Hallym University

Atopic dermatitis (AD) is a prevalent chronic inflammatory skin disease arising from a multifaceted interplay between genetic susceptibility and environmental exposures. Longitudinal cohort studies have been instrumental in elucidating the contribution of environmental factors to both the onset and persistence of AD. This review synthesizes evidence from such studies to delineate key environmental determinants across various domains. Early‐life exposures, including delivery mode and antibiotic exposure, modulate microbial composition and function, thereby influencing immune development and predisposing individuals to AD. Both outdoor and indoor air pollutants, such as particulate matter and volatile organic compounds, have been shown to impair skin barrier integrity and dysregulate immune responses, facilitating the initiation and progression of AD. Nutritional factors, encompassing maternal and infant dietary patterns, shape gut microbiota and metabolite profiles and systemic immune activity, further modulating AD risk. Moreover, psychological stress during the prenatal and postnatal periods has been associated with alterations in immune function and epigenetic programming, which may heighten susceptibility to AD. Environmental influences also appear to vary by AD phenotype and trajectory, underscoring the need for individualized prevention strategies. Advances in exposome research, encompassing both external and internal environmental components, have enhanced mechanistic understanding and facilitated the identification of candidate biomarkers. Collectively, current evidence supports the notion that early‐life environmental exposures act not as isolated determinants but in concert with genetic, microbial, and immunologic factors to shape AD pathogenesis. A comprehensive framework integrating exposomics and multiomics may ultimately inform the development of targeted preventive and therapeutic strategies for children with AD. image

In this article, we consider some types of derivations in Banach algebras. In detail, we investigate the question of whether the superstability can be achieved under some conditions for some types of derivations, such as Jordan derivations, generalized Lie 2-derivations, and generalized Lie derivations.

With the proliferation of devices connected through the Internet of Things (IoT), the focus on biometric authentication (BA) has intensified, driven by the increasing demand for convenience and security. Photoplethysmography (PPG) signals are recognized for their high usability, emerging as a viable method for user identification and authentication in remote medical environments. This study introduces PPGAIHI, a PPG-based authentication in healthcare IoT designed for practical applications and utilizing 1-D convolutional neural networks for feature extraction. Thorough performance evaluations of both model-and distance-based recognition methods were conducted. Comparative analyses of various PPG-based techniques were performed across ideal and real-world conditions using three distinct datasets to assess the trade-off between performance and learning/recognition time, leading to system configuration recommendations. In ideal conditions, model-based methods such as the support vector machine (SVM) and shallow fully connected networks achieved perfect scores–similarly, all distance-based methods achieved a score of one, contingent upon the selected threshold. In practical scenarios, the SVM achieved optimal accuracy, reaching one with more than six segments during the recognition phase. Conversely, the distance-based Minkowski model achieved a superior accuracy of 0.9779 using 100 segments (160 s) for registration and 15 segments (24 s) for recognition. Furthermore, this investigation provides practical guidelines for selecting optimal thresholds in distance-based recognition tasks, significantly improving the feasibility of PPG-based BA systems in real-world applications. This research demonstrates significant advancements in developing adaptable and practical PPG-based BA systems, achieving high accuracy under both ideal and practical conditions while effectively balancing system performance and recognition time.

Aging is characterized by changes in cellular identity and function over time, with alterations in epigenetic patterns potentially serving as an underlying mechanism that drives human aging. Recent studies have identified sets of individual methylation sites, whose combined DNA methylation status serves as a measure of chronological age, known as the DNA methylation clock. These sites, highly enriched in Polycomb repressive complexes binding locations and associated with chromatin factor localization, are located near genes implicated in mammalian development and longevity. Age-related alterations in the human DNA methylome are believed to contribute to age-related diseases, including neurodegenerative diseases. The two most common neurodegenerative diseases, Alzheimer’s disease (AD) and Parkinson’s disease (PD), arise from a combination of genetic and environmental factors, with aging being a significant risk factor. DNA methylation changes are among the key hallmarks of neurodegeneration, reflecting both the aging process and environmental influences. In AD, DNA methylation aging accelerates as the disease advances, while in PD, accelerated aging correlates with earlier onset. Widespread epigenetic dysregulation is linked to neurodegenerative changes through accelerated aging and pathogenesis in AD and PD. In this chapter, we explore the intricate relationship between DNA methylation changes, aging, and neurodegenerative diseases.

