BACKGROUND/OBJECTIVES: Obesity is a complex metabolic disorder characterized by excessive adipose tissue accumulation, dysregulated lipid metabolism, and insulin resistance, leading to an increased risk of metabolic disease. While Withania somnifera (AS) and Chrysanthemum zawadskii Herbich var. latilobum (Maxim.) Kitamura (C) have traditionally been used for their metabolic regulatory properties, their combined effects on adipogenesis, lipogenesis, lipolysis, and insulin signaling remain unexplored. Therefore, this study aimed to evaluate the anti-obesity effects of AS, C, and their optimal combination (ASC) in 3T3-L1 adipocytes by investigating their impact on lipid metabolism and glucose homeostasis.MATERIALS/METHODS: Following adipogenic differentiation, 3T3-L1 adipocytes were treated with AS, C, and ASC at different concentrations. RESULTS: AS, C, and ASC significantly inhibited adipogenesis by downregulating cyclic adenosine monophosphate (cAMP) response element-binding protein, peroxisome proliferator-activated receptor gamma, CCAAT/enhancer-binding proteins alpha and beta phosphorylation, thereby reducing lipid accumulation in adipocytes. They also suppressed lipogenesis by downregulating the expression of dephosphorylated acetyl-CoA carboxylase, fatty acid synthase, and lipoprotein lipase. In contrast, they markedly enhanced lipolysis, as evidenced by increased hormone-sensitive lipase and protein kinase A expression, along with elevated glycerol release and cAMP levels. Furthermore, AS, C, and ASC activated energy metabolism pathways, as indicated by the upregulation of AMP-activated protein kinase, uncoupling protein 1, and carnitine palmitoyltransferase 1A, suggesting a transition toward enhanced mitochondrial fatty acid oxidation. Notably, AS, C, and ASC significantly improved insulin signaling by restoring insulin receptor substrate 1, phosphoinositide 3-kinase, and Akt phosphorylation, while upregulating glucose transporter type 4 expression, indicating enhanced glucose uptake. Among all treatments, ASC showed relatively greater efficacy compared to AS or C alone, suggesting a potential additive effect. CONCLUSION These findings demonstrate that ASC effectively modulates multiple metabolic pathways in adipocytes, including adipogenesis, lipogenesis, lipolysis, energy metabolism, and insulin signaling, to exert its anti-obesity effects.

Objectives: Large language models (LLMs) demonstrate remarkable potential in healthcare communication. However, whether they can process complex, high-volume medical information, such as stroke-related content, remains insufficiently validated. This study aimed to evaluate the natural language processing capabilities of LLMs in handling such content and to develop an evaluation instrument. Methods: A survey compared educational materials generated by two LLMs (ChatGPT 4.0 and Claude 3) with neurologist-authored content on stroke. The materials were based on two clinical scenarios representing distinct stroke etiologies: cardioembolism and large-artery atherosclerosis. They were evaluated in terms of accuracy, legality, ethics, comprehensiveness, and information delivery. Scores for comprehensiveness and information delivery were compared according to participants’ agreement with the use of LLMs in healthcare. Results: ChatGPT received the highest scores across all domains, except for legality in Scenario 2. In Scenario 1, the ranking for accuracy and summarization of clinical information was, from highest to lowest, ChatGPT, Claude, and the neurologist (η2 = 0.140, p < 0.001; η2 = 0.175, p < 0.001). The same hierarchy was observed in Scenario 2 for accuracy (η2 = 0.077, p < 0.001) and summarization (η2 = 0.194, p < 0.001). Participants who agreed with the use of LLMs in healthcare assigned higher scores for the comprehensiveness (Scenario 1, p = 0.005; Scenario 2, p = 0.007) and information delivery (Scenario 1, p = 0.003; Scenario 2, p = 0.026) of ChatGPT-generated materials than participants who did not agree. Conclusions LLMs demonstrated adequate capability to convey complex content, such as stroke-related information, in an accessible and understandable manner for non-experts.

Frailty is a condition marked by a progressive decline in physiological functioning due to aging, leading to increased disease morbidity and rising healthcare costs. Therefore, it is important to prevent frailty through proper management. Not only does a lack of physical activity contribute to frailty, but inadequate dietary intake, resulting from decreased appetite and malabsorption, is also a significant risk factor. This study aimed to develop a 16-week algorithm to assess the nutritional risks associated with frailty, provide personalized nutritional solutions, and manage frailty risk factors through continuous monitoring and periodic reassessment. A total of 20 indicators were selected to create a nutritional health score. The selected indicators encompassed nutritional risk factors for frailty, disease history, and hematological factors. Reassessments were designed to occur at four-week intervals to revise personalized management goals and adjust solutions. Metrics were prioritized to provide a personalized solution. A user-friendly monitoring system was developed that leveraged voice recognition technology to determine compliance. It is anticipated that the algorithm will serve several purposes. First, this outcome will allow us to help prevent and delay the onset of frailty by utilizing a mobile app. Second, it will reduce the time and economic costs associated with nutritional management. Finally, it will facilitate future professional counseling and monitoring.

