Background and Objectives: Studies investigating the correlation between papillary thyroid carcinoma (PTC) tumor size and the prevalence of the B-type Raf kinase (BRAF) mutation have yielded conflicting results. Therefore, we evaluated the prevalence of BRAF mutation according to tumor size in a large cohort of PTC patients to clarify this association. Materials and Methods: We retrospectively analyzed 6,438 patients with surgically diagnosed classic PTC between January 2009 and December 2017 at Samsung Medical Center, Seoul, Republic of Korea.During the study period, BRAF mutation testing was attempted on all fine-needle aspiration specimens, except for a small number of inadequate specimens. All other histologic subtypes were excluded. The prevalence of BRAF mutation was assessed based on tumor size, and further analyzed by age group and sex according to tumor size. Results: The overall prevalence of BRAF mutation was 79.2%. When PTCs ≤1 cm were excluded, the prevalence was 77.2%. The prevalence significantly decreased with increasing tumor size (p for trend ＜0.001). It was significantly higher in men than in women (p=0.013), but did not differ by age. The inverse correlation between tumor size and prevalence was prominent in patients aged 20-49 years but was less distinct in those aged 50 years and older. Conclusion In this large cohort of patients with PTC, the prevalence of BRAF mutation significantly decreased with increasing tumor size. These findings suggest that BRAF mutation is enriched in smaller surgically treated classic PTCs and may provide a hypothesis-generating clue regarding its role in early PTC development, although selection bias cannot be excluded.

Objective: Reliable staging of dementia is critical for both clinical practice and research. Although the Clinical Dementia Rating (CDR) is widely used to assess dementia severity, it is subject to subjectivity and inter-rater variability. Resting-state electroencephalography (EEG) may provide objective neurophysiological information associated with clinical severity. Methods: Resting-state EEG data from 600 adults aged ≥60 years in Korea were analyzed. Relative power spectral density was extracted from 19 electrodes across six frequency bands. Ordinal logistic regression and linear regression analyses exam-ined associations between resting-state EEG spectral features and CDR Global and CDR Sum of Boxes (CDR-SB) scores. All models adjusted for age, sex, education, and depressive symptoms, with false-discovery-rate correction. Results: Ordinal logistic regression demonstrated that higher resting-state theta-band EEG power was significantly associatedwith greater CDR Global severity, particularly in frontal, temporal, central, and parietal regions. Consistently, linear regressionanalyses revealed that higher CDR-SB scores were associated with increased theta in widespread cortical areas. No significantassociations were observed in other frequency bands. Conclusion EEG spectral slowing—marked by elevated theta power—was consistently related to CDR-defined dementia se-verity. Resting-state EEG can potentially serve as an objective neurophysiological correlate that informs clinician-rated staging and enhances the reliability of dementia severity assessment.

Objective: This study performed a longitudinal molecular characterization of P. multocidaisolates from pigs with respiratory lesions in South Korea (2016–2023), including subspecies identification, capsular serogrouping, virulence gene profiling, and multilocus sequence typing (MLST). Methods: A total of 1,358 pneumonic lung samples were collected from 960 pig farms between 2016 and 2023, yielding 169 P. multocida isolates. Subspecies were assigned by 16S rRNA gene sequencing. Capsular serogroups were determined using multiplex Virulence genes (pfhA, hgbB, tbpA, and toxA) were detected by PCR assays. MLST was conducted using the RIRDC scheme, and phylogenetic analysis of concatenated loci assessed clonal relationships. Results: Serogroup A predominated (109/169, 64.5%), followed by D (47/169, 27.8%) and B (3/169, 1.8%). Serogroups F and A/D were each detected once (0.6%), and 8 isolates were untypable (4.7%). Co-detection of hgbB and pfhA occurred in multiple isolates. Notably, two serogroup B isolates carried both genes, representing the first such finding in Korea and suggesting enhanced virulence potential. MLST identified 5 clonal complexes and 15 sequence types, including ST9 linked to serogroup B. Conclusions and Relevance: The emergence of serogroup B/ST9 isolates co-harboring hgbB and pfhA highlights evolving virulence patterns in Korean swine and supports continued molecular and genomic surveillance to guide control strategies and reduce health risks.

