Purpose: The classification of asthma phenotypes frequently depends on the age of onset. However, the rationale for specific age cutoffs remains unclear. This study aimed to explore the distribution of asthma onset age, to define subgroups based on onset age, and to examine their characteristics within a broad Korean population. Methods: An analysis of cross-sectional data involving 56,632 participants from the Korean National Health and Nutrition Examination Survey (2010–2016) was conducted. Data on asthma history, including diagnosis, self-reported age of asthma onset, and current disease status, were collected using structured questionnaires. Results: The distribution of asthma onset age showed a distinct peak in early childhood, with a decline between the ages 15 and 20.Based on this distribution, asthma was categorized into childhood-onset ( ≤ 18 years) and adult-onset ( > 18 years) for further analysis.Multivariate analyses indicated that adult-onset asthma was associated with older age, female sex, obesity, and a history of smoking, whereas childhood-onset asthma was linked to younger age, male sex, allergic rhinitis, and atopic dermatitis. Among the adultonset group, current asthma had a later onset age, increased history of smoking history, and atopic dermatitis compared to past asthma. Conclusion This analysis of nationwide general population data suggests that an age threshold around 18 years may be relevant for defining adult-onset asthma.

Purpose: The classification of asthma phenotypes frequently depends on the age of onset. However, the rationale for specific age cutoffs remains unclear. This study aimed to explore the distribution of asthma onset age, to define subgroups based on onset age, and to examine their characteristics within a broad Korean population. Methods: An analysis of cross-sectional data involving 56,632 participants from the Korean National Health and Nutrition Examination Survey (2010–2016) was conducted. Data on asthma history, including diagnosis, self-reported age of asthma onset, and current disease status, were collected using structured questionnaires. Results: The distribution of asthma onset age showed a distinct peak in early childhood, with a decline between the ages 15 and 20.Based on this distribution, asthma was categorized into childhood-onset ( ≤ 18 years) and adult-onset ( > 18 years) for further analysis.Multivariate analyses indicated that adult-onset asthma was associated with older age, female sex, obesity, and a history of smoking, whereas childhood-onset asthma was linked to younger age, male sex, allergic rhinitis, and atopic dermatitis. Among the adultonset group, current asthma had a later onset age, increased history of smoking history, and atopic dermatitis compared to past asthma. Conclusion This analysis of nationwide general population data suggests that an age threshold around 18 years may be relevant for defining adult-onset asthma.

Purpose: The classification of asthma phenotypes frequently depends on the age of onset. However, the rationale for specific age cutoffs remains unclear. This study aimed to explore the distribution of asthma onset age, to define subgroups based on onset age, and to examine their characteristics within a broad Korean population. Methods: An analysis of cross-sectional data involving 56,632 participants from the Korean National Health and Nutrition Examination Survey (2010–2016) was conducted. Data on asthma history, including diagnosis, self-reported age of asthma onset, and current disease status, were collected using structured questionnaires. Results: The distribution of asthma onset age showed a distinct peak in early childhood, with a decline between the ages 15 and 20.Based on this distribution, asthma was categorized into childhood-onset ( ≤ 18 years) and adult-onset ( > 18 years) for further analysis.Multivariate analyses indicated that adult-onset asthma was associated with older age, female sex, obesity, and a history of smoking, whereas childhood-onset asthma was linked to younger age, male sex, allergic rhinitis, and atopic dermatitis. Among the adultonset group, current asthma had a later onset age, increased history of smoking history, and atopic dermatitis compared to past asthma. Conclusion This analysis of nationwide general population data suggests that an age threshold around 18 years may be relevant for defining adult-onset asthma.

Purpose: The classification of asthma phenotypes frequently depends on the age of onset. However, the rationale for specific age cutoffs remains unclear. This study aimed to explore the distribution of asthma onset age, to define subgroups based on onset age, and to examine their characteristics within a broad Korean population. Methods: An analysis of cross-sectional data involving 56,632 participants from the Korean National Health and Nutrition Examination Survey (2010–2016) was conducted. Data on asthma history, including diagnosis, self-reported age of asthma onset, and current disease status, were collected using structured questionnaires. Results: The distribution of asthma onset age showed a distinct peak in early childhood, with a decline between the ages 15 and 20.Based on this distribution, asthma was categorized into childhood-onset ( ≤ 18 years) and adult-onset ( > 18 years) for further analysis.Multivariate analyses indicated that adult-onset asthma was associated with older age, female sex, obesity, and a history of smoking, whereas childhood-onset asthma was linked to younger age, male sex, allergic rhinitis, and atopic dermatitis. Among the adultonset group, current asthma had a later onset age, increased history of smoking history, and atopic dermatitis compared to past asthma. Conclusion This analysis of nationwide general population data suggests that an age threshold around 18 years may be relevant for defining adult-onset asthma.

