Objective: To explore the relationship between snack consumption and depressive symptoms in first year junior high school students with different left behind experiences in Yunnan Province, so as to provide a basis for improving depressive symptoms among first year junior high school students with different left behind experiences. Methods: From October to December 2022,a cluster random sampling method was used to select 8 500 first year junior high school students from 11 ethnic minority areas (Fugong County, Longling County, Longyang District, Luchun County, Mojiang County, Nanjian County, Qiaojia County, Shuangjiang County, Tengchong City, Yuanmou County, Zhenyuan County) in Yunnan Province for a questionnaire survey. The Chinese version of Depression Anxiety Stress Scale-21 was applied to assess depressive symptoms in first year junior high school students, and snack consumption was collected by employing food frequency questionnaire. The generalized linear model was used to analyze the association between first year junior high school students snack consumption and depressive symptoms, and the analysis was stratified according to left behind experience. Results: The detection rates of depressive symptoms among firstyear junior high school students with and without left behind experience were 36.25% and 26.91%, respectively. After controlling for confounding variables, the generalized linear model analysis showed that sweet snacks ( β=0.16, 95%CI =0.07-0.25), fast food ( β=0.14, 95%CI =0.04-0.23) and carbonated drinks ( β=0.09, 95%CI =0.01-0.17) of first year junior high school students with left behind experience (all P <0.05). Compared with those without such behavior, the risk of depressive symptoms was higher in consumption of fast food ( β=0.13, 95%CI =0.07-0.18) and carbonated drinks ( β=0.10, 95%CI =0.06-0.15)among first year junior high school students without left behind experience (both P <0.05). Conclusion Snack consumption among first year junior high school students in Yunnan may increase the risk of developing depressive symptoms, while first year junior high school students with left behind experience may have a greater risk of developing depressive symptoms.

Background: Long working hours have been associated with adverse physical and mental health outcomes; however, evidence regarding their relationship with satisfaction with work environment remains limited, particularly when long working hours are defined using a specific daily threshold. This study examined the association between working ≥10 hours per day and satisfaction with work environment among Korean wage workers, focusing on the cumulative number of such workdays per month. Methods: This cross-sectional study analyzed data from the 7th Korean Working Conditions Survey. A total of 24,269 wage workers aged ≥18 years were included after excluding self-employed workers, unpaid family workers, shift workers, and respondents with missing data. Working ≥10 hours per day was categorized as 0, 1–9, and ≥10 days per month. Satisfaction with work environment was categorized as satisfied or dissatisfied. Multiple logistic regression analyses were conducted to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for dissatisfaction with work environment according to the number of long working days, adjusting for demographic and occupational characteristics. Results: Compared with workers who did not work ≥10 hours per day, those who worked 1–9 days and ≥10 days per month showed progressively higher levels of dissatisfaction with work environment. After adjustment for demographic and occupational factors, the odds of dissatisfaction with work environment were significantly higher among workers who worked ≥10 hours per day for 1–9 days per month (OR: 1.380; 95% CI: 1.145–1.665) and ≥10 days per month (OR: 2.106; 95% CI: 1.627–2.725), demonstrating a dose–response relationship. Conclusions Among the analytic sample of Korean wage workers included in this study, working ≥10 hours per day was associated with greater dissatisfaction with work environment, with a dose–response pattern according to the number of long working days per month.

Purpose: This study aimed to develop and evaluate the effectiveness of the Trauma-nursing Education and Skill Support (TESS) program based on the ADDIE model (Analysis, Design, Development, Implementation, Evaluation model). The program was designed to enhance trauma nurses’ clinical competencies, including trauma-related knowledge, self-efficacy, and problem-solving ability, through the integration of theoretical education and simulation-based practice. Methods: A quasi-experimental study using a non-equivalent control group pretest–posttest design was conducted. Participants included 108 trauma nurses from regional trauma centers, military trauma centers, and emergency care facilities, who were assigned to an experimental group (n=52) or a control group (n=56). The TESS program consisted of a 2-day, 14-hour blended-learning course that included eight lecture sessions and four simulation-based practice stations. Data were collected at baseline, immediately after the intervention, and at 6 months using validated instruments measuring trauma-related knowledge, self-efficacy, and problem-solving ability. Two-way repeated-measures analysis of variance was used for data analysis. Results: The experimental group demonstrated significant improvements in trauma-related knowledge, self-efficacy, and problem-solving ability compared with baseline (all p<.001). These improvements were sustained at 6 months, although trauma-related knowledge scores showed a slight decline compared with immediate posttest levels. Between-group analyses confirmed significant group-by-time interaction effects for all outcomes: trauma-related knowledge (η2=0.12, p<.001), self-efficacy (η2=0.09, p=.002), and problem-solving ability (η2=0.08, p=.003). Conclusion The TESS program effectively enhanced trauma nurses’ trauma-related knowledge, self-efficacy, and problem-solving ability, with effects sustained for up to 6 months. Incorporating blended learning and simulation-based training into standardized trauma nursing education may strengthen clinical competencies and ultimately contribute to improved patient outcomes.

