Objective: Suicide is a leading cause of death among adolescents, and despite the need to distinguish between suicidal consideration and suicide attempts, research focused on suicide attempts remains insufficient. Therefore, this study aims to investigate the influence of self-perceived weight on suicide attempts. Methods: This study utilized data from the Korea Youth Risk Behavior Web-based Survey for its analysis from 2005 to 2023, including a total of 1,156,728 participants. This study utilized various analytical methods to examine the influence of self-perceived weight on suicide attempts. We estimated weighted prevalence and used linear regression to assess temporal trend β coefficients and their differences (βdiff) with 95% confidence intervals (CIs), and survey-weighted logistic regression to estimate weighted odds ratios (wORs) and 95% CIs for the association between self-perceived weight and suicide attempts. Results: A comparison of suicide attempts based on self-perceived weight suggested that individuals who perceived themselves as overweight (weighted prevalence, 3.97% [95% CI, 3.89 to 4.04]) had the highest rate of suicide attempts, followed by those who perceived themselves as underweight (3.36% [95% CI, 3.28 to 3.44]), while those who perceived themselves as having a normal weight (3.20% [95% CI, 3.14 to 3.27]) had the lowest rate. Additionally, females (underweight: 4.47% [95% CI, 4.32 to 4.62]; normal weight: 3.91% [95% CI, 3.81 to 4.01]; overweight: 5.23% [5.11 to 5.35]) experienced more suicide attempts than males (underweight: 2.73% [95% CI, 2.65 to 2.82]; normal weight: 2.43% [95% CI, 2.35 to 2.51]; overweight: 2.60% [95% CI, 2.52 to 2.69]). Conclusion Findings from the present study suggest that self-perceived weight was associated with suicide attempts and interaction analyses indicated a potential sex-based difference in the impact of body image distortion. Therefore, this study suggests the introduction of programs and campaigns aimed at correcting distorted self-perceived weight.

OBJECTIVE: Despite the global decrease in influenza infections during the coronavirus disease 2019 (COVID-19) pandemic, seasonal influenza remains a significant health issue. South Korea, known for its robust pandemic response and high influenza vaccination rates, offers a unique context for examining changes in vaccination trends during the pandemic. Using nationally representative data, we aimed to understand the impact of the pandemic on influenza vaccination behavior over a 12-year period and to identify vulnerable groups. METHODS: We analyzed influenza vaccination rates in South Korea between 2011-2022, focusing on pandemic-related impacts. The data of 2,426,139 adults (≥ 19 years) from the Korea Community Health Survey were used to assess demographic and sociological factors influencing vaccination behaviors. RESULTS: We observed an increase in influenza vaccination rates during the pre-COVID-19 period from 2011-2013 (weighted prevalence: 46.68% [95% confidence interval ( CI): 46.55-46.82]) to 2017-2019 (weighted prevalence: 52.50% [95% CI: 52.38-52.63]). However, a significant decline was observed in 2022, the late-COVID-19 pandemic period (weighted prevalence: 55.78% [95% CI: 55.56-56.01]), compared with the mid-pandemic period in 2021 (weighted prevalence: 59.12% [95% CI: 58.91-59.32]), particularly among populations traditionally prioritized for influenza vaccination, including older adults (≥ 65 years) and patients with chronic diseases and low educational and income levels. CONCLUSION The influenza vaccination rate in South Korea was significantly affected by the COVID-19 pandemic, showing a notable decrease among vulnerable demographic groups. This suggests the need for targeted public health strategies to address vaccine hesitancy and improve vaccination rates, particularly among high-risk populations.
Humans ; Republic of Korea/epidemiology* ; COVID-19/epidemiology* ; Adult ; Middle Aged ; Influenza Vaccines/administration & dosage* ; Male ; Female ; Influenza, Human/epidemiology* ; Aged ; Vaccination/statistics & numerical data* ; Young Adult ; Pandemics ; SARS-CoV-2