Purpose: The classification of asthma phenotypes frequently depends on the age of onset. However, the rationale for specific age cutoffs remains unclear. This study aimed to explore the distribution of asthma onset age, to define subgroups based on onset age, and to examine their characteristics within a broad Korean population. Methods: An analysis of cross-sectional data involving 56,632 participants from the Korean National Health and Nutrition Examination Survey (2010–2016) was conducted. Data on asthma history, including diagnosis, self-reported age of asthma onset, and current disease status, were collected using structured questionnaires. Results: The distribution of asthma onset age showed a distinct peak in early childhood, with a decline between the ages 15 and 20.Based on this distribution, asthma was categorized into childhood-onset ( ≤ 18 years) and adult-onset ( > 18 years) for further analysis.Multivariate analyses indicated that adult-onset asthma was associated with older age, female sex, obesity, and a history of smoking, whereas childhood-onset asthma was linked to younger age, male sex, allergic rhinitis, and atopic dermatitis. Among the adultonset group, current asthma had a later onset age, increased history of smoking history, and atopic dermatitis compared to past asthma. Conclusion This analysis of nationwide general population data suggests that an age threshold around 18 years may be relevant for defining adult-onset asthma.

Purpose: This study aims to analyze the learning curve of hand-assisted laparoscopic living donor nephrectomy (HLDN) conducted by a trained gastrointestinal surgeon. Methods: A retrospective analysis was performed on the perioperative clinical data of 96 consecutive patients who underwent HLDN from May 2013 to March 2023. The learning curve was evaluated using the cumulative sum (CUSUM) test based on operation time and risk-adjusted CUSUM for postoperative complications. Patients were divided into three groups (novice, development, and competency phases) based on changes in operation time. Patient demographics and perioperative outcomes were compared between each group. Results: Among the patients, 35 were male, with a mean age of 48.9 ± 11.3 years and a mean body mass index (BMI) of 24.5 ± 3.2 kg/m 2 . The novice phase (phase 1) included the first 30 cases, with the development phase (phase 2) up to the 65th case. Operation times were significantly different across phases, averaging 263.2 ± 33.4, 211.1 ± 34.4, and 161.1 ± 31.3 minutes for phases 1, 2, and 3, respectively (P < 0.001). Blood loss decreased gradually across phases (phase 1, 264.7 ± 144.4 mL; phase 2, 239.7 ± 166.3 mL; phase 3, 198.8 ± 103.5 mL), though not statistically significant. BMI impacted operation time only in phase 1. Overall postoperative complications occurred in 13 cases (Clavien-Dindo grade I, 4 cases;grade II, 9 cases), with no significant differences across phases. Conclusion HLDN can be safely performed by a trained gastrointestinal surgeon, with approximately 30 cases needed to achieve proficiency.

Background/Aims: Shifts in the gut microbiota and metabolites are interrelated with liver cirrhosis progression and complications. However, causal relationships have not been evaluated comprehensively. Here, we identified complication-dependent gut microbiota and metabolic signatures in patients with liver cirrhosis. Methods: Microbiome taxonomic profiling was performed on 194 stool samples (52 controls and 142 cirrhosis patients) via V3-V4 16S rRNA sequencing. Next, 51 samples (17 controls and 34 cirrhosis patients) were selected for fecal metabolite profiling via gas chromatography mass spectrometry and liquid chromatography coupled to timeof-flight mass spectrometry. Correlation analyses were performed targeting the gut-microbiota, metabolites, clinical parameters, and presence of complications (varices, ascites, peritonitis, encephalopathy, hepatorenal syndrome, hepatocellular carcinoma, and deceased). Results: Veillonella bacteria, Ruminococcus gnavus, and Streptococcus pneumoniae are cirrhosis-related microbiotas compared with control group. Bacteroides ovatus, Clostridium symbiosum, Emergencia timonensis, Fusobacterium varium, and Hungatella_uc were associated with complications in the cirrhosis group. The areas under the receiver operating characteristic curve (AUROCs) for the diagnosis of cirrhosis, encephalopathy, hepatorenal syndrome, and deceased were 0.863, 0.733, 0.71, and 0.69, respectively. The AUROCs of mixed microbial species for the diagnosis of cirrhosis and complication were 0.808 and 0.847, respectively. According to the metabolic profile, 5 increased fecal metabolites in patients with cirrhosis were biomarkers (AUROC >0.880) for the diagnosis of cirrhosis and complications. Clinical markers were significantly correlated with the gut microbiota and metabolites. Conclusions Cirrhosis-dependent gut microbiota and metabolites present unique signatures that can be used as noninvasive biomarkers for the diagnosis of cirrhosis and its complications.