Messenger ribonucleic acid (mRNA) vaccines have become a prevalent immunization method, even as the coronavirus disease 2019 (COVID-19) pandemic recedes. However, the potential adverse effects using mRNA vaccines need to be explored in this evolving landscape. In this study, 60 participants were randomly assigned to receive either an mRNA vaccine, specifically for COVID-19, or a conventional vaccine for meningococcal disease. Symptom records and blood samples were collected on Days 0, 3, and 7 after vaccination. Results showed that recipients of mRNA vaccines exhibited elevated levels of serum acute-phase proteins, such as haptoglobin and C-reactive protein, alongside decreased white blood cell counts compared to those receiving conventional vaccines. Proteomic analysis identified significant changes in nine proteins, including interactions involving complement component C9, haptoglobin, and alpha-1-acid glycoprotein, suggesting implications for complement activation and inflammatory responses. Furthermore, variability in anti-polyethylene glycol antibody levels was noted among mRNA vaccine recipients compared to conventional vaccine recipients. This research aims to provide useful information to help develop future vaccination strategies and shape research directions to mitigate individual adverse effects.

Background Oral hygiene behavior has been increasingly recognized as a potential contributor to chronic disease prevention. This scoping review aimed to synthesize existing evidence on the associations between tooth brushing behavior (as a proxy for oral hygiene) and major chronic health outcomes, including cardiovascular events (e.g., myocardial infarction, atrial fibrillation, heart failure), stroke (ischemic, hemorrhagic, and subarachnoid), hypertension (HTN), metabolic syndrome (MetS), and chronic kidney disease (CKD). Methods A comprehensive literature search was conducted using four databases: Ovid-MEDLINE, EMBASE, CINAHL, and the Cochrane Library. The study selection process followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Two reviewers independently screened and selected eligible studies and extracted relevant data. A total of 142 full-text articles were assessed for eligibility. Results Twenty-one studies were included in the final review. Most studies reported that a lower frequency of tooth brushing was associated with a higher risk of cardiovascular events, stroke, HTN, MetS, and CKD. Additionally, several studies suggested that maintaining good oral hygiene in individuals with HTN or diabetes mellitus (DM) may be associated with a reduced risk of cardiovascular complications. Conclusion Frequent tooth brushing, as a key component of oral hygiene, may be associated with a reduced risk of several chronic health outcomes, particularly cardiovascular and metabolic diseases. However, the current body of evidence is predominantly based on observational studies. Further longitudinal and interventional research is warranted to clarify the directionality and potential causal pathways linking oral hygiene behavior to systemic health outcomes.

The lack of a standardized classification system poses challenges for accurately assessing ultra-processed food (UPF) consumption in Korea. This scoping review compared the UPF consumption levels reported in Korean studies and analyzed the discrepancies to highlight the need for a unified UPF classification system tailored to Korea. Four online databases were searched to identify studies conducted in Korea. From 147 papers, 20 papers that examined UPFs as the main variables were selected and reviewed. Their consumption levels are reported. Ninety percent of the papers were cross-sectional studies, while 10% were prospective cohort studies. Most studies (72%) measured UPF consumption using the 24-h dietary recall (24HR), while 28% used food frequency questionnaires (FFQs). The most popular topic (65%) was the association between UPF consumption and health outcomes, with obesity-related outcomes being examined most frequently. A wide range of UPF consumption was observed: from 4.9% to 32.8% of total energy intake. The estimates from food-based FFQs were the lowest compared to those from the dish-based FFQs or 24HR. Significant variations in UPF consumption levels were observed across dietary assessment methods, researchers, and data sources. This scoping review highlights the need for an objective and standardized UPF classification system, developed through collaboration among researchers, to minimize the potential misclassification issues when estimating UPF consumption or examining its associations with the health outcomes in Korea. Establishing a “UPF Working Group” could serve as an effective starting point for this initiative, and it is expected to attract participation from more researchers interested in UPF studies.

