Objectives: This study aimed to investigate the prevalence of depressive symptoms among hospital nurses and their associations with psychosocial and job-related factors. Methods: A total of 515 nurses at one university hospital participated in this study. Data on demographics, job-related factors, burnout, social conflict, happiness, and ego-resiliency were collected using self-reported questionnaires. Depressive symptoms were assessed using the depressive subscale of the Hospital Anxiety and Depression Scale, with scores of 8 or higher indicating their presence. Demographics, job-related, and psychosocial factors were compared based on the presence of depressive symptoms. Logistic regression analysis was conducted to identify factors associated with depressive symptoms. In addition, the mediating effect of ego-resiliency on the relationship between burnout and depressive symptoms was examined. Results: A total of 226 participants (43.9%) were identified as having depressive symptoms. Happiness was associated with a lower risk of depressive symptoms, whereas burnout and social conflict were associated with a higher risk. Furthermore, ego-resiliency showed a significant mediating effect on the relationship between burnout and depressive symptoms. Being a basic or charge nurse; experiencing job dissatisfaction; being exposed to higher levels of burnout and social conflict; and having lower levels of happiness and ego-resiliency were significantly correlated with depressive symptoms. Conclusions Our findings suggest that intervention strategies aimed at enhancing happiness and ego-resiliency while reducing burnout and social conflict may help prevent and manage depressive symptoms among hospital nurses.

Metaverse-based mental health interventions have recently emerged as an extension of telepsychiatry, digital therapeutics (DTx), and virtual reality (VR)–based treatments for children and adolescents. The coronavirus disease 2019 pandemic accelerated the adoption of remote psychological assessment and intervention, raising critical questions regarding their clinical validity, developmental appropriateness, and long-term applicability in youth populations. Accumulating evidence has demonstrated that remote assessments and internet-based interventions can achieve clinical outcomes comparable to face-to-face approaches in selected domains, particularly anxiety, depression, and social communication difficulties. In parallel, immersive technologies such as VR and metaverse-based platforms have been applied to exposure-based interventions, social skills training, and emotion regulation in controlled and repeatable environments. This narrative review examined 48 empirical and review studies on telepsychiatry, internet-based cognitive behavioral therapy, DTx, VR, and metaverse-based interventions for children and adolescents. Neurobiological mechanisms relevant to fear extinction, executive function, emotion regulation, and social cognition were critically reviewed. In addition, real-world implementation experiences from a hospital-based Wee Center in Korea were integrated to evaluate feasibility within school-linked mental health services. The findings suggested that metaverse-based counseling should not be conceptualized as a stand-alone replacement for face-to-face treatment, but rather as a complementary, hybrid platform linking remote screening, structured digital intervention, and in-person clinical care. While current evidence supported feasibility and conditional clinical utility, further longitudinal, controlled, and cost-effectiveness studies were required to establish its role in pediatric mental health.

Background: This study aims to compare the effects of short- and intermediate-acting erythropoiesis-stimulating agents (ESAs) on hemoglobin (Hb) variability in patients undergoing maintenance hemodialysis. Methods: This retrospective cohort study included 119 patients who were classified into two groups based on ESA type: short-acting ESA (epoetin alfa/beta [EPO], n=48) and intermediate-acting ESA (darbepoetin alfa [DPO], n=71). Hb levels were measured 11 times at 4-week intervals from ESA therapy initiation to 40 weeks of follow-up. This study used established metrics from previous research, including standard deviation (SD), coefficient of variation (CV), and residual SD, to quantify Hb variability. Results: Over the 40-week study period with 4-week measurement intervals, mean Hb levels were comparable between the two ESA groups. In the EPO and DPO groups, SD was 0.67±0.19 g/dL and 0.69±0.22 g/dL, respectively (p=0.516), CV was 0.06±0.02 and 0.07±0.02, respectively (p=0.480), and residual SD was 0.73±0.21 g/dL and 0.76±0.26 g/dL, respectively (p=0.463). No significant differences in Hb variability were observed between the groups over 40 weeks using SD, CV, and residual SD. Conclusion This retrospective cohort study showed comparable mean Hb levels and Hb variability indices between groups. With respect to Hb variability, the findings reveal no clear basis for prioritizing either short-acting or intermediate-acting ESA.

Objectives: Large language models (LLMs) demonstrate remarkable potential in healthcare communication. However, whether they can process complex, high-volume medical information, such as stroke-related content, remains insufficiently validated. This study aimed to evaluate the natural language processing capabilities of LLMs in handling such content and to develop an evaluation instrument. Methods: A survey compared educational materials generated by two LLMs (ChatGPT 4.0 and Claude 3) with neurologist-authored content on stroke. The materials were based on two clinical scenarios representing distinct stroke etiologies: cardioembolism and large-artery atherosclerosis. They were evaluated in terms of accuracy, legality, ethics, comprehensiveness, and information delivery. Scores for comprehensiveness and information delivery were compared according to participants’ agreement with the use of LLMs in healthcare. Results: ChatGPT received the highest scores across all domains, except for legality in Scenario 2. In Scenario 1, the ranking for accuracy and summarization of clinical information was, from highest to lowest, ChatGPT, Claude, and the neurologist (η2 = 0.140, p < 0.001; η2 = 0.175, p < 0.001). The same hierarchy was observed in Scenario 2 for accuracy (η2 = 0.077, p < 0.001) and summarization (η2 = 0.194, p < 0.001). Participants who agreed with the use of LLMs in healthcare assigned higher scores for the comprehensiveness (Scenario 1, p = 0.005; Scenario 2, p = 0.007) and information delivery (Scenario 1, p = 0.003; Scenario 2, p = 0.026) of ChatGPT-generated materials than participants who did not agree. Conclusions LLMs demonstrated adequate capability to convey complex content, such as stroke-related information, in an accessible and understandable manner for non-experts.

