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 NO₂ and particulate matter 10 (PM₁₀) 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 NO₂, OR = 1.14 (95% CI = 1.12–1.16), per 1 ppb SO₂, OR = 1.04 (95% CI = 1.02–1.05), per 10 µg/m³ PM₁₀, 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 PM₁₀: per 10 µg/m³ PM₁₀, OR = 1.05 (95% CI = 1.01–1.09).

Background Low-dose chest CT screening is recommended for smokers with the potential for lung function abnormality, but its role in predicting lung function remains unclear. Purpose To develop a deep learning algorithm to predict pulmonary function with low-dose CT images in participants using health screening services. Materials and Methods In this retrospective study, participants underwent health screening with same-day low-dose CT and pulmonary function testing with spirometry at a university affiliated tertiary referral general hospital between January 2015 and December 2018. The data set was split into a development set (model training, validation, and internal test sets) and temporally independent test set according to first visit year. A convolutional neural network was trained to predict the forced expiratory volume in the first second of expiration (FEV₁) and forced vital capacity (FVC) from low-dose CT. The mean absolute error and concordance correlation coefficient (CCC) were used to evaluate agreement between spirometry as the reference standard and deep-learning prediction as the index test. FVC and FEV₁ percent predicted (hereafter, FVC% and FEV₁%) values less than 80% and percent of FVC exhaled in first second (hereafter, FEV₁/FVC) less than 70% were used to classify participants at high risk. Results A total of 16 148 participants were included (mean age, 55 years ± 10 [SD]; 10 981 men) and divided into a development set (n = 13 428) and temporally independent test set (n = 2720). In the temporally independent test set, the mean absolute error and CCC were 0.22 L and 0.94, respectively, for FVC and 0.22 L and 0.91 for FEV₁. For the prediction of the respiratory high-risk group, FVC%, FEV₁%, and FEV₁/FVC had respective accuracies of 89.6% (2436 of 2720 participants; 95% CI: 88.4, 90.7), 85.9% (2337 of 2720 participants; 95% CI: 84.6, 87.2), and 90.2% (2453 of 2720 participants; 95% CI: 89.1, 91.3) in the same testing data set. The sensitivities were 61.6% (242 of 393 participants; 95% CI: 59.7, 63.4), 46.9% (226 of 482 participants; 95% CI: 45.0, 48.8), and 36.1% (91 of 252 participants; 95% CI: 34.3, 37.9), respectively. Conclusion A deep learning model applied to volumetric chest CT predicted pulmonary function with relatively good performance. © RSNA, 2023 Supplemental material is available for this article.

An effective technique of phase contrast synchrotron radiation computed tomography was established for the quantitative analysis of the microstructures in the respiratory zone of a mouse lung. Heitzman’s method was adopted for the whole-lung sample preparation, and Canny’s edge detector was used for locating the air-tissue boundaries. This technique revealed detailed morphology of the respiratory zone components, including terminal bronchioles and alveolar sacs, with sufficiently high resolution of 1.74 µm isotropic voxel size. The technique enabled visual inspection of the respiratory zone components and comprehension of their relative positions in three dimensions. To check the method’s feasibility for quantitative imaging, morphological parameters such as diameter, surface area and volume were measured and analyzed for sixteen randomly selected terminal branching units, each consisting of a terminal bronchiole and a pair of succeeding alveolar sacs. The four types of asymmetry ratios concerning alveolar sac mouth diameter, alveolar sac surface area, and alveolar sac volume are measured. This is the first ever finding of the asymmetry ratio for the terminal bronchioles and alveolar sacs, and it is noteworthy that an appreciable degree of branching asymmetry was observed among the alveolar sacs at the terminal end of the airway tree, despite the number of samples was small yet. The series of efficient techniques developed and confirmed in this study, from sample preparation to quantification, is expected to contribute to a wider and exacter application of phase contrast synchrotron radiation computed tomography to a variety of studies.

Media reports of a celebrity’s suicide may be followed by copycat suicides, and the impact may vary in different age and sex subgroups. We proposed a quantitative framework to assess the vulnerability of age and sex subgroups to copycat suicide and used this method to investigate copycat suicides in relation to the suicides of 10 celebrities in South Korea from 1993 to 2013. By applying a detrending model to control for annual and seasonal fluctuations, we estimated the expected number of suicides within a copycat suicide period. The copycat effect was assessed in two ways: the magnitude of copycat suicide by dividing the observed by the expected number of suicides, and the mortality rate by subtracting the expected from the observed number of suicides. Females aged 20–29 years were the most vulnerable subgroup according to both the magnitude of the copycat effect (2.31-fold increase over baseline) and the mortality rate from copycat suicide (22.7-increase). Males aged 50–59 years were the second most vulnerable subgroup according to the copycat suicide mortality rate (20.5- increase). We hope that the proposed quantitative framework will be used to identify vulnerable subgroups to copycat effect, thereby helping devise strategies for prevention.

In February and March 2020, COVID-19 epidemic in South Korea met a large black swan effect by a Sincheonji cult mass infection in Daegu-Gyeongbuk area. The black swan made it difficult to evaluate that the current policies for infection prevention including social distancing, closing schools, hand washing, and wearing masks good enough or not. Therefore, in this study, we evaluated basic reproduction number (R ₀ ) and time-dependent reproduction number (R _t ) of confirmed cases based on various kinds of populations, including total, Daegu-Geyoengbuk, except-Daegu-Gyeongbuk, Sincheonji, and except-Sincheonji. In total, it seems the infection is going to be under control, but this is never true because in the except-Sincheonji and except-Daegu-Geyongbuk cases, R ₀ is still above 1.0, and R _t is drifting around 1.0. This study could be used to determine government policies in the near future.