Purpose: This study evaluated the postoperative quality of life (QoL) after various types of gastrectomy for gastric cancer. Materials and Methods: A multicenter prospective observational study was conducted in Korea using the Korean Quality of Life in Stomach Cancer Patients Study (KOQUSS)-40, a new QoL assessment tool focusing on postgastrectomy syndrome. Overall, 496 patients with gastric cancer were enrolled, and QoL was assessed at 5 time points: preoperatively and at 1, 3, 6, and 12 months after surgery. Results: Distal gastrectomy (DG) and pylorus-preserving gastrectomy (PPG) showed significantly better outcomes than total gastrectomy (TG) and proximal gastrectomy (PG) with regard to total score, indigestion, and dysphagia. DG, PPG, and TG also showed significantly better outcomes than PG in terms of dumping syndrome and worry about cancer. Postoperative QoL did not differ significantly according to anastomosis type in DG, except for Billroth I anastomosis, which achieved better bowel habit change scores than the others. No domains differed significantly when comparing double tract reconstruction and esophagogastrostomy after PG. The total QoL score correlated significantly with postoperative body weight loss (more than 10%) and extent of resection (P<0.05 for both).Reflux as assessed by KOQUSS-40 did not correlate significantly with reflux observed on gastroscopy 1 year postoperatively (P=0.064). Conclusions Our prospective observation using KOQUSS-40 revealed that DG and PPG lead to better QoL than TG and PG. Further study is needed to compare postoperative QoL according to anastomosis type in DG and PG.

Purpose: This study evaluated the postoperative quality of life (QoL) after various types of gastrectomy for gastric cancer. Materials and Methods: A multicenter prospective observational study was conducted in Korea using the Korean Quality of Life in Stomach Cancer Patients Study (KOQUSS)-40, a new QoL assessment tool focusing on postgastrectomy syndrome. Overall, 496 patients with gastric cancer were enrolled, and QoL was assessed at 5 time points: preoperatively and at 1, 3, 6, and 12 months after surgery. Results: Distal gastrectomy (DG) and pylorus-preserving gastrectomy (PPG) showed significantly better outcomes than total gastrectomy (TG) and proximal gastrectomy (PG) with regard to total score, indigestion, and dysphagia. DG, PPG, and TG also showed significantly better outcomes than PG in terms of dumping syndrome and worry about cancer. Postoperative QoL did not differ significantly according to anastomosis type in DG, except for Billroth I anastomosis, which achieved better bowel habit change scores than the others. No domains differed significantly when comparing double tract reconstruction and esophagogastrostomy after PG. The total QoL score correlated significantly with postoperative body weight loss (more than 10%) and extent of resection (P<0.05 for both).Reflux as assessed by KOQUSS-40 did not correlate significantly with reflux observed on gastroscopy 1 year postoperatively (P=0.064). Conclusions Our prospective observation using KOQUSS-40 revealed that DG and PPG lead to better QoL than TG and PG. Further study is needed to compare postoperative QoL according to anastomosis type in DG and PG.

Purpose: This study evaluated the postoperative quality of life (QoL) after various types of gastrectomy for gastric cancer. Materials and Methods: A multicenter prospective observational study was conducted in Korea using the Korean Quality of Life in Stomach Cancer Patients Study (KOQUSS)-40, a new QoL assessment tool focusing on postgastrectomy syndrome. Overall, 496 patients with gastric cancer were enrolled, and QoL was assessed at 5 time points: preoperatively and at 1, 3, 6, and 12 months after surgery. Results: Distal gastrectomy (DG) and pylorus-preserving gastrectomy (PPG) showed significantly better outcomes than total gastrectomy (TG) and proximal gastrectomy (PG) with regard to total score, indigestion, and dysphagia. DG, PPG, and TG also showed significantly better outcomes than PG in terms of dumping syndrome and worry about cancer. Postoperative QoL did not differ significantly according to anastomosis type in DG, except for Billroth I anastomosis, which achieved better bowel habit change scores than the others. No domains differed significantly when comparing double tract reconstruction and esophagogastrostomy after PG. The total QoL score correlated significantly with postoperative body weight loss (more than 10%) and extent of resection (P<0.05 for both).Reflux as assessed by KOQUSS-40 did not correlate significantly with reflux observed on gastroscopy 1 year postoperatively (P=0.064). Conclusions Our prospective observation using KOQUSS-40 revealed that DG and PPG lead to better QoL than TG and PG. Further study is needed to compare postoperative QoL according to anastomosis type in DG and PG.

