Bioassay-guided fractionation of the methanolic extract of Artemisia iwayomogi Kitamura led to the isolation of 12 known compounds (1‒12). Notably, this study marks the first report of 3-epimeridinol (1) being isolated and structurally characterized from a natural source. Additionally, compounds 3, 4, and 7 were isolated from the Asteraceae family for the first time. The structural elucidation of the isolated compound was achieved through analysis of 1D, 2D NMR, and MS data. Upon evaluation of their inhibitory effects against lipopolysaccharideinduced nitric oxide production, compound 12 demonstrated significant inhibitory activity with greater potency than the reference compound quercetin. These results established A. iwayomogi as a promising source of antiinflammatory agents.

The roots of Astragalus membranaceus Bunge have long been used in herbal medicine for their diversebiological activities. Notably, its potential anti-diabetic properties have been extensively studied, highlighting promising therapeutic prospects. In this study, we conducted a comprehensive investigation focusing on flavonoid components from the roots of A. membranaceus and their PTP1B inhibitory activity. As a result, we isolated a total of 24 flavonoids, among which formonentin (1), pratensein (3), and vesticarpan (19) emerged as the most potent inhibitors against PTP1B with IC50 value of 10.9 ± 1.09 μM, 10.0 ± 1.71 μM, and 10.3 ± 1.31 μM, respectively.Additionally, through the enzyme kinetic analysis, the inhibition mode of compound 19 was determined as a competitive inhibitor, with Ki value of 7.6 ± 1.17 μM. Furthermore, the molecular docking simulation elucidated the binding mechanism of compound 19 with PTP1B, mainly through van der Waals forces and hydrogen bonds.This study highlights the PTP1B inhibitory potential of the flavonoid constituents derived from the roots of A. membranaceus. Moreover, discovering vesticarpan (19) as a novel PTP1B inhibitor provides a significant foundation for further investigations to develop innovative therapeutic strategies for diabetes treatment.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Objectives: We analyzed the demographic and clinical characteristics of patients diagnosedwith coronavirus disease 2019 (COVID-19), focusing specifically on severe/critical cases, andassessed the trends and rates of severity and fatality among these patients in the Republic of Korea. Methods: Clinical data on patients with COVID-19 from January 20, 2020 to August 30, 2023were collected from the Korea Disease Control and Prevention Agency’s database. We identified patients who progressed to severe/critical conditions and analyzed their demographic and clinical profiles. Severity and fatality rates were calculated and compared annually to track thedisease progression over time. Results: During the surveillance period, 34,572,554 COVID-19 cases were confirmed, among whom 38,112 (0.11%) progressed to severe/critical conditions. Most severe/critical cases occurred in individuals aged ≥60 years, with a notable increase in patients aged ≥80 years from 2022.The overall severity rate was 0.19%, with a fatality rate of 0.10%. However, the severity of cases gradually diminished during the study period. In 2022, the severity and fatality rates decreased to 0.14% and 0.09%, respectively. In 2023, while the severity rate remained stable at 0.15%, thefatality rate further decreased to 0.06%. Notably, throughout the study period, individuals aged ≥80 years had a significantly higher severity rate (2.44%), with a fatality rate of 1.75%. Conclusion These findings underscore the importance of prioritizing protection and management strategies for older adults and high-risk groups to mitigate the impact ofCOVID-19. Continued surveillance and analysis are essential to effectively control COVID-19 and minimize its burden on public health.

Bioassay-guided fractionation of the methanolic extract of Artemisia iwayomogi Kitamura led to the isolation of 12 known compounds (1‒12). Notably, this study marks the first report of 3-epimeridinol (1) being isolated and structurally characterized from a natural source. Additionally, compounds 3, 4, and 7 were isolated from the Asteraceae family for the first time. The structural elucidation of the isolated compound was achieved through analysis of 1D, 2D NMR, and MS data. Upon evaluation of their inhibitory effects against lipopolysaccharideinduced nitric oxide production, compound 12 demonstrated significant inhibitory activity with greater potency than the reference compound quercetin. These results established A. iwayomogi as a promising source of antiinflammatory agents.

