Background: The COVID-19 pandemic increased the worldwide prevalence of metabolic syndrome. The purpose of this study was to assess health behavior adherence during the pandemic in adults who had engaged in a metabolic syndrome management program for at least 6 months. This assessment included an evaluation of health behavior changes, factors influencing adherence, and clinical parameters. The city-wide program was operated by the Seoul Metropolitan Government. Methods: Baseline and follow-up data were compared in 116 participants who engaged in the program for at least 6 months prior to the pandemic. Health behaviors and clinical parameters were examined. Generalized estimating equation analysis was used to identify sociodemographic variables influencing health behavior adherence over time. Results: Systolic blood pressure, waist circumference, and blood glucose improved (all P<0.05), and risk factors decreased (P<0.001) from baseline to follow-up (mean±standard deviation, 1.13±0.91 years). All six health behaviors, physical activity and weight control, eating habits, alcohol consumption and smoking, stress management, sleep and rest, and medication compliance and medical examination improved (all P<0.001) from baseline to follow-up (2.37±1.05 years). Smoking and employment negatively influenced adherence to health behaviors (P<0.05). Participants felt the most beneficial part of the program was receiving sequential medical examination results with follow-up consultations by public health professionals without charge. Conclusion Our study demonstrated the durability of the impact of the Seoul Program on all six targeted health behaviors as well as clinical parameters. Findings encourage participation in such broad-based programs and development of novel approaches to facilitate success for smokers and employed participants.

Background: The COVID-19 pandemic increased the worldwide prevalence of metabolic syndrome. The purpose of this study was to assess health behavior adherence during the pandemic in adults who had engaged in a metabolic syndrome management program for at least 6 months. This assessment included an evaluation of health behavior changes, factors influencing adherence, and clinical parameters. The city-wide program was operated by the Seoul Metropolitan Government. Methods: Baseline and follow-up data were compared in 116 participants who engaged in the program for at least 6 months prior to the pandemic. Health behaviors and clinical parameters were examined. Generalized estimating equation analysis was used to identify sociodemographic variables influencing health behavior adherence over time. Results: Systolic blood pressure, waist circumference, and blood glucose improved (all P<0.05), and risk factors decreased (P<0.001) from baseline to follow-up (mean±standard deviation, 1.13±0.91 years). All six health behaviors, physical activity and weight control, eating habits, alcohol consumption and smoking, stress management, sleep and rest, and medication compliance and medical examination improved (all P<0.001) from baseline to follow-up (2.37±1.05 years). Smoking and employment negatively influenced adherence to health behaviors (P<0.05). Participants felt the most beneficial part of the program was receiving sequential medical examination results with follow-up consultations by public health professionals without charge. Conclusion Our study demonstrated the durability of the impact of the Seoul Program on all six targeted health behaviors as well as clinical parameters. Findings encourage participation in such broad-based programs and development of novel approaches to facilitate success for smokers and employed participants.

Background: The COVID-19 pandemic increased the worldwide prevalence of metabolic syndrome. The purpose of this study was to assess health behavior adherence during the pandemic in adults who had engaged in a metabolic syndrome management program for at least 6 months. This assessment included an evaluation of health behavior changes, factors influencing adherence, and clinical parameters. The city-wide program was operated by the Seoul Metropolitan Government. Methods: Baseline and follow-up data were compared in 116 participants who engaged in the program for at least 6 months prior to the pandemic. Health behaviors and clinical parameters were examined. Generalized estimating equation analysis was used to identify sociodemographic variables influencing health behavior adherence over time. Results: Systolic blood pressure, waist circumference, and blood glucose improved (all P<0.05), and risk factors decreased (P<0.001) from baseline to follow-up (mean±standard deviation, 1.13±0.91 years). All six health behaviors, physical activity and weight control, eating habits, alcohol consumption and smoking, stress management, sleep and rest, and medication compliance and medical examination improved (all P<0.001) from baseline to follow-up (2.37±1.05 years). Smoking and employment negatively influenced adherence to health behaviors (P<0.05). Participants felt the most beneficial part of the program was receiving sequential medical examination results with follow-up consultations by public health professionals without charge. Conclusion Our study demonstrated the durability of the impact of the Seoul Program on all six targeted health behaviors as well as clinical parameters. Findings encourage participation in such broad-based programs and development of novel approaches to facilitate success for smokers and employed participants.

The emergence of Chat Generative Pre-trained Transformer (ChatGPT), a chatbot developed by OpenAI, has garnered interest in the application of generative artificial intelligence (AI) models in the medical field. This review summarizes different generative AI models and their potential applications in the field of medicine and explores the evolving landscape of Generative Adversarial Networks and diffusion models since the introduction of generative AI models. These models have made valuable contributions to the field of radiology. Furthermore, this review also explores the significance of synthetic data in addressing privacy concerns and augmenting data diversity and quality within the medical domain, in addition to emphasizing the role of inversion in the investigation of generative models and outlining an approach to replicate this process. We provide an overview of Large Language Models, such as GPTs and bidirectional encoder representations (BERTs), that focus on prominent representatives and discuss recent initiatives involving language-vision models in radiology, including innovative large language and vision assistant for biomedicine (LLaVa-Med), to illustrate their practical application.This comprehensive review offers insights into the wide-ranging applications of generative AI models in clinical research and emphasizes their transformative potential.

