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Breast cancer is one of the major female health problems worldwide. Although there is growing evidence indicating that air pollution increases the risk of breast cancer, there is still inconsistency among previous studies. Unlike the previous studies those had case-control or cohort study designs, we performed a nationwide, whole-population census study. In all 252 administrative districts in South Korea, the associations between ambient NO 2 and particulate matter 10 (PM 10 ) concentration, and age-adjusted breast cancer mortality rate in females (from 2005 to 2016, N mortality  = 23,565), and incidence rate (from 2004 to 2013, N incidence  = 133,373) were investigated via multivariable beta regression. Population density, altitude, rate of higher education, smoking rate, obesity rate, parity, unemployment rate, breastfeeding rate, oral contraceptive usage rate, and Gross Regional Domestic Product per capita were considered as potential confounders. Ambient air pollutant concentrations were positively and significantly associated with the breast cancer incidence rate: per 100 ppb CO increase, Odds Ratio OR = 1.08 (95% Confidence Interval CI = 1.06–1.10), per 10 ppb NO 2 , OR = 1.14 (95% CI = 1.12–1.16), per 1 ppb SO 2 , OR = 1.04 (95% CI = 1.02–1.05), per 10 µg/m 3 PM 10 , OR = 1.13 (95% CI = 1.09–1.17). However, no significant association between the air pollutants and the breast cancer mortality rate was observed except for PM 10 : per 10 µg/m 3 PM 10 , OR = 1.05 (95% CI = 1.01–1.09).

Realistic image synthesis based on deep learning is an invaluable technique for developing high-performance computer aided diagnosis systems while protecting patient privacy. However, training a generative adversarial network (GAN) for image synthesis remains challenging because of the large amounts of data required for training various kinds of image features. This study aims to synthesize retinal images indistinguishable from real images and evaluate the efficacy of the synthesized images having a specific disease for augmenting class imbalanced datasets. The synthesized images were validated via image Turing tests, qualitative analysis by retinal specialists, and quantitative analyses on amounts and signal-to-noise ratios of vessels. The efficacy of synthesized images was verified by deep learning-based classification performance. Turing test shows that accuracy, sensitivity, and specificity of 54.0 ± 12.3%, 71.1 ± 18.8%, and 36.9 ± 25.5%, respectively. Here, sensitivity represents correctness to find real images among real datasets. Vessel amounts and average SNR comparisons show 0.43% and 1.5% difference between real and synthesized images. The classification performance after augmenting synthesized images outperforms every ratio of imbalanced real datasets. Our study shows the realistic retina images were successfully generated with insignificant differences between the real and synthesized images and shows great potential for practical applications.

Heterogeneous clinical manifestations and progression of chronic obstructive pulmonary disease (COPD) affect patient health risk assessment, stratification, and management. Pulmonary function tests are used to diagnose and classify the severity of COPD, but they cannot fully represent the type or range of pathophysiologic abnormalities of the disease. To evaluate whether deep radiomics from chest computed tomography (CT) images can predict mortality in patients with COPD, we designed a convolutional neural network (CNN) model for extracting representative features from CT images and then performed random survival forest to predict survival in COPD patients. We trained CNN-based binary classifier based on six-minute walk distance results (> 440 m or not) and extracted high-throughput image features (i.e., deep radiomics) directly from the last fully connected layer of it. The various sizes of fully connected layers and combinations of deep features were experimented using a discovery cohort with 344 patients from the Korean Obstructive Lung Disease cohort and an external validation cohort with 102 patients from Penang General Hospital in Malaysia. In the integrative analysis of discovery and external validation cohorts, with combining 256 deep features from the coronal slice of the vertebral body and two sagittal slices of the left/right lung, deep radiomics for survival prediction achieved concordance indices of 0.8008 (95% CI, 0.7642–0.8373) and 0.7156 (95% CI, 0.7024–0.7288), respectively. Deep radiomics from CT images could be used to predict mortality in COPD patients.

