Background: Rosacea is a chronic inflammatory skin disorder characterized by erythema, papules, ocular symptoms, and heightened sensitivity. Patients with neurogenic symptoms such as burning or stinging remain particularly difficult to manage. Current guidelines often underrepresent energy-based devices (EBDs), pigmentary sequelae, psychosocial burden, and ocular comorbidities. Objective: To examine Korean dermatologists’ expert perspectives on rosacea management, focusing on skin sensitivity, neurogenic symptoms, pigmentary changes, psychosocial impact, ocular involvement, and EBD use. Methods: A web-based, 29-item survey was administered to 25 board-certified Korean dermatologists (May–June 2025). Quantitative and qualitative responses were analyzed. Results: Erythematotelangiectatic and papulopustular phenotypes with sensitivity skin predominated. EBDs (pulsed dye laser, intense pulsed light) were frequently used but limited by cost and sensitivity issues. Neurogenic symptoms were recognized but rarely treated with neuromodulators. Post-inflammatory hyperpigmentation was infrequent, yet monitoring was inconsistent.Psychosocial and ocular aspects were acknowledged but seldomly systematically addressed.Respondents expressed interest in emerging adjunctive treatments such as cold plasma, skin boosters, and holistic care approaches. Conclusion Korean dermatologists adopt individualized strategies for rosacea, yet practice gaps remain regarding neurogenic symptoms, pigmentary complications, and psychosocial and ocular comorbidities. Findings support the need for updated multidisciplinary, phenotype-driven guidelines aligned with real-world practice.

Objective: This study aimed to evaluate the accuracy of predicting final adult height (FAH) in Korean girls with central precocious puberty (CPP) using artificial intelligence (AI)-derived bone age assessments integrated into the Bayley–Pinneau (BP) or Korean National Growth Chart (KGC) prediction models. Materials and Methods: This single-center, retrospective study included 122 Korean girls with CPP who received gonadotropinreleasing hormone agonist (GnRHa) treatment for at least two years between January 2000 and November 2022. We assessed bone age and predicted adult height at the initiation and completion of GnRHa treatment. We used three bone age assessment methods: human expert assessment based on the Greulich-Pyle (GP) atlas (Human-GP), AI-derived GP (AI-GP), and AI-weighted GP scoring (AI-GPw). We calculated predicted adult heights (PAHs) using both the BP and KGC models, generating 12 PAH estimates per patient (2 time points x 3 bone-age methods x 2 height-prediction models). We assessed prediction accuracy and agreement with FAH using linear regression analysis and Bland–Altman plots and performed multivariable analysis to identify significant predictors of FAH. Results: Human-GP, AI-GP, and AI-GPw demonstrated comparable overall performance in predicting FAH (R 2 : 0.470–0.646 and 0.691–0.822 for treatment initiation and completion, respectively). AI-GPw combined with BP yielded slightly better point estimates but showed no statistically significant differences. At both time points, the BP model demonstrated consistently narrower 95% limits of agreement (LoA) than the KGC model. Multivariable analysis identified AI-GPw-BP and height percentile score as significant predictors of FAH at both time points; mid-parental height was significant only at treatment initiation. Conclusion Human-GP, AI-GP, and AI-GPw demonstrated comparable accuracy in predicting FAH. The BP model demonstrated consistently narrower 95% LoA than did the KGC model. AI-GPw-BP was an independent predictor of FAH. These findings support the clinical utility of AI-derived bone age assessments for individualized FAH prediction in patients with CPP.

Purpose: This study evaluates the Korean Cancer Study Group Geriatric Score-7 (KG-7) frailty screening tool’s effectiveness in elderly multiple myeloma (MM) patients to prevent under and overtreatment. Materials and Methods: This prospective pilot cohort study included 100 elderly patients aged 70 and older with newly diagnosed MM who had not undergone transplantation from August 2020 to January 2022. Results: The median age was 77 years, and 73.0% of patients were classified at International Staging System stages 2 or 3. Using a 5-point cutoff on the KG-7 index (non-frail, score ≥ 5; frail, score < 5), 31% were categorized as frail. After a median follow-up of 26.8 months, the 3-year overall survival rate was 73.0%. There was no statistically significant association between any frailty index and the risk of death. However, frail patients defined by the simplified frailty index (hazard ratio [HR], 2.49; 95% confidence interval [CI], 1.09 to 5.95; p=0.030) and by KG-7 (HR, 2.43; 95% CI, 1.03 to 5.86; p=0.043) had a significantly higher risk of grade 3-4 non-hematologic toxicity, whereas the International Myeloma Working Group definition did not. Over a 24-month tracking period, vulnerability as measured by KG-7 either improved or deteriorated. Conclusion The pilot study, which had a limited number of participants, did not demonstrate KG-7’s effectiveness in predicting survival; however, it successfully predicted severe non-hematologic toxicities. We plan to conduct larger studies in elderly MM patients to determine whether KG-7 can help tailor their treatment regimens.

