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
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
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 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
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
The rising prevalence of chronic multimorbidity poses substantial challenges to healthcare systems,necessitating the development of innovative management strategies to optimize patient care and system efficiency.The s...The rising prevalence of chronic multimorbidity poses substantial challenges to healthcare systems,necessitating the development of innovative management strategies to optimize patient care and system efficiency.The study by Fontalba-Navas et al investigates the implementation of a novel high complexity unit(HCU)specifically designed to improve the management of patients with chronic complex conditions.By adopting a multidisciplinary approach,the HCU aims to provide comprehensive,patient-centered care that enhances health outcomes and alleviates the strain on traditional hospital services.Utilizing a longitudinal analysis of data from the Basic Minimum Data Set,this study compares hospitalization metrics among the HCU,Internal Medicine,and other departments within a regional hospital throughout 2022.The findings reveal that the HCU's integrated care model significantly reduces readmission rates and boosts patient satisfaction compared to conventional care practices.The study highlights the HCU's potential as a replicable model for managing chronic multimorbidity,emphasizing its effectiveness in minimizing unnecessary hospitalizations and enhancing the overall quality of patient care.This innovative approach not only addresses the complexities associated with chronic multimorbid conditions but also offers a sustainable framework for healthcare systems confronting similar challenges.展开更多
This letter critically reviews a recent longitudinal network study by Bai et al examining the dynamic,symptom-level interplay among peer bullying victimization,depression,anxiety,and aggression in Chinese adolescents....This letter critically reviews a recent longitudinal network study by Bai et al examining the dynamic,symptom-level interplay among peer bullying victimization,depression,anxiety,and aggression in Chinese adolescents.The study highlights that key symptoms,such as persistent sad mood,sleep disturbances,and cyberbullying victimization play a pivotal role in reinforcing the vicious cycle between mental health issues and bullying experiences.While the application of cross-lagged panel network analysis offers a nuanced understanding of these bidirectional relationships,several limitations remain,including the use of selfreported measures and a region-specific sample.Nevertheless,the findings underscore the urgent need for early screening and targeted interventions in school settings,particularly those addressing both emotional symptoms and digital forms of bullying.Moving forward,integrated and culturally sensitive approaches are essential to prevent escalation and break the link between peer victimization and adolescent psychopathology.Future research should incorporate multi-informant data and broaden the cultural context to strengthen generalizability and intervention design.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
Depression is a prevalent mental health disorder characterized by high relapse rates,highlighting the need for effective preventive interventions.This paper reviews the potential of reinforcement learning(RL)in preven...Depression is a prevalent mental health disorder characterized by high relapse rates,highlighting the need for effective preventive interventions.This paper reviews the potential of reinforcement learning(RL)in preventing depression relapse.RL,a subset of artificial intelligence,utilizes machine learning algorithms to analyze behavioral data,enabling early detection of relapse risk and optimization of personalized interventions.RL's ability to tailor treatment in real-time by adapting to individual needs and responses offers a dynamic alternative to traditional therapeutic approaches.Studies have demonstrated the efficacy of RL in customizing e-Health interventions and integrating mobile sensing with machine learning for adaptive mental health systems.Despite these advantages,challenges remain in algorithmic complexity,ethical considerations,and clinical implementation.Addressing these issues is crucial for the successful integration of RL into mental health care.This paper concludes with recommendations for future research directions,emphasizing the need for larger-scale studies and interdisciplinary collaboration to fully realize RL’s potential in improving mental health outcomes and preventing depression relapse.展开更多
This letter critically examines a recent study by Zhang et al investigating the mediating role of overweight in the association between depression and new-onset diabetes among middle-aged and older adults.The study pr...This letter critically examines a recent study by Zhang et al investigating the mediating role of overweight in the association between depression and new-onset diabetes among middle-aged and older adults.The study provides com-pelling evidence that overweight mediates approximately 61%of this relationship,suggesting that depression may contribute to diabetes by influencing behaviors that lead to weight gain.This aligns with the understanding that depression can impact appetite regulation and physical activity.While the study employs a longitudinal design and robust statistical methods,limitations such as reliance on self-reported data and body mass index measurements warrant consideration.This analysis emphasizes the need for integrated interventions that address both mental and metabolic health for effective diabetes prevention.Future research should further explore the interplay of lifestyle factors,biological pathways,and social determinants in the development of this complex relationship.Ultimately,an integrated approach targeting both behavioral and biological components is crucial for the prevention and management of new-onset diabetes.展开更多