Janus kinase (JAK) inhibitors are an important treatment option for ulcerative colitis, providing rapid onset of action, oral administration, and efficacy even after biologic failure. The 3 approved agents—tofacitinib, filgotinib, and upadacitinib—differ in JAK isoform selectivity, leading to clinically meaningful differences in efficacy and safety. Evidence from network meta-analyses, clinical trials, and real-world studies consistently shows that upadacitinib provides the highest efficacy for induction and maintenance of remission, whereas filgotinib demonstrates the most favorable safety profile. The strong efficacy of upadacitinib and tofacitinib is particularly relevant in patients with severe disease, including acute severe ulcerative colitis, and upadacitinib maintains high efficacy regardless of prior advanced therapy exposure. JAK inhibitors also benefit extraintestinal manifestations. Although risks such as herpes zoster, serious infection, thromboembolism, and major cardiovascular events differ among agents, long-term data suggest generally acceptable safety when used appropriately. Intraclass JAK-to-JAK cycling is feasible, with about half of patients achieving steroid-free clinical remission in retrospective cohorts. Based on mechanistic, clinical, and real-world evidence, filgotinib may be a first-line option for patients with lower disease activity or when safety is a priority, whereas upadacitinib or tofacitinib may be preferred in higher disease activity. Strategically selecting agents may improve durability and outcomes.

This study aimed to develop a predictive model for the early identification of patients at risk of sepsis, using routinely available clinical information and laboratory test results collected during the initial phase of patient care. Methods: This retrospective analysis included electronic medical records of 22,400 adult patients who presented with suspected infection to a tertiary care university hospital in Korea between January 2013 and May 2024. Patients were classified according to Systemic Inflammatory Response Syndrome (score ≥ 2) or Quick Sequential Organ Failure Assessment (score ≥ 2), in combination with sepsis-related International Classification of Diseases, 10th revision codes. Four different machine learning models were trained and validated using five-fold cross-validation. In addition, Shapley additive explanations analysis was performed to interpret the contribution and clinical relevance of key predictive variables. Results: Among the evaluated models, CatBoost demonstrated the strongest predictive performance. Notably, platelet distribution width, alveolar–arterial oxygen difference, procalcitonin, and the arterial/alveolar oxygen ratio consistently emerged as major predictors. Importantly, several variables that did not reach statistical significance in univariate analysis nevertheless contributed substantially to overall model performance, highlighting the importance of complex, multidimensional interactions among clinical factors. Conclusion: These findings indicate that a model based on simple, routinely collected clinical data can achieve high predictive accuracy and strong generalizability. Such a tool may support early clinical decision-making by multidisciplinary teams, including nurses, across diverse real-world care settings. Further prospective studies are warranted to validate its clinical utility and to assess its potential effects on patient outcomes.

Objective: The NOD-like receptor family pyrin domain-containing 3 (NLRP3) inflammasome has been proposed to be a downstream mediator of neuroinflammation in individuals with Parkinson’s disease (PD). However, its involvement across disease stages and related synucleinopathies, such as multiple system atrophy (MSA), remains unclear. We aimed to analyze the peripheral mRNA expression of NLRP3-related genes and cytokines across individuals with isolated REM sleep behavior disorder (iRBD), early-stage PD, late-stage PD, and MSA. Methods: Peripheral blood mononuclear cells (PBMCs) were collected from 151 participants: 35 healthy controls (HCs), 31 patients with iRBD, 41 patients with early-stage PD, 21 patients with late-stage PD, and 23 patients with MSA. mRNA expression was measured using quantitative real-time polymerase chain reaction. Statistical comparisons were performed using analysis of variance (ANOVA) or Welch’s ANOVA, and associations with clinical variables were analyzed through stepwise multiple linear regression. Results: NLRP3 expression was significantly lower in patients with iRBD (p=0.0263) and patients with early-stage PD (p= 0.0101) than in HCs. NIMA-related kinase 7 (NEK7) expression progressively decreased across the disease spectrum (HCs vs. patients with early-stage PD, p=0.0008; vs. patients with late-stage PD, p<0.0001). In contrast, apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC) and caspase-1 were elevated in patients with PD, especially those in the late stages. Levels of patients with MSA resembled those of HCs but differed from those of patients with PD. Interleukin (IL)-1β and IL-18 levels were not significantly different. In patients with early-stage PD, NLRP3 expression was negatively correlated with disease duration, the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale Part II score, and the cognitive score. Conclusion Our findings challenge the prevailing hypothesis that NLRP3 inflammasome activation directly contributes to PD pathogenesis. Instead, the observed increase in ASC and caspase-1 expression suggests the potential involvement of alternative inflammasome pathways during disease progression.

