Sarcopenia is an age-related condition which is characterized by a decrease in the mass and function of skeletal muscle and can affect the muscles of the trunk and extremities and the muscles to be used in swallowing. Early diagnosis and appropriate intervention of sarcopenic dysphagia is very crucial for older adults. Sarcopenic dysphagia can be diagnosed by measuring the function, strength, and mass of the affected muscle. In addition, dietary function assessments, functional eating scales and food swallow level scales, video fluoroscopic assessments, and electromyography or manometry can be used to diagnose the sarcopenic dysphagia. Although studies have been published using ultrasound to determine the amount and function of muscle in the body and physical function in sarcopenia, few studies have used ultrasound to diagnose sarcopenia.This paper aims to introduce the use of ultrasonography to assess sarcopenic dysphagia and suggest future research directions.

Sarcopenia is an age-related condition which is characterized by a decrease in the mass and function of skeletal muscle and can affect the muscles of the trunk and extremities and the muscles to be used in swallowing. Early diagnosis and appropriate intervention of sarcopenic dysphagia is very crucial for older adults. Sarcopenic dysphagia can be diagnosed by measuring the function, strength, and mass of the affected muscle. In addition, dietary function assessments, functional eating scales and food swallow level scales, video fluoroscopic assessments, and electromyography or manometry can be used to diagnose the sarcopenic dysphagia. Although studies have been published using ultrasound to determine the amount and function of muscle in the body and physical function in sarcopenia, few studies have used ultrasound to diagnose sarcopenia.This paper aims to introduce the use of ultrasonography to assess sarcopenic dysphagia and suggest future research directions.

Sarcopenia is an age-related condition which is characterized by a decrease in the mass and function of skeletal muscle and can affect the muscles of the trunk and extremities and the muscles to be used in swallowing. Early diagnosis and appropriate intervention of sarcopenic dysphagia is very crucial for older adults. Sarcopenic dysphagia can be diagnosed by measuring the function, strength, and mass of the affected muscle. In addition, dietary function assessments, functional eating scales and food swallow level scales, video fluoroscopic assessments, and electromyography or manometry can be used to diagnose the sarcopenic dysphagia. Although studies have been published using ultrasound to determine the amount and function of muscle in the body and physical function in sarcopenia, few studies have used ultrasound to diagnose sarcopenia.This paper aims to introduce the use of ultrasonography to assess sarcopenic dysphagia and suggest future research directions.

Sarcopenia is an age-related condition which is characterized by a decrease in the mass and function of skeletal muscle and can affect the muscles of the trunk and extremities and the muscles to be used in swallowing. Early diagnosis and appropriate intervention of sarcopenic dysphagia is very crucial for older adults. Sarcopenic dysphagia can be diagnosed by measuring the function, strength, and mass of the affected muscle. In addition, dietary function assessments, functional eating scales and food swallow level scales, video fluoroscopic assessments, and electromyography or manometry can be used to diagnose the sarcopenic dysphagia. Although studies have been published using ultrasound to determine the amount and function of muscle in the body and physical function in sarcopenia, few studies have used ultrasound to diagnose sarcopenia.This paper aims to introduce the use of ultrasonography to assess sarcopenic dysphagia and suggest future research directions.

Noroviruses (NoV) are the major viral pathogen causing epidemic acute gastroenteritis and outbreaks of foodborne and waterborne illness. During the local festival in Chungnam province, group food poisoning occurred outbreak by NoV infections in Jan 2019. In this study, epidemiological analysis and molecular characterization were conducted such as genotyping, phylogeny. The prevalent genotypes of food poisoning events were NoV GII.3 and GII.17, and NoV GII.3 and GII.17 isolates of this study were completely matched in nucleotide sequence comparison of capsid gene region, respectively. In underground water and stream water, various multiple genotypes of noroviruses were detected including NoV GII.3, GII.8 and GI.4 in aquatic environment of the local festival site. Among 32 worker samples, various NoVs of five genotypes (GI.7, GI.8, GII.3, GII.8, GII.17) were detected in 12 samples and expected to causing NoV contaminated by exposure to groundwater. NoV genotype GII.3, which was detected from groundwater 2, was completely consistent with that of patients and workers. Therefore, groundwater within the local festival site could be main cause of food poisoning event. Because NoV outbreaks are caused by fecal to oral transmission, proper management of sewage purification facilities, groundwater and sanitary toilets is required for many visitors, and efforts are needed to maintain clean environment.
Base Sequence ; Capsid ; Chungcheongnam-do ; Disease Outbreaks ; Epidemiologic Studies ; Foodborne Diseases ; Gastroenteritis ; Genotype ; Groundwater ; Holidays ; Humans ; Korea ; Norovirus ; Phylogeny ; Rivers ; Sewage ; Water