Objective We explored the impact of food insecurity on three different measures of obesity, that is, estimated percentage body fat (PBF), BMI, and waist circumference (WC), and examined the presence of sociodemographic disparities. Methods This cross‐sectional study used data from the Korea National Health and Nutrition Examination Survey 2019–2021 (n = 12,447, aged ≥30 years). Food insecurity was evaluated using an 18‐item modified version of the US Household Food Security Survey Module. Three obesity measures were defined: PBF ≥25% for men or ≥35% for women, BMI ≥25 kg/m², and WC ≥90 cm for men or ≥85 cm for women. Logistic regression models were employed to estimate odds ratio (OR) values with 95% CI. Results Of those measured, 4% of households experienced food insecurity (men 3.7%, women 4.6%). In women, food insecurity was positively associated with PBF‐defined obesity (OR: 1.37, 95% CI: 1.03–1.81) but not with BMI‐ or WC‐defined obesity. When analyzed by sociodemographic factors, positive associations between food insecurity and PBF‐defined obesity were observed in older women and urban residents but not in their counterparts. Conclusions Food insecurity seems to have a stronger positive association with PBF than with BMI or WC among Korean women, especially those who are older and reside in urban areas.

This research proposes an explanatory deep learning-based music generation approach, where the output of a deep learning model is validated through a set of predefined musical rules, with a refinement process applied when inaccuracies are detected. The study focuses on gamelan, a traditional form of Indonesian music. A Long Short-Term Memory (LSTM) network is used to generate musical compositions, while a modified Genetic Algorithm (GA), omitting the selection and crossover operators, performs validation and, when necessary, refinement via mutation. The LSTM network produces initial compositions, and the GA module ensures compliance with musical rules, enhancing both explainability and creativity. The model successfully generates new bars and lines with notation sequences not found in the original dataset, indicating creative variation. Whether produced directly by the LSTM or refined through GA, the generated output demonstrates the system’s ability to innovate while preserving core musical characteristics. Furthermore, the GA-based validation allows the generated music to be interpreted in terms of the underlying rule constraints. Statistical analysis, including correlation and regression tests using the t-test, reveals a near-perfect relationship between the original and generated compositions. These findings support the effectiveness of the proposed Automatic Music Generation (AMG) model in fostering creative exploration of new tonal patterns aligned with the target genre.

Background Ethical competence is an essential attribute for professional nurses. Before designing ethics education programmes for nurses, it is crucial to conduct a systematic and accurate analysis of the needs of nurses to optimise the effectiveness of such programmes. This study performed an educational-needs analysis aimed at assessing and prioritising ethical competence requirements among nurses. Methods We developed a 35-item questionnaire based on the Four Components Model of moral behaviour and adjusted it according to findings from a review of the pertinent nursing literature and input from expert panel discussions. The questionnaire, which comprised questions across four ethical competencies (ethical sensitivity, ethical judgment, ethical motivation, and ethical implementation) was distributed to 400 nurses working in three general hospitals in Seoul and Gyeonggi Province, Republic of Korea. Of the 400 questionnaires returned, 307 met our inclusion criteria. The responses were analysed with the aim of determining in which areas nurses needed more training to achieve the required competence. The three-step educational-needs analysis first employed paired t-tests to determine where reliable differences lie between the nurses’ present competence level and required competence level, and then we employed Borich Needs Assessment and the Locus for Focus models to rank and prioritise the identified educational needs. Results Our analysis revealed significant differences between the nurses’ present competence levels and the required competence levels in all 35 items (p < .001) of the questionnaire. Borich Needs Assessment and the Locus for Focus models identified four items in the questionnaire that had the highest priorities for educational needs in ethical competence: three ethical judgment items and one ethical implementation item. These items deal with ethical judgment grounded in ethical theories such as virtue ethics and the principle of justice within principlism; and ethical implementation that specifically addresses issues related to the concealment of clear medical errors. Conclusions This study suggests that specific ethical competencies, identified as high-priority needs through the educational-needs analysis, should be included in student and post-graduate nursing education and in institutional policies in order to foster good nursing practices in daily care.