Managing type 2 diabetes mellitus remains a significant challenge,particularly for individuals with persistently poor glycemic control.Although inadequate glycemic regulation is a well-established public health concer...Managing type 2 diabetes mellitus remains a significant challenge,particularly for individuals with persistently poor glycemic control.Although inadequate glycemic regulation is a well-established public health concern and a major contributor to diabetes-related complications,evidence on the effectiveness of intensive and supportive interventions across diverse patient subgroups is scarce.This editorial examines findings from a prospective study evaluating the influence of glycemic history on treatment outcomes in poorly controlled diabetes.The study highlights that personalized care models outperform generalized approaches by addressing the unique trajectories of glycemic deterioration.Newly diagnosed patients demonstrated the most favorable response to intervention,while those with consistently elevated glycated hemoglobin(≥10%)faced the greatest challenges in achieving glycemic control.These findings underscore the limitations of a onesize-fits-all strategy,reinforcing the need for patient-centered care that integrates individualized monitoring and timely intervention.Diabetes management requires prioritizing personalized treatment strategies that mitigate therapeutic inertia and ensure equitable,effective care for all patients.展开更多
Post-stroke depression(PSD)is a prevalent but often underdiagnosed complication affecting stroke survivors,with significant implications for recovery and quality of life.Emerging evidence suggests that central obesity...Post-stroke depression(PSD)is a prevalent but often underdiagnosed complication affecting stroke survivors,with significant implications for recovery and quality of life.Emerging evidence suggests that central obesity,as measured by the weight-to-waist index(WWI),may play a crucial role in PSD risk and severity.Traditional obesity metrics,such as body mass index,may not accurately capture the impact of visceral fat distribution on neuropsychiatric outcomes.This letter highlights the growing recognition of WWI as a precise indicator of metabolic and inflammatory disturbances linked to post-stroke mental health.Integrating WWI into routine stroke rehabilitation assessments could facilitate early identification of high-risk patients and improve intervention strategies.Further research is needed to establish standardized WWI cutoff values and explore potential therapeutic targets for PSD prevention.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
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 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(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 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 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.
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 rising prevalence of chronic multimorbidity poses substantial challenges to healthcare systems,necessitating the development of innovative management strategies to optimize patient care and system efficiency.The study by Fontalba-Navas et al investigates the implementation of a novel high complexity unit(HCU)specifically designed to improve the management of patients with chronic complex conditions.By adopting a multidisciplinary approach,the HCU aims to provide comprehensive,patient-centered care that enhances health outcomes and alleviates the strain on traditional hospital services.Utilizing a longitudinal analysis of data from the Basic Minimum Data Set,this study compares hospitalization metrics among the HCU,Internal Medicine,and other departments within a regional hospital throughout 2022.The findings reveal that the HCU's integrated care model significantly reduces readmission rates and boosts patient satisfaction compared to conventional care practices.The study highlights the HCU's potential as a replicable model for managing chronic multimorbidity,emphasizing its effectiveness in minimizing unnecessary hospitalizations and enhancing the overall quality of patient care.This innovative approach not only addresses the complexities associated with chronic multimorbid conditions but also offers a sustainable framework for healthcare systems confronting similar challenges.
基金Supported by the 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 letter critically reviews a recent longitudinal network study by Bai et al examining the dynamic,symptom-level interplay among peer bullying victimization,depression,anxiety,and aggression in Chinese adolescents.The study highlights that key symptoms,such as persistent sad mood,sleep disturbances,and cyberbullying victimization play a pivotal role in reinforcing the vicious cycle between mental health issues and bullying experiences.While the application of cross-lagged panel network analysis offers a nuanced understanding of these bidirectional relationships,several limitations remain,including the use of selfreported measures and a region-specific sample.Nevertheless,the findings underscore the urgent need for early screening and targeted interventions in school settings,particularly those addressing both emotional symptoms and digital forms of bullying.Moving forward,integrated and culturally sensitive approaches are essential to prevent escalation and break the link between peer victimization and adolescent psychopathology.Future research should incorporate multi-informant data and broaden the cultural context to strengthen generalizability and intervention design.
