the average throughput by more than 440% compared to the case without a response.
基金funding from the Korea Institute for Advancement of Technology(KIAT)through a grant provided by the Korean Government Ministry of Trade,Industry,and Energy(MOTIE)(RS-2024-00415520,Training Industrial Security Specialist for High-Tech Industry)Additional support was received from the Ministry of Science and ICT(MSIT)under the ICAN(ICT Challenge and Advanced Network of HRD)program(No.IITP-2022-RS-2022-00156310)overseen by the Institute of Information&Communication Technology Planning and Evaluation(IITP).
文摘The Internet of Things(IoT)is extensively applied across various industrial domains,such as smart homes,factories,and intelligent transportation,becoming integral to daily life.Establishing robust policies for managing and governing IoT devices is imperative.Secure authentication for IoT devices in resource-constrained environments remains challenging due to the limitations of conventional complex protocols.Prior methodologies enhanced mutual authentication through key exchange protocols or complex operations,which are impractical for lightweight devices.To address this,our study introduces the privacy-preserving software-defined range proof(SDRP)model,which achieves secure authentication with low complexity.SDRP minimizes the overhead of confidentiality and authentication processes by utilizing range proof to verify whether the attribute information of a user falls within a specific range.Since authentication is performed using a digital ID sequence generated from indirect personal data,it can avoid the disclosure of actual individual attributes.Experimental results demonstrate that SDRP significantly improves security efficiency,increasing it by an average of 93.02%compared to conventional methods.It mitigates the trade-off between security and efficiency by reducing leakage risk by an average of 98.7%.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2023-00245084)by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(RS-2024-00415938,HRD Program for Industrial Innovation)and Soonchunhyang University.
文摘This paper proposes a deep learning-based 3D LiDAR perception framework designed for applications such as autonomous robots and vehicles.To address the high dependency on large-scale annotated data—an inherent limitation of deep learning models—this study introduces a hybrid perception architecture that incorporates expertdriven LiDAR processing techniques into the deep neural network.Traditional 3DLiDAR processingmethods typically remove ground planes and apply distance-or density-based clustering for object detection.In this work,such expert knowledge is encoded as feature-level inputs and fused with the deep network,therebymitigating the data dependency issue of conventional learning-based approaches.Specifically,the proposedmethod combines two expert algorithms—Patchwork++for ground segmentation and DBSCAN for clustering—with a PointPillars-based LiDAR detection network.We design four hybrid versions of the network depending on the stage and method of integrating expert features into the feature map of the deep model.Among these,Version 4 incorporates a modified neck structure in PointPillars and introduces a new Cluster 2D Pseudo-Map Branch that utilizes cluster-level pseudo-images generated from Patchwork++and DBSCAN.This version achieved a+3.88%improvement mean Average Precision(mAP)compared to the baseline PointPillars.The results demonstrate that embedding expert-based perception logic into deep neural architectures can effectively enhance performance and reduce dependency on extensive training datasets,offering a promising direction for robust 3D LiDAR object detection in real-world scenarios.
文摘Nanohairs, which can be found on the epidermis of Tokay gecko＇s toes, contribute to the adhesion by means of van der Waals force, capillary force, etc. This structure has inspired many researchers to fabricate the attachable nano-scale structures. However, the efficiency of artificial nano-scale structures is not reliable sufficiently. Moreover, the mechanical parameters related to the nano-hair attachment are not yet revealed qualitatively. The mechanical parameters which have influence on the ability of adhesive nano-hairs were investigated through numerical simulation in which only van der Waals force was considered. For the numerical analysis, finite element method was utilized and van der Waals force, assumed as 12-6 Lennard-Jones potential, was implemented as the body force term in the finite element formulation.
基金supplemented by a paper presented at the 6th International Symposium on Mobile Internet Security(MobiSec 2022).