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

The effects of atherogenic indices on kidney function remain unclear. This study evaluated the association between atherogenic indices and risk of chronic kidney disease (CKD) in adults with metabolic derangements. Methods: A total of 4,176 participants from the Gangnam Severance Medical Cohort (2006–2021), which consisted of participants who had at least one disease related to metabolic derangements including diabetes mellitus, fatty liver, and hypertension were enrolled and atherogenic indices (lipid ratios including atherogenic index of plasma [AIP]) were assessed. The study endpoint was a composite kidney outcome (estimated glomerular filtration rate [eGFR] of <60 mL/min/1.73 m2 in at least two measurements in participants with baseline eGFR of ≥60 mL/min/1.73 m2; ≥30% decrease in eGFR from baseline in participants with baseline eGFR of <60 mL/min/1.73 m2; or the initiation of dialysis or kidney transplantation). Results: During a median follow-up of 6.0 years (interquartile range, 2.5–11.0 years), 1,266 composite kidney outcomes (30.3%) occurred. The highest quartile of AIP showed a higher risk of composite kidney outcome than the lowest quartile (hazard ratio [HR], 1.31; 95% confidence interval [CI], 1.12–1.54). This association was consistent when the AIP was treated as a continuous variable (HR per 1.0 increase, 1.51; 95% CI, 1.21–1.88). However, other atherogenic indices did not show significant associations with composite kidney outcome. Adding AIP to the traditional risk model to predict composite kidney outcomes significantly improved the C-index, net reclassification index, and integrated discrimination improvement. The association between high AIP and an increased risk of composite kidney outcome was consistent regardless of subgroup. Conclusion: High AIP was associated with an increased risk of CKD in adults with metabolic derangements.

BACKGROUND/OBJECTIVES: This study investigated the relationship between adolescent food-related lifestyles and food-related media use and eating behavior in Korea. SUBJECTS/METHODS: Participants were 392 Korean adolescents, ranging in age from 12 to 18, recruited via convenience sampling. They completed a self-report questionnaire survey consisting of questions about food-related lifestyle, food-related media use, food consumption behavior, food literacy, and nutrition quotient. Data analysis was conducted using SPSS 29.0. (IBM Co., Armonk, NY, USA). RESULTS: The factor analysis of food-related lifestyles identified four factors. Based on the cluster analysis results, participants were classified into three clusters reflecting different levels of interest: high interest in food, moderate interest in food, and low interest in food. The analysis revealed significant differences between groups in food-related liestyle factors (P < 0.05). Notably, the high-interest group demonstrated proactive engagement with food-related content, a willingness to explore diverse culinary experiences, and a conscientious consideration of nutritional labeling during food purchases. In contrast, the low-interest group reported tendencies toward overeating or succumbing to stimulating food consumption post-exposure to food-related content, coupled with a disregard for nutritional labeling when making food choices. A stronger inclination toward a food-related lifestyle was positively correlated with higher levels of food literacy and nutrition quotient. CONCLUSION This study proposes that the implementation of a nutrition education program using media could effectively promote a healthy diet among adolescents with a high level of interest in their dietary habits. For adolescents with low interest in their dietary habits, it suggests that introducing an education program with a primary focus on enhancing food literacy could be beneficial in fostering a healthy diet. Our research findings provide insight for the development of tailored nutritional education programs and establishment of effective nutrition policies.

Purpose: There are clinical unmet needs in predicting therapeutic response and precise strategy for the patient with advanced biliary tract cancer (BTC). We aimed to identify genomic alterations predicting therapeutic response and resistance to gemcitabine and cisplatin (Gem/Cis)-based chemotherapy in advanced BTC. Materials and Methods: Genomic analysis of advanced BTC multi-institutional cohorts was performed using targeted panel sequencing. Genomic alterations were analyzed integrating patients’ clinicopathologic data, including clinical outcomes of Gem/Cis-based therapy. Significance of genetic alterations was validated using clinical next-generation sequencing (NGS) cohorts from public repositories and drug sensitivity data from cancer cell lines. Results: 193 BTC patients from three cancer centers were analyzed. Most frequent genomic alterations were TP53 (55.5%), KRAS (22.8%), ARID1A (10.4%) alterations, and ERBB2 amplification (9.8%). Among 177 patients with BTC receiving Gem/Cis-based chemotherapy, ARID1A alteration was the only independent predictive molecular marker of primary resistance showing disease progression for 1st-line chemotherapy in the multivariate regression model (odds ratio, 3.12; p=0.046). In addition, ARID1A alteration was significantly correlated with inferior progression-free survival on Gem/Cis-based chemotherapy in the overall patient population (p=0.033) and in patients with extrahepatic cholangiocarcinoma (CCA) (p=0.041). External validation using public repository NGS revealed that ARID1A mutation was a significant predictor for poor survival in BTC patients. Investigation of multi-OMICs drug sensitivity data from cancer cell lines revealed that cisplatin-resistance was exclusively observed in ARID1A mutant bile duct cancer cells. Conclusion Integrative analysis with genomic alterations and clinical outcomes of the first-line Gem/Cis-based chemotherapy in advanced BTC revealed that patients with ARID1Aalterations showed a significant worse clinical outcome, especially in extrahepatic CCA. Well-designed prospective studies are mandatory to validate the predictive role of ARID1Amutation.