Frailty is a condition marked by a progressive decline in physiological functioning due to aging, leading to increased disease morbidity and rising healthcare costs. Therefore, it is important to prevent frailty through proper management. Not only does a lack of physical activity contribute to frailty, but inadequate dietary intake, resulting from decreased appetite and malabsorption, is also a significant risk factor. This study aimed to develop a 16-week algorithm to assess the nutritional risks associated with frailty, provide personalized nutritional solutions, and manage frailty risk factors through continuous monitoring and periodic reassessment. A total of 20 indicators were selected to create a nutritional health score. The selected indicators encompassed nutritional risk factors for frailty, disease history, and hematological factors. Reassessments were designed to occur at four-week intervals to revise personalized management goals and adjust solutions. Metrics were prioritized to provide a personalized solution. A user-friendly monitoring system was developed that leveraged voice recognition technology to determine compliance. It is anticipated that the algorithm will serve several purposes. First, this outcome will allow us to help prevent and delay the onset of frailty by utilizing a mobile app. Second, it will reduce the time and economic costs associated with nutritional management. Finally, it will facilitate future professional counseling and monitoring.

Frailty is a condition marked by a progressive decline in physiological functioning due to aging, leading to increased disease morbidity and rising healthcare costs. Therefore, it is important to prevent frailty through proper management. Not only does a lack of physical activity contribute to frailty, but inadequate dietary intake, resulting from decreased appetite and malabsorption, is also a significant risk factor. This study aimed to develop a 16-week algorithm to assess the nutritional risks associated with frailty, provide personalized nutritional solutions, and manage frailty risk factors through continuous monitoring and periodic reassessment. A total of 20 indicators were selected to create a nutritional health score. The selected indicators encompassed nutritional risk factors for frailty, disease history, and hematological factors. Reassessments were designed to occur at four-week intervals to revise personalized management goals and adjust solutions. Metrics were prioritized to provide a personalized solution. A user-friendly monitoring system was developed that leveraged voice recognition technology to determine compliance. It is anticipated that the algorithm will serve several purposes. First, this outcome will allow us to help prevent and delay the onset of frailty by utilizing a mobile app. Second, it will reduce the time and economic costs associated with nutritional management. Finally, it will facilitate future professional counseling and monitoring.

Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.

Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.

Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.

Background: The Pandemic Grief Risk Factors (PGRFs) was developed as a self-report tool to compile a comprehensive list of unique risk factors related to grief when experiencing a coronavirus disease 2019 (COVID-19) loss. We explored the reliability and validity of the PGRF among healthcare workers who witnessed their patients’ deaths during the COVID-19 pandemic. Further, we examined whether the general severity of PGRF may have been associated with work-related stress and pandemic grief reactions. Methods: An online survey was conducted among tertiary hospital healthcare workers (doctors and nursing professionals) who had witnessed the deaths of patients they cared for.Pandemic Grief Scale for healthcare workers, the Stress and Anxiety to Viral Epidemics-3 items, the Patient Health Questionnaire-9, and the Generalized Anxiety Disorder-7 responses were collected. Results: In total, 267 responses were analyzed. The single-factor structure of the Korean version of the PGRF showed a good fit for the model. The scale demonstrated good internal consistency and convergent validity with other depression and anxiety rating scales. The mediation analysis revealed that work-related stress directly influenced pandemic grief reactions positively, and depression, anxiety, and general severity of grief risk factors partially mediated the association positively. Conclusion Among healthcare workers who witnessed the deaths of their patients due to COVID-19, the Korean version of the PGRF was valid and reliable for measuring the overall severity of PGRF. The PGRF can be used to identify individuals at risk for dysfunctional grief.

Objective: The rising prevalence of mild cognitive impairment (MCI) has spurred interest in innovative cognitive rehabilitation approaches, including serious games. This review summarizes randomized clinical trials (RCTs) exploring the impact of serious games on MCI patients. Methods: We conducted a comprehensive data search using key terms such as “gamification,” “digital therapy,” “cognition,” “mild cognitive impairment,” and “Alzheimer’s disease.” We exclusively considered published RCTs, excluding animal studies and basic research. Results: We identified eight RCTs. Four RCTs examined the effects of serious games using cognitive training for MCI patients. Notably, one study found that non-specific training (Nintendo Wii) significantly enhanced cognitive function and quality of life compared to cognition-specific computer training (CoTras). Among the remaining three RCTs, one specifically demonstrated that personalized serious game-based cognitive training yielded superior cognitive outcomes and reduced depressive symptoms. One RCT focused on serious games incorporating physical exercise, highlighting the effectiveness of kinetic-based exergaming in enhancing overall cognition. Three RCT focused on combined cognitive training and physical exercise. A double-blind RCT revealed that progressive resistance training or standalone physical exercise outperformed the combined approach in improving executive function and global cognition. Two additional RCTs reported positive outcomes, including improvements in cognitive function and electroencephalogram patterns associated with game-based interventions. Conclusion Serious games, whether focusing on cognitive training, physical exercise, or a combination of both, have potential to improve cognitive and functional outcomes in individuals with MCI. Further research and standardization of protocols are needed to better understand the full potential of serious games in MCI.