The Korea National Health and Nutrition Examination Survey (KNHANES) is a national health survey mandated by Article 16 of the National Health Promotion Act to assess the health and nutritional status of the Korean population. Over the past 2 decades, the survey has continuously introduced timely survey components while strengthening its survey methodology and operational systems to enhance both the policy relevance and scientific utility of its findings. The 10th KNHANES (2025–2027) preserves the statistical representativeness of its sampling design while expanding the use of web-based self-administered questionnaires to improve participant convenience. In response to Korea’s transition into a super-aged society, the 10th KNHANES incorporates enhanced older-adult health surveys, including osteoporosis assessment and older adults’ life functioning, and newly introduces items addressing social support as part of the social determinants of health. In addition, a longitudinal follow-up survey system has been established to monitor long-term changes in population health. Collectively, these changes are expected to improve understanding of aging-related health issues, support evidence-based national health policy development, and expand the applicability of KNHANES data for academic research.

Background: The spread of antimicrobial-resistant bacteria has close relationships among humans, animals, and the environment, necessitating the implementation of the “One Health” approach. In previous study, the prevalence and molecular epidemiology of extended-spectrum β-lactamase-producing Escherichia coli (ESBL-EC) were investigated across multiple pig industry sectors in Korea. In this study, animal developmental stages and antimicrobial usage policies on farms were also considered. Methods: A total of 6,288 non-duplicated samples were collected from pig farms, slaughterhouses, and their personnel from three provinces in Korea between 2020 and 2022.Antimicrobial susceptibility and ESBL gene characterization were performed on the samples. Results: Overall, 1,084 ESBL-EC isolates were collected, with positivity detection rates of 24.7%, 23.3%, 12.7%, 3.9%, 17.2% in pigs, workers, the environment, meat, and total samples, respectively. Common ESBL types were blaCTX-M-55 (N = 628), blaCTX-M-15 (N = 204), and blaCTX-M-14 (N = 127). Pig-derived E. coli isolates had antimicrobial resistance (AMR) rates of 80.4%, 47.2%, 32.6%, 36.7%, 43.4%, 62.4%, and 72.1% to ampicillin, piperacillin, cefazoline, ciprofloxacin, cotrimoxazole, chloramphenicol, and tetracycline, respectively.AMR rates were lower in the finishing stage pigs than in the lactating stage pigs and tended to decrease during animal development for most antimicrobials. AMR rates were higher in pig farms with a customary antimicrobial use policy than in those with a low-level antimicrobial use policy. Conclusions The findings highlight the significance of elucidating AMR networks among humans, animals, and the environment. Prudent antimicrobial usage and continuous AMR monitoring in the pig industry are crucial to safeguard food safety.

Background: Bacteroides fragilis group (BFG) isolates are the most frequently isolated gram-negative anaerobic bacteria and exhibit higher levels of antimicrobial resistance than other anaerobic bacteria. Reliable susceptibility testing is needed because of reports of resistance to the most active antibiotics. Recently, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) introduced disk zone diameter breakpoints. We evaluated the disk diffusion test (DDT) for susceptibility testing of BFG isolates compared with the agar dilution method. Methods: In total, 150 BFG isolates were collected from three institutes in Korea. The agar dilution method was conducted according to the CLSI guidelines. DDT was performed following the EUCAST guideline. Fastidious anaerobe agar supplemented with 5% defibrinated horse blood was used as the culture medium. Nine antimicrobials were evaluated:penicillin, cefoxitin, cefotetan, imipenem, meropenem, piperacillin-tazobactam, clindamycin, moxifloxacin, and metronidazole. Results: The categorical agreement (CA) between the two methods was > 90.0% for imipenem, meropenem, clindamycin, and metronidazole. However, the CA for piperacillintazobactam was low, at 83.2%. Major errors were found: 5.4% for imipenem, 7.4% for meropenem, and 12.8% for piperacillin-tazobactam. All minor errors were < 10%. We propose using the area of technical uncertainty (ATU) zone-overlapping area for susceptible and resistant strains to reduce errors in the DDT. Outside the ATU, the CAs of cefoxitin, cefotetan, and piperacillin-tazobactam were > 90.0%, whereas that of moxifloxacin was increased to 88.5%. Conclusions The DDT can be a useful alternative antimicrobial susceptibility test for BFG isolates when using the ATU zone to reduce errors.

