Purpose: Drainage fluid may serve as a biologically informative indicator of immune and infectious status during postsurgical recovery after trauma. However, microbiome shifts in drainage fluid associated with clinical resilience have not yet been characterized. This study aimed to investigate microbial dynamics in drainage fluid across the intensive care unit (ICU) and ward recovery phases in Korean trauma patients. Methods: A total of 25 drainage and 10 stool samples were collected from 10 trauma patients who underwent abdominal surgery at a regional trauma center. Microbial composition was analyzed using 16S ribosomal RNA amplicon sequencing.Alpha and beta diversity were compared between sample types and recovery stages. Linear mixed-effects models were used to identify recovery-associated taxa while adjusting for clinical variables, and predicted metabolic pathways were assessed using PICRUSt2. Results: Drainage fluid harbored distinct microbial communities independent of the intestinal microbiota. Shared taxa between drainage and stool increased significantly in patients with bowel injury, suggesting microbial translocation.Seven genera and 5 species showed significantly decreased abundance during the ward stage, with Modestobacter and Blastococcus tunisiensis demonstrating the highest discriminative ability between recovery stages (area under the curve = 0.721). Predicted metabolic pathways related to fatty acid degradation, amino acid degradation, and pro-inflammatory processes were more active during the ICU stage. Conclusion These findings provide preliminary evidence that drainage fluid microbiome profiles may reflect recovery dynamics following trauma, supporting its potential utility for microbiome-based monitoring and biomarker discovery in trauma surgery.

Background: Left bundle branch block (LBBB) significantly increases the risk of left ventricular systolic dysfunction (LVSD) due to cardiac dyssynchrony. Although artificial intelligence–enabled electrocardiography (AI-ECG) mod‑ els show promise in detecting LVSD, their performance in LBBB patients remains underexplored. We hypothesized that an AI-ECG model clinically validated for detecting LVSD would accurately detect LVSD and predict future clinical outcomes in LBBB patients. Methods: In this retrospective multicenter study, 5,689 expert-validated LBBB ECGs collected from 2,813 patients between 2016 and 2024 were analyzed using a previously developed and validated AI-ECG model. LVSD was defined as an ejection fraction of ≤ 40%. Model performance was assessed using the area under the receiver operating char‑ acteristic curve (AUROC), the area under the precision-recall curve (AUPRC), sensitivity, and specificity. Patients were stratified into high- and low-risk groups based on a threshold that achieved 90% sensitivity. A Kaplan–Meier analysis was used to compare clinical outcomes. Results: Among the 2,813 LBBB patients (mean age, 70.7 years; male sex, 43.7%), hypertension and a history of heart failure were common. The AiTiALVSD model showed strong diagnostic performance for LVSD (AUROC, 0.930 [95% CI, 0.924–0.937]; AUPRC, 0.913 [95% CI, 0.902–0.923]; sensitivity, 0.979; specificity, 0.473). During the mean follow-up of 4.1 years, high-risk patients had significantly higher hazards than low-risk patients for all-cause mortality (adjusted hazard ratio [HR], 1.87; 95% CI, 1.53–2.28), implantable cardioverter defibrillator/cardiac resynchronization therapy implantation (adjusted HR, 15.2; 95% CI, 7.51–30.77), and cardiovascular hospitalization (adjusted HR, 1.11; 95% CI, 0.96–1.28). Conclusions AiTiALVSD effectively detects LVSD and stratifies long-term cardiovascular risk in LBBB patients, support‑ ing its clinical utility for early detection and patient management.

