Objectives: In alignment with the World Health Organization’s goal of eliminating hepatitis C, this study assessed the current treatment status and reasons for non-treatment among patients with hepatitis C in Jeonbuk State, Republic of Korea, to inform strategies for improving care engagement. Methods: Among 311 individuals diagnosed with hepatitis C and reported through the NationalNotifiable Infectious Disease Surveillance system between January 2023 and June 2024, 208 patients were surveyed after excluding those who had died or could not be contacted.Statistical analyses included the chi-square test, the Cochran-Armitage test for trend, and logistic regression. Results: Overall, 116 participants (55.8%) reported having received antiviral therapy. Among the 92 untreated individuals, the most common reason for non-treatment was the absence of symptoms (n = 23; 25.0%), followed by the burden of drug costs (n = 21; 22.8%). Conclusion These findings highlight suboptimal treatment uptake and key barriers that may hinder progress toward hepatitis C elimination. Expanding screening and strengthening linkage-to-care strategies, while addressing financial barriers, will be essential to achievingnational elimination targets.

Burning mouth syndrome remains a frequent source of diagnostic delay, repeated consultations, and fragmented care because it has long been treated as a “diagnosis of exclusion.” Recent updates in orofacial pain classification and research diagnostic frameworks support a practical shift: from an exclusionary label toward a structured diagnostic algorithm and staged, phenotype-guided management. This narrative, algorithm-focused clinical review aims to summarize clinically actionable evidence and to propose a “minimum sufficient workup” that prioritizes safety, efficiency, and patient-centered communication.We first outline pragmatic diagnostic criteria and key symptom patterns, then integrate current concepts of mixed mechanisms—peripheral neuropathic features, centralociplastic amplification, and psychosocial modulators—that account for symptom fluctuation and discordance between symptom severity and objective findings. The core of this review is a one-page stepwise algorithm: (1) confirm chronicity and absence of visible mucosal disease, (2) screen for red flags requiring urgent evaluation, (3) address common local contributors (e.g., candidiasis, irritants, contact allergy, xerostomia), (4) perform a targeted systemic/medication/deficiency panel, and (5) phenotype patients to guide staged treatment choices and follow-up. Finally, we provide a practical management framework emphasizing education, trigger control, and individualized combinations of topical, systemic, and behavioral interventions with predefined reassessment intervals. Future priorities include phenotype-stratified randomized trials and implementation outcomes that quantify reductions in diagnostic delay, misdiagnosis, and unnecessary testing.

In forensic medicine, age estimation commonly involves assessing age-related changes in teeth and skeletal structures. Vertebral morphological alterations, such as osteophyte formation, serve as age indicators. Recent studies using deep-learning techniques, such as neural networks, for age estimation from radiographic images have been conducted, reporting significantly higher accuracy than previous studies. This study aimed to estimate age using neural network-based deep-learning techniques applied to computed tomography (CT) cross-sectional images of the spine and evaluate its feasibility. Postmortem CT scans of 214 cadavers with varying decomposition levels were used. Coronal and sagittal cross-sectional images penetrating the center of each vertebral body were extracted for the 11th and 12th thoracic vertebrae and the first to fifth lumbar vertebrae. Using these images, along with the chronological ages of deceased individuals, an age estimation model was developed through regression analysis in PyTorch, employing a convolutional neural networks architecture with five-fold cross-validation. The model achieved a mean absolute error of 5.385 years, root mean squared error of 7.029 years, and coefficient of determination of 0.793. Although the sample size was relatively small, the results suggested the potential applicability of vertebral imagingbased age estimation in the Korean population. Further research using a larger dataset may improve the accuracy and reliability of the model.

Alzheimer’s disease (AD) is characterized by progressive cognitive deterioration and significant depression. However, the mechanisms linking depression to AD pathology remain unclear. Here, we investigated whether Notch2 signaling mediates depressionlike behaviors in presenilin-2 (PS2) N141I mutant mice, an early-onset AD model. PS2 wild-type (WT) and mutant (MT) mice aged 12-15 months were subjected to unpredictable chronic mild stress (UCMS) for 4 weeks, followed by sucrose preference, tail-hanging, and forced swimming tests. Behavioral assessments showed that UCMS exacerbated anhedonia and immobility only in PS2 MT mice. Molecular analysis revealed concomitant increases in plasma corticosterone, hippocampal γ-secretase activity, and Notch2 expression, and elevated total and phosphorylated glucocorticoid receptor levels in PS2 MT-UCMS mice. Gene expression profiling of human hippocampal datasets confirmed upregulation of NOTCH2 in Alzheimer’s disease and depression.Pharmacological inhibition of γ-secretase and Notch signaling with DAPT normalizes depressive behavior, reduces corticosterone release, attenuates GR phosphorylation, and inhibits Notch2 signaling in PS2 MT mice. These findings identify Notch2 as a pivotal mediator linking chronic stress to molecular changes associated with depression and AD, and suggest that targeting Notch2 signaling may provide therapeutic benefits for comorbid mood and neurodegenerative disorders.