Objective: This study was performed to evaluate the efficacy and safety of lurasidone (160 mg/day) compared to quetiapine XR (QXR; 600 mg/day) in the treatment of acutely psychotic patients with schizophrenia. Methods: Patients were randomly assigned to 6 weeks of double-blind treatment with lurasidone 160 mg/day (n=105) or QXR 600 mg/day (n=105). Primary efficacy measure was the change from baseline to week 6 in Positive and Negative Syndrome Scale (PANSS) total score and Clinical Global Impressions severity (CGI-S) score. Adverse events, body measurements, and laboratory parameters were assessed. Results: Lurasidone demonstrated non-inferiority to QXR on the PANSS total score. Adjusted mean±standard error change at week 6 on the PANSS total score was -26.42±2.02 and -27.33±2.01 in the lurasidone and QXR group, respectively. The mean difference score was -0.91 (95% confidence interval -6.35–4.53). The lurasidone group showed a greater reduction in PANSS total and negative subscale on week 1 and a greater reduction in end-point CGI-S score compared to the QXR group. Body weight, body mass index, and waist circumference in the lurasidone group were reduced, with significantly lower mean change compared to QXR. Endpoint changes in glucose, cholesterol, triglycerides, and low-density lipoprotein levels were also significantly lower. The most common adverse drug reactions with lurasidone were akathisia and nausea. Conclusion Lurasidone 160 mg/day was found to be non-inferior to QXR 600 mg/day in the treatment of schizophrenia with comparable efficacy and tolerability. Adverse effects of lurasidone were generally tolerable, and beneficial effects on metabolic parameters can be expected.

Purpose: Tinnitus is one of the most common health conditions worldwide. Although various methods of treatment have been used, the condition is still difficult to manage or cure. This study aimed to evaluate the therapeutic effects of transcutaneous trigeminal electrical stimulation (TTES) combined with notched sound therapy (NST) on patients with tinnitus. Materials and Methods: A clinical trial was conducted prospectively from September 2020 to September 2021 at a single center in South Korea. In total, 14 patients took part in this trial. Periodic visits and tele-monitoring were used to assess treatment compliance and collect data, including electroencephalography (EEG), photoplethysmography (PPG), tinnitus handicap inventory (THI), tinnitus magnitude index, Beck Depression Inventory (BDI), Pittsburgh Sleep Quality Index (PSQI), and 36-item short-form survey (SF-36) results. Results: Changes after intervention were analyzed with paired t-test. This study showed that alpha waves in the left hemisphere measured by EEG (p=0.024), autonomic nervous system balance (p=0.007), and stress level (p=0.022) measured by PPG significantly changed after intervention. Also, THI scores especially emotional symptoms (p=0.029) and catastrophic symptoms (p=0.043) decreased after treatment. The SF-36 score, both mental component summary and physical component summary score (each p<0.001), increased significantly, whereas the PSQI score (p<0.001) and BDI score (p<0.001) decreased after TTES and NST. Conclusion Based on the results of our study, we could confirm that TTES combined with NST can significantly improve tinnitus, catastrophic symptoms, and the overall quality of life of patients.

Purpose: Germline mutations in DNA damage repair (DDR) genes such as BRCA2 have been associated with prostate cancer (PC) risk but has not been thoroughly evaluated for metastatic prostate cancer (mPC) in Asian men. This study attempts to evaluate frequency of DDR mutations in the largest cohort of Koreans. Materials and Methods: We recruited 340 patients with mPC unselected for family history of cancer and compared to 495 controls. Whole genome sequencing was applied to assess germline pathogenic/likely pathogenic variants (PV/LPVs) in 26 DDR genes and HOXB13, including 7 genes (ATM, BRCA1/2, CHEK2, BRIP1, PALB2, and NBN) associated with hereditary PC. Comparisons to published Caucasian and Japanese cohorts were performed. Results: Total of 28 PV/LPVs were identified in 30 (8.8%) patients; mutations were found in 13 genes, including BRCA2 (15 men [4.41%]), ATM (2 men [0.59%]), NBN (2 men [0.59%], and BRIP1 (2 men [0.59%]). Only one patient had HOXB13 mutation (0.29%). A lower rate of overall germline variant frequency was observed in Korean mPC compared to Caucasians (8.8% vs. 11.8%), but individual variants notably differed from Caucasian and geographically similar Japanese cohorts. PV/LPVs in DDR genes tended to increase gradually with higher Gleason scores (GS 7, 7.1%; GS 8, 7.5%; GS 9–10, 9.9%). Conclusions BRCA2 was the most frequently mutated gene common to different cohorts supporting its importance, but differences in variant distribution in Korean mPC underscore the need for ethnic-specific genetic models. Future ethnic-specific analyses are warranted to verify our findings.

