Purpose: This developmental study explored the conceptual feasibility and applicability of a metaverse-based clinical assessment platform as a complementary tool to conventional objective structured clinical examinations in undergraduate medical education. Methods: A targeted literature review and expert consensus process were conducted to identify domains of clinical competence in which metaverse technologies could provide added value. Based on these findings, prototype virtual patient simulations were developed within a metaverse environment. Large language models (LLMs) were integrated to support dynamic, interactive history-taking simulations, and pilot modules for physical examination were also created. Results: Integration of LLMs into virtual patient scenarios enabled realistic, context-sensitive medical interviews, facilitating interactive dialogue between examinees and simulated patients. In contrast, physical examination modules faced technical limitations, particularly in replicating procedures requiring tactile or haptic feedback, such as palpation and percussion. Nevertheless, the metaverse environment enabled delivery of consistent and reproducible scenarios, supporting objective assessment of communication and diagnostic reasoning skills. Conclusion Metaverse-based simulations augmented by LLMs offer a promising approach to scalable and standardized clinical assessment, particularly within cognitive and interpersonal competency domains. Although current technological constraints limit the fidelity of physical examination simulations, rapid advancements in immersive and haptic technologies may help overcome these barriers in the near future. Further research is needed to evaluate the educational efficacy, validity, and feasibility of deploying such platforms in summative, high-stakes assessment contexts.

Electroconvulsive therapy (ECT) is performed under general anesthesia, and the choice of anesthetic induction agents critically influences multiple aspects of the treatment, including the seizure quality, cardiovascular stability, and post-procedural recovery. Developing anesthetic strategies that reliably achieve loss of consciousness while preserving adequate seizure expression has been a central challenge in ECT practice because most commonly used induction agents possess anticonvulsant properties. Dexmedetomidine (DEX), recently introduced primarily for sedation in intensive care and various procedural settings, is a selective α2-adrenergic receptor agonist that has minimal impacts on the seizure threshold and seizure duration while providing a complex profile of sedation, analgesia, anxiolysis, and sympatholysis. These properties help reduce the acute increases in blood pressure and heart rate induced by electrical stimulation, and DEX also offers advantages for airway management because it induces virtually no respiratory depression.Importantly, DEX has also been consistently reported to reduce pre-procedural anxiety and postictal agitation or aggression, suggesting that it may allow more stable management throughout the entire ECT course compared with conventional anesthetic agents. This review examines the pharmacological characteristics and clinical implications of DEX in anesthetic induction for ECT and discusses its potential roles in future clinical practice.

Background: The aim of this study is to evaluate the prevalence and associated psychosocial factors of behavioral addictions among school adolescents living in the Gwangju and Jeonnam regions in Korea. Methods: A self-reported survey was conducted from December 4, 2023, to January 31, 2024, including 855 middle and high school students residing in the Gwangju-Jeonnam regions. Aside from the information on demographic characteristics, data on depression, anxiety, Internet gaming addiction, gambling problems, and resilience was obtained. Results: The prevalence of Internet gaming addiction among adolescents was 5.4%, while the prevalence of gambling problems was 3.3%. The male adolescents had a significantly higher risk of behavioral addiction compared with the female adolescents. The logistic regression analysis revealed that male and depression were significant risk factors for Internet gaming addiction. For gambling problems, male was identified as a significant risk factor. Conclusion The findings of this study suggested that the prevalence of behavioral addiction among school adolescents has been relatively higher than that of previous studies, emphasizing the need for community-based prevention and intervention strategies tailored to the sex difference and psychological factors associated with adolescent behavioral addictions.

Background: The aim of this study is to evaluate the prevalence and associated psychosocial factors of behavioral addictions among school adolescents living in the Gwangju and Jeonnam regions in Korea. Methods: A self-reported survey was conducted from December 4, 2023, to January 31, 2024, including 855 middle and high school students residing in the Gwangju-Jeonnam regions. Aside from the information on demographic characteristics, data on depression, anxiety, Internet gaming addiction, gambling problems, and resilience was obtained. Results: The prevalence of Internet gaming addiction among adolescents was 5.4%, while the prevalence of gambling problems was 3.3%. The male adolescents had a significantly higher risk of behavioral addiction compared with the female adolescents. The logistic regression analysis revealed that male and depression were significant risk factors for Internet gaming addiction. For gambling problems, male was identified as a significant risk factor. Conclusion The findings of this study suggested that the prevalence of behavioral addiction among school adolescents has been relatively higher than that of previous studies, emphasizing the need for community-based prevention and intervention strategies tailored to the sex difference and psychological factors associated with adolescent behavioral addictions.

Background: The aim of this study is to evaluate the prevalence and associated psychosocial factors of behavioral addictions among school adolescents living in the Gwangju and Jeonnam regions in Korea. Methods: A self-reported survey was conducted from December 4, 2023, to January 31, 2024, including 855 middle and high school students residing in the Gwangju-Jeonnam regions. Aside from the information on demographic characteristics, data on depression, anxiety, Internet gaming addiction, gambling problems, and resilience was obtained. Results: The prevalence of Internet gaming addiction among adolescents was 5.4%, while the prevalence of gambling problems was 3.3%. The male adolescents had a significantly higher risk of behavioral addiction compared with the female adolescents. The logistic regression analysis revealed that male and depression were significant risk factors for Internet gaming addiction. For gambling problems, male was identified as a significant risk factor. Conclusion The findings of this study suggested that the prevalence of behavioral addiction among school adolescents has been relatively higher than that of previous studies, emphasizing the need for community-based prevention and intervention strategies tailored to the sex difference and psychological factors associated with adolescent behavioral addictions.