Purpose: Hereditary spastic paraplegia (HSP) refers to a group of genetic neurodegenerative diseases marked by gradually worsening spasticity and hyperreflexia in the lower extremities. This study aimed to describe the clinical and genetic characteristics of Korean patients with spastic paraplegia. Materials and Methods: We retrospectively reviewed medical records of 69 patients with spastic paraplegia from 54 unrelated families between 2002 and 2024. Genetic, clinical, electrophysiological, and radiological features were comprehensively analyzed. Results: Causative genes were identified in 34 (63%) of 54 unrelated families; SPAST, detected in 26 families, was the most prevalent. Seven novel pathogenic variants were identified. Clinically, the median age of symptom onset was 25 years [14.0–37.0]. Out of 69 patients with spastic paraplegia, 51 (74%) presented with the pure form of spastic paraplegia, which included all patients with SPG4. Spastic gait was a universal feature in all patients. Urinary dysfunction was present in 42 (61%) patients. Additional neurologic manifestations included peripheral neuropathy 9 (13%), cognitive impairment 5 (7%), upper limb weakness 4 (6%), dysarthria 4 (6%), dysphagia 3 (4%), ataxia 3 (4%), and scoliosis 1 (3%). Brain MRI findings demonstrated a thin corpus callosum in two patients with SPG11; all patients with SPG4 had normal findings. Spine MRI revealed spinal cord atrophy in 16 (27%) patients, including 6 (21%) patients with SPG4. Conclusion The study comprehensively reviewed genetic and clinical spectra of spastic paraplegia in Korean patients, emphasizing the predominance of SPAST as the causative gene and underscoring the genetic and phenotypic heterogeneity of spastic paraplegia.

OBJECTIVES: This study examined whether individuals with consistently normal liver enzyme levels are protected from alcohol-related liver disease and investigated whether heavy drinking increases liver disease risk independent of normal biomarker status. METHODS: We conducted a nationwide cohort study using Korean National Health Insurance Service data (2002–2019), including 19,035 participants aged ≥40 years who maintained normal liver enzyme levels (aspartate aminotransferase ≤40 μ/L, alanine aminotransferase ≤40 μ/L, gamma-glutamyl transferase ≤63 μ/L for men and ≤35 μ/L for women) across multiple examinations conducted between 2002 and 2008. Participants were categorized as abstainers (≤1 time/mo), moderate drinkers (≤2 times/wk), and heavy drinkers (≥3 times/wk). Primary outcomes included incident liver disease identified using diagnostic codes. Cox proportional hazards models were used to estimate hazard ratios (HRs), with age-stratified analyses performed. RESULTS: Heavy drinkers demonstrated a significantly higher risk of liver disease than abstainers (HR, 1.73; 95% confidence interval [CI], 1.40 to 2.14), whereas moderate drinkers showed no significant association (HR, 1.06; 95% CI, 0.94 to 1.19). Consistent patterns emerged for middle-aged adults (40–69 years), with attenuated effects among participants aged ≥70 years. When alcoholic liver disease was analyzed specifically, both moderate (HR, 1.29; 95% CI, 1.05 to 1.58) and heavy drinkers (HR, 2.86; 95% CI, 2.09 to 3.91) exhibited significantly increased hazards. CONCLUSIONS Despite normal liver enzyme levels, heavy alcohol consumption was associated with a significantly increased risk of liver disease. These findings challenge current reactive screening paradigms relying solely on biomarker abnormalities and support proactive alcohol counseling regardless of enzyme results. More sensitive strategies are needed for early detection of alcohol-related liver injury.

In patients with differentiated thyroid cancer (DTC), initial recurrence risk stratification based on clinical, histopathological, and perioperative data remains the key determinant for guiding management strategies during the first 1-2 years post-treatment. However, the adoption of ongoing risk stratification (ORS), which dynamically reassesses risk by integrating longitudinal clinical data and treatment response, enables more precise long-term prognostic assessment and facilitates highly individualized management. Building upon recent guidelines, the 2026 KTA guideline has been further refined by incorporating robust evidence from large-scale national cohorts and comprehensive systematic reviews. These updated recommendations outline contemporary concepts of ORS, risk-adapted TSH suppression targets, optimized surveillance modalities for recurrence detection, and disease-specific long-term follow-up strategies. Reflecting the paradigm shift toward de-escalated treatment, this revision integrates evolved perspectives on TSH suppression intensity, the clinical interpretation of thyroglobulin levels, and tailored follow-up intervals. These evidence-based recommendations aim to minimize unnecessary treatment and excessive surveillance in the large proportion of patients with excellent prognosis after initial therapy, while ensuring that each patient receives appropriately tailored and effective long-term management.