Background: Lifestyle modifications are critical in addressing metabolic dysfunction-associated steatotic liver disease (MASLD); however, the specific macronutrients that most significantly influence the disease’s progression are uncertain. In this study, we aimed to explore the role of carbohydrate, fat, and protein intake in MASLD development using decision trees, random forest models, and cluster analysis. Methods: Participants (n=3,951) from the Korean Genome and Epidemiology Study were included. We used the classification and regression tree analysis to classify participants into subgroups based on variables associated with the incidence of new-onset MASLD. Random forest analyses were used to assess the relative importance of each variable. Participants were grouped into homogeneous clusters based on carbohydrate, protein, fat, and total caloric intake using hierarchical cluster analysis. Subsequently, we used the Cox proportional hazards regression models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for MASLD risk across the clusters. Results: Carbohydrate intake was identified as the most significant predictor of new-onset MASLD, followed by fat, protein, and total caloric intake. Participants in cluster 3, who consumed a lower proportion of carbohydrate but had higher total caloric, protein, and fat intake, had a lower risk of new-onset MASLD than those in cluster 1 after adjusting for confounders (cluster 1 as a reference; cluster 3: HR, 0.90; 95% CI, 0.82 to 0.99). Conclusion The study’s results highlight the critical role of macronutrient composition, particularly carbohydrate intake, in MASLD development. The findings suggest that dietary strategies focusing on optimizing macronutrients, rather than simply reducing caloric intake, may be more effective in preventing MASLD.

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.

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.

Objectives: ：Among the various risk factors for suicide, geographic factors have different effects on males and females. This study aimed to identify differences between genders in spatiotemporal dependence and spatiotemporal patterns of suicide mortality over the preceding decade. Methods: ：This research analyzed the age-adjusted suicide mortality rate per 100,000 population, spanning from 2012 to 2021, for intentional suicides across each administrative district (229 Si, Gun, Gu) in Korea. Data were sourced from the National Statistical Office of the Korean Statistical Information Service. The Moran’s I in-dex for spatial autocorrelation of the suicide mortality rates was computed. An emerging hot spot analysis was conducted to examine the community-level spatiotemporal distribution patterns, thus providing insight into the re-gional clustering characteristics that reflect the temporal-spatial clusters of suicide mortality rates. Results: ：TIn males, the Moran’s I indices were almost above 0 (p-value<0.05) for most years, indicating sig-nificant spatial autocorrelation. Conversely, no significant regional clustering was observed among females dur-ing the same period. The emerging hot spot analysis, focusing on the temporal trends in the spatial distributionof male suicide mortality rates from 2012 to 2021, identified two distinct time series patterns and a total of 12 hot spot areas: seven new spots and five sporadic spots. Conclusions ：This study is the first to intuitively demonstrate the disparities in spatiotemporal dependencies and patterns of suicide mortality rates in Korea between genders. The findings highlight the necessity for tailoredsuicide prevention strategies that are sensitive to gender differences.

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.

Objectives: ：Among the various risk factors for suicide, geographic factors have different effects on males and females. This study aimed to identify differences between genders in spatiotemporal dependence and spatiotemporal patterns of suicide mortality over the preceding decade. Methods: ：This research analyzed the age-adjusted suicide mortality rate per 100,000 population, spanning from 2012 to 2021, for intentional suicides across each administrative district (229 Si, Gun, Gu) in Korea. Data were sourced from the National Statistical Office of the Korean Statistical Information Service. The Moran’s I in-dex for spatial autocorrelation of the suicide mortality rates was computed. An emerging hot spot analysis was conducted to examine the community-level spatiotemporal distribution patterns, thus providing insight into the re-gional clustering characteristics that reflect the temporal-spatial clusters of suicide mortality rates. Results: ：TIn males, the Moran’s I indices were almost above 0 (p-value<0.05) for most years, indicating sig-nificant spatial autocorrelation. Conversely, no significant regional clustering was observed among females dur-ing the same period. The emerging hot spot analysis, focusing on the temporal trends in the spatial distributionof male suicide mortality rates from 2012 to 2021, identified two distinct time series patterns and a total of 12 hot spot areas: seven new spots and five sporadic spots. Conclusions ：This study is the first to intuitively demonstrate the disparities in spatiotemporal dependencies and patterns of suicide mortality rates in Korea between genders. The findings highlight the necessity for tailoredsuicide prevention strategies that are sensitive to gender differences.

Objectives: ：Among the various risk factors for suicide, geographic factors have different effects on males and females. This study aimed to identify differences between genders in spatiotemporal dependence and spatiotemporal patterns of suicide mortality over the preceding decade. Methods: ：This research analyzed the age-adjusted suicide mortality rate per 100,000 population, spanning from 2012 to 2021, for intentional suicides across each administrative district (229 Si, Gun, Gu) in Korea. Data were sourced from the National Statistical Office of the Korean Statistical Information Service. The Moran’s I in-dex for spatial autocorrelation of the suicide mortality rates was computed. An emerging hot spot analysis was conducted to examine the community-level spatiotemporal distribution patterns, thus providing insight into the re-gional clustering characteristics that reflect the temporal-spatial clusters of suicide mortality rates. Results: ：TIn males, the Moran’s I indices were almost above 0 (p-value<0.05) for most years, indicating sig-nificant spatial autocorrelation. Conversely, no significant regional clustering was observed among females dur-ing the same period. The emerging hot spot analysis, focusing on the temporal trends in the spatial distributionof male suicide mortality rates from 2012 to 2021, identified two distinct time series patterns and a total of 12 hot spot areas: seven new spots and five sporadic spots. Conclusions ：This study is the first to intuitively demonstrate the disparities in spatiotemporal dependencies and patterns of suicide mortality rates in Korea between genders. The findings highlight the necessity for tailoredsuicide prevention strategies that are sensitive to gender differences.

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.

Purpose: The purpose of this study is to build a prediction model for estimating graft weight about different graft volumetry methods combined with other variables. Methods: Donors who underwent living-donor right hepatectomy from March 2021 to March 2023 were included. Estimated graft volume measured by conventional method and 3-dimensional (3D) software were collected as well as the actual graft weight. Linear regression was used to build a prediction model. Donor groups were divided according to the 3D volumetry of <700 cm3 , 700–899 cm3 , and ≥900 cm3 to compare the performance of different models. Results: A total of 119 donors were included. Conventional volumetry showed R2 of 0.656 (P < 0.001) while 3D software showed R2 of 0.776 (P < 0.001). The R2 of the multivariable model was 0.842 (P < 0.001) including for 3D volume (β = 0.623, P < 0.001), body mass index (β = 7.648, P < 0.001), and amount of weight loss (β = –7.252, P < 0.001). The median errors between different models and actual graft weight did not differ in donor groups (<700 and 700–899 cm3 ), while the median error of univariable linear model using 3D software (122.5; interquartile range [IQR], 61.5–179.8) was significantly higher than multivariable-adjusted linear model (41.5; IQR, 24.8–69.8; P = 0.003) in donors with estimated graft weight ≥900 cm3 . Conclusion The univariable 3D volumetry model showed an acceptable outcome for donors with an estimated graft volume <900 cm3 . For donors with an estimated graft volume ≥900 cm3 , the multivariable-adjusted linear model showed higher accuracy.