Digitizing molecular structure information of diverse compounds is essential for AI-driven drug discovery. While a significant amount of molecular structure data is already stored and managed in digital formats for in silico predictions of drug stability and side effects, a significant portion remains in analog documents. To address this, deep learning-based optical chemical structure recognition (OCSR) technologies have been developed. Although these models excel with abundant digitally rendered chemical structure images, they underperform on hand-drawn images due to lack of sufficient training data for such images. This study proposes OCSAug, a diffusion model-based data augmentation technique to improve OCSR performance on hand-drawn molecular images. Using a denoising diffusion probabilistic model to capture irregularities and distortions, and the RePaint algorithm to generate augmented data, OCSAug significantly enhances recognition accuracy. Evaluation on the DECIMER dataset shows an improvement of 1.918–3.820 times over results without augmentation.

The infant gut microbiome is essential for long-term health and is linked to atopic dermatitis (AD), although the underlying mechanisms are not fully understood. This study investigated gut microbiome-host interactions in 31 infants with AD and 29 healthy controls using multi-omics approaches, including metagenomic, host transcriptomic, and metabolomic analyses. Microbial diversity was significantly altered in AD, with Bifidobacterium longum and Clostridium innocuum associated with these changes. At the strain-level, only B. longum differed significantly between groups, with pangenome analyses identifying genetic variations potentially affecting amino acid and lipid metabolites. Notably, B. longum subclade I, which was more prevalent in healthy controls, correlated with host transcriptomic pathways involved in phosphatidylinositol 3-kinase-AKT signaling and neuroactive ligand-receptor pathways, as well as specific metabolites, including tetrahydrocortisol and ornithine. These findings highlight the role of B. longum strain-level variation in infants, offering new insights into microbiome-host interactions related to AD.

Rationale: Tissue engineering through three-dimensional (3D) bioprinting has emerged as a highly promising strategy for creating custom-designed 3D bioconstructs that closely mimic native tissue architecture. However, ongoing advancements in bioink formulation and bioprinting processes are required to achieve precise replication of target tissues. In particular, effective vascularization and extracellular remodeling are essential for successful gingival tissue regeneration. Methods: To achieve this, we propose a cell-laden collagen bioink formulation containing omega-3 fatty acids, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), for gingival tissue regeneration. To enhance the mechanotransduction of human gingival fibroblasts (hGFs) encapsulated in the bioink, we employed a shear-induced bioprinting process to activate key signaling pathways, including mechanosensitive channels, which are involved in gingival tissue regeneration. Results: Bioprinted cell constructs subjected to both biochemical and biophysical cues exhibited promising gene expression profiles related to collagen production and angiogenesis, demonstrating the potential of integrating bioprinting with mechanical and biochemical stimulation for gingival tissue engineering. Furthermore, when hGF-laden bioconstructs containing EPA/DHA were implanted subcutaneously into mice, the formation of blood vessel-like structures was clearly observed at four weeks post-transplantation. Conclusion: These results suggest that the engineered bioconstruct, incorporating EPA/DHA-assisted bioinks and mechanical stimulation, may offer a promising strategy for gingival tissue regeneration and the development of a 3D biomimetic model within an oral organ-on-a-chip system.