Some preservatives are naturally contained in raw food materials, while in some cases may have been introduced in food by careless handling or fermentation. However, it is difficult to distinguish between intentionally added preservatives and the preservatives naturally produced in food. The objective of this study was to evaluate the minimum inhibitory concentration (MIC) of propionic acid, sorbic acid, and benzoic acid for inhibiting food spoilage microorganisms in animal products, which can be useful in determining if the preservatives are natural or not. The broth microdilution method was used to determine the MIC of preservatives for 57 microorganisms. Five bacteria that were the most sensitive to propionic acid, benzoic acid, and sorbic acid were inoculated in unprocessed and processed animal products. A hundred microliters of the preservatives were then spiked in samples. After storage, the cells were counted to determine the MIC of the preservatives. The MIC of the preservatives in animal products ranged from 100 to 1,500 ppm for propionic acid, from 100 to >1,500 ppm for benzoic acid, and from 100 to >1,200 ppm for sorbic acid. Thus, if the concentrations of preservatives are below the MIC, the preservatives may not be added intentionally. Therefore, the MIC result will be useful in determining if preservatives are added intentionally in food.

Background We aim to identify the multifaceted risk factors that can affect the development of severe radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC) treated with curative high-dose radiotherapy with or without concurrent chemotherapy. Methods We retrospectively reviewed the medical records of 175 patients with stage-I-III NSCLC treated with curative thoracic X-ray radiotherapy at the Korea University Guro Hospital between June 2019 and June 2022. Treatment-related complications were evaluated using the Common Terminology Criteria for Adverse Events (version 4.03). Results The median follow-up duration was 15 months (range: 3–47 months). Idiopathic pulmonary fibrosis (IPF) as an underlying lung disease ( P  < 0.001) and clinical stage, regarded as the concurrent use of chemotherapy ( P  = 0.009), were associated with a high rate of severe RP. In multivariate analyses adjusting confounding variables, the presence of IPF as an underlying disease was significantly associated with severe RP (odds ratio [95% confidence interval] = 48.4 [9.09–347]; P  < 0.001). In a subgroup analysis of stage-I-II NSCLC, the incidence of severe RP in the control, chronic obstructive pulmonary disease (COPD), and IPF groups was 3.2%, 4.3%, and 42.9%, respectively ( P  < 0.001). The incidence of severe RP was 15.2%, 10.7%, and 75.0% in the control, COPD, and IPF groups, respectively ( P  < 0.001) in the stage-III NSCLC group. Conclusions This study revealed that IPF as an underlying lung disease and the concurrent use of chemotherapy are associated with a high rate of severe RP. In contrast, COPD did not increase the risk of pulmonary toxicity after receiving curative high-dose radiotherapy.

Birthweight is a strong determinant of a neonate’s health. The SARS-CoV-2 pandemic’s impact on birthweight has not been investigated in-depth, with inconsistent conclusions from initial studies. To assess changes in preterm birth and inappropriate birthweight between the SARS-CoV-2 pandemic and pre-pandemic periods. A nationwide birth micro-data consisted with exhaustive census of all births in 2011–2020 in South Korea was accessed to examine whether the mean birthweight and rates of under/overweight births changed significantly during the SARS-CoV-2 pandemic year (2020) compared to those of the pre-pandemic period (2011–2019). A total of 3,736,447 singleton births were analyzed. Preterm birth was defined as < 37 weeks of gestation. Low birthweight (LBW) and macrosomia were defined as birthweights < 2.5 kg and ≥ 4.0 kg, respectively. Small for gestational age (SGA) and large for gestational age (LGA) were defined as birthweights below the 10th and above 90th percentiles for sex and gestational age, respectively. Inappropriate birthweight was defined as one or more LBW, macrosomia, SGA, or LGA. Generalized linear models predicted birth outcomes and were adjusted for parental age and education level, marital status, parity, gestational age, and months from January 2011. There were 3,481,423 and 255,024 singleton births during the pre-pandemic and pandemic periods, respectively. Multivariable generalized linear models estimated negative associations between the pandemic and preterm birth (odds ratio [OR], 0.968; 95% confidence interval [CI] 0.958–0.978), LBW (OR: 0.967, 95% CI 0.956–0.979), macrosomia (OR: 0.899, 95% CI 0.886–0.912), SGA (OR: 0.974, 95% CI 0.964–0.983), LGA (OR: 0.952, 95% CI 0.945–0.959), and inappropriate birthweight (OR: 0.958, 95% CI 0.952–0.963), indicating a decline during the pandemic compared to pre-pandemic period. An 8.98 g decrease in birthweight (95% CI 7.98–9.99) was estimated during the pandemic. This is the largest and comprehensive nationwide study to date on the impact of the SARS-CoV-2 pandemic on preterm birth and inappropriate birthweight. Birth during the pandemic was associated with lower odds of being preterm, underweight, and overweight. Further studies are required to understand the dynamics underlying this phenomenon.

