Metabolic dysfunction-associated steatotic liver disease (MASLD) has emerged as a global health issue due to its increasing prevalence associated with lifestyle changes and its strong correlation with metabolic syndrome. Farnesoid X receptor (FXR) is a nuclear receptor that plays a pivotal role in regulating bile acid, lipid, and glucose metabolism, making it an attractive therapeutic target for liver and metabolic diseases. In this study, we investigated the effects of MHY5456, a synthetic agonist of FXR, on hepatic metabolism and fibrosis. MHY5456 enhanced the transcriptional activity of FXR in a concentration-dependent manner.Treatment of AC2F rat liver-derived cells with MHY5456 resulted in the downregulation of genes involved in lipid accumulation and an upregulation of mitochondrial-related gene expression. Additionally, MHY5456 significantly reduced oleic acid (OA)-induced lipid accumulation. To assess its anti-fibrotic potential, we tested its effects on transforming growth factor-beta (TGF-β)-induced fibrosis in LX2 human hepatic stellate cells (HSCs). MHY5456 significantly suppressed the expression of fibrosis-related genes and proteins. In vivo, administration of MHY5456 to mice fed a methionine-choline-deficient (MCD) diet alleviated hepatic fibrosis, inflammation, and lipid accumulation. These results show that FXR activation by MHY5456 modulates lipid metabolism and fibrotic pathways, suggesting its potential as a pharmacological candidate for liver and metabolic disorders, including MASLD. Further pharmacological and toxicological studies are needed to confirm its therapeutic relevance.

OBJECTIVES: Poor cardiovascular health (CVH) and the high prevalence of depressive symptoms represent significant public health concerns, underscoring the importance of examining their association. This study aimed to investigate the association between CVH, as defined by the American Heart Association’s 2022 Life’s Essential 8 (LE8) framework, and depressive symptoms. METHODS: This study used data from the 2014, 2016, 2018, and 2020 Korea National Health and Nutrition Examination Survey. Overall CVH, measured using LE8, was categorized into 3 groups: low score (0–<50), moderate score (50–<80), and high score (80–100). LE8 comprises 2 subdomains: health behaviors and health factors. Depressive symptoms were defined as a total score ≥10 on the Patient Health Questionnaire-9. Multiple logistic regression analyses were performed, adjusting for sex, age, socioeconomic status, and current drinking status. RESULTS: Among 17,294 adults, 257 male and 681 female reported significant depressive symptoms. Compared with individuals in the low LE8 category (reference), the odds ratio (OR) for depressive symptoms was 0.29 (95% confidence interval [CI], 0.21 to 0.40) for those in the high LE8 category. The OR for depressive symptoms was 0.39 (95% CI, 0.29 to 0.53) for individuals with a high health behavior score compared with those with a low health behavior score. In contrast, the health factor score was not significantly associated with depressive symptoms. CONCLUSIONS These findings suggest that overall CVH, particularly the health behavior subdomain, was associated with lower odds of depressive symptoms. Prospective longitudinal studies are warranted to validate these findings and clarify the directionality of the observed associations.

This case report investigates the effects of semi-occluded vocal tract exercises (SOVTEs) on voice improvement in elderly female patients with presbyphonia. Elderly female patients with presbyphonia commonly present with symptoms such as hoarseness, decreased vocal intensity, and phonatory difficulties. These symptoms are often associated with age-related vocal fold atrophy, leading to compensatory muscle tension and inefficient phonation, which necessitate targeted therapeutic interventions. In this study, two elderly female patients, aged 73 years and 71 years, participated in a voice therapy program centered on SOVTEs designed to promote vocal fold vibration efficiency and reduce compensatory tension. Case 1 underwent five therapy sessions, while Case 2 completed sixteen sessions. Pre- and post-treatment voice assessments revealed notable improvements in vocal quality, suggesting that SOVTEs may be an effective therapeutic approach for managing presbyphonia in elderly women.

This case report investigates the effects of semi-occluded vocal tract exercises (SOVTEs) on voice improvement in elderly female patients with presbyphonia. Elderly female patients with presbyphonia commonly present with symptoms such as hoarseness, decreased vocal intensity, and phonatory difficulties. These symptoms are often associated with age-related vocal fold atrophy, leading to compensatory muscle tension and inefficient phonation, which necessitate targeted therapeutic interventions. In this study, two elderly female patients, aged 73 years and 71 years, participated in a voice therapy program centered on SOVTEs designed to promote vocal fold vibration efficiency and reduce compensatory tension. Case 1 underwent five therapy sessions, while Case 2 completed sixteen sessions. Pre- and post-treatment voice assessments revealed notable improvements in vocal quality, suggesting that SOVTEs may be an effective therapeutic approach for managing presbyphonia in elderly women.

