This study analyzed the prevalence of metabolic syndrome and influencing factors among adult workers aged 19 to 64. Methods: Data from the ninth Korea National Health and Nutrition Examination Survey conducted in 2022 were utilized. The sample comprised 685 individuals who had measurements of fasting glucose, triglycerides, high-density lipoprotein cholesterol, blood pressure, and waist circumference, which are necessary to diagnose metabolic syndrome. Data analysis was performed using SPSS 26.0, and complex sample logistic regression was conducted to identify factors influencing metabolic syndrome. Results: Among the participants, 34.3% were diagnosed with metabolic syndrome. Significant differences were observed in sex, age, region, marital status, regular worker, and physical activity between participants with and without metabolic syndrome (p < .05). Factors influencing the prevalence of metabolic syndrome included sex, age (with the 19~29 age group showing a significantly lower risk), physical activity, smoking, and perceived stress levels (p < .05). Conclusion: To prevent metabolic syndrome in adult workers, it is essential to promote physical activity, discourage smoking, and encourage effective stress management.

This study analyzed the prevalence of metabolic syndrome and influencing factors among adult workers aged 19 to 64. Methods: Data from the ninth Korea National Health and Nutrition Examination Survey conducted in 2022 were utilized. The sample comprised 685 individuals who had measurements of fasting glucose, triglycerides, high-density lipoprotein cholesterol, blood pressure, and waist circumference, which are necessary to diagnose metabolic syndrome. Data analysis was performed using SPSS 26.0, and complex sample logistic regression was conducted to identify factors influencing metabolic syndrome. Results: Among the participants, 34.3% were diagnosed with metabolic syndrome. Significant differences were observed in sex, age, region, marital status, regular worker, and physical activity between participants with and without metabolic syndrome (p < .05). Factors influencing the prevalence of metabolic syndrome included sex, age (with the 19~29 age group showing a significantly lower risk), physical activity, smoking, and perceived stress levels (p < .05). Conclusion: To prevent metabolic syndrome in adult workers, it is essential to promote physical activity, discourage smoking, and encourage effective stress management.

This study analyzed the prevalence of metabolic syndrome and influencing factors among adult workers aged 19 to 64. Methods: Data from the ninth Korea National Health and Nutrition Examination Survey conducted in 2022 were utilized. The sample comprised 685 individuals who had measurements of fasting glucose, triglycerides, high-density lipoprotein cholesterol, blood pressure, and waist circumference, which are necessary to diagnose metabolic syndrome. Data analysis was performed using SPSS 26.0, and complex sample logistic regression was conducted to identify factors influencing metabolic syndrome. Results: Among the participants, 34.3% were diagnosed with metabolic syndrome. Significant differences were observed in sex, age, region, marital status, regular worker, and physical activity between participants with and without metabolic syndrome (p < .05). Factors influencing the prevalence of metabolic syndrome included sex, age (with the 19~29 age group showing a significantly lower risk), physical activity, smoking, and perceived stress levels (p < .05). Conclusion: To prevent metabolic syndrome in adult workers, it is essential to promote physical activity, discourage smoking, and encourage effective stress management.

This study analyzed the prevalence of metabolic syndrome and influencing factors among adult workers aged 19 to 64. Methods: Data from the ninth Korea National Health and Nutrition Examination Survey conducted in 2022 were utilized. The sample comprised 685 individuals who had measurements of fasting glucose, triglycerides, high-density lipoprotein cholesterol, blood pressure, and waist circumference, which are necessary to diagnose metabolic syndrome. Data analysis was performed using SPSS 26.0, and complex sample logistic regression was conducted to identify factors influencing metabolic syndrome. Results: Among the participants, 34.3% were diagnosed with metabolic syndrome. Significant differences were observed in sex, age, region, marital status, regular worker, and physical activity between participants with and without metabolic syndrome (p < .05). Factors influencing the prevalence of metabolic syndrome included sex, age (with the 19~29 age group showing a significantly lower risk), physical activity, smoking, and perceived stress levels (p < .05). Conclusion: To prevent metabolic syndrome in adult workers, it is essential to promote physical activity, discourage smoking, and encourage effective stress management.

Background: Information on the effectiveness of nirmatrelvir/ritonavir against the omicron is limited. The clinical response and viral kinetics to therapy in the real world need to be evaluated. Methods: Mild to moderate coronavirus disease 2019 (COVID-19) patients with risk factors for severe illness were prospectively enrolled as a treatment group with nirmatrelvir/ritonavir therapy versus a control group with supportive care. Serial viral load and culture from the upper respiratory tract were evaluated for seven days, and clinical responses and adverse reactions were evaluated for 28 days. Results: A total of 51 patients were analyzed including 40 in the treatment group and 11 in the control group. Faster symptom resolution during hospitalization (P= 0.048) was observed in the treatment group. Only minor adverse reactions were reported in 27.5% of patients. The viral load on Day 7 was lower in the treatment group (P = 0.002). The viral culture showed a positivity of 67.6% (25/37) vs. 100% (6/6) on Day 1, 0% (0/37) vs. 16.7 (1/6) on Day 5, and 0% (0/16) vs. 50.0% (2/4) on Day 7 in the treatment and control groups, respectively. Conclusions Nirmatrelvir/ritonavir against the omicron was safe and resulted in negative viral culture conversion after Day 5 of treatment with better symptomatic resolution.