Background: An activating mutation (c.617A>C/p.Lys206Arg, L206R) in protein kinase cAMP-activated catalytic subunit alpha (PRKACA) has been reported in 35% to 65% of cases of cortisol-producing adenomas (CPAs). We aimed to compare the clinical characteristics and transcriptome analysis between PRKACA L206R mutants and wild-type CPAs in Korea. Methods: We included 57 subjects with CPAs who underwent adrenalectomy at Seoul National University Hospital. Sanger sequencing for PRKACA was conducted in 57 CPA tumor tissues. RNA sequencing was performed in 13 fresh-frozen tumor tissues. Results: The prevalence of the PRKACA L206R mutation was 51% (29/57). The mean age of the study subjects was 42±12 years, and 87.7% (50/57) of the patients were female. Subjects with PRKACA L206R mutant CPAs showed smaller adenoma size (3.3±0.7 cm vs. 3.8±1.2 cm, P=0.059) and lower dehydroepiandrosterone sulfate levels (218±180 ng/mL vs. 1,511±3,307 ng/mL, P=0.001) than those with PRKACA wild-type CPAs. Transcriptome profiling identified 244 differentially expressed genes (DEGs) between PRKACA L206R mutant (n=8) and wild-type CPAs (n=5), including five upregulated and 239 downregulated genes in PRKACA L206R mutant CPAs (|fold change| ≥2, P<0.05). Among the upstream regulators of DEGs, CTNNB1 was the most significant transcription regulator. In several pathway analyses, the Wnt signaling pathway was downregulated and the steroid biosynthesis pathway was upregulated in PRKACA mutants. Protein-protein interaction analysis also showed that PRKACA downregulates Wnt signaling and upregulates steroid biosynthesis. Conclusion The PRKACA L206R mutation in CPAs causes high hormonal activity with a limited proliferative capacity, as supported by transcriptome profiling.

Purpose: This study aimed to evaluate and optimize microbial DNA extraction methods from urine, a non-invasive sample source, to enhance DNA quality, purity, and reliability for urinary microbiome research and biomarker discovery in bladder cancer. Materials and Methods: A total of 302 individuals (258 with genitourinary cancers and 44 with benign urologic diseases) participated in this study. Urine samples were collected via sterile catheterization, resulting in 445 vials for microbial analysis. DNA extraction was performed using three protocols: the standard protocol (SP), water dilution protocol (WDP), and chelation-assisted protocol (CAP). DNA quality (concentration, purity, and contamination levels) was assessed using NanoDrop spectrophotometry.Microbial analysis was conducted on 138 samples (108 cancerous and 30 benign) using 16S rRNA sequencing. Prior to sequencing on the Illumina MiSeq platform, Victor 3 fluorometry was used for validation. Results: WDP outperformed other methods, achieving significantly higher 260/280 and 260/230 ratios, indicating superior DNA purity and reduced contamination, while maintaining reliable DNA yields. CAP was excluded due to poor performance across all metrics. Microbial abundance was significantly higher in WDP-extracted samples (p<0.0001), whereas SP demonstrated higher alpha diversity indices (p<0.01), likely due to improved detection of low-abundance taxa. Beta diversity analysis showed no significant compositional differences between SP and WDP (p=1.0), supporting the reliability of WDP for microbiome research. Conclusions WDP is a highly effective and reliable method for microbial DNA extraction from urine, ensuring high-quality and reproducible results. Future research should address sample variability and crystal precipitation to further refine microbiome-based diagnostics and therapeutics.

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.

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.

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.