Background: The aim of this study is to evaluate the prevalence and associated psychosocial factors of behavioral addictions among school adolescents living in the Gwangju and Jeonnam regions in Korea. Methods: A self-reported survey was conducted from December 4, 2023, to January 31, 2024, including 855 middle and high school students residing in the Gwangju-Jeonnam regions. Aside from the information on demographic characteristics, data on depression, anxiety, Internet gaming addiction, gambling problems, and resilience was obtained. Results: The prevalence of Internet gaming addiction among adolescents was 5.4%, while the prevalence of gambling problems was 3.3%. The male adolescents had a significantly higher risk of behavioral addiction compared with the female adolescents. The logistic regression analysis revealed that male and depression were significant risk factors for Internet gaming addiction. For gambling problems, male was identified as a significant risk factor. Conclusion The findings of this study suggested that the prevalence of behavioral addiction among school adolescents has been relatively higher than that of previous studies, emphasizing the need for community-based prevention and intervention strategies tailored to the sex difference and psychological factors associated with adolescent behavioral addictions.

Background: The aim of this study is to evaluate the prevalence and associated psychosocial factors of behavioral addictions among school adolescents living in the Gwangju and Jeonnam regions in Korea. Methods: A self-reported survey was conducted from December 4, 2023, to January 31, 2024, including 855 middle and high school students residing in the Gwangju-Jeonnam regions. Aside from the information on demographic characteristics, data on depression, anxiety, Internet gaming addiction, gambling problems, and resilience was obtained. Results: The prevalence of Internet gaming addiction among adolescents was 5.4%, while the prevalence of gambling problems was 3.3%. The male adolescents had a significantly higher risk of behavioral addiction compared with the female adolescents. The logistic regression analysis revealed that male and depression were significant risk factors for Internet gaming addiction. For gambling problems, male was identified as a significant risk factor. Conclusion The findings of this study suggested that the prevalence of behavioral addiction among school adolescents has been relatively higher than that of previous studies, emphasizing the need for community-based prevention and intervention strategies tailored to the sex difference and psychological factors associated with adolescent behavioral addictions.

BACKGROUND/OBJECTIVES: Mushroom consumption, rich in diverse nutrients and bioactive compounds, is suggested as a potential significant contributor to preventing cardiometabolic diseases (CMDs). This systematic review aimed to explore the association between mushrooms and cardiometabolic health outcomes, utilizing data from prospective cohort studies and clinical trials focusing on the general population, with mushrooms themselves as a major exposure. SUBJECTS/METHODS: All original articles, published in English until July 2023, were identified through searches on PubMed, Ovid-Embase, and google scholar. Of 1,328 studies, we finally selected 5 prospective cohort studies and 4 clinical trials. RESULTS: Existing research is limited, typically consisting of 1 to 2 studies for each CMD and cardiometabolic condition. Examination of articles revealed suggestive associations in some cardiometabolic conditions including blood glucose (both fasting and postprandial), high-density lipoprotein cholesterol related indices, high-sensitivity C-reactive protein, and obesity indices (body weight, body mass index, and waist circumference). However, mushroom consumption showed no association with the mortality and morbidity of cardiovascular diseases, stroke, and type 2 diabetes, although there was a potentially beneficial connection with all cause-mortality, hyperuricemia, and metabolic syndrome. CONCLUSION Due to the scarcity of available studies, drawing definitive conclusions is premature. Further comprehensive investigations are needed to clarify the precise nature and extent of this relationship before making conclusive recommendations for the general population.

Objective: This study aimed to develop a machine learning model to predict cognitive decline in community-dwelling older adults. By integrating multimodal data, including demographic, psychosocial, and neuroimaging information, we sought to en-hance early detection of cognitive decline. Methods: Data were obtained from 159 participants in the Biobank Innovations for Chronic Cerebrovascular Disease with Alzheimer’s Disease Study. Participants underwent clinical assessments, neuropsychological testing, and magnetic resonance im-aging scans. Cognitive decline was defined as an increase in the Clinical Dementia Rating-Sum of Boxes of greater than 2.05 points per year at follow-up. Models were developed using the logistic classification, combining demographic, psychosocial as-sessments, and neuroimaging data. Model performance was evaluated using area under the curve (AUC), accuracy, and F1 score, while Shapley additive explanation values were used to assess feature importance. Results: The model that incorporated all data types achieved the highest performance, with an AUC of 0.834. The top predictor of cognitive decline was years of education, underscoring the importance of non-invasive, easily accessible data for prediction. Conclusion This machine learning model demonstrates significant potential for early cognitive decline prediction, offering a scalable tool for improving dementia screening and timely intervention, especially in resource-limited settings.

Objective: This study aimed to develop a machine learning model to predict cognitive decline in community-dwelling older adults. By integrating multimodal data, including demographic, psychosocial, and neuroimaging information, we sought to en-hance early detection of cognitive decline. Methods: Data were obtained from 159 participants in the Biobank Innovations for Chronic Cerebrovascular Disease with Alzheimer’s Disease Study. Participants underwent clinical assessments, neuropsychological testing, and magnetic resonance im-aging scans. Cognitive decline was defined as an increase in the Clinical Dementia Rating-Sum of Boxes of greater than 2.05 points per year at follow-up. Models were developed using the logistic classification, combining demographic, psychosocial as-sessments, and neuroimaging data. Model performance was evaluated using area under the curve (AUC), accuracy, and F1 score, while Shapley additive explanation values were used to assess feature importance. Results: The model that incorporated all data types achieved the highest performance, with an AUC of 0.834. The top predictor of cognitive decline was years of education, underscoring the importance of non-invasive, easily accessible data for prediction. Conclusion This machine learning model demonstrates significant potential for early cognitive decline prediction, offering a scalable tool for improving dementia screening and timely intervention, especially in resource-limited settings.