Purpose: This study aimed to compare work-life balance, turnover intention, and teamwork between nurses in wards participating in a shift work improvement pilot program and those in non-participating wards. Methods: In this comparative descriptive study, 201 nurses working at D University hospital located in B city, including 71 participants in the pilot program and 130 non-participants. Data were collected from August 1 to August 31, 2025, using a structured questionnaire. The collected data were analyzed using IBM SPSS Statistics 29.0 including descriptive statistics, t-tests, χ2 tests, and one-way ANOVA. Results: The results of this study indicated that participation in the shift improvement pilot program significantly enhanced nurses' work-life balance (t=2.03, p=.044), particularly in the work-leisure (t=2.42, p=.016) and work-growth (t=2.00, p=.047), whereas no significant differences were found in turnover intention or teamwork between the groups. Regarding individual characteristics, work-life balance was significantly higher among unmarried and male nurses (t=2.23, p=.029). Turnover intention was notably higher among those with a bachelor’s degree or lower (t=2.56, p=.012) and those with 3 to 5 years of experience in their current department (F=4.63, p=.012). Teamwork was reported at higher levels by nurses with less than 3 years of experience (F=3.45, p=.035). Conclusion Participation in the shift work improvement pilot program was associated with higher work-life balance, particularly in relation to leisure and professional growth. However, participation was not associated with differences in turnover intention or teamwork, suggesting that these outcomes may be influenced by factors beyond shift work arrangements.

Purpose: This study aimed to identify predictive factors affecting adolescents’ subjective happiness using data from the 2023 Korea Youth Risk Behavior Survey. A random forest model was applied to determine the strongest predictive factors, and its predictive performance was compared with traditional regression models. Methods: Responses from a total of 44,320 students from grades 7 to 12 were analyzed. Data pre-processing involved handling missing values and selecting variables to construct an optimal dataset. The random forest model was employed for prediction, and SHAP (Shapley Additive Explanations) analysis was used to assess variable importance. Results: The random forest model demonstrated a stable predictive performance, with an R2 of .37. Mental and physical health factors were found to significantly affect subjective happiness. Adolescents’ subjective happiness was most strongly influenced by perceived stress, perceived health, experiences of loneliness, generalized anxiety disorder, suicidal ideation, economic status, fatigue recovery from sleep, and academic performance. Conclusion This study highlights the utility of machine learning in identifying factors influencing adolescents’ subjective happiness, addressing limitations of traditional regression approaches. These findings underscore the need for multidimensional interventions to improve mental and physical health, reduce stress and loneliness, and provide integrated support from schools and communities to enhance adolescents’ subjective happiness.

Background: and Purpose: Plasma biomarkers, including phosphorylated tau (ptau217), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL), are promising tools for detecting Alzheimer’s disease (AD) pathology. However, cross-laboratory reproducibility remains a challenge, even when using identical analytical platforms such as single-molecule array (Simoa). This study aimed to compare plasma biomarker measurements (ptau217, GFAP, and NfL) between 2 laboratories, the University of Gothenburg (UGOT) and DNAlink, and evaluate their associations with amyloid positron emission tomography (PET) imaging. Methods: Plasma biomarkers were measured using Simoa platforms at both laboratories:the UGOT and DNAlink Incorporation. Diagnostic performance for predicting amyloid PET positivity, cross-laboratory agreement, and the impact of normalization techniques were assessed. Bland-Altman plots and correlation analyses were employed to evaluate agreement and variability. Results: Plasma ptau217 concentrations exhibited strong correlations with amyloid PET global centiloid values, with comparable diagnostic performance between laboratories (area under the curve=0.94 for UGOT and 0.95 for DNAlink). Cross-laboratory agreement for ptau217 was excellent (r=0.96), improving further after natural log transformation. GFAP and NfL also demonstrated moderate to strong correlations (r=0.86 for GFAP and r=0.99 for NfL), with normalization reducing variability. Conclusions Plasma biomarker measurements were consistent across laboratories using identical Simoa platforms, with strong diagnostic performance and improved agreement after normalization. These findings support the scalability of plasma biomarkers for multicenter studies and underscore their potential for standardized applications in AD research and clinical practice.

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 Purpose: Plasma biomarkers, including phosphorylated tau (ptau217), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL), are promising tools for detecting Alzheimer’s disease (AD) pathology. However, cross-laboratory reproducibility remains a challenge, even when using identical analytical platforms such as single-molecule array (Simoa). This study aimed to compare plasma biomarker measurements (ptau217, GFAP, and NfL) between 2 laboratories, the University of Gothenburg (UGOT) and DNAlink, and evaluate their associations with amyloid positron emission tomography (PET) imaging. Methods: Plasma biomarkers were measured using Simoa platforms at both laboratories:the UGOT and DNAlink Incorporation. Diagnostic performance for predicting amyloid PET positivity, cross-laboratory agreement, and the impact of normalization techniques were assessed. Bland-Altman plots and correlation analyses were employed to evaluate agreement and variability. Results: Plasma ptau217 concentrations exhibited strong correlations with amyloid PET global centiloid values, with comparable diagnostic performance between laboratories (area under the curve=0.94 for UGOT and 0.95 for DNAlink). Cross-laboratory agreement for ptau217 was excellent (r=0.96), improving further after natural log transformation. GFAP and NfL also demonstrated moderate to strong correlations (r=0.86 for GFAP and r=0.99 for NfL), with normalization reducing variability. Conclusions Plasma biomarker measurements were consistent across laboratories using identical Simoa platforms, with strong diagnostic performance and improved agreement after normalization. These findings support the scalability of plasma biomarkers for multicenter studies and underscore their potential for standardized applications in AD research and clinical practice.