Objective: To provide a decade-long molecular and phenotypic characterization of APP isolates from Korean pig farms, focusing on the serovar distribution, apx-based toxingene profiles, and antimicrobial susceptibility. Methods: Between 2015 and 2025, 1,215 pneumonic lung samples from 965 pig farms yielded 132 APP isolates. The species identity was confirmed by 16S rRNA sequencing. The serovars were determined using capsule polysaccharide (CPS) gene-based multiplex polymerase chain reaction (PCR). Toxin genes (apxIA–apxIVA) were profiled, and the antimicrobial susceptibility to 29 agents was assessed by broth microdilution according to the Clinical and Laboratory Standards Institute guidelines. Results: Serovar 1 was predominant (66.7%), followed by serovars 5 (17.4%) and 2 (6.8%).CPS multiplex PCR identified three isolates (2.3%) as serovar 15, which displayed heterogeneous toxin gene profiles, including apxIICA-deficient profiles. Most isolates exhibited classical repeats-in-toxin operon arrangements, suggesting ongoing diversification of toxin gene profiles. High resistance rates were observed for oxytetracycline (90.9%) and florfenicol (50.8%), and recurrent multidrug-resistant combinations were frequently detected. Conclusions and Relevance: Serovar 1 is dominant in Korea, but the emergence of atypical toxin gene profiles in serovar 15 may carry immunological implications. Persistent resistance to older drug classes underscores the necessity for long-term molecular surveillance, evaluation of vaccine coverage against evolving strains, and enhanced antimicrobial stewardship to strengthen the control efforts for porcine pleuropneumonia in Korea.

Background: The Korean Occupational Stress Scale (KOSS) was developed in 2004. During this time, industrial structures have evolved, and societal awareness of occupational stress has changed. This study aims to develop and validate a revised version of the Korean Occupational Stress Scale (KOSS®19), tailored for workers, reflecting these changes. Methods: The KOSS®19 was developed based on the 26-item KOSS–short form (SF) through a review by eight experts. A survey was conducted including 359 service industry workers, comprising the KOSS®19, Burnout, and Depression scales. The KOSS®19 subscales were restructured, and their reliability and validity were evaluated. Results: The KOSS®19 composed of eight subscales: hazardous physical environment (2 items), high job demand (3 items), insufficient job control (2 items), low social support (2 items), job insecurity (2 items), organizational injustice (4 items), lack of reward (2 items), and work-life imbalance (2 items). The reliability and validity of the KOSS®19 were found to be satisfactory. Conclusions The KOSS®19 is a suitable tool for assessing occupational stress, effectively replacing the original KOSS and KOSS-SF.

Background: Emotional labor refers to the management of emotions and expressions to meet the emotional requirements of a job role. This study aimed to develop a revised version of the Korean Emotional Labor Scale (KELS®11), based on the first edition (KELS-24) introduced in 2014, and to provide practical applications and guidelines for its use in the Korean workplace through a validation process. Methods: The revised version of KELS®11 was derived from the 24-item KELS, following a review process involving eight experts. To validate the scale’s reliability and validity, a self-administered survey was conducted among 359 service industry workers using KELS®11, burnout, and depression scales. KELS®11 was reclassified, and its reliability and validity were evaluated. Receiver operating characteristic curve analysis was conducted to establish sex-specific cutoff values (normal vs. high-risk groups). Results: KELS®11 was designed to account for individual, organizational, and cultural contexts. It consists of four subscales and 11 items: “emotional regulation” (2 items), “emotional dissonance” (3 items), “organizational monitoring” (2 items), and “organizational protective system for emotional labor” (4 items). KELS®11 demonstrated good validity (content validity ratio: 0.84; item convergence/discriminant validity success rates: 100%; correlation with burnout: r = 0.185–0.436, p < 0.01; correlation with depression: r = 0.128–0.339, p < 0.05) and reliability (Cronbach’s alpha: 0.597–0.795). Additionally, sex-specific reference values were established to determine risk groups based on the intensity of emotional labor exposure. Conclusions KELS®11 is a validated and reliable measurement tool designed to assess the intensity and magnitude of emotional labor in the workplace. The revised tool reflects critical considerations in the development of emotional labor measurement scales.

Background: Workplace violence refers to any act or threat of physical violence, verbal abuse, harassment, intimidation, bullying, mobbing, or other aggressive and disruptive behaviors that occur at work. This study aims to develop and validate a revision of the Korean Workplace Violence Scale (KWVS®13), based on the first edition of the Korean Workplace Violence Scale (KWVS-24), and to provide practical applications and guidelines for the Korean workplace environment. Methods: The revised KWVS®13 was developed by restructuring the 24-item KWVS through a review process involving eight experts. To validate the reliability and validity of KWVS®13, a self-administered survey comprising KWVS®13, burnout, and depression scales was conducted among 359 service industry workers. KWVS®13 was reclassified, and its reliability and validity were assessed. Receiver operating characteristic curve analysis was performed to establish sex-specific cutoff values (normal vs. risk) of the scale. Results: KWVS®13 consists of 13 items across four subscales: “psychological and sexual violence from customers” (4 items), “psychological and sexual violence from supervisors or coworkers” (4 items), “physical assault from customers, supervisors, or coworkers” (2 items), and “organizational protective system for workplace violence” (3 items). We found that KWVS®13 shows relatively good validity (content validity ratio for content validity: 0.888; success rate of item convergent and discriminant validity: 100%, and significant correlation coefficient with burnout (r = 0.115–0.83, p < 0.05) and depression (r = 0.098–0.348, p < 0.05) with the exception of Organizational Violence Protection System for Workplace Violence) and reliability (Cronbach’s alpha: 0.827–0.860). The reference values for determining risk groups according to the intensity of exposure to workplace violence are presented separately by sex. Conclusions KWVS®13 is a robust and useful measurement tool to objectively and quantitatively assess the intensity and magnitude of workplace violence. It incorporates important considerations for workplace violence assessment and provides a reliable framework for evaluating workplace violence in various professional settings.