文摘As time and space constraints decrease due to the development of wireless communication network technology,the scale and scope of cyber-attacks targeting the Internet of Things(IoT)are increasing.However,it is difficult to apply high-performance security modules to the IoT owing to the limited battery,memory capacity,and data transmission performance depend-ing on the size of the device.Conventional research has mainly reduced power consumption by lightening encryption algorithms.However,it is difficult to defend large-scale information systems and networks against advanced and intelligent attacks because of the problem of deteriorating security perfor-mance.In this study,we propose wake-up security(WuS),a low-power security architecture that can utilize high-performance security algorithms in an IoT environment.By introducing a small logic that performs anomaly detection on the IoT platform and executes the security module only when necessary according to the anomaly detection result,WuS improves security and power efficiency while using a relatively high-complexity security module in a low-power environment compared to the conventional method of periodically exe-cuting a high-performance security module.In this study,a Python simulator based on the UNSW-NB15 dataset is used to evaluate the power consumption,latency,and security of the proposed method.The evaluation results reveal that the power consumption of the proposed WuS mechanism is approxi-mately 51.8%and 27.2%lower than those of conventional high-performance security and lightweight security modules,respectively.Additionally,the laten-cies are approximately 74.8%and 65.9%lower,respectively.Furthermore,the WuS mechanism achieved a high detection accuracy of approximately 96.5%or greater,proving that the detection efficiency performance improved by approximately 33.5%compared to the conventional model.The performance evaluation results for the proposed model varied depending on the applied anomaly-detection model.Therefore,they can be used in various ways by selecting suitable models based on the performance levels required in each industry.
基金supported by a Korea Institute for Advancement of Technology(KIAT)Grant funded by theKorean Government(MOTIE)(P0008703,The Competency Development Program for Industry Specialists)the MSIT under the ICAN(ICT Challenge and Advanced Network ofHRD)program(No.IITP-2022-RS-2022-00156310)supervised by the Institute of Information Communication Technology Planning and Evaluation(IITP).
文摘With the introduction of 5G technology,the application of Internet of Things(IoT)devices is expanding to various industrial fields.However,introducing a robust,lightweight,low-cost,and low-power security solution to the IoT environment is challenging.Therefore,this study proposes two methods using a data compression technique to detect malicious traffic efficiently and accurately for a secure IoT environment.The first method,compressed sensing and learning(CSL),compresses an event log in a bitmap format to quickly detect attacks.Then,the attack log is detected using a machine-learning classification model.The second method,precise re-learning after CSL(Ra-CSL),comprises a two-step training.It uses CSL as the 1st step analyzer,and the 2nd step analyzer is applied using the original dataset for a log that is detected as an attack in the 1st step analyzer.In the experiment,the bitmap rule was set based on the boundary value,which was 99.6%true positive on average for the attack and benign data found by analyzing the training data.Experimental results showed that the CSL was effective in reducing the training and detection time,and Ra-CSL was effective in increasing the detection rate.According to the experimental results,the data compression technique reduced the memory size by up to 20%and the training and detection times by 67%when compared with the conventional technique.In addition,the proposed technique improves the detection accuracy;the Naive Bayes model with the highest performance showed a detection rate of approximately 99%.
基金supported by a National Research Foundation of Korea (NRF)grant funded by the Ministry of Science and ICT (MSIT) (No.2020R1F1A1061107)the Korea Institute for Advancement of Technology (KIAT)grant funded by the Korean Government (MOTIE) (P0008703,The Competency Development Program for Industry Specialists)the MSIT under the ICAN (ICT Challenge and Advanced Network of HRD)program (No.IITP-2022-RS-2022-00156310)supervised by the Institute of Information&Communication Technology Planning and Evaluation (IITP).
文摘With the development of the 5th generation of mobile communi-cation(5G)networks and artificial intelligence(AI)technologies,the use of the Internet of Things(IoT)has expanded throughout industry.Although IoT networks have improved industrial productivity and convenience,they are highly dependent on nonstandard protocol stacks and open-source-based,poorly validated software,resulting in several security vulnerabilities.How-ever,conventional AI-based software vulnerability discovery technologies cannot be applied to IoT because they require excessive memory and com-puting power.This study developed a technique for optimizing training data size to detect software vulnerabilities rapidly while maintaining learning accuracy.Experimental results using a software vulnerability classification dataset showed that different optimal data sizes did not affect the learning performance of the learning models.Moreover,the minimal data size required to train a model without performance degradation could be determined in advance.For example,the random forest model saved 85.18%of memory and improved latency by 97.82%while maintaining a learning accuracy similar to that achieved when using 100%of data,despite using only 1%.