This review examines the challenges associated with occupational disease surveillance in Korea, particularly emphasizing the limitations of current data sources such as the Industrial Accident Compensation Insurance (IACI) statistics and special health examinations. The IACI system undercounts cases due to its emphasis on severe diseases and restrictions on approvals. Special health examinations, although they cover a broad workforce, are constrained by their annual scheduling, which leads to missed acute illnesses and subclinical conditions. The paper also explores the history of occupational disease surveillance in Korea, highlighting the fragmented and disease-specific approach of earlier systems. The authors introduce the newly established Korea Occupational Disease Surveillance Center (KODSC), a comprehensive nationwide system designed to gather, analyze, and interpret data on occupational diseases through a network of regional centers. By incorporating hospital-based surveillance and focusing on acute poisonings and other sentinel events, the KODSC aims to overcome the limitations of previous systems and promote collaboration with various agencies. Although it is still in the early stages of implementation, the KODSC demonstrates potential for improving data accuracy and contributing valuable insights for public health policy.

Background/Aims: The coronavirus disease 2019 (COVID-19) pandemic has significantly impacted global health, exacerbated metabolic health issues, and altered lifestyle behaviors. This study examined the sex-specific impact of the COVID-19 outbreak on the incidence of metabolic syndrome using data from the Korea National Health and Nutrition Examination Survey (KNHANES). Methods: Data from the KNHANES VII (2018) and VIII (2019–2021), including 15,499 participants, were analyzed. The study population was stratified by sex, and further subdivisions were conducted based on the timeframe relative to the COVID-19 outbreak. Variables such as age, education level, household income, smoking status, and high-risk drinking were analyzed to assess their influence on the prevalence of metabolic syndrome. Results: The overall prevalence of metabolic syndrome significantly increased from 28.11% before the outbreak to 29.69% after the outbreak. Both males and females reported significant increases in waist circumference and fasting glucose levels. Age and education level differentially influenced the prevalence of metabolic syndrome between the sex. Smoking was significantly associated with increased prevalence in males, whereas high-risk drinking was associated with increased prevalence in males and decreased prevalence in females. Conclusions The COVID-19 pandemic has significantly increased the prevalence of metabolic syndrome with notable sex-specific differences. These findings highlight the need for sex-specific public health interventions to mitigate the impact of the pandemic on metabolic health.

Background: The development of frailty at hospital discharge affects the clinical outcomes in severe coronavirus disease 2019 (COVID-19) survivors who had no frailty before hospitalization. We aimed to describe the prevalence of new frailty using the clinical frailty scale (CFS) and evaluate its associated factors in patients with severe COVID-19 without pre-existing frailty before hospitalization. Methods: We performed a secondary analysis of clinical data from a nationwide retrospective cohort collected from 22 hospitals between January 1, 2020 and August 31, 2021. The patients were at least 19 years old and survived until discharge after admission to the intensive care unit (ICU) because of severe COVID-19. Development of new frailty was defined as a CFS score ≥5 at hospital discharge. Results: Among 669 severe COVID-19 survivors without pre-existing frailty admitted to the ICU, the mean age was 65.2±12.8 years, 62.5% were male, and 50.2% received mechanical ventilation (MV). The mean CFS score at admission was 2.4±0.9, and new frailty developed in 27.8% (186/483). In multivariate analysis, older age, cardiovascular disease, CFS score of 3–4 before hospitalization, increased C-reactive protein level, longer duration of corticosteroid treatment, and use of MV and extracorporeal membrane oxygenation were identified as factors associated with new-onset frailty. Conclusion Our study suggests that new frailty is not uncommon and is associated with diverse factors in survivors of severe COVID-19 without pre-existing frailty.

Purpose: Cancer poses a significant global health challenge, demanding precise genomic testing for individualized treatment strategies. Targeted-panel sequencing (TPS) has improved personalized oncology but often lacks comprehensive coverage of crucial cancer alterations. Whole-genome sequencing (WGS) addresses this gap, offering extensive genomic testing. This study demonstrates the medical potential of WGS. Materials and Methods: This study evaluates target-enhanced WGS (TE-WGS), a clinical-grade WGS method sequencing both cancer and matched normal tissues. Forty-nine patients with various solid cancer types underwent both TE-WGS and TruSight Oncology 500 (TSO500), one of the mainstream TPS approaches. Results: TE-WGS detected all variants reported by TSO500 (100%, 498/498). A high correlation in variant allele fractions was observed between TE-WGS and TSO500 (r=0.978). Notably, 223 variants (44.8%) within the common set were discerned exclusively by TE-WGS in peripheral blood, suggesting their germline origin. Conversely, the remaining subset of 275 variants (55.2%) were not detected in peripheral blood using the TE-WGS, signifying them as bona fide somatic variants. Further, TE-WGS provided accurate copy number profiles, fusion genes, microsatellite instability, and homologous recombination deficiency scores, which were essential for clinical decision-making. Conclusion TE-WGS is a comprehensive approach in personalized oncology, matching TSO500’s key biomarker detection capabilities. It uniquely identifies germline variants and genomic instability markers, offering additional clinical actions. Its adaptability and cost-effectiveness underscore its clinical utility, making TE-WGS a valuable tool in personalized cancer treatment.