Purpose: This study examined longitudinal changes in interstitial lung abnormalities (ILAs) and predictors of clinically significant interstitial lung diseases (ILDs) in a screening population with ILAs. Materials and Methods: We retrieved 36891 low-dose chest CT records from screenings between January 2003 and May 2021. After identifying 101 patients with ILAs, the clinical findings, spirometry results, and initial and follow-up CT findings, including visual and artificial intelligence-based quantitative analyses, were compared between patients diagnosed with ILD (n = 23, 23%) and those who were not (n = 78, 77%). Logistic regression analysis was used to identify significant parameters for the clinical diagnosis of ILD. Results: Twenty-three patients (n = 23, 23%) were subsequently diagnosed with clinically significant ILDs at follow-up (mean, 8.7 years). Subpleural fibrotic ILAs on initial CT and signs of progression on follow-up CT were common in the ILD group (both p < 0.05). Logistic regression analysis revealed that emerging respiratory symptoms (odds ratio [OR], 5.56; 95% confidence interval [CI], 1.28–24.21; p = 0.022) and progression of ILAs at follow-up chest CT (OR, 4.07; 95% CI, 1.00–16.54; p = 0.050) were significant parameters for clinical diagnosis of ILD. Conclusion Clinically significant ILD was subsequently diagnosed in approximately one-quarter of the screened population with ILAs. Emerging respiratory symptoms and progression of ILAs at followup chest CT can be predictors of clinically significant ILDs.

Purpose: This study examined longitudinal changes in interstitial lung abnormalities (ILAs) and predictors of clinically significant interstitial lung diseases (ILDs) in a screening population with ILAs. Materials and Methods: We retrieved 36891 low-dose chest CT records from screenings between January 2003 and May 2021. After identifying 101 patients with ILAs, the clinical findings, spirometry results, and initial and follow-up CT findings, including visual and artificial intelligence-based quantitative analyses, were compared between patients diagnosed with ILD (n = 23, 23%) and those who were not (n = 78, 77%). Logistic regression analysis was used to identify significant parameters for the clinical diagnosis of ILD. Results: Twenty-three patients (n = 23, 23%) were subsequently diagnosed with clinically significant ILDs at follow-up (mean, 8.7 years). Subpleural fibrotic ILAs on initial CT and signs of progression on follow-up CT were common in the ILD group (both p < 0.05). Logistic regression analysis revealed that emerging respiratory symptoms (odds ratio [OR], 5.56; 95% confidence interval [CI], 1.28–24.21; p = 0.022) and progression of ILAs at follow-up chest CT (OR, 4.07; 95% CI, 1.00–16.54; p = 0.050) were significant parameters for clinical diagnosis of ILD. Conclusion Clinically significant ILD was subsequently diagnosed in approximately one-quarter of the screened population with ILAs. Emerging respiratory symptoms and progression of ILAs at followup chest CT can be predictors of clinically significant ILDs.

Background: Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department. Methods: This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department. Patients were enrolled from 19 hospitals between September 2019 and December 2020. For acquired data from 3,657 survivors and 1,455 deaths, six ML models (logistic regression, support vector machine, random forest, extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting [CatBoost]) were constructed using fivefold cross-validation to predict mortality. Through these models, 44 clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components (PaO 2 /FIO 2 [PF], platelets (PLT), bilirubin, cardiovascular, Glasgow Coma Scale score, and creatinine).The confidence interval (CI) was obtained by performing 10,000 repeated measurements via random sampling of the test dataset. All results were explained and interpreted using Shapley’s additive explanations (SHAP). Results: Of the 5,112 participants, CatBoost exhibited the highest area under the curve (AUC) of 0.800 (95% CI, 0.756–0.840) using clinical variables. Using the SOFA components for the same patient, XGBoost exhibited the highest AUC of 0.678 (95% CI, 0.626–0.730). As interpreted by SHAP, albumin, lactate, blood urea nitrogen, and international normalization ratio were determined to significantly affect the results. Additionally, PF and PLTs in the SOFA component significantly influenced the prediction results. Conclusion Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.