Objectives: Assessing healthcare quality at the national level is essential for evaluating health system performance and identifying areas requiring improvement. This study examined long-term trends in healthcare quality in Korea from 2008 to 2023 using internationally comparable indicators. Methods: We conducted a trend analysis of healthcare quality in Korea using the Organization for Economic Cooperation and Development (OECD) Health Care Quality and Outcomes framework and quality indicators. Indicators across multiple domains, including acute care, primary care, prescribing in primary care, mental healthcare, and patient experiences, were analyzed and compared with OECD averages. Results: Healthcare quality in Korea improved across several domains. Thirty-day mortality for acute myocardial infarction decreased from 14.2% in 2008 to 10.2% in 2023, while mortality for ischemic stroke declined from 8.7% to 5.5%. Avoidable hospitalizations decreased substantially, with hospitalizations for chronic obstructive pulmonary disease declining by 59.7%. The proportion of patients with diabetes receiving cholesterol-lowering treatment increased from 44.1% to 82.5%. However, the proportion of broad-spectrum antibiotic prescriptions remained substantially higher than the OECD average (40.3 vs. 15.5%). In mental healthcare, excess mortality ratios increased from 4.3 in 2010 to 4.9 in 2023 for schizophrenia and from 3.5 to 4.3 for bipolar disorder, while post-discharge suicide rates showed little improvement. Patient experience indicators related to patient-physician communication improved and approached OECD averages. Conclusions Healthcare quality in Korea improved substantially between 2008 and 2023, particularly in acute care outcomes and chronic disease management. However, persistent challenges remain in areas such as antibiotic use, polypharmacy, and mental healthcare. These findings provide internationally comparable evidence to inform future health policy and healthcare quality improvement efforts.

Objective: The prognostic value of coronary artery calcium (CAC) volume and density was derived from an automated artificial intelligence (AI)-based analysis of non-electrocardiogram-gated chest CT. Materials and Methods: In this retrospective study, 7,552 asymptomatic adults who underwent chest CT as part of a national health screening program between 2007 and 2014 at two tertiary hospitals were examined for eligibility, of whom 1,109 with detectable CAC were analyzed. CAC density was derived by back-calculation from the Agatston score and CAC volume, both of which were obtained using AI software on chest CT. Differences in the probability of being free from major adverse cardiovascular events (MACE) across the four combined CAC volume-density groups were assessed using Kaplan–Meier curves and restricted mean survival time (RMST). Multivariable Cox proportional hazards models were used to assess the association between CAC volume and density and MACE. Results: Among the 1,109 participants with nonzero CAC (median age, 60.3 years; 87% men), 207 experienced MACE during a median follow-up of 7.7 years. Ten-year RMSTs were 9.45 years in the low-volume-high-density group, 9.07 years in the lowvolume-low-density group, 8.03 years in the high-volume-high-density group, and 7.68 years in the high-volume-low-density group. Differences in time to MACE were predominantly driven by CAC volume, with no significant density-related differences within the volume strata. CAC density demonstrated a significant, independent, inverse association after adjusting for CAC volume and clinical covariates (hazard ratio [HR] per increase by standard deviation [SD], 0.786; 95% confidence interval [CI], 0.659–0.936; P = 0.007). CAC volume also remained independently associated with an increased risk of MACE (HR per increase by SD, 2.608; 95% CI, 2.016–3.374; P < 0.001). Conclusion CAC density derived from chest CT using automated AI quantification was independently and inversely associated with MACE, providing additional prognostic value when added to CAC volume.