Central line-related bloodstream infection (CRBSI) is a common complication during hospital admissions; however, there is insufficient data regarding CRBSI in the emergency department. Therefore, we evaluated the incidence and clinical impact of CRBSI using a single-center retrospective study to analyze medical data of 2189 adult patients (median age: 65 years, 58.8% males) who underwent central line insertion in ED from 2013 to 2015. CRBSI was defined if the same pathogens were identified at peripheral and catheter tips or the differential time to positivity was > 2 h. CRBSI-related in-hospital mortality and risk factors were evaluated. CRBSI occurred in 80 patients (3.7%), of which 51 survived and 29 died; those with CRBSI had higher incidence of subclavian vein insertion and retry rates. Staphylococcus epidermidis was the most common pathogen, followed by Staphylococcus aureus , Enterococcus faecium , and Escherichia coli . Using multivariate analysis, we found that CRBSI development was an independent risk factor for in-hospital mortality (adjusted odds ratio: 1.93, 95% confidence intervals: 1.19–3.14, p  < 0.01). Our findings suggest that CRBSI after central line insertion in the emergency department is common and associated with poor outcomes. Infection prevention and management measures to reduce CRBSI incidence are essential to improve clinical outcomes.

Objectives: This study aimed to evaluate the setup accuracy and intrafractional geometric stability of surface-guided radiation therapy (SGRT) under frameless immobilization in lung cancer radiotherapy and to assess its clinical utility in a relatively large patient cohort. Materials and Methods: A total of 678 treatment fractions from 52 patients with primary non-small cell lung cancer (NSCLC), treated between October 2024 and November 2025, were retrospectively analyzed. Patient setup was performed using SGRT with the Identify system, and cone-beam computed tomography (CBCT) served as the reference for internal target localization Intrafractional setup displacements, center-of-mass (COM) shifts, residual setup errors, and intrafractional clinical target volume (CTV) variations were evaluated. Spatial agreement between planned and intrafractional tumor volumes was quantified using the Dice Similarity Coefficient (DSC). Results: The mean CBCT-based intrafractional shifts were −0.01 mm (vertical), 0.03 mm (longitudinal), and 0.01 mm (lateral), indicating negligible systematic errors. The greatest variability was observed in the longitudinal direction (standard deviation, 1.32 mm), with a maximum displacement of 4.58 mm. COM-based analysis demonstrated near-zero mean displacements in all directions, with standard deviations ranging from 0.01 to 0.02 mm. DSC values ranged from 0.91 to 0.98, with a mean of 0.96, indicating excellent spatial agreement between planned and intrafractional tumor volumes. Residual setup errors were predominantly within ±1 mm, and the mean intrafractional CTV volume change was minimal (0.27 cm3). Conclusions: SGRT-based frameless lung cancer radiotherapy demonstrated high setup accuracy and robust intrafractional geometric stability. Although slightly greater variability was observed in the longitudinal direction, overall positional deviations and volumetric changes remained within clinically acceptable limits. These findings support the feasibility of integrating SGRT with CBCT-guided radiotherapy and suggest potential benefits for workflow efficiency and planning target volume margin optimization.

