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.

BACKGROUND/OBJECTIVES: Dietary factors act on lower urinary tract symptoms (LUTS).This study examined the relationship between the overall diet quality and LUTS. SUBJECTS/METHODS: This study analyzed the data from examinees who visited a general hospital in Korea (October 13, 2014−March 12, 2020). The number of subjects in the study was 6,506 adult men. The recommended food score was used to evaluate the overall quality of the diet, and the International Prostate Symptom Score was used to quantify LUTS. Logistic regression analysis was used to derive the odds ratio (OR) and 95% confidence interval (CI) to evaluate the relationship between the dietary quality and LUTS. The influence of age, education, marital history, income, occupation, smoking, drinking, exercise, metabolic syndrome, body mass index (BMI), and prostate-specific antigen (PSA) level was analyzed to determine the net influence of the overall diet quality on LUTS. RESULTS: The group with high overall dietary quality showed a lower incidence of LUTS than the group with relatively lower dietary quality (OR, 0.88; 95% CI, 0.79–0.99). These results were also observed after correcting for other risk factors and associated variables: age, education, smoking, metabolic syndrome, BMI, and PSA level. CONCLUSION The overall diet quality and LUTS were correlated. Nevertheless, further research will be needed to find the relationship between diet quality and LUTS.

BACKGROUND/OBJECTIVES: Rosa hybrida has been demonstrated to exert biological effects on several cell types. This study investigated the efficacy of the edible ethanol extract of R.hybrida (EERH) against human colorectal carcinoma cell line (HCT116) cells.MATERIALS/METHODS: HCT116 cells were cultured with different concentrations of EERH (0, 400, 600, 800, and 1,000 µg/mL) in Dulbecco’s modified Eagle medium. Cell viability was measured using the 3-(4,5-dimethylthiazol-2-yl)-2,5 diphenyl tetrazolium bromide and viable cell counting assays. Cell cycle pattern was observed by flow cytometry analysis. The wound-healing migration assay, invasion assay, and zymography were used to determine the migratory and invasive level of HCT116 cells treated with EERH. The protein expression and binding ability level of HCT116 cells following EERH treatment were analyzed via immunoblotting and the electrophoretic mobility shift assay. RESULTS: EERH suppressed HCT116 cell proliferation, thus arresting the G1-phase cell cycle.It also reduced cyclin-dependent kinases and cyclins, which are associated with p27KIP1 expression. Additionally, EERH differentially regulated the phosphorylation of extracellular signal-regulated kinase 1/2, c-Jun NH2-terminal kinase, p38, and protein kinase B. Moreover, EERH treatment inhibited the enzymatic activity of matrix metalloproteinase-9 (MMP-9) and MMP-2, resulting in HCT116 cell migration and invasion. The EERH-induced inhibition of MMP-9 and MMP-2 was attributed to the reduced transcriptional binding of activator protein-1, specificity protein-1, and nuclear factor-κB motifs in HCT116 cells. Kaempferol was identified as the main compound contributing to EERH's antitumor activity. CONCLUSION EERH inhibits HCT116 cell proliferation and metastatic potential. Therefore, it is potentially useful as a preventive and curative nutraceutical agent against colorectal cancer.

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: Previous studies have reported an association between cancer-related symptoms and perceived social support (PSS). The objective of this study was to analyze whether Chemotherapy-Induced Peripheral Neuropathy (CIPN), a prevalent side effect of chemotherapy, varies according to PSS level using a validated tool for CIPN at prospective follow-up. Methods: A total of 39 breast cancer patients were evaluated for PSS using the Multidimensional Scale of Perceived Social Support (MSPSS) prior to chemotherapy and were subsequently grouped into one of two categories for each subscale: low-to-moderate PSS and high PSS. CIPN was prospectively evaluated using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Chemotherapy-Induced Peripheral Neuropathy 20 (CIPN20) at five time points. A linear mixed-effects model with square root transformation was employed to investigate whether the CIPN20 scales varied by PSS level and time point. Results: Statistical analysis of the MSPSS total scale and subscales revealed a significant effect of the friends subscale group and time point on the CIPN20 sensory scale. The sensory scale score of CIPN20 was found to be lower in participants with high PSS from friends in comparison to those with low-to-moderate PSS at 1 month post-chemotherapy (p=0.010). Conclusion This is the first study to prospectively follow the long-term effect of pre-treatment PSS from friends on CIPN. Further studies based on larger samples are required to analyze the effects of PSS on the pathophysiology of CIPN.

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.