Breast cancer prevention and treatment have become major issues that urgently need to be addressed in the field of global public health. As a key pathological transitional stage in the progression of breast cancer， preneoplastic breast cancer （PBC） carries a significant risk of clinical transformation. Effective intervention in the progression of PBC is of great clinical significance in preventing the occurrence of breast cancer. Pathological studies have shown that abnormal angiogenesis is a key mechanism driving the transformation of PBC into breast cancer. Vascular endothelial growth factor （VEGF）， as a core regulatory molecule that promotes angiogenesis， plays a pivotal role in this process. The malignant transformation of PBC is closely associated with the abnormal activation of the VEGF-mediated pro-angiogenic network. Although modern medicine has achieved certain therapeutic effects through surgery and endocrine therapy， clinical limitations such as invasiveness， drug resistance， and adverse reactions still exist. Recent studies have demonstrated that the VEGF signaling system mediates the phosphatidylinositol 3-kinase-protein kinase B （PI3K/Akt） signaling pathway and the mitogen-activated protein kinase （MAPK） pathway. In addition， the hypoxia-inducible factor-1α （HIF-1α）/VEGF signaling pathway and the delta-like ligand 4 （DLL4）/Notch receptor 1 （Notch1） signaling pathway， together with other pathways， form a complex regulatory network that plays a central role in angiogenesis during PBC. Traditional Chinese medicine （TCM）， characterized by multi-component synergy， multi-pathway regulation， and high safety， demonstrates significant advantages in inhibiting pathological angiogenesis and blocking PBC progression by targeting the VEGF signaling pathway. From the perspective of VEGF pathway regulation， this paper systematically reviews the latest research progress on TCM in inhibiting angiogenesis and intervening in PBC， and discusses its mechanisms and application value in the early prevention and treatment of PBC， with the aim of providing references for optimizing clinical intervention strategies for PBC.

Objective:To develop a preoperative CT-based radiomics model integrating multidimensional features for the accurate prediction of TFE3-rearranged renal cell carcinoma（TFE3-rRCC）.Methods:This study retrospectively enrolled 865 pathologically confirmed renal cell carcinoma（RCC）patients in The First Affiliated Hospital of Naval Medical University from June 2013 to June 2023，including 60 cases of TFE3-rRCC and 805 cases of non-TFE3 RCC（comprising clear cell RCC，papillary RCC，and chromophobe RCC）. Among them，627 were male and 238 were female，with a mean age of（54.1 ± 12.7）years（range：14?82 years）. The median maximum tumor diameter was 4.0（2.6，6.0）cm. Based on the chronological order of CT examinations，the patients were divided into training（ n=478），validation（ n=206），and test（ n=181）sets in an approximate 6∶2∶2 ratio. Using precontrast and corticomedullary phase CT images，we extracted peritumoral imaging features，habitat features，3D radiomic features，and 2.5D deep learning radiomic features. A deep learning radiomics score（DLR-SCORE）prediction model was constructed using least absolute shrinkage and selection operator（LASSO）regression. The diagnostic performance of the model was evaluated by receiver operating characteristic（ROC）curve analysis，with the area under the curve（AUC）as the primary metric. Additionally，sensitivity，specificity，and accuracy were calculated based on the confusion matrix. Results:A total of 12 442 features were extracted from non-contrast and corticomedullary phase CT images，from which eight key features were selected to construct the DLR-SCORE model. The model demonstrated diagnostic accuracies for TFE3-rRCC of 98.5%（471/478）in the training set，81.6%（168/206）in the validation set，and 86.2%（156/181）in the test set. The AUC of ROC curve was 0.98（95% CI 0.96?1.00）in the training set，0.83（95% CI 0.71?0.94）in the validation set，and 0.88（95% CI 0.76?1.00）in the test set. In the test set，the DLR-SCORE model achieved a sensitivity of 88.9%（16/18）and a specificity of 85.9%（140/163）for detecting TFE3-rRCC. Conclusions:The DLR-SCORE model integrating multidimensional CT radiomics features demonstrated favorable predictive performance for TFE3-rRCC，offering a promising noninvasive tool to assist preoperative diagnosis.

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.

Background: and Purpose: This clinical practice guideline provides evidence-based recommendations for treatment of dementia, focusing on cholinesterase inhibitors and N-methyl-D-aspartate (NMDA) receptor antagonists for Alzheimer’s disease (AD) and other types of dementia. Methods: Using the Population, Intervention, Comparison, Outcomes (PICO) framework, we developed key clinical questions and conducted systematic literature reviews. A multidisciplinary panel of experts, organized by the Korean Dementia Association, evaluated randomized controlled trials and observational studies. Recommendations were graded for evidence quality and strength using Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology. Results: Three main recommendations are presented: (1) For AD, cholinesterase inhibitors (donepezil, rivastigmine, galantamine) are strongly recommended for improving cognition and daily function based on moderate evidence; (2) Cholinesterase inhibitors are conditionally recommended for vascular dementia and Parkinson’s disease dementia, with a strong recommendation for Lewy body dementia; (3) For moderate to severe AD, NMDA receptor antagonist (memantine) is strongly recommended, demonstrating significant cognitive and functional improvements. Both drug classes showed favorable safety profiles with manageable side effects. Conclusions This guideline offers standardized, evidence-based pharmacologic recommendations for dementia management, with specific guidance on cholinesterase inhibitors and NMDA receptor antagonists. It aims to support clinical decision-making and improve patient outcomes in dementia care. Further updates will address emerging treatments, including amyloid-targeting therapies, to reflect advances in dementia management.

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.