Background/Aims: Direct-acting antivirals (DAAs) effectively eradicate hepatitis C virus. This study investigated whether metabolic dysfunction influences the likelihood of fibrosis regression after DAA treatment in patients with chronic hepatitis C (CHC). Methods: This multicenter, retrospective study included 8,819 patients diagnosed with CHC who were treated with DAAs and achieved a sustained virological response (SVR) between January 2014 and December 2022. Fibrosis regression was defined as a 20% reduction in noninvasive surrogates for liver fibrosis, such as liver stiffness (LS) measured by vibration-controlled transient elastography (VCTE) and the fibrosis-4 (FIB-4) score. Hypercholesterolemia (h-TC) was defined as >200 mg/dL. Results: The median age of the study population was 59.6 years, with a predominance of male patients (n=4,713, 57.3%). Genotypes 1, 2, and others were confirmed in 3,872 (46.2%), 3,487 (41.6%), and 1,024 (12.2%) patients, respectively. Diabetes mellitus (DM) was present in 1,442 (17.2%) patients and the median LS was 7.50 kPa (interquartile range, 5.30–12.50). Multivariate analysis revealed that the presence of DM and pre-DAA h-TC were independently associated with a decreased probability of fibrosis regression by VCTE. Additionally, pre-DAA h-TC was independently associated with a decreased probability of fibrosis regression by the FIB-4. Conclusions Metabolic dysfunction has an unfavorable influence on fibrosis regression in patients with CHC who achieve SVR after DAA treatment.

Background/Aims: This study aimed to evaluate the performance of the Model for End-Stage Liver Disease (MELD) 3.0 for predicting mortality and liver-related complications compared with the Child-Pugh classification, albumin-bilirubin (ALBI) grade, the MELD, and the MELD sodium (MELDNa) score. Methods: We evaluated a multicenter retrospective cohort of incorporated patients with cirrhosis between 2013 and 2019. We conducted comparisons of the area under the receiver operating characteristic curve (AUROC) of the MELD3.0 and other models for predicting 3-month mortality. Additionally, we assessed the risk of cirrhosis-related complications according to the MELD3.0 score. Results: A total of 3,314 patients were included. The mean age was 55.9±11.3 years, and 70.2% of the patients were male. Within the initial 3 months, 220 patients (6.6%) died, and the MELD3.0had the best predictive performance among the tested models, with an AUROC of 0.851, outperforming the Child-Pugh classification, ALBI grade, MELD, and MELDNa. A high MELD3.0score was associated with an increased risk of mortality. Compared with that of the group with a MELD3.0 score <10 points, the adjusted hazard ratio of the group with a score of 10–20 pointswas 2.176, and that for the group with a score of ≥20 points was 4.892. Each 1-point increase inthe MELD3.0 score increased the risk of cirrhosis-related complications by 1.033-fold. The risk of hepatorenal syndrome showed the highest increase, with an adjusted hazard ratio of 1.149, followed by hepatic encephalopathy and ascites. Conclusions The MELD3.0 demonstrated robust prognostic performance in predicting mortality in patients with cirrhosis. Moreover, the MELD3.0 score was linked to cirrhosis-related complications, particularly those involving kidney function, such as hepatorenal syndrome and ascites.