Background The association between long-term exposure to air pollutants, including nitrogen dioxide (NO 2 ), carbon monoxide (CO), sulfur dioxide (SO 2 ), ozone (O 3 ), and particulate matter 10 μm or less in diameter (PM 10 ), and mortality by ischemic heart disease (IHD), cerebrovascular disease (CVD), pneumonia (PN), and chronic lower respiratory disease (CLRD) is unclear. We investigated whether living in an administrative district with heavy air pollution is associated with an increased risk of mortality by the diseases through an ecological study using South Korean administrative data over 19 years. Methods A total of 249 Si-Gun-Gus, unit of administrative districts in South Korea were studied. In each district, the daily concentrations of CO, SO 2 , NO 2 , O 3 , and PM 10 were averaged over 19 years (2001–2018). Age-adjusted mortality rates by IHD, CVD, PN and CLRD for each district were averaged for the same study period. Multivariate beta-regression analysis was performed to estimate the associations between air pollutant concentrations and mortality rates, after adjusting for confounding factors including altitude, population density, higher education rate, smoking rate, obesity rate, and gross regional domestic product per capita. Associations were also estimated for two subgrouping schema: Capital and non-Capital areas (77:172 districts) and urban and rural areas (168:81 districts). Results For IHD, higher SO 2 concentrations were significantly associated with a higher mortality rate, whereas other air pollutants had null associations. For CVD, SO 2 and PM 10 concentrations were significantly associated with a higher mortality rate. For PN, O 3 concentrations had significant positive associations with a higher mortality rate, while SO 2 , NO 2 , and PM 10 concentrations had significant negative associations. For CLRD, O 3 concentrations were associated with an increased mortality rate, while CO, NO 2 , and PM 10 concentrations had negative associations. In the subgroup analysis, positive associations between SO 2 concentrations and IHD mortality were consistently observed in all subgroups, while other pollutant-disease pairs showed null, or mixed associations. Conclusion Long-term exposure to high SO 2 concentration was significantly and consistently associated with a high mortality rate nationwide and in Capital and non-Capital areas, and in urban and rural areas. Associations between other air pollutants and disease-related mortalities need to be investigated in further studies.

Background Emergency department overcrowding negatively impacts critically ill patients and could lead to the occurrence of cardiac arrest. However, the association between emergency department crowding and the occurrence of in-hospital cardiac arrest has not been thoroughly investigated. This study aimed to evaluate the correlation between emergency department occupancy rates and the incidence of in-hospital cardiac arrest. Methods A single-center, observational, registry-based cohort study was performed including all consecutive adult, non-traumatic in-hospital cardiac arrest patients between January 2014 and June 2017. We used emergency department occupancy rates as a crowding index at the time of presentation of cardiac arrest and at the time of maximum crowding, and the average crowding rate for the duration of emergency department stay for each patient. To calculate incidence rate, we divided the number of arrest cases for each emergency department occupancy period by accumulated time. The primary outcome is the association between the incidence of in-hospital cardiac arrest and emergency department occupancy rates. Results During the study period, 629 adult, non-traumatic cardiac arrest patients were enrolled in our registry. Among these, 187 patients experienced in-hospital cardiac arrest. Overall survival discharge rate was 24.6%, and 20.3% of patients showed favorable neurologic outcomes at discharge. Emergency department occupancy rates were positively correlated with in-hospital cardiac arrest occurrence. Moreover, maximum emergency department occupancy in the critical zone had the strongest positive correlation with in-hospital cardiac arrest occurrence (Spearman rank correlation ρ  = 1.0, P  < .01). Meanwhile, occupancy rates were not associated with the ED mortality. Conclusion Maximum emergency department occupancy was strongly associated with in-hospital cardiac arrest occurrence. Adequate monitoring and managing the maximum occupancy rate would be important to reduce unexpected cardiac arrest.