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Background: Elderly patients with breast cancer are highly heterogeneous, and tumor load and comorbidities affect patient prognosis. Prediction models can help clinicians to implement tailored treatment plans for elderly patients with breast cancer. This study aimed to establish a prediction model for breast cancer, including comorbidities and tumor characteristics, in elderly patients with breast cancer. Methods: All patients were ≥65 years old and admitted to the Peking Union Medical College Hospital. The clinical and pathological characteristics, recurrence, and death were observed. Overall survival (OS) was analyzed using the Kaplan-Meier curve and a prediction model was constructed using Cox proportional hazards model regression. The discriminative ability and calibration of the nomograms for predicting OS were tested using concordance (C)- statistics and calibration plots. Clinical utility was demonstrated using decision curve analysis (DCA). Results: Based on 2,231 patients, the 5- and 10-year OS was 91.3% and 78.4%, respectively. We constructed an OS prediction nomogram for elderly patients with early breast cancer (PEEBC). The C-index for OS in PEEBC in the training and validation cohorts was 0.798 and 0.793, respectively. Calibration of the nomogram revealed a good predictive capability, as indicated by the calibration plot. DCA demonstrated that our model is clinically useful. Conclusion: The nomogram accurately predicted the 3-year, 5-year, and 10-year OS in elderly patients with early breast cancer. [ABSTRACT FROM AUTHOR]

Aerosol optical depth (AOD) is the key parameter for determining the aerosol radiative effects and air quality variation. It is important to quantify nighttime aerosols using satellite-based night light images to understand their diurnal variations. This study selected high-resolution low light images from the Glimmer Imager (GLI) aboard the SDGSAT-1 satellite to examine spatial–temporal changes in night light emitted from the urban surface of Beijing. The radiance observed by SDGSAT-1/GLI was used to discern the AOD changes using the radiance background method (RB) and standard deviation method (SD) based on the characterization of the radiance from artificial light sources. Cloud cleaning processes were conducted to reduce the influence of the cloud cover in the glimmer images of the derived AOD. The results showed that there are good consistencies between the derived AOD results from the remote sensing and nighttime site observations. The radiance background method is better than the standard deviation method for deriving AOD using SDGSAT-1/GLI with the RMSE of its RB (0.0984) being greater than that of the SD (0.7653). The influence of moonlight, atmospheric absorption, and positioning errors on the results is briefly discussed. This paper shows that SDGSAT-1 can obtain relatively reliable night AOD values based on our investigations using the available satellite images taken in winter and spring, and that it has the potential to provide the scientific products of nighttime AOD. [ABSTRACT FROM AUTHOR]

Introduction: Although geriatric assessment (GA) has been used for a long time in the field of geriatrics and internal medicine, there are few studies on its application in the field of breast surgery. Therefore, the utility of specific GA domains for the assessment of older patients with breast cancer remains unclear. The aim of the present study was to evaluate the association between specificGA domains and the survival rate of older patients with breast cancer. Methods: We used the database of Peking Union Medical College Hospital to identify older patients who were newly diagnosed with breast cancer between 2012 and 2018 and retrospectively analysed the data of 541 patients aged =65 years. Patients with metastatic cancer and those with missing vital status data were excluded. The primary outcomes were overall survival (OS) and breast cancer-specific survival. The GA domains used in this study included functional status, comorbidities, and psychological state. Multivariate regression analysis was used to estimate hazard ratios for these three domains. Results: After a median follow-up of 72 months, we observed a significant relationship between functional impairment and mortality (adjusted HR: 3.06, 95% confidence interval [CI]: 1.83-5.10, P<0.001). Similarly, patients with severe comorbidities (adjusted HR: 2.35; 95% CI: 1.16-4.75, P=0.017) and an impaired psychological state (adjusted HR: 2.82, 95% CI: 1.45-5.50, P=0.002) showed worse OS rates. Accordingly, addition of the three GA domains to the basic model, which included age, tumour stage, lymph node stage, and intrinsic molecular subtype as baseline variables, yielded higher C-statistics for mortality analysis (from 0.713 to 0.740). Conclusion: To our knowledge, this is the first study to include specific GA domains in a prognostic model for older patients with breast cancer in China. Three domains, namely functional status, comorbidities, and psychological state, should be considered for survival analyses in this particular population. The full model including these three GA domains may be more accurate in predicting the survival of older patients with breast cancer. [ABSTRACT FROM AUTHOR]