Purpose: This study aimed to identify predictive factors affecting adolescents’ subjective happiness using data from the 2023 Korea Youth Risk Behavior Survey. A random forest model was applied to determine the strongest predictive factors, and its predictive performance was compared with traditional regression models. Methods: Responses from a total of 44,320 students from grades 7 to 12 were analyzed. Data pre-processing involved handling missing values and selecting variables to construct an optimal dataset. The random forest model was employed for prediction, and SHAP (Shapley Additive Explanations) analysis was used to assess variable importance. Results: The random forest model demonstrated a stable predictive performance, with an R2 of .37. Mental and physical health factors were found to significantly affect subjective happiness. Adolescents’ subjective happiness was most strongly influenced by perceived stress, perceived health, experiences of loneliness, generalized anxiety disorder, suicidal ideation, economic status, fatigue recovery from sleep, and academic performance. Conclusion This study highlights the utility of machine learning in identifying factors influencing adolescents’ subjective happiness, addressing limitations of traditional regression approaches. These findings underscore the need for multidimensional interventions to improve mental and physical health, reduce stress and loneliness, and provide integrated support from schools and communities to enhance adolescents’ subjective happiness.

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 Purpose: Plasma biomarkers, including phosphorylated tau (ptau217), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL), are promising tools for detecting Alzheimer’s disease (AD) pathology. However, cross-laboratory reproducibility remains a challenge, even when using identical analytical platforms such as single-molecule array (Simoa). This study aimed to compare plasma biomarker measurements (ptau217, GFAP, and NfL) between 2 laboratories, the University of Gothenburg (UGOT) and DNAlink, and evaluate their associations with amyloid positron emission tomography (PET) imaging. Methods: Plasma biomarkers were measured using Simoa platforms at both laboratories:the UGOT and DNAlink Incorporation. Diagnostic performance for predicting amyloid PET positivity, cross-laboratory agreement, and the impact of normalization techniques were assessed. Bland-Altman plots and correlation analyses were employed to evaluate agreement and variability. Results: Plasma ptau217 concentrations exhibited strong correlations with amyloid PET global centiloid values, with comparable diagnostic performance between laboratories (area under the curve=0.94 for UGOT and 0.95 for DNAlink). Cross-laboratory agreement for ptau217 was excellent (r=0.96), improving further after natural log transformation. GFAP and NfL also demonstrated moderate to strong correlations (r=0.86 for GFAP and r=0.99 for NfL), with normalization reducing variability. Conclusions Plasma biomarker measurements were consistent across laboratories using identical Simoa platforms, with strong diagnostic performance and improved agreement after normalization. These findings support the scalability of plasma biomarkers for multicenter studies and underscore their potential for standardized applications in AD research and clinical practice.

Purpose: This study aimed to identify predictive factors affecting adolescents’ subjective happiness using data from the 2023 Korea Youth Risk Behavior Survey. A random forest model was applied to determine the strongest predictive factors, and its predictive performance was compared with traditional regression models. Methods: Responses from a total of 44,320 students from grades 7 to 12 were analyzed. Data pre-processing involved handling missing values and selecting variables to construct an optimal dataset. The random forest model was employed for prediction, and SHAP (Shapley Additive Explanations) analysis was used to assess variable importance. Results: The random forest model demonstrated a stable predictive performance, with an R2 of .37. Mental and physical health factors were found to significantly affect subjective happiness. Adolescents’ subjective happiness was most strongly influenced by perceived stress, perceived health, experiences of loneliness, generalized anxiety disorder, suicidal ideation, economic status, fatigue recovery from sleep, and academic performance. Conclusion This study highlights the utility of machine learning in identifying factors influencing adolescents’ subjective happiness, addressing limitations of traditional regression approaches. These findings underscore the need for multidimensional interventions to improve mental and physical health, reduce stress and loneliness, and provide integrated support from schools and communities to enhance adolescents’ subjective happiness.

Background: and Purpose: Plasma biomarkers, including phosphorylated tau (ptau217), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL), are promising tools for detecting Alzheimer’s disease (AD) pathology. However, cross-laboratory reproducibility remains a challenge, even when using identical analytical platforms such as single-molecule array (Simoa). This study aimed to compare plasma biomarker measurements (ptau217, GFAP, and NfL) between 2 laboratories, the University of Gothenburg (UGOT) and DNAlink, and evaluate their associations with amyloid positron emission tomography (PET) imaging. Methods: Plasma biomarkers were measured using Simoa platforms at both laboratories:the UGOT and DNAlink Incorporation. Diagnostic performance for predicting amyloid PET positivity, cross-laboratory agreement, and the impact of normalization techniques were assessed. Bland-Altman plots and correlation analyses were employed to evaluate agreement and variability. Results: Plasma ptau217 concentrations exhibited strong correlations with amyloid PET global centiloid values, with comparable diagnostic performance between laboratories (area under the curve=0.94 for UGOT and 0.95 for DNAlink). Cross-laboratory agreement for ptau217 was excellent (r=0.96), improving further after natural log transformation. GFAP and NfL also demonstrated moderate to strong correlations (r=0.86 for GFAP and r=0.99 for NfL), with normalization reducing variability. Conclusions Plasma biomarker measurements were consistent across laboratories using identical Simoa platforms, with strong diagnostic performance and improved agreement after normalization. These findings support the scalability of plasma biomarkers for multicenter studies and underscore their potential for standardized applications in AD research and clinical practice.