Background: This study aimed to examine the effect of artificial intelligence (AI) literacy on attitudes toward digital technology and the mediating effect of technostress on this relationship among health professions students. Methods: An online survey was conducted from May to October 2025 with 1,314 students enrolled in medical schools, nursing schools, dental schools, and graduate schools of dentistry nationwide. Structural equation modeling and bootstrapping analyses were performed. Results: The analysis revealed that AI literacy significantly reduced technostress and enhanced attitudes toward digital technology. Technostress also had a negative effect on attitudes toward digital technology, and a partial mediating effect was identified in the relationship between AI literacy and attitudes toward digital technology. In other words, higher levels of AI literacy were associated with lower technostress, which, in turn, led to more positive attitudes toward digital technology. Multigroup analysis further showed that the effect of AI literacy on technostress differed across majors, being significant for medical and nursing students, but not for dental students. Conclusion This study confirmed that improving AI literacy reduces technology-related stress and promotes positive attitudes toward digital technology. These findings suggest the need for AI and digital technology education designs that consider the psychological factors of learners in medical education. Furthermore, the observed group differences suggest that AI literacy may function differently depending on discipline-specific technological and educational contexts.

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.

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.

Background: Primary cutaneous CD30+ lymphoproliferative disorders (pcCD30-LPDs) are a diseases with various clinical and prognostic characteristics. Objective: Increasing our knowledge of the clinical characteristics of pcCD30-LPDs and identifying potential prognostic variables in an Asian population. Methods: Clinicopathological features and survival data of pcCD30-LPD cases obtained from 22 hospitals in South Korea were examined. Results: A total of 413 cases of pcCD30-LPDs (lymphomatoid papulosis [LYP], n=237; primary cutaneous anaplastic large cell lymphoma [C-ALCL], n=176) were included. Ninety percent of LYP patients and roughly 50% of C-ALCL patients presented with multiple skin lesions. Both LYP and C-ALCL affected the lower limbs most frequently. Multiplicity and advanced T stage of LYP lesions were associated with a chronic course longer than 6 months. Clinical morphology with patch lesions and elevated serum lactate dehydrogenase were significantly associated with LPDs during follow-up in LYP patients. Extracutaneous involvement of C-ALCL occurred in 13.2% of patients. Lesions larger than 5 cm and increased serum lactate dehydrogenase were associated with a poor prognosis in C-ALCL. The survival of patients with C-ALCL was unaffected by the anatomical locations of skin lesions or other pathological factors. Conclusion The multiplicity or size of skin lesions was associated with a chronic course of LYP and survival among patients with C-ALCL.

Hangovers from alcohol consumption cause symptoms like headaches, nausea, and fatigue, disrupting daily activities and overall well-being. Over time, they can also lead to inflammation and oxidative stress. Effective hangover relief alleviates symptoms, prevents dehydration, and replenishes energy needed for daily tasks. Natural foods considered high in antioxidants and antiinflammatory properties may aid in the hepatic breakdown of alcohol. The study aims to investigate the impact of glutathione or its enriched yeast extract, which is recognized for its antioxidant characteristics, on alcohol metabolism and alleviating hangovers in a rat model exposed to binge drinking. In this study, glutathione and its enriched yeast extract controlled hangover behaviour patterns, including locomotor activity. Additionally, it enhanced the activities of alcohol dehydrogenase (ADH) and aldehyde dehydrogenase (ALDH) following ethanol ingestion (3 g/kg). Further, the incorporation of glutathione led to an increase in the expression of antioxidant enzymes, such as SOD and catalase, by activating the nuclear erythroid 2-related factor 2 (Nrf2) signaling pathway.This activation reduced the excessive production of reactive oxygen species (ROS) and malondialdehyde. Next, glutathione modulated the activity of cytochrome P450 2E1 (CYP2E1) and the protein expressions of Bax and Bcl2. Besides, in vitro and in vivo investigations with glutathione demonstrated a regulating effect on the pan-s-glutathionylation and its associated protein expression, glutaredoxin 1 (Grx1), glutathione-S-transferase Pi (GST-π), and glutathione reductase (GR). Together, these findings suggest that glutathione or its enriched yeast extract as a beneficial dietary supplement for alleviating hangover symptoms by enhancing alcohol metabolism and its associated Nrf2/Keap1 signalings.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.