Bacteroides fragilis is the most common opportunistic anaerobic pathogen. In the absence of appropriate antimicrobial therapy, mortality rates associated with B. fragilis group infections can reach as high as 50%. Therefore, we aimed to elucidate the clinical characteristics and outcomes of B. fragilis infections and the molecular genetic characteristics of B. fragilis isolates. Forty B. fragilis clinical isolates were collected at Hanyang University Hospital between January 2022 and December 2023. Antimicrobial susceptibility was tested using the agar dilution method. Whole-genome sequencing was conducted using the Illumina platform (Illumina, San Diego, CA, USA). Various multilocus sequence types of B. fragilis were identified, including ST149 (N = 4), ST11 (N = 4), ST1 (N = 3), ST21 (N = 2), and ST157 (N = 1). The insertion sequence (IS) IS1187, located upstream of cfiA, was associated with high-level carbapenem resistance in the ST157 isolate. B. fragilis toxin genes (bft) were identified in 30% of isolates. The most common comorbidities were diabetes mellitus (26.5%) and non-metastatic cancer (23.5%). Five patients (14.7%) died within 30 days, and two (5.9%) deaths were directly attributable to B. fragilis infection. The emergence of high-level MIC carbapenem-resistant B. fragilis ST157 has led to caution in the presence of B. fragilis infections.

Objective: Recently, various mental health promotion programs using virtual reality (VR) technology have been tried in the psychiatric field. Considering the importance of mental health in pregnant women, VR-assisted mental health promotion programs for pregnant women are needed to be developed. This study aimed to prospectively investigate the efficacy of a VR-assisted mental health promotion program for pregnant women. Methods: Fifty-five pregnant women were participated in this study. Mental health states such as depression, anxiety, perceived stress, and quality of life were assessed by Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), Perceived Stress Scale, and World Health Organization Quality of Life Scale Abbreviated Version (WHOQOL-BREF) before and after using VR program treatment. Based on the mental health types of pregnant women classified by cluster analysis, the effects of the VR program were analyzed by paired t test and Wilcoxon signed rank test. Results: The VR program significantly reduced PHQ-9 (p = 0.033) and GAD-7 (p = 0.046) scores, and significantly increased WHOQOL-BREF (p = 0.026) score of the participants. Additionally, when classifying the mental health types of pregnant women, the VR program was significantly effective to improve depression, anxiety, and quality of life in the most severe group based on the cluster analysis. Conclusion The results of this study suggest that VR program may be effective to improve the mental health of pregnant women. Particularly, VR programs for the high-risk group of depression might be the most effective. Therefore, VR-assisted mental health promotion programs could be useful for managing the mental health in pregnant women.

Objective: Recently, various mental health promotion programs using virtual reality (VR) technology have been tried in the psychiatric field. Considering the importance of mental health in pregnant women, VR-assisted mental health promotion programs for pregnant women are needed to be developed. This study aimed to prospectively investigate the efficacy of a VR-assisted mental health promotion program for pregnant women. Methods: Fifty-five pregnant women were participated in this study. Mental health states such as depression, anxiety, perceived stress, and quality of life were assessed by Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), Perceived Stress Scale, and World Health Organization Quality of Life Scale Abbreviated Version (WHOQOL-BREF) before and after using VR program treatment. Based on the mental health types of pregnant women classified by cluster analysis, the effects of the VR program were analyzed by paired t test and Wilcoxon signed rank test. Results: The VR program significantly reduced PHQ-9 (p = 0.033) and GAD-7 (p = 0.046) scores, and significantly increased WHOQOL-BREF (p = 0.026) score of the participants. Additionally, when classifying the mental health types of pregnant women, the VR program was significantly effective to improve depression, anxiety, and quality of life in the most severe group based on the cluster analysis. Conclusion The results of this study suggest that VR program may be effective to improve the mental health of pregnant women. Particularly, VR programs for the high-risk group of depression might be the most effective. Therefore, VR-assisted mental health promotion programs could be useful for managing the mental health in pregnant women.

Objective: Recently, various mental health promotion programs using virtual reality (VR) technology have been tried in the psychiatric field. Considering the importance of mental health in pregnant women, VR-assisted mental health promotion programs for pregnant women are needed to be developed. This study aimed to prospectively investigate the efficacy of a VR-assisted mental health promotion program for pregnant women. Methods: Fifty-five pregnant women were participated in this study. Mental health states such as depression, anxiety, perceived stress, and quality of life were assessed by Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), Perceived Stress Scale, and World Health Organization Quality of Life Scale Abbreviated Version (WHOQOL-BREF) before and after using VR program treatment. Based on the mental health types of pregnant women classified by cluster analysis, the effects of the VR program were analyzed by paired t test and Wilcoxon signed rank test. Results: The VR program significantly reduced PHQ-9 (p = 0.033) and GAD-7 (p = 0.046) scores, and significantly increased WHOQOL-BREF (p = 0.026) score of the participants. Additionally, when classifying the mental health types of pregnant women, the VR program was significantly effective to improve depression, anxiety, and quality of life in the most severe group based on the cluster analysis. Conclusion The results of this study suggest that VR program may be effective to improve the mental health of pregnant women. Particularly, VR programs for the high-risk group of depression might be the most effective. Therefore, VR-assisted mental health promotion programs could be useful for managing the mental health in pregnant women.

