Hallym Polytechnic University

Prion diseases, also known as transmissible spongiform encephalopathies, are characterized by the accumulation of misfolded prion proteins (PrPSc), which resists clearance due to impaired removal mechanisms and immune tolerance. Although strategies aimed at enhancing intracellular clearance pathways, including autophagy, have shown promise, effective in vivo interventions remain limited. Innate immune stimulation with CpG oligodeoxynucleotides (CpG ODN), a Toll-like receptor 9 agonist that mimics bacterial DNA containing unmethylated CpG motifs, has been reported to extend survival in prion-infected mice; however, the underlying mechanisms involved remain incompletely defined. In this study, we examined the effects of CpG ODN in both in vitro and in vivo models of prion disease using the 22L scrapie strain. Mice treated with CpG ODN prior to infection exhibited prolonged mean survival compared with vehicle-treated controls and showed reduced PrPSc accumulation in spleen and brain tissues at 60 and 170 days post-infection, respectively. These changes were correlated with increased AMPK (Tyr172) phosphorylation and alterations in autophagy-associated signaling markers in the brain tissue. In 22L scrapie-infected neuronal cells, CpG ODN treatment significantly reduced PrPSc levels, and this effect was attenuated by pharmacological inhibition of autophagy-associated and lysosomal degradative processes. Collectively, these findings indicate that CpG ODN reduces PrPSc accumulation and prolongs survival in prion-infected mice, supporting the involvement of autophagy-associated degradative processes without establishing enhanced autophagic flux.

Purpose: This study compared key health indicators of poor, older urban adults attending a free clinic with those of the general older population, using data from the 2023 Korea National Health and Nutrition Examination Survey (KNHANES). Methods: This cross-sectional comparative study included 60 adults aged ≥60 years who attended the Raphael Nanum Homeless Clinic in Seoul. Participants completed a questionnaire, underwent anthropometric assessment, and provided fasting blood samples for measurement of total cholesterol, low density lipoprotein (LDL)-cholesterol, and triglycerides (TG). Obesity, current smoking, monthly alcohol use, poor self-rated health, and strength exercise (≥ 2 days/week) were defined according to 2023 KNHANES criteria and compared with age-matched 2023 KNHANES estimates for adults aged ≥ 60 years using independent t-tests and two-proportion z-tests. Results: Participants were predominantly men (80.0%) with a mean age of 79.9 years; 70.0% reported no regular income, and 46.7% rated their health as poor. Compared with their 2023 KNHANES counterparts, the clinic group had a higher prevalence of obesity (50.0% vs. 35.0%), particularly among men, and a more atherogenic lipid profile characterized by higher LDL-cholesterol despite similar total cholesterol levels and lower TG. The prevalence of current smoking (3.3% vs. 10.6%) and monthly alcohol use (31.7% vs. 53.0%) was significantly lower, whereas participation in strength exercise was low in both groups, with no significant differences observed. Conclusion: Poor, older urban adults exhibited multidimensional health disparities, including obesity, adverse lipid profiles, and markedly poorer self-rated health, despite lower levels of smoking and alcohol consumption. Community-based interventions targeting nutrition, physical activity, and chronic disease management are needed to reduce health inequalities in this vulnerable population.

The purpose of this study was to investigate the association between single-nucleotide polymorphisms (SNPs) of bone-related and cartilage-related genes and the anteroposterior phenotypes of skeletal class III malocclusion. The samples consisted of 310 Korean young adult patients. K-means cluster analysis was performed using the ordinal scales of SNA, SNB, and ANB (normal, moderate, or severe, based on ±1 SD and ±2 SD from the ethnic norm). Three main groups comprising 5 clusters were obtained: (1) class-I group: cluster-4 (normal maxilla and normal mandible), (2) class-II group: cluster-1 (normal maxilla and retrusive mandible), and (3) class-III group: (a) cluster-2 (retrusive maxilla and normal mandible), (b) cluster-5 (normal maxilla and moderately protrusive mandible), and (c) cluster-3 (normal maxilla and severely protrusive mandible). Genotype-phenotype associations were investigated for fibroblast growth factor-10 ( FGF10 )-rs593307, fibrillin-3 precursor gene ( FBN3 )–rs7351083, matrilin1 ( MATN1 )-rs1149042, Wnt family member 11 ( WNT11) -rs1533767, growth hormone receptor ( GHR )-rs2973015 and rs6184, and collagen type-I α1 ( COL1A1 )-rs2249492. Compared with mandibular retrusion (cluster-1), mandibular prognathism was significantly associated with FGF10 -rs593307 and FBN3 -rs7351083 (cluster-3, OR = 2.46~5.12; cluster-3 + cluster-5, OR = 2.46~3.61; all P < 0.05). Compared with a normal mandible (cluster-2), mandibular prognathism was associated with MATN1 -rs1149042 (cluster-3, OR = 4.87; cluster-3 + cluster-5, OR = 3.38; all P < 0.05). Compared with a normal maxilla and retrusive mandible (cluster-1), the combination of a retrusive maxilla and a normal mandible (cluster-2) was associated with FBN3 -rs7351083 (OR = 4.48, P < 0.05). FGF10 -rs593307, FBN3 –rs7351083, and MATN1 -rs1149042 might be associated with skeletal class III malocclusion, specifically through the expression of mandibular prognathism in cluster-3 and cluster-5, and maxillary retrusion in cluster-2.