Objectives: This study was conducted to identify the level of mental health and burnout of medical staff at hospitals and public health centers, which responded to the coronavirus disease 2019 (COVID-19) in South Korea and to suggest appropriate management plans. Methods: The mental health and burnout levels of medical staff responding to COVID-19 were evaluated through an online survey conducted during the period July 1–14, 2021. To analyze the mental health status of the participants according to their demographic characteristics, the chisquare test, t-test, and an analysis of variance were conducted, followed by Scheffe post-hoc tests for pairwise comparisons. Results: A total of 773 participants were included in the study. An analysis of the clinically significant symptoms of the group showed that 29.5% had posttraumatic stress symptoms, 30.6% had depression, 15.8% had anxiety, 39.8% had somatic symptoms, and 4.5% were seen to be at risk of suicide. The average scores for burnout were as follows: 3.55±1.75 for emotional exhaustion, 2.68±1.73 for depersonalization, and 3.89±1.40 for personal accomplishment. Mental health problems and burnout were found to be high in women, those in the 20–39-year age bracket, those with less than 5 years of work experience, and public health center workers. Conclusion The medical staff responding to COVID-19 were observed to experience high levels of mental health and burnout problems. Work-related characteristics, such as younger age, lower work experience, and employment at a public health center, may have a negative impact on mental health and cause burnout. Therefore, individualized and systemic support for mental health and to prevent the burnout of medical staff responding to cases of COVID-19 is needed.

Background/Aims: Although the incidence of nontuberculous mycobacteria pulmonary disease (NTM-PD), a chronic infectious disease, is increasing, lung and gut microbiota dysbiosis in NTM patients has rarely been studied and was therefore the focus of this study. Methods: We analyzed the microbiota diversity in sputum and stool samples from 10 healthy subjects and 10 patients with NTM-PD through sequencing of the V3 and V4 regions of the 16S rRNA gene. In NTM-PD patients, we comparatively evaluated the microbiota diversity according to the body mass index (BMI), with BMI ≤ 18.5 kg/m2 defined as “underweight” and BMI > 18.5 kg/m2 as “others.” Results: The sputum microbiota from NTM-PD patients tended to have lower index values of amplicon sequence variant richness, Shannon evenness, and beta diversity than those from the control group. Furthermore, NTM-PD patients with a low BMI had a lower microbiota diversity than patients with high BMI. Fecal samples from NTM-PD patients also significantly differed in alpha and beta diversity compared with the control group and exhibited a diversity pattern similar to that found in sputum samples. Conclusions Our results reveal that the lung and gut microbiota of patients with NTM-PD exhibit an altered distribution and reduced richness and diversity.

Educational evaluation involves data collection and the analysis of various education-related factors to make decisions that improve educational quality. Systematic educational evaluation is essential for enhancing the quality of education. This study reports a case of student-conducted process evaluation of a medical school’s student support system and the procedure for devising improvement plans. Sixteen Inje University College of Medicine students participated in the Education Evaluation Committee (IUCM-EEC) to understand the educational improvement process as learners and actively achieve improvement. The Quality Improvement Committee of the Inje University College of Medicine (IUCM-QIC) decided to reform its student support system based on a previous educational evaluation in 2019. The evaluation of the student support system was conducted for 10 months in 2021 by the student subcommittee, under the guidance of the IUCM-EEC. The CIPP (context-input-process-product) evaluation model was used for a systematic evaluation. Accordingly, the subcommittee developed evaluation criteria and indicators, and analyzed relevant data collected from surveys and the previous literature. For further recommendations and revision ideas, the student subcommittee members interviewed faculty members from six other medical schools and also conducted a focus group interview with the dean and vice deans of IUCM. Finally, the student subcommittee submitted a report to the IUCM-QIC. Communication with various stakeholders is essential for a successful evaluation process. In this case, students, as key stakeholders in education, evaluated the student support system. Their active participation helped improve their understanding of the evaluation process.

Background: Aging is one of major causes triggering neurophysiological changes in many brain substructures, including the hippocampus, which has a major role in learning and memory. Thioredoxin (Trx) is a class of small redox proteins. Among the Trx family, Trx2 plays an important role in the regulation of mitochondrial membrane potential and is controlled by TrxR2. Hitherto, age-dependent alterations in Trx2 and TrxR2 in aged hippocampi have been poorly investigated. Therefore, the aim of this study was to examine changes in Trx2 and TrxR2 in mouse and rat hippocampi by age and to compare their differences between mice and rats. Results: Trx2 and TrxR2 levels using Western blots in mice were the highest at young age and gradually reduced with time, showing that no significant differences in the levels were found between the two subfields. In rats, however, their expression levels were the lowest at young age and gradually increased with time. Nevertheless, there were no differences in cellular distribution and morphology in their hippocampi when it was observed by cresyl violet staining. In addition, both Trx2 and TrxR2 immunoreactivities in the CA1-3 fields were mainly shown in pyramidal cells (principal cells), showing that their immunoreactivities were altered like changes in their protein levels. Conclusions Our current findings suggest that Trx2 and TrxR2 expressions in the brain may be different according to brain regions, age and species. Therefore, further studies are needed to examine the reasons of the differences of Trx2 and TrxR2 expressions in the hippocampus between mice and rats.