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 aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

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 aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

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 aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Background: The clinical implications of myelin oligodendrocyte glycoprotein autoantibodies (MOG-Abs) are increasing. Establishing MOG-Ab assays is essential for effectively treating patients with MOG-Abs. We established an in-house cell-based assay (CBA) to detect MOG-Abs to identify correlations with patients’ clinical characteristics. Methods: We established the CBA using HEK 293 cells transiently overexpressing fulllength human MOG, tested it against 166 samples from a multicenter registry of central nervous system (CNS) inflammatory disorders, and compared the results with those of the Oxford MOG-Ab-based CBA and a commercial MOG-Ab CBA kit. We recruited additional patients with MOG-Abs and compared the clinical characteristics of MOG-Ab-associated disease (MOGAD) with those of neuromyelitis optica spectrum disorder (NMOSD). Results: Of 166 samples tested, 10 tested positive for MOG-Abs, with optic neuritis (ON) being the most common manifestation (4/15, 26.7%). The in-house and Oxford MOG-Ab CBAs agreed for 164/166 (98.8%) samples (κ = 0.883, P < 0.001); two patients (2/166, 1.2%) were only positive in our in-house CBA, and the CBA scores of the two laboratories correlated well (r = 0.663, P < 0.001). The commercial MOG-Ab CBA kit showed one falsenegative and three false-positive results. The clinical presentation at disease onset differed between MOGAD and NMOSD; ON was the most frequent manifestation in MOGAD, and transverse myelitis was most frequent in NMOSD. Conclusions The in-house CBA for MOG-Abs demonstrated reliable results and can potentially be used to evaluate CNS inflammatory disorders. A comprehensive, long-term study with a large patient population would clarify the clinical significance of MOG-Abs.

This research group (forensic research via omics markers in environmental health vulnerable areas: FROM) aimed to develop biomarkers for exposure to environmental hazards and diseases, assess environmental diseases, and apply and verify these biomarkers in environmentally vulnerable areas. Environmentally vulnerable areas—including refineries, abandoned metal mines, coal-fired power plants, waste incinerators, cement factories, and areas with high exposure to particulate matter—along with control areas, were selected for epidemiological investigations. A total of 1,157 adults, who had resided in these areas for over 10 years, were recruited between June 2021 and September 2023. Personal characteristics of the study participants were gathered through a survey. Biological samples, specifically blood and urine, were collected during the field investigations, separated under refrigerated conditions, and then transported to the laboratory for biomarker analysis. Analyses of heavy metals, environmental hazards, and adducts were conducted on these blood and urine samples. Additionally, omics analyses of epigenomes, proteomes, and metabolomes were performed using the blood samples. The biomarkers identified in this study will be utilized to assess the risk of environmental disease occurrence and to evaluate the impact on the health of residents in environmentally vulnerable areas, following the validation of diagnostic accuracy for these diseases.