Global health security threats in the post-coronavirus disease 2019 era include dense urban populations, increased human–animal proximity, migration driven by political or economic instability, climate change, humanitarian crises, antimicrobial resistance (AMR), and the misuse of biological research—including the accidental or intentional release of high-risk pathogens. The foundational preparation for these threats is to establish a robust, resilient public health system based on universal health coverage. The World Health Organization’s International Health Regulations must continue to promote global solidarity by maintaining core capacities such as surveillance, national laboratories, and epidemiological investigations of emerging infectious diseases, with timely reporting and information sharing within the global health security community. A One Health approach is essential for addressing AMR.Infection prevention and control must be enhanced to reduce healthcare-associated infections in medical facilities. Additionally, regulations concerning biosafety and biosecurity should address dual-use research of concern as well as the accidental or intentional release of highrisk pathogens from laboratories. Global health security is a collective responsibility because these threats know no borders and require coordinated action.

Objectives: The study aimed to analyze the core capacities to implement World Health Organization International Health Regulations (IHR) in 6 countries: Indonesia, Cambodia, Vietnam, the Dominican Republic, Ghana, and the Republic of Korea. Methods: Secondary data from relevant databases and reports, including the electronic State Party Self-Assessment Annual Reporting mechanism and global health security index, were used to assess health security in these countries. Descriptive statistics summarized the basic features of the scores, and a strengths, weaknesses, opportunities, and threats (SWOT) analysis wassubsequently performed to identify factors affecting health security scores while highlighting key similarities and differences between countries. Results: Early warning and event management emerged as the primary strength in most countries.Common opportunities included international commitments and immunization programs.In contrast, many countries shared weaknesses related to the policy, legal, and normative frameworks for IHR implementation, as well as challenges in human resources, chemical event management, and radiation emergency preparedness. Recurring threats involved issues such as biosafety, biosecurity, dual-use research and the culture of responsible science, infection control practices, coordination between public health and security authorities, laboratory supply chain vulnerabilities, and communication with healthcare workers during public health emergencies. Conclusion In order to counter future global health threats, countries should prioritize enhancing surveillance capacity (early warning and event management) as well as the immunization indicator (vaccination rates for human and animal diseases, including the national vaccine delivery system).

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: Klebsiella pneumoniae is known as one of the most common causes of hospitalacquired infections. Its prevalence poses substantial challenges to both hospital and publichealth systems, particularly due to the rise of multidrug‐resistant strains. Understandingthe epidemiology and resistance properties of K. pneumoniae can inform antimicrobial stewardship and infection control programs. A cross-sectional study was employed from November 2021 to November 2023. Methods: A total of 24 isolates underwent antimicrobial susceptibility testing using the disk diffusion method, an extended-spectrum beta-lactamase (ESBL) production test, and molecular gene detection. Results: The study found that 95.8% of clinical isolates were classified as multidrug-resistant.All isolates were resistant to ampicillin (100%). A high percentage of isolates were resistant to cefazolin (91.7%), ceftriaxone (87.5%), cefotaxime (87.5%), cefepime (87.5%), ciprofloxacin (83.3%), and sulfamethoxazole-trimethoprim (83.3%). Of the 24 isolates, 87.5% harbored ESBL genes, while the frequencies for GES, NDM, SIM, and OXA-48 were 16.7%, 20.8%, 8.3%, and 41.7%, respectively. Notably, the OXA-23 and OXA-51 genes, which are typically associated with Acinetobacter baumannii, were detected in 16.7% and 20.8% of isolates, respectively.Moreover, the prevalence of virulence genes rmpA, acrAB, and tolC was 0%, 95.8%, and 87.5%, respectively. Conclusion This study demonstrated a high level of antibiotic resistance and a significant presence of virulence genes among K. pneumoniae isolates. Consequently, these findings represent a critical public health issue that requires heightened awareness among all stakeholders, including health workers.

Objectives: This study aimed to describe and compare health-related quality of life (QoL) as measured by the World Health Organization Quality of Life–BREF (WHOQoL-BREF) and the EuroQol-5 Dimensions (EQ-5D) among the Malaysian population, examining differences by sociodemographic characteristics including age, income, sex, ethnicity, educational level, and occupation. Methods: This cross-sectional study used data from 19,402 individuals collected as part of a health and demographic surveillance system survey conducted in the Segamat district of Malaysia in 2018–2019. Descriptive statistics and measures of central tendency were produced.Differences in QoL among demographic sub-groups were examined using the t-test and analysisof variance, while the correlations between the WHOQoL-BREF and EQ-5D were evaluated usingPearson correlation coefficients. Results: Based on complete case analysis (n = 19,129), the average scores for the 4 WHOQoLBREF domains were 28.2 (physical), 24.1 (psychological), 12.0 (social relationships), and 30.4 (environment). The percentages of participants not in full health for each EQ-5D dimension were 12.8% (mobility), 3.1% (self-care), 6.9% (usual activities), 20.9% (pain/discomfort), and 6.8% (anxiety/depression). Correlations between the 4 WHOQoL-BREF domains and the 5 EQ-5D dimensions were relatively weak, ranging from –0.06 (social relationships with self-care and pain/discomfort; p < 0.001) to –0.42 (physical with mobility; p < 0.001). Conclusion Although health-related QoL as measured by the WHOQoL-BREF and the EQ-5D are correlated, these 2 measures should not be considered interchangeable. The choice betweenthem should be guided by the specific research questions and the intended use of the data.