Chronic and hard-to-heal wounds remain burdensome because microbial contamination, dysregulated inflammation, and fragile tissue regeneration slow closure, while passive dressings often injure new tissue during removal. This review synthesizes polymer- and lipid-based nanostructures through a barrier-resolved lens that links composition, architecture, and processing to performance in protease- and salt-rich exudate across topical and transdermal routes. Quantitative trends include effective diameters of approximately 50–300 nm, practical constraints of sterile filtration at 0.2 μm, and therapeutic windows that prioritize contamination control on the first day, support proliferation around day three, and sustain remodeling beyond one week. Mechanistic evidence indicates that interfacial charge and the protein corona govern residence and uptake, lipid bilayers enable dual loading, degradable polymer matrices provide depot-like behavior, and hybrid constructs temper the early burst while improving storage stability.

Orthohantavirus hantanense (HTNV) is a zoonotic pathogen transmitted by rodents and the causative agent of hemorrhagic fever with renal syndrome (HFRS) in East Asia. Long-term reservoir population ecology studies have enhanced our understanding of hantavirus infection patterns and support disease risk assessments critical for military and civilian populations in HTNV high-risk areas. Here, we evaluated fluctuations in the population dynamics of Apodemus agrarius, the primary reservoir of HTNV, assessed hantavirus seroprevalence, and conducted a descriptive analysis of HFRS disease risks in the Republic of Korea (ROK). From 2000 to 2019, a total of 12,476 small mammals representing 15 species were captured across locations spanning northern to southern regions. A. agrarius was the most frequently collected species in the ROK, with juvenile populations peaking during the late fall/winter seasons. A high proportion of A. agrarius captured at military installations/training sites near the Demilitarized Zone tested seropositive for immunoglobulin G antibodies against HTNV, whereas none of those trapped in Jeollanam Province were seropositive. Risk assessment identified high-risk HFRS zones in northern Gyeonggi and Gangwon Provinces. This study provides important insights for forecasting heightened viral transmission risks to humans and implementing targeted mitigation strategies in HFRS high-risk regions of the ROK.

Early warning score (EWS) have become an essential component of patient safety strategies in healthcare environments worldwide. These systems aim to identify patients at risk of clinical deterioration by evaluating vital signs and other physiological parameters, enabling timely intervention by rapid response teams. Despite proven benefits and widespread adoption, conventional EWS have limitations that may affect their ability to effectively detect and respond to patient deterioration. There is growing interest in integrating continuous multimodal monitoring technologies and advanced analytics, particularly artificial intelligence (AI) and machine learning (ML)-based approaches, to address these limitations and enhance EWS performance. This review provides a comprehensive overview of the current state and potential future directions of AI-based bio-signal monitoring in early warning system. It examines emerging trends and techniques in AI and ML for bio-signal analysis, exploring the possibilities and potential applications of various bio-signals such as electroencephalography, electrocardiography, electromyography in early warning system. However, significant challenges exist in developing and implementing AI-based bio-signal monitoring systems in early warning system, including data acquisition strategies, data quality and standardization, interpretability and explainability, validation and regulatory approval, integration into clinical workflows, and ethical and legal considerations. Addressing these challenges requires a multidisciplinary approach involving close collaboration between healthcare professionals, data scientists, engineers, and other stakeholders. Future research should focus on developing advanced data fusion techniques, personalized adaptive models, real-time and continuous monitoring, explainable and reliable AI, and regulatory and ethical frameworks. By addressing these challenges and opportunities, the integration of AI and bio-signals into early warning systems can enhance patient monitoring and clinical decision support, ultimately improving healthcare quality and safety. In conclusion, integrating AI and bio-signals into the early warning system represents a promising approach to improve patient care outcomes and support clinical decision-making. As research in this field continues to evolve, it is crucial to develop safe, effective, and ethically responsible solutions that can be seamlessly integrated into clinical practice, harnessing the power of innovative technology to enhance patient care and improve individual and population health and well-being.