The roots of Astragalus membranaceus Bunge have long been used in herbal medicine for their diversebiological activities. Notably, its potential anti-diabetic properties have been extensively studied, highlighting promising therapeutic prospects. In this study, we conducted a comprehensive investigation focusing on flavonoid components from the roots of A. membranaceus and their PTP1B inhibitory activity. As a result, we isolated a total of 24 flavonoids, among which formonentin (1), pratensein (3), and vesticarpan (19) emerged as the most potent inhibitors against PTP1B with IC50 value of 10.9 ± 1.09 μM, 10.0 ± 1.71 μM, and 10.3 ± 1.31 μM, respectively.Additionally, through the enzyme kinetic analysis, the inhibition mode of compound 19 was determined as a competitive inhibitor, with Ki value of 7.6 ± 1.17 μM. Furthermore, the molecular docking simulation elucidated the binding mechanism of compound 19 with PTP1B, mainly through van der Waals forces and hydrogen bonds.This study highlights the PTP1B inhibitory potential of the flavonoid constituents derived from the roots of A. membranaceus. Moreover, discovering vesticarpan (19) as a novel PTP1B inhibitor provides a significant foundation for further investigations to develop innovative therapeutic strategies for diabetes treatment.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Objectives: We analyzed the demographic and clinical characteristics of patients diagnosedwith coronavirus disease 2019 (COVID-19), focusing specifically on severe/critical cases, andassessed the trends and rates of severity and fatality among these patients in the Republic of Korea. Methods: Clinical data on patients with COVID-19 from January 20, 2020 to August 30, 2023were collected from the Korea Disease Control and Prevention Agency’s database. We identified patients who progressed to severe/critical conditions and analyzed their demographic and clinical profiles. Severity and fatality rates were calculated and compared annually to track thedisease progression over time. Results: During the surveillance period, 34,572,554 COVID-19 cases were confirmed, among whom 38,112 (0.11%) progressed to severe/critical conditions. Most severe/critical cases occurred in individuals aged ≥60 years, with a notable increase in patients aged ≥80 years from 2022.The overall severity rate was 0.19%, with a fatality rate of 0.10%. However, the severity of cases gradually diminished during the study period. In 2022, the severity and fatality rates decreased to 0.14% and 0.09%, respectively. In 2023, while the severity rate remained stable at 0.15%, thefatality rate further decreased to 0.06%. Notably, throughout the study period, individuals aged ≥80 years had a significantly higher severity rate (2.44%), with a fatality rate of 1.75%. Conclusion These findings underscore the importance of prioritizing protection and management strategies for older adults and high-risk groups to mitigate the impact ofCOVID-19. Continued surveillance and analysis are essential to effectively control COVID-19 and minimize its burden on public health.

Objective: Adult attachment style is linked to how an individual responds to threats or stress and is known to be related to the onset of psychiatric symptoms such as depression. However, as the current assessment of attachment type mainly relies on self-report questionnaires and can be prone to bias, there is a need to incorporate physiological factors along with psychological symptoms and history in this process. We aimed to predict the measurement of two important types of adult attachment with heart rate variability (HRV), early life stress experience, and subjective psychiatric symptoms. Methods: Five hundred eighty-two subjects with depressive disorder were recruited retrospectively from January 2015 to June 2021. The experience of early life stress and psychiatric symptoms were collected, and HRV measures were obtained as input for an ensembled Voting Regressor model of machine learning-based regression models, including linear regression, ElasticNet, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). Results: Model performances evaluated with R-squared score averaged across 30 seeds were 0.377 and 0.188 for anxious- and avoidant-attachment, respectively. Mean absolute error averaged to 13.251 and 12.083, respectively. Shapley value importance analysis indicated that for both attachment types, the most important feature was the trait-anxiety, followed by emotional abuse, state-anxiety or self-reported depressive symptoms, and fear or helplessness felt in the moment of an early life stressor. Conclusion Our results provide the evidence base that may be utilized in clinical settings to predict the degree of attachment type using bio-psychological factors.

Purpose: This study aimed to identify the most delirium assessment tool with high predictive validity for elderly patients with stroke. Methods: This was a prospective observational study. The subjects were 219 stroke patients aged 60 years or older admitted to the neurology ward of a general hospital and data collection was conducted from August 2022 to February 2023. The collected data were analyzed using descriptive statistics, x 2 -test, independent t-test, and Fisher's exact test. The predictive validity of the Delirium Observation Screening Scale (DOS), the Nursing Delirium Screening Scale(Nu-DESC), and the 4'A's test (4AT) were assessed based on the sensitivity, specificity, positive and negative predictive values, and area under the receiver operating characteristic curve (AUC). Results: Of the 38 patients screened for delirium using delirium assessment tools, 32 (14.6%) were diagnosed with delirium by a neurologist. The sensitivity, specificity, positive predictive value, and negative predictive value were 84.4%, 91.1%, 71.1%, and 97.2% for DOS; 93.8%, 96.8%, 83.3%, and 98.9% for Nu-DESC; and 100.0%, 96.8%, 84.2%, and 100.0% for 4AT, respectively. The AUC was shown to be 0.89 for the DOS, 0.95 for the Nu-DESC, and 0.98 for the 4AT. Conclusion The results indicate that the 4AT was the most valid delirium assessment tool for elderly patients with stroke. Therefore, the active use of 4AT by nurses to screen for delirium of elderly stroke patients in clinical settings can contribute to the prevention and early management of delirium.