Objectives: This study aimed to investigate the combined effects of individual, household, and community-level factors on coronavirus disease 2019 (COVID-19) vaccination intention using a multilevel analysis. Methods: This cross-sectional study analyzed raw data from the 2021 Korea Community Health Survey and regional statistics from 255 health centers, with a final sample of 229,216 individuals.Multilevel analysis was conducted, focusing on individual, household, and community-level factors. Individual-level factors included demographics, health status, and COVID-19 concerns;household-level factors included income and marital status; and community-level factors included city type and vaccination rates. Results: At the individual level, significant differences were observed across all variables. At the household level, higher vaccination intention was associated with households of 4 or more members and a monthly income of 4 million Korean won or more. At the community level, higher flu vaccination rates, greater concern about COVID-19 infection, and higher COVID-19 vaccination rates were linked to increased vaccination intention. Conclusion This study highlights that COVID-19 vaccination intention is influenced by factors at the individual, household, and community levels. Vaccination strategies that integrate household and community-level approaches may be more effective. Policymakers should consider both individual and social health factors when designing vaccination policies.

Objectives: This study systematically reviewed and analyzed epidemiological evidence regarding the association between dietary total antioxidant capacity (DTAC) and both the risk of developing diabetes and glycemic biomarker levels. Methods: We searched the PubMed, Scopus, ScienceDirect, and Google Scholar databases through July 2024 without imposing any date restrictions. Original studies that examined the relationship between DTAC and either the risk of developing diabetes or glycemic biomarkerlevels—specifically fasting blood glucose (FBG), hemoglobin A1C (HbA1C), insulin, and the homeostatic model assessment for insulin resistance (HOMA-IR)—were eligible for inclusion.After eliminating duplicates and irrelevant records, relevant studies were selected, and data were extracted through rigorous critical analysis. Results: A total of 32 articles were included in the review. Of the 19 studies that evaluated diabetes risk, 15 reported a lower risk among subjects with higher DTAC values. All 4 studies examining prediabetes risk found lower risk in participants with high DTAC scores. Additionally, significant inverse relationships were observed between DTAC values and FBG (9/15 studies), HbA1C (1/6 studies), insulin (5/6 studies), and HOMA-IR (8/9 studies). Conclusion The majority of evidence indicates that high adherence to an antioxidant-rich diet may reduce diabetes risk and improve glycemic biomarkers, including FBG, insulin, and HOMA-IR.

Objectives: This study aimed to investigate the relationship between blood microbiota,specifically bacterial DNA, and cognitive decline in individuals with subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI). The objective was to identify potential microbial signatures that could serve as biomarkers for cognitive deterioration. Methods: Forty-seven participants were recruited, including 13 with aMCI, 20 with SCD, and 14 normal cognition (NC). Blood samples were collected, and microbial DNA was analyzed using 16S rRNA sequencing on the Illumina MiSeq platform. Bioinformatics analyses—including α- and β-diversity measures and differential abundance testing (using edgeR)—were employed to assess microbial diversity and differences in bacterial composition among groups. Logistic regression models were used to evaluate the predictive impact of the microbiota on cognitive decline. Results: Microbial diversity differed significantly between groups, with NC exhibiting the highest α-diversity. Both the aMCI and SCD groups showed reduced diversity. Taxa such as Bacteroidia, Alphaproteobacteria, and Clostridia were significantly decreased in the aMCI group compared to NC (p < 0.05). In contrast, Gammaproteobacteria increased significantly in the aMCI group compared to both NC and SCD, indicating progressive microbial changes from SCD to aMCI. No significant differences were found between the NC and SCD groups. Conclusion Distinct bacterial taxa—particularly the increase in Gammaproteobacteria along with decreases in Bacteroidia, Alphaproteobacteria, and Clostridia—are associated with the progression of cognitive decline. These findings suggest that blood microbiota could serve as potential biomarkers for the early detection of aMCI. However, the small sample size and the lack of control for confounding factors such as diet and medication limit the findings. Larger studies are needed to validate these results and further explore the role of microbiota in neurodegeneration.

Objectives: This study aimed to explore factors influencing satisfaction with medical services among medically underserved populations at the free medical clinic, providing data to improve free medical services for these populations. Methods: We employed a descriptive correlational study design involving 112 individuals (aged 19 years and older) from medically underserved populations who visited the clinic. Data were collected through face-to-face surveys from September to October 2023, and statistical analyses (t-tests, analysis of variance, Pearson correlation, and hierarchical multiple regression) were used to identify key predictors of satisfaction. Results: Perceived support from healthcare providers emerged as the strongest predictor ofsatisfaction with medical services, demonstrating a significant positive association. While socialsupport was positively correlated with perceived support from healthcare providers, it did not independently predict satisfaction. Conclusion These findings underscore the importance of healthcare provider and social supportin increasing satisfaction with medical services among medically underserved populations.Developing tailored healthcare programs and specialized healthcare provider training are essential strategies to improve healthcare access and outcomes for these vulnerable groups.