Background/Aims: Cardiovascular disease remains a major cause of mortality in patients with metabolic dysfunction-associated steatotic liver disease (MASLD). This study evaluated the association between subclinical carotid atherosclerosis (SCA) and MASLD or MASLD and increased alcohol intake (MetALD) in asymptomatic individuals. Methods: This cross-sectional study included 56,889 adults undergoing health check-ups in South Korea. Hepatic steatosis was diagnosed by ultrasound, and SCA was defined by carotid plaques or increased intima-media thickness. Liver fibrosis was evaluated using the fibrosis-4 index and elastography. Results: SCA was identified in 13.5%. MASLD and MetALD were significantly associated with SCA in models adjusted for demographic and lifestyle factors (adjusted odds ratio [aOR], 1.26;95% confidence interval [CI], 1.19 to 1.33; aOR, 1.43; 95% CI, 1.30 to 1.58; respectively, p<0.001for both). However, these associations attenuated and lost statistical significance when metabolic risk factors were further adjusted. The risk of SCA increased with greater hepatic steatosis and liver fibrosis severity. In patients with MASLD, aORs were 1.70 (hepatic steatosis index >36),1.23 (fibrosis-4 index ≥1.3), and 1.78 (liver stiffness measurement ≥5.6 kPa), compared to indi-viduals without MASLD. Similar trends were observed in the MetALD group. Additionally, hyper-tension and clustering of ≥3 cardiometabolic risk factors were significantly associated with SCA inthe MASLD group, supporting the role of metabolic burden in SCA development. Conclusions MASLD and MetALD were associated with increased SCA risk, particularly in individuals with hepatic steatosis and fibrosis. These findings suggest that metabolic burden and liver disease severity jointly contribute to subclinical atherosclerosis risk.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

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: Allograft is predominantly used interbody spacers for anterior cervical discectomy and fusion (ACDF). The corticocancellous allograft has weaker mechanical strength as it is an artificial composite of the cancellous and cortical parts. Additionally, whether utilizing a firmer allograft, such as the cortical ring, leads to better outcomes is unclear. Therefore, we aimed to compare the surgical outcomes of cortical ring and cortico-cancellous allografts in ACDF. Methods: Patients who underwent ACDF using allograft and were followed up for > 1 year were retrospectively reviewed. Patient characteristics, including fusion rates (assessed by interspinous motion [ISM], intra-graft bone bridging, and extra-graft bone bridging), subsidence, allograft complications (e.g., allograft fracture and resorption), and patient-reported outcome measures (neck pain visual analog scale [VAS], arm pain VAS, and neck disability index), were assessed. Patients were divided into 2 groups based on the allograft used: cortical ring and cortico-cancellous allograft groups. Subgroup analysis was subsequently conducted in singleand multi-level operation groups. Results: A total of 227 patients were included. Of them, 134 (59.0%) and 93 (41.0%) underwent ACDF using cortical ring and corticocancellous allograft, respectively. In single-level operations, the cortico-cancellous allograft significantly frequented allograft resorption (24 / 66, 36.4%) than the cortical ring allograft (1 / 28, 3.7%) (p = 0.001). The cortico-cancellous allograft group demonstrated significantly greater subsidence. However, the fusion rates did not significantly differ between the 2 groups. In multi-level operations, the cortico-cancellous allograft (5 / 27, 18.5%) resulted in a significantly higher fracture rate than the cortical ring allograft (5 / 105, 4.7%) (p = 0.030). The fusion rate at 1-year postoperative assessed using ISM (63.2% vs. 55.5%) and intra-graft bone bridging (66.7% vs. 40.7%) was higher in the cortical ring group; however, the difference was not significant. The patient-reported outcomes at 1-year postoperative did not demonstrate significant intergroup differences both in single- and multi-level operations. Conclusions Allograft resorption or fracture occurs more frequently with cortico-cancellous than cortical ring allografts. Despite the frequent occurrence of allograft-related complications with cortico-cancellous allografts, the fusion rate was not significantly affected. Due to the higher rate of allograft resorption or fractures and greater subsidence with cortico-cancellous allografts, cortical ring allografts might yield more stable results in ACDF.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.