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.
文摘Depression is a prevalent mental health disorder characterized by high relapse rates,highlighting the need for effective preventive interventions.This paper reviews the potential of reinforcement learning(RL)in preventing depression relapse.RL,a subset of artificial intelligence,utilizes machine learning algorithms to analyze behavioral data,enabling early detection of relapse risk and optimization of personalized interventions.RL's ability to tailor treatment in real-time by adapting to individual needs and responses offers a dynamic alternative to traditional therapeutic approaches.Studies have demonstrated the efficacy of RL in customizing e-Health interventions and integrating mobile sensing with machine learning for adaptive mental health systems.Despite these advantages,challenges remain in algorithmic complexity,ethical considerations,and clinical implementation.Addressing these issues is crucial for the successful integration of RL into mental health care.This paper concludes with recommendations for future research directions,emphasizing the need for larger-scale studies and interdisciplinary collaboration to fully realize RL’s potential in improving mental health outcomes and preventing depression relapse.
基金Supported by the New Professor Research Program of KOREATECH,No.202501930001.
文摘This letter critically examines a recent study by Zhang et al investigating the mediating role of overweight in the association between depression and new-onset diabetes among middle-aged and older adults.The study provides com-pelling evidence that overweight mediates approximately 61%of this relationship,suggesting that depression may contribute to diabetes by influencing behaviors that lead to weight gain.This aligns with the understanding that depression can impact appetite regulation and physical activity.While the study employs a longitudinal design and robust statistical methods,limitations such as reliance on self-reported data and body mass index measurements warrant consideration.This analysis emphasizes the need for integrated interventions that address both mental and metabolic health for effective diabetes prevention.Future research should further explore the interplay of lifestyle factors,biological pathways,and social determinants in the development of this complex relationship.Ultimately,an integrated approach targeting both behavioral and biological components is crucial for the prevention and management of new-onset diabetes.
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Managing type 2 diabetes mellitus remains a significant challenge,particularly for individuals with persistently poor glycemic control.Although inadequate glycemic regulation is a well-established public health concern and a major contributor to diabetes-related complications,evidence on the effectiveness of intensive and supportive interventions across diverse patient subgroups is scarce.This editorial examines findings from a prospective study evaluating the influence of glycemic history on treatment outcomes in poorly controlled diabetes.The study highlights that personalized care models outperform generalized approaches by addressing the unique trajectories of glycemic deterioration.Newly diagnosed patients demonstrated the most favorable response to intervention,while those with consistently elevated glycated hemoglobin(≥10%)faced the greatest challenges in achieving glycemic control.These findings underscore the limitations of a onesize-fits-all strategy,reinforcing the need for patient-centered care that integrates individualized monitoring and timely intervention.Diabetes management requires prioritizing personalized treatment strategies that mitigate therapeutic inertia and ensure equitable,effective care for all patients.
基金The New Professor Research Program of Korean Technology in 2025.
文摘Post-stroke depression(PSD)is a prevalent but often underdiagnosed complication affecting stroke survivors,with significant implications for recovery and quality of life.Emerging evidence suggests that central obesity,as measured by the weight-to-waist index(WWI),may play a crucial role in PSD risk and severity.Traditional obesity metrics,such as body mass index,may not accurately capture the impact of visceral fat distribution on neuropsychiatric outcomes.This letter highlights the growing recognition of WWI as a precise indicator of metabolic and inflammatory disturbances linked to post-stroke mental health.Integrating WWI into routine stroke rehabilitation assessments could facilitate early identification of high-risk patients and improve intervention strategies.Further research is needed to establish standardized WWI cutoff values and explore potential therapeutic targets for PSD prevention.
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%.