Objective: Recently, various mental health promotion programs using virtual reality (VR) technology have been tried in the psychiatric field. Considering the importance of mental health in pregnant women, VR-assisted mental health promotion programs for pregnant women are needed to be developed. This study aimed to prospectively investigate the efficacy of a VR-assisted mental health promotion program for pregnant women. Methods: Fifty-five pregnant women were participated in this study. Mental health states such as depression, anxiety, perceived stress, and quality of life were assessed by Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), Perceived Stress Scale, and World Health Organization Quality of Life Scale Abbreviated Version (WHOQOL-BREF) before and after using VR program treatment. Based on the mental health types of pregnant women classified by cluster analysis, the effects of the VR program were analyzed by paired t test and Wilcoxon signed rank test. Results: The VR program significantly reduced PHQ-9 (p = 0.033) and GAD-7 (p = 0.046) scores, and significantly increased WHOQOL-BREF (p = 0.026) score of the participants. Additionally, when classifying the mental health types of pregnant women, the VR program was significantly effective to improve depression, anxiety, and quality of life in the most severe group based on the cluster analysis. Conclusion The results of this study suggest that VR program may be effective to improve the mental health of pregnant women. Particularly, VR programs for the high-risk group of depression might be the most effective. Therefore, VR-assisted mental health promotion programs could be useful for managing the mental health in pregnant women.

Objective: Suicide is a significant public health issue, particularly among older adults, where the risk is heightened.Early identification of individuals at risk for suicidal ideation is essential for timely interventions, greatly improving prevention efforts. This study aimed to develop a predictive model for suicidal ideation in community-dwelling older adults using psychiatric self-report scales and machine learning classifiers. Methods: A total of 238 older adults were assessed using the Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7, Perceived Stress Scale-10, and World Health Organization Quality of Life Scale – Abbreviated Version.A nested 5-fold cross-validation procedure repeated 100 times was used for feature selection and model evaluation.Various classifiers—including support vector machines, random forest, logistic regression, linear discriminant analysis, and gradient boosting—were employed. Results: As the number of PHQ-9 items increased from two to six, the area under the curve (AUC) rose from 0.835 to 0.892. When a set of nine features—selected based on feature stability across iterations—was used, the AUC further improved to 0.904. This progression indicates that inclusion of additional informative items enhances classification performance. Conclusion This study demonstrates that psychiatric self-report scales can effectively predict suicidal ideation risk in community-dwelling older adults. By utilizing efficient features, the predictive accuracy of the model can be enhanced, offering valuable insights for developing early identification systems for high-risk groups. These findings suggest that a community-based suicide prevention program could be promoted by implementing a screening system to identify individuals at high risk for suicidal ideation among the elderly.

Objective: This study aimed to develop a machine learning-based classification model to differentiate bipolar disorder from major depressive disorder using self-report scales, including the Mood Disorder Questionnaire (MDQ), Bipolar Spectrum Diagnostic Scale (BSDS), and Anger Coping Scale (ACS). Methods: A total of 122 bipolar and 67 depressive patients participated. Recursive feature elimination with 1,000 iterations was used to identify the most informative features. Machine learning classifiers assessed combinations of MDQ, BSDS, and ACS items for classification performance. Results: The AUC values for MDQ and BSDS were 0.8212 and 0.7934, respectively. Combining MDQ and BSDS increased the AUC to 0.8477, which improved further to 0.8548 when ACS was included. For MDQ, the best performance was achieved when all 13 items were included. In contrast, the combined model of MDQ, BSDS, and ACS showed optimal performance when BSDS items 18 (conflicts with colleagues or police), 19 (alcohol or substance use), and ACS item 15 (beating others) were excluded. Conclusion Integrating anger coping styles with mood symptoms enhanced diagnostic accuracy, particularly when items related to undesirable behaviors were excluded. This machine learning approach shows potential for effectively evaluating bipolarity and underscores the importance of refining self-report scales to optimize diagnostic tools. Future research should incorporate clinical and objective data to enhance classification models.