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To address challenges in screening for chronic kidney disease (CKD), we devised a deep learning-based CKD screening model named UWF-CKDS. It utilizes ultra-wide-field (UWF) fundus images to predict the presence of CKD. We validated the model with data from 23 tertiary hospitals across China. Retinal vessels and retinal microvascular parameters (RMPs) were extracted to enhance model interpretability, which revealed a significant correlation between renal function and RMPs. UWF-CKDS, utilizing UWF images, RMPs, and relevant medical history, can accurately determine CKD status. Importantly, UWF-CKDS exhibited superior performance compared to CTR-CKDS, a model developed using the central region (CTR) cropped from UWF images, underscoring the contribution of the peripheral retina in predicting renal function. The study presents UWF-CKDS as a highly implementable method for large-scale and accurate CKD screening at the population level. [ABSTRACT FROM AUTHOR]

Background: Ozone (O3) and nitrogen dioxide (NO2) are substances with oxidizing ability in the atmosphere. Only considering the impact of a single substance is not comprehensive. However, people's understanding of "total oxidation capacity" (Ox) and "weighted average oxidation" (Oxwt) is limited. Objectives: This investigation aims to assess the impact of Ox and Oxwt on the novel coronavirus disease (COVID-19). We also compared the relationship between the different calculation methods of Ox and Oxwt and the COVID-19 infection rate. Method: We recorded confirmed COVID-19 cases and daily pollutant concentrations (O3 and NO2) in 34 provincial capital cities in China. The generalized additive model (GAM) was used to analyze the nonlinear relationship between confirmed COVID-19 cases and Ox and Oxwt. Result: Our results indicated that the correlation between Ox and COVID-19 was more sensitive than Oxwt. The hysteresis effect of Ox and Oxwt decreased with time. The most obvious statistical data was observed in Central China and South China. A 10 µg m−3 increase in mean Ox concentrations were related to a 23.1% (95%CI: 11.4%, 36.2%) increase, and a 10 µg m−3 increase in average Oxwt concentration was related to 10.7% (95%CI: 5.2%, 16.8%) increase in COVID-19. In conclusion, our research results show that Ox and Oxwt can better replace the single pollutant research on O3 and NO2, which is used as a new idea for future epidemiological research. [ABSTRACT FROM AUTHOR]

Heavy metals (HMs) in farmland soils lead to adverse influences on ecosystem and human health. Despite that, data on quantitative risk from different sources are still scarce. In this study, 100 farmland soil samples in Jiuyuan District were collected and analyzed for selected HMs (As, Cd, Co, Cr, Cu, Mn, Ni, Pb, V and Zn) content characteristics and pollution statuses. The positive matrix factorization (PMF) model combined with the Nemerow integrated risk index (NIRI) and human health risk assessment (HHRA) was used to quantitatively identify the primary risk sources. The results indicated that the mean contents or median values (mg/kg) of 10 HMs were all higher than the background values. The contamination factor (CF) and pollution load index (PLI) revealed that the soil was severely polluted. Based on PMF, the main source of HM pollution was anthropogenic activities, accounting for 78.91%. Sewage irrigation represented the biggest input but was not associated with the highest risk. The results of PMF-based NIRI and PMF-based HHRA showed that the chemical fertilizers and pesticides were the largest and priority risk sources with contribution rates of 38.10% to ecological risk and 34.61 and 32.82% to non-carcinogenic and carcinogenic risk, respectively. In addition, non-carcinogenic risk of children was higher than that of adults, while the carcinogenic risk was the opposite. The integrated approaches were beneficial for priority risk quantification from different sources and can provide direct risk information and effective policy recommendations for management and control of key risk sources. [ABSTRACT FROM AUTHOR]