Rising temperatures can increase the risk of mental disorders. As climate change intensifies, the future disease burden due to mental disorders may be underestimated. Using data on the number of daily emergency department visits for mental disorders at 30 hospitals in Beijing, China during 2016–2018, the relationship between daily mean temperature and such visits was assessed using a quasi-Poisson model integrated with a distributed lag nonlinear model. Emergency department visits for mental disorders attributed to temperature changes were projected using 26 general circulation models under four climate change scenarios. Stratification analyses were then conducted by disease subtype, sex, and age. The results indicate that the temperature-related health burden from mental disorders was projected to increase consistently throughout the 21st century, mainly driven by high temperatures. The future temperature-related health burden was higher for patients with mental disorders due to the use of psychoactive substances and schizophrenia as well as for women and those aged <65 years. These findings enhance our knowledge of how climate change could affect mental well-being and can be used to advance and refine targeted approaches to mitigating and adapting to climate change with a view on addressing mental disorders. • Future temperature-related health burden for mental disorders (MDs) will increase. • High temperatures pose a greater future health burden for MDs. • Those with substance use disorders and schizophrenia were at higher risk. • Female patients and patients aged <65 years were more susceptible to temperature. [ABSTRACT FROM AUTHOR]

Nitrate (NO3−) is a major contributing species to haze formation in Northern China. So far, formation processes and source apportionments of nitrate aerosols during haze pollution have not yet been well understood. In this study, the PM2.5 samples were collected in Beijing from 13 November to 24 December 2018. In addition to water‐soluble ions, oxygen (δ18O‐NO3−) and nitrogen (δ15N‐NO3−) isotopes in particulate NO3− were also determined, in order to investigate the formation pathways and potential sources of NO3− aerosols. The results showed that NO3− was a dominant species (43%) of secondary inorganic aerosols during the sampling period. The δ18O‐NO3− and δ15N‐NO3− values averaged at 83.8 ± 13.4 and 11.5 ± 5.0‰, respectively. Combining isotope compositions and Bayesian isotope mixing model, we found that heterogeneous reaction and gas phase oxidation contributed equally to nitrate formation during the sampling period. However, the contribution of heterogeneous processes to nitrate increased from 39% during the clean period to 64% during the haze period. On average, coal combustion, biomass burning, vehicle emissions, and soil emission contributed 50%, 26%, 20%, and 4%, respectively, to nitrate aerosols during the sampling period. Compared to the result in 2013, the significant decrease (~21%) of relative contribution of coal combustion to nitrate was due to strict reduction of coal consumption in Beijing. Finally, the relative contribution of traffic emissions to nitrate increased from 18% during the clean period to 30% during the haze period, suggesting that control of traffic emissions would be an important way to decrease nitrate concentrations and improve the air quality in Beijing. Key Points: Nitrate aerosols in Beijing during the haze events were mainly produced by hydrolysis of N2O5 under high humidity conditionsThe contribution of coal combustion to atmospheric nitrate decreased significantly after clean air actions implemented by Chinese governmentStrict control of traffic emissions would be an important way to decrease nitrate concentrations and improve the air quality in Beijing [ABSTRACT FROM AUTHOR]

Air pollution is among the top threats to human health in China. As air toxicants, polycyclic aromatic hydrocarbons (PAHs) could bring significant risks to population; however, the exposure to PAHs in China and its health impact are not fully understood. In 2012, a summer exchange program allowed 10 students to travel from Los Angeles to Beijing and stay there for 10 weeks. Based on the program, this study investigated the difference in urinary concentration of 12 hydroxylated-PAHs (Σ12OH-PAHs) and malondialdehyde (MDA) between the two cities. The median concentration of Σ12OH-PAHs in Beijing (14.1 µg g-1 creatinine) was significantly higher than that in Los Angeles (5.78 µg g-1 creatinine), indicating a higher exposure in Beijing. The ratios of homogeneous OH-PAHs (e.g., 1-/2-OH-NAP) changed significantly between the two cities (p < 0.01), which might suggest a potential alteration in metabolism subsequent to exposure. A significant association between Σ12OH-PAHs and MDA (p < 0.01) was observed, with the association varying between the two cities. This study suggests that exposure to PAHs might be linked to metabolism alteration and calls for future studies to investigate the role this possible alteration played in the health effects of PAHs exposure. [ABSTRACT FROM AUTHOR]

