This study examined the inhibitory effects of pluviatolide, a lignan derived from Podophyllum hexandrum, on mast cell activation and IgE-mediated type I hypersensitivity, focusing on FcεRI-dependent and calcium-mediated pathways. Using bone marrowderived mast cells (BMMCs) and rat basophilic leukemia (RBL)-2H3 cells, we found that pluviatolide significantly decreased β-hexosaminidase release and suppressed the expression and secretion of TNF-α and IL-6 in a concentration-dependent manner, without causing cytotoxicity. While we initially hypothesized that it would selectively modulate antigen-specific FcεRI signaling, pluviatolide also inhibited degranulation induced by calcium ionophore and thapsigargin, indicating its effects extend to receptorindependent, Ca2+-dependent activation mechanisms. Immunoblot analyses revealed decreased phosphorylation of proximal kinases (Lyn, Syk), adaptor proteins (LAT, PLCγ1), MAPKs (ERK1/2, JNK, p38), and NF-κB p65. In a passive cutaneous anaphylaxis (PCA) mouse model, oral administration of pluviatolide significantly reduced Evans blue extravasation and mast cell degranulation in ear tissues. These findings demonstrate that pluviatolide suppresses both early and late-phase mast cell responses through multi-nodal inhibition of activation pathways, highlighting its potential as a therapeutic candidate for both IgE-mediated and non-IgE-mediated allergic disorders.

Artificial intelligence-based segmentation and quantification is a hot topic in cardiovascular imaging. However, the development of clinically useful artificial intelligence-based models for congenital heart disease is challenging owing to morphologic heterogeneity and limited data for training. Although cardiovascular computed tomography is increasingly used in evaluating patients with congenital heart disease, only a few reports have evaluated artificial intelligence-based segmentation algorithms for congenital heart disease. To be familiarized with this trend, we need to understand state-of-the-art segmentation techniques for cardiovascular digital twins as well as basic concept, current challenges, and potential solutions of artificial intelligence-based methods. Additionally, in this review, our learnings from the literature and our pilot study are summarized, and future directions are suggested.

Pediatric kidney disease has a relatively lower prevalence than do other pediatric conditions and has a notably different etiology from kidney diseases observed in adults. Furthermore, the pediatric population is unique in that they experience ongoing growth and development, distinguishing them from adult patients. Consequently, pediatric patients with kidney disease require more specialized and meticulous nutritional management than do adults. To address this need and promote optimal dietary practices for pediatric patients with kidney disease, pediatric nephrologists from the Korean Society of Pediatric Nephrology and nutritionists from the Korean Society of Clinical Nutrition have collaborated to establish nutritional guidelines specifically tailored to Korean dietary patterns. These guidelines offer detailed, nutrient-specific recommendations covering energy, protein, calcium, phosphorus, and potassium consumption while providing practical, culturally relevant guidance intended to support both pediatric patients and their caregivers.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Pediatric kidney disease has a relatively lower prevalence than do other pediatric conditions and has a notably different etiology from kidney diseases observed in adults. Furthermore, the pediatric population is unique in that they experience ongoing growth and development, distinguishing them from adult patients. Consequently, pediatric patients with kidney disease require more specialized and meticulous nutritional management than do adults. To address this need and promote optimal dietary practices for pediatric patients with kidney disease, pediatric nephrologists from the Korean Society of Pediatric Nephrology and nutritionists from the Korean Society of Clinical Nutrition have collaborated to establish nutritional guidelines specifically tailored to Korean dietary patterns. These guidelines offer detailed, nutrient-specific recommendations covering energy, protein, calcium, phosphorus, and potassium consumption while providing practical, culturally relevant guidance intended to support both pediatric patients and their caregivers.

Background: Liver fibrosis is a common outcome of chronic liver disease and is primarily driven by hepatic stellate cell (HSC) activation. Irisin, a myokine released during physical exercise, is beneficial for metabolic disorders and mitochondrial dysfunction. This study aimed to explore the effects of irisin on liver fibrosis in HSCs, a bile duct ligation (BDL) mouse model, and the associated mitochondrial dysfunction. Methods: In vitro experiments utilized LX-2 cells, a human HSC line, stimulated with transforming growth factor-β1 (TGF-β1), a major regulator of HSC fibrosis, with or without irisin. Mitochondrial function was assessed using mitochondrial fission markers, transmission electron microscopy, mitochondrial membrane potential, and adenosine triphosphate (ATP) production. In vivo, liver fibrosis was induced in mice via BDL, followed by daily intraperitoneal injections of irisin (100 μg/kg/day) for 10 days. Results: In vitro, irisin mitigated HSC activation and reduced reactive oxygen species associated with the TGF-β1/Smad signaling pathway. Irisin restored TGF-β1-induced increases in fission markers (Fis1, p-DRP1) and reversed the decreased expression of TFAM and SIRT3. Additionally, irisin restored mitochondrial membrane potential and ATP production lowered by TGF-β1 treatment. In vivo, irisin ameliorated the elevated liver-to-body weight ratio induced by BDL and alleviated liver fibrosis, as evidenced by Masson’s trichrome staining. Irisin also improved mitochondrial dysfunction induced by BDL surgery. Conclusion Irisin effectively attenuated HSC activation, ameliorated liver fibrosis in BDL mice, and improved associated mitochondrial dysfunction. These findings highlight the therapeutic potential of irisin for the treatment of liver fibrosis.