Purpose: Recently, there has been a rise in the interest to understand the composition of indoor dust due to its association with lung diseases such as asthma, chronic obstructive pulmonary disease (COPD) and lung cancer. Furthermore, it has been found that bacterial extracellular vesicles (EVs) within indoor dust particles can induce pulmonary inflammation, suggesting that these might play a role in lung disease. Methods: We performed microbiome analysis of indoor dust EVs isolated from mattresses in apartments and hospitals. We developed diagnostic models based on the bacterial EVs antibodies detected in serum samples via enzyme-linked immunosorbent assay (ELISA) in this analysis. Results: Proteobacteria was the most abundant bacterial EV taxa observed at the phylum level while Pseudomonas, Enterobacteriaceae (f) and Acinetobacter were the most prominent organisms at the genus level, followed by Staphylococcus. Based on the microbiome analysis, serum anti-bacterial EV immunoglobulin G (IgG), IgG1 and IgG4 were analyzed using ELISA with EV antibodies that targeted Staphylococcus aureus, Acinetobacter baumannii, Enterobacter cloacae and Pseudomonas aeruginosa. The levels of anti-bacterial EV antibodies were found to be significantly higher in patients with asthma, COPD and lung cancer compared to the healthy control group. We then developed a diagnostic model through logistic regression of antibodies that showed significant differences between groups with smoking history as a covariate. Four different variable selection methods were compared to construct an optimal diagnostic model with area under the curves ranging from 0.72 to 0.81. Conclusions The results of this study suggest that ELISA-based analysis of anti-bacterial EV antibodies titers can be used as a diagnostic tool for lung disease. The present findings provide insights into the pathogenesis of lung disease as well as a foundation for developing a novel diagnostic methodology that synergizes microbial EV metagenomics and immune assays.

Background: Hong Kong's construction industry currently faces a manpower crisis. Blue-collar workers are a disadvantaged group and suffer higher levels of chronic diseases, for example, cancer, than the wider population. Cancer risk factors are likely to cluster together. We documented prevalence of cancer-associated lifestyle risk behaviors and their correlates among Hong Kong construction workers. Methods: Data were collected from workers at 37 railway-related construction worksites throughout Hong Kong during May 2014. Tobacco use, alcohol consumption, unbalanced nutrition intake, and physical inactivity were included in the analysis. Latent class analysis and multivariable logistic regression were performed to identify the patterns of risk behaviors related to cancer, as well as their impact factors among construction workers in Hong Kong. Results: Overall, 1,443 workers participated. Latent class analysis identified four different behavioral classes in the sample. Fully adjusted multiple logistic regression identified age, gender, years of Hong Kong residency, ethnicity, educational level, and living status differentiated behavioral classes. Conclusion High levels of lifestyle-related cancer-risk behaviors were found in most of the Hong Kong construction workers studied. The present study contributes to understanding how cancer-related lifestyle risk behaviors cluster among construction workers and relative impact factors of risk behaviors. It is essential to tailor health behavior interventions focused on multiple risk behaviors among different groups for further enlarging the effects on cancer prevention.

Purpose: Recently, there has been a rise in the interest to understand the composition of indoor dust due to its association with lung diseases such as asthma, chronic obstructive pulmonary disease (COPD) and lung cancer. Furthermore, it has been found that bacterial extracellular vesicles (EVs) within indoor dust particles can induce pulmonary inflammation, suggesting that these might play a role in lung disease. Methods: We performed microbiome analysis of indoor dust EVs isolated from mattresses in apartments and hospitals. We developed diagnostic models based on the bacterial EVs antibodies detected in serum samples via enzyme-linked immunosorbent assay (ELISA) in this analysis. Results: Proteobacteria was the most abundant bacterial EV taxa observed at the phylum level while Pseudomonas, Enterobacteriaceae (f) and Acinetobacter were the most prominent organisms at the genus level, followed by Staphylococcus. Based on the microbiome analysis, serum anti-bacterial EV immunoglobulin G (IgG), IgG1 and IgG4 were analyzed using ELISA with EV antibodies that targeted Staphylococcus aureus, Acinetobacter baumannii, Enterobacter cloacae and Pseudomonas aeruginosa. The levels of anti-bacterial EV antibodies were found to be significantly higher in patients with asthma, COPD and lung cancer compared to the healthy control group. We then developed a diagnostic model through logistic regression of antibodies that showed significant differences between groups with smoking history as a covariate. Four different variable selection methods were compared to construct an optimal diagnostic model with area under the curves ranging from 0.72 to 0.81. Conclusions The results of this study suggest that ELISA-based analysis of anti-bacterial EV antibodies titers can be used as a diagnostic tool for lung disease. The present findings provide insights into the pathogenesis of lung disease as well as a foundation for developing a novel diagnostic methodology that synergizes microbial EV metagenomics and immune assays.