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Abstract Diabetes retinopathy (DR) is a critical clinical disease with that causes irreversible visual damage in adults, and may even lead to permanent blindness in serious cases. Early identification and treatment of DR is critical. Our aim was to train and externally validate a prediction nomogram for early prediction of DR. 2381 patients with type 2 diabetes mellitus (T2DM) were retrospective study from the First Affiliated Hospital of Xinjiang Medical University in Xinjiang, China, hospitalised between Jan 1, 2019 and Jun 30, 2022. 962 patients with T2DM from the Suzhou BenQ Hospital in Jiangsu, China hospitalised between Jul 1, 2020 to Jun 30, 2022 were considered for external validation. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression was performed to identify independent predictors and establish a nomogram to predict the occurrence of DR. The performance of the nomogram was evaluated using a receiver operating characteristic curve (ROC), a calibration curve, and decision curve analysis (DCA). Neutrophil, 25-hydroxyvitamin D3 [25(OH)D3], Duration of T2DM, hemoglobin A1c (HbA1c), and Apolipoprotein A1 (ApoA1) were used to establish a nomogram model for predicting the risk of DR. In the development and external validation groups, the areas under the curve of the nomogram constructed from the above five factors were 0.834 (95%CI 0.820–0.849) and 0.851 (95%CI 0.829–0.874), respectively. The nomogram demonstrated excellent performance in the calibration curve and DCA. This research has developed and externally verified that the nomograph model shows a good predictive ability in assessing DR risk in people with type 2 diabetes. The application of this model will help clinicians to intervene early, thus effectively reducing the incidence rate and mortality of DR in the future, and has far-reaching significance in improving the long-term health prognosis of diabetes patients.

Abstract Contamination of soils by Molybdenum (Mo) has raised increasing concern worldwide. Both fulvic acid (FA) and humic acid (HA) possess numerous positive properties, such as large specific surface areas and microporous structure that facilitates the immobilization of the heavy metal in soils. Despite these characteristics, there have been few studies on the microbiology effects of FA and HA. Therefore, this study aimed to assess the Mo immobilization effects of FA and HA, as well as the associated changes in microbial community in Mo-contaminated soils (with application rates of 0%, 0.5% and 1.0%). The result of the incubation demonstrated a decrease in soil pH (from 8.23 ~ 8.94 to 8.05 ~ 8.77). Importantly, both FA and HA reduced the exchangeable fraction and reducible fraction of Mo in the soil, thereby transforming Mo into a more stable form. Furthermore, the application of FA and HA led to an increase in the relative abundance of Actinobacteriota and Firmicutes, resulting in alterations to the microbial community structure. However, it is worth noting that due to the differing structures and properties of FA and HA, these outcomes were not entirely consistent. In summary, the aging of FA and HA in soil enhanced their capacity to immobilization Mo as a soil amendment. This suggests that they have the potential to serve as effective amendments for the remediation of Mo-contaminated soils.

Abstract Xinjiang is an important power production base in China, and its electric energy production needs not only meet the demand of Xinjiang's electricity consumption, but also make up for the shortage of electricity in at least 19 provinces or cities in China. Therefore, it is of great significance to know ahead of time the electric energy production of Xinjiang in the future. In such terms, accurate electric energy production forecasts are imperative for decision makers to develop an optimal strategy that includes not only risk reduction, but also the betterment of the economy and society as a whole. According to the characteristics of the historical data of monthly electricity generation in Xinjiang from January 2001 to August 2020 , the suitable and widely used SARIMA (Seasonal autoregressive integrated moving mean model) method and Holt-winter method were used to construct the monthly electric energy production in Xinjiang for the first time. The results of our analysis showed that the established SARIMA((1,2,3,4,6,7,11),2,1)(1,0,1)12 model had higher prediction accuracy than that of the established Holt-Winters' multiplicative model. We predicted the monthly electric energy production from August 2021 to August 2022 by the SARIMA((1,2,3,4,6,7,11),2,1)(1,0,1)12 model, and errors are very small compared to the actual values, indicating that our model has a very good prediction performance. Therefore, based on our study, we provided a simple and easy scientific tool for the future power output prediction in Xinjiang. Our research methods and research ideas can also provide scientific reference for the prediction of electric energy production elsewhere.