Elemental carbon (EC) or black carbon (BC) in the atmosphere has a strong influence on both climate and human health. In this study, radiocarbon (14C) based source apportionment is used to distinguish between fossil fuel and biomass burning sources of EC isolated from aerosol filter samples collected in Beijing from June 2010 to May 2011. The 14C results demonstrate that EC is consistently dominated by fossil-fuel combustion throughout the whole year with a mean contribution of 79% ± 6% (ranging from 70% to 91%), though EC has a higher mean and peak concentrations in the cold season. The seasonal molecular pattern of hopanes (i.e., a class of organic markers mainly emitted during the combustion of different fossil fuels) indicates that traffic-related emissions are the most important fossil source in the warm period and coal combustion emissions are significantly increased in the cold season. By combining 14C based source apportionment results and picene (i.e., an organic marker for coal emissions) concentrations, relative contributions from coal (mainly from residential bituminous coal) and vehicle to EC in the cold period were estimated as 25 ± 4% and 50 ± 7%, respectively, whereas the coal combustion contribution was negligible or very small in the warm period. [ABSTRACT FROM AUTHOR]

Haze pollution occurs frequently over northern China Plain (NCP) during winter, resulting in great threats to human health. So far, limited studies have linked between emission sources and health risks from ambient field measurements. In this work, PM 2.5 samples were collected in Beijing in the 2018 winter; 23 metallic elements were determined to assess the health risks induced by PM-bound metals. During the sampling period, metallic elements constituted a minor fraction (5%) in PM 2.5 , but exhibited significant threats to human health. The cancer risk (CR) value induced by PM-bound metals was 1.41 × 10−4 for adults and 2.59 × 10−4 for children while non-cancer risk (NCR) was 2.59 for adults and 3.37 for children. Nickel (Ni), As and Cr(IV) were the major contributing elements to carcinogenic risk. Arsenic (As) contributed a major (57%) of NCR for children, and Mn (30%), Ni (27%) and As (24%) were the major contributing species to NCR for adults. Using positive matrix factorization (PMF) model, traffic-related emission (25% for children and 30% for adults) and heavy oil combustion (39% for children and 28% for adults) were the dominant sources of CR values. Moreover, the CR value induced by local emissions was higher than that by long-range transported air pollution. Local traffic emission and long-range transported heavy oil combustion enhanced CR values, threatening the human health. As results, we highlighted that control of traffic emissions and heavy oil combustion on local and regional scales should be seriously considered by the Chinese government in reduction of health risk in Beijing. Image 1 • The CR value was 1.41 × 10−4 for adults and 2.59 × 10−4 for children while NCR value was 2.59 for adults and 3.37 for children. • Traffic-related emission and heavy oil combustion were the predominant sources of CR in Beijing. • Control of local traffic emission and long-range transported heavy oil combustion is an important way to reduce health risk. [ABSTRACT FROM AUTHOR]

Urban flood risk assessment plays a crucial role in disaster prevention and mitigation. A scientifically accurate assessment and risk stratification method are of paramount importance for effective flood risk management. This study aims to propose a comprehensive urban flood risk assessment approach by coupling GeoDetector-Dematel and Clustering Method to enhance the accuracy of urban flood risk evaluation. Based on simulation results from hydraulic models and existing literature, the research established a set of urban flood risk assessment indicators comprising 10 metrics across two dimensions: hazard factors and vulnerability factors, among which vulnerability factors include exposure factors, sensitivity factors, and adaptability factors. Subsequently, the research introduced the GeoDetector-Dematel method to determine indicator weights, significantly enhancing the scientific rigor and precision of weight calculation. Finally, the research employed the K-means clustering method to risk zonation, providing a more scientifically rational depiction of the spatial distribution of urban flood risks. This novel comprehensive urban flood risk assessment method was applied in the Fangzhuang area of Beijing. The results demonstrated that this integrated approach effectively enhances the accuracy of urban flood risk assessment. In conclusion, this research offers a new methodology for urban flood risk assessment and contributes to decision-making in disaster prevention and control measures. [Display omitted] • A novel integrated urban flood risk assessment approach is proposed. • A set of urban flood risk assessment index system is established. • A novel method using GeoDetector-Dematel determines index weights. • K-means method is introduced to obtain more scientific flood risk. [ABSTRACT FROM AUTHOR]