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 Purpose In young patients (aged 18–60 years) with patent foramen ovale (PFO)- associated stroke, percutaneous closure has been found to be useful for preventing recurrent ischemic stroke or transient ischemic attack (TIA). However, it remains unknown whether PFO closure is also beneficial in older patients. Methods: Patients aged ≥60 years who had a cryptogenic stroke and PFO from ten hospitals in South Korea were included. The effect of PFO closure plus medical therapy over medical therapy alone was assessed by a propensity-score matching method in the overall cohort and in those with a high-risk PFO, characterized by the presence of an atrial septal aneurysm or a large shunt. Results: Out of the 437 patients (mean age, 68.1), 303 (69%) had a high-risk PFO and 161 (37%) patients underwent PFO closure. Over a median follow-up of 3.9 years, recurrent ischemic stroke or TIA developed in 64 (14.6%) patients. In the propensity score-matched cohort of the overall patients (130 pairs), PFO closure was associated with a significantly lower risk of a composite of ischemic stroke or TIA (hazard ratio [HR]: 0.45; 95% confidence interval [CI]: 0.24–0.84; P=0.012), but not for ischemic stroke. In a subgroup analysis of confined to the high-risk PFO patients (116 pairs), PFO closure was associated with significantly lower risks of both the composite of ischemic stroke or TIA (HR: 0.40; 95% CI: 0.21–0.77; P=0.006) and ischemic stroke (HR: 0.47; 95% CI: 0.23–0.95; P=0.035). Conclusion Elderly patients with cryptogenic stroke and PFO have a high recurrence rate of ischemic stroke or TIA, which may be significantly reduced by device closure.

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.

Purpose: Hereditary diffuse gastric cancer (HDGC) presents a significant genetic predisposition, notably linked to mutations in the CDH1 and CTNNA1. However, the genetic basis for over half of HDGC cases remains unidentified. The aim of this study is to identify novel pathogenic variants in HDGC and evaluate their protein expression. Materials and Methods: Among 20 qualifying families, two were selected based on available pedigree and DNA. Whole genome sequencing (WGS) on DNA extracted from blood and whole exome sequencing on DNA from formalin-fixed paraffin-embedded tissues were performed to find potential pathogenic variants in HDGC. After selection of a candidate variant, functional validation, and enrichment analysis were performed. Results: As a result of WGS, three candidate germline mutations (EPHA5, MCOA2, and RHOA) were identified in one family. After literature review and in-silico analyses, the RHOA mutation (R129W) was selected as a candidate. This mutation was found in two gastric cancer patients within the family. In functional validation, it showed RhoA overexpression and a higher GTP-bound state in the RhoaR129W mutant. Decreased phosphorylation at Ser127/397 suggested altered YAP1 regulation in the Rho-ROCK pathway. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses linked RhoaR129W overexpression to changed migration/adhesion in MKN1 cell line. However, this RHOA mutation (R129W) was not found in index patients in other families. Conclusion The RHOA mutation (R129W) emerges as a potential causative gene for HDGC, but only in one family, indicating a need for further studies to understand its role in HDGC pathogenesis fully.

Background/Aims: The gut microbiome has emerged as a key player that mechanistically links various risk factors to colorectal cancer (CRC) etiology. However, the role of the gut microbiome in CRC pathogenesis remains unclear. This study aimed to characterize the gut microbiota in healthy controls (HCs) and patients with colorectal adenoma (AD) and CRC in subgroups based on sex and age. Methods: Study participants who visited the hospital for surveillance of CRC or gastrointestinal symptoms were prospectively enrolled, and the gut microbiome was analyzed based on fecal samples. Results: In terms of HC-AD-CRC sequence, commensal bacteria, including lactate-producing (Streptococcus salivarius) and butyrate-producing (Faecalibacterium prausnitzii, Anaerostipes hadrus, and Eubacterium hallii) bacteria, were more abundant in the HC group than in the AD and CRC groups. In the sex comparison, the female HC group had more lactate-producing bacteria (Bifidobacterium adolescentis, Bifidobacterium catenulatum, and Lactobacillus ruminis) than the male HC group. In age comparison, younger subjects had more butyrate-producing bacteria (Agathobaculum butyriciproducens and Blautia faecis) than the older subjects in the HC group.Interestingly, lactate-producing bacteria (B. catenulatum) were more abundant in females than males among younger HC group subjects. However, these sex- and age-dependent differences were not observed in the AD and CRC groups. Conclusions The gut microbiome, specifically lactate- and butyrate-producing bacteria, which were found to be abundant in the HC group, may play a role in preventing the progression of CRC. In particular, lactate-producing bacteria, which were found to be less abundant in healthy male controls may contribute to the higher incidence of CRC in males.