基金Supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education,No.RS-2023-00237287。
文摘This study addressed the critical need for an integrated,personalized approach to perimenopausal mental health,addressing both biological and psychosocial fac-tors.Current research highlighted the influence of hormonal fluctuations,genetic predispositions,and lifestyle factors in shaping perimenopausal mental health outcomes.This transitional period is marked by significant hormonal fluctuations contributing to heightened anxiety,depression,and sleep disturbances,affecting the women’s quality of life.Traditional pharmacological treatments,including selective serotonin reuptake inhibitors and hormone replacement therapy,have limitations due to variable efficacy and side effects,emphasizing the need for precision medicine.Advancements in pharmacogenomics and metabolomics provide new avenues for individualized treatments,with genetic markers(e.g.,Solute carrier organic anion transporter family member 1B1,estrogen receptor 1/estrogen receptor 2,and tachykinin receptor 3)guiding hormone therapy resp-onses.Emerging digital health technologies,such as artificial intelligence-driven diagnostics,wearable monitoring,and telehealth platforms,offer scalable,real-time mental health support,though regulatory and clinical validation challenges remain.Furthermore,integrative treatment models combining hormone-based therapy with non-pharmacological interventions demonstrate significant efficacy in alleviating perimenopausal symptoms.Future directions should prioritize the clinical validation and ethical implementation of digital health solutions,ensuring safety,efficacy,and user accessibility.A multidisciplinary,patient-centric model,incorporating genetics,endocrinology,digital health,and psychosocial interventions,is essential for optimizing perimenopausal mental health outcomes.
基金Supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education,No.NRF-RS-2023-00237287.
文摘Sleep disorders,particularly insomnia,have emerged as a critical public health challenge,with the situation worsened by the coronavirus disease-2019 pandemic.Insomnia symptoms,which affected up to 45%of the population during this period,highlight the urgent need to understand the mechanisms linking sleep disturbances to mental health outcomes.Recent findings suggest that cognitive failures,such as memory lapses and attentional deficits,mediate the relationship between insomnia and emotional disorders such as anxiety and depression.The role of personality traits,particularly neuroticism,adds further complexity,as it may either exacerbate or buffer these effects under specific conditions.This review explores the study by Li et al,which offers valuable insights into the cognitive-emotional pathways influenced by sleep disturbances.The study makes significant contributions by identifying key cognitive mechanisms and proposing the dual role of neuroticism in shaping emotional outcomes.To advance these findings,this letter advocates for future longitudinal research and the integration of targeted interventions,such as cognitive-behavioral therapy for insomnia,into public health frameworks.By addressing insomnia-induced cognitive dysfunction,these strategies can enhance emotional regulation and foster resilience,particularly in vulnerable populations facing the mental health impacts of the pandemic.
基金Supported by Basic Science Research Program through the National Research Foundation of Korea Funded by the Ministry of Education,No.NRF-RS-2023-00237287.
文摘Yu et al's study has advanced the understanding of the neural mechanisms underlying major depressive disorder(MDD)in adolescents,emphasizing the significant role of the amygdala.While traditional diagnostic methods have limitations in objectivity and accuracy,this research demonstrates a notable advancement through the integration of machine learning techniques with neuroimaging data.Utilizing resting-state functional magnetic resonance imaging(fMRI),the study investigated functional connectivity(FC)in adolescents with MDD,identifying notable reductions in regions such as the left inferior temporal gyrus and right lingual gyrus,alongside increased connectivity in Vermis-10.The application of support vector machines(SVM)to resting-state fMRI(rs-fMRI)data achieved an accuracy of 83.91%,sensitivity of 79.55%,and specificity of 88.37%,with an area under the curve of 0.6765.These results demonstrate how SVM analysis of rs-fMRI data represents a significant improvement in diagnostic precision,with reduced FC in the right lingual gyrus emerging as a particularly critical marker.These findings underscore the critical role of the amygdala in MDD pathophysiology and highlight the potential of rs-fMRI and SVM as tools for identifying reliable neuroimaging biomarkers.