Background: Adrenocortical carcinomas (ACCs) are rare tumors with aggressive but varied prognosis. Stage, Grade, Resection status, Age, Symptoms (S-GRAS) score, based on clinical and pathological factors, was found to best stratify the prognosis of European ACC patients. This study assessed the prognostic performance of modified S-GRAS (mS-GRAS) scores including modified grade (mG) by integrating mitotic counts into the Ki67 index (original grade), in Korean ACC patients. Methods: Patients who underwent surgery for ACC between January 1996 and December 2022 at three medical centers in Korea were retrospectively analyzed. mS-GRAS scores were calculated based on tumor stage, mG (Ki67 index or mitotic counts), resection status, age, and symptoms. Patients were divided into four groups (0–1, 2–3, 4–5, and 6–9 points) based on total mS-GRAS score. The associations of each variable and mS-GRAS score with recurrence and survival were evaluated using Cox regression analysis, Harrell’s concordance index (C-index), and the Kaplan–Meier method. Results: Data on mS-GRAS components were available for 114 of the 153 patients who underwent surgery for ACC. These 114 patients had recurrence and death rates of 61.4% and 48.2%, respectively. mS-GRAS score was a significantly better predictor of recurrence (C-index=0.829) and death (C-index=0.747) than each component (P<0.05), except for resection status. mS-GRAS scores correlated with shorter progression-free survival (P=8.34E-24) and overall survival (P=2.72E-13). Conclusion mS-GRAS scores showed better prognostic performance than tumor stage and grade in Asian patients who underwent surgery for ACC.

Background: To evaluate the chemosensitivity to doxorubicin (DOX) in two primary cells derived from a tumor of FVB/N-Trp53tm1Hw1 knockout (KO) mice with TALEN-mediated Trp53 mutant gene, we evaluated the cell survivability, cell cycle distribution, apoptotic cell numbers and apoptotic protein expression in solid tumor cells and ascetic tumor cells treated with DOX. Results: The primary tumor cells showed a significant (P < 0.05) defect for UV-induced upregulation of the Trp53 pro-tein, and consisted of different ratios of leukocytes, fibroblasts, epithelial cells and mesenchymal cells. The ­ IC50 level to DOX was lower in both primary cells ­(IC50 = 0.12 μM and 0.20 μM) as compared to the CT26 cells ­(IC50 = 0.32  μM), although the solid tumor was more sensitive. Also, the number of cells arrested at the G0/G1 stage was significantly decreased (24.7–23.1% in primary tumor cells treated with DOX, P < 0.05) while arrest at the G2 stage was enhanced to 296.8–254.3% in DOX-treated primary tumor cells compared with DOX-treated CT26 cells. Furthermore, apoptotic cells of early and late stage were greatly increased in the two primary cell-lines treated with DOX when compared to same conditions for CT26 cells. However, the Bax/Bcl-2 expression level was maintained constant in the primary tumor and CT26 cells. Conclusions To the best of our knowledge, these results are the first to successfully detect an alteration in chemosensitivity to DOX in solid tumor cells and ascetic tumor cells derived from tumor of FVB/N-Trp53tm1Hw1 mice TALENmediated Trp53 mutant gene.

The accuracy and convenience of continuous glucose monitoring (CGM), which efficiently evaluates glycemic variability and hypoglycemia, are improving. There are two types of CGM: professional CGM and personal CGM. Personal CGM is subdivided into real-time CGM (rt-CGM) and intermittently scanned CGM (isCGM). CGM is being emphasized in both domestic and foreign diabetes management guidelines. Regardless of age or type of diabetes, CGM is useful for diabetic patients undergoing multiple insulin injection therapy or using an insulin pump. rt-CGM is recommended for all adults with type 1 diabetes (T1D), and can also be used in type 2 diabetes (T2D) treatments using multiple insulin injections. In some cases, short-term or intermittent use of CGM may be helpful for patients with T2D who use insulin therapy other than multiple insulin injections and/or oral hypoglycemic agents. CGM can help to achieve A1C targets in diabetes patients during pregnancy. CGM is a safe and cost-effective alternative to self-monitoring blood glucose in T1D and some T2D patients. CGM used in diabetes management works optimally with proper education, training, and follow up. To achieve the activation of CGM and its associated benefits, it is necessary to secure sufficient repetitive training and time for data analysis, management, and education. Various supports such as compensation, insurance coverage expansion, and reimbursement are required to increase the effectiveness of CGM while considering the scale of benefit recipients, policy priorities, and financial requirements.