This case report investigates the effects of semi-occluded vocal tract exercises (SOVTEs) on voice improvement in elderly female patients with presbyphonia. Elderly female patients with presbyphonia commonly present with symptoms such as hoarseness, decreased vocal intensity, and phonatory difficulties. These symptoms are often associated with age-related vocal fold atrophy, leading to compensatory muscle tension and inefficient phonation, which necessitate targeted therapeutic interventions. In this study, two elderly female patients, aged 73 years and 71 years, participated in a voice therapy program centered on SOVTEs designed to promote vocal fold vibration efficiency and reduce compensatory tension. Case 1 underwent five therapy sessions, while Case 2 completed sixteen sessions. Pre- and post-treatment voice assessments revealed notable improvements in vocal quality, suggesting that SOVTEs may be an effective therapeutic approach for managing presbyphonia in elderly women.

Cardiovascular disease is an important risk factor for mortality among kidney transplant recipients. In this study, we aimed to investigate the association between cardiovascular risk score at kidney transplantation and long-term outcomes of patients. Methods: In this prospective, observational cohort study, we enrolled kidney transplant recipients who participated in the Korean Organ Transplantation Registry and underwent transplantation between April 2014 and December 2019. The cardiovascular risk status of kidney transplant recipients was assessed using the Framingham risk score. All-cause mortality, major adverse cardiovascular events, allograft failure, estimated glomerular filtration rates (eGFRs), and composite outcomes were evaluated after kidney transplantation. Results: Of the 4,682 kidney transplant recipients, 96 died during 30.7 ± 19.1 months of follow-up. The Kaplan-Meier survival analysis results showed that high Framingham risk scores were associated with all-cause mortality, major adverse cardiovascular events, and composite outcomes. According to the multivariable Cox analysis, high Framingham risk scores were associated with an increased risk of mortality (hazard ratio [HR], 3.20; 95% confidence interval [CI], 1.30–7.91), major adverse cardiovascular events (HR, 8.43; 95% CI, 2.41–29.52), and composite outcomes (HR, 2.05; 95% CI, 1.19–3.46). The eGFRs after transplantation were significantly higher among patients in the low Framingham risk score group (p < 0.001). However, Framingham risk scores were not associated with graft loss or rapid decline in eGFRs. Conclusion: The Framingham risk score is a useful indicator of cardiovascular events, mortality, and kidney function after kidney transplantation.

Background: Posttransplant diabetes mellitus (PTDM) complicates kidney transplant recipients (KTRs) in morbidity and mortality. This study aimed to predict PTDM risk in KTRs using machine learning and deep learning models. Methods: Data were obtained from the Korea Organ Transplantation Registry, a nationwide cohort study of KTRs. Four machine learning algorithms, including eXtreme Gradient Boosting (XGBoost), CatBoost, light gradient boosting machine and logistic regression, and deep learning were implemented on 41 pretransplant and 31 posttransplant variables to predict PTDM. Model performance was assessed using the area under the curve (AUC) of the receiver operating characteristic curve, accuracy, precision, recall, and F1 score. Results: Among 3,213 KTRs, 497 patients (15.5%) developed PTDM within 1 year. The PTDM group had higher age, body mass index (BMI), triglyceride level, and prevalence of hypertension and cardiovascular disease, and lower total cholesterol level at baseline than the No-PTDM group. The XGBoost model showed the highest AUC (0.738) and F1 score (0.42), and modest accuracy (0.86), while the CatBoost model exhibited the highest accuracy (0.87) and precision (0.79). Feature importance in XGBoost was highest for recipient age, followed by baseline BMI, triglyceride level at posttransplant 6 months, baseline glycated hemoglobin and high-density lipoprotein cholesterol level, white blood cell (WBC) count and serum uric acid level at 6 months, baseline WBC count, and tacrolimus trough level at discharge. Conclusion The XGBoost model demonstrated the best performance for predicting PTDM within 1 year, offering an accurate tool for early identification and personalized care of high-risk KTRs for PTDM.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Purpose: This longitudinal cohort study aimed to evaluate the relationship between osteonecrosis of the jaw and pulp and periapical disease in patients who were administered bisphosphonates. Methods: Using data from a nationwide cohort, we examined the association among dental caries, pulp and periapical disease, and osteonecrosis of the jaw in women aged >50 years who received bisphosphonates for more than 1 year between 2002 and 2015.Because of ambiguities in the diagnosis of osteonecrosis of the jaw in population-based data, we operationally defined and categorized the condition into established and potential osteonecrosis of the jaw. Results: Pulp and periapical disease significantly increased the development of both established and potential osteonecrosis of the jaw (hazard ratio, 2.21; 95% confidence interval, 1.40–3.48; and hazard ratio, 2.22; 95% confidence interval, 1.65–2.98, respectively). Root canal treatment did not have any influence on the development of osteonecrosis of the jaw. Conclusions Pulp and periapical disease may be a major risk factor for osteonecrosis of the jaw. The study findings suggest that patients should undergo regular dental examinations to detect pulp and periapical disease before or during the administration of bisphosphonates and that root canal treatment should be considered to decrease the risk of osteonecrosis of the jaw.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.