Explore the vast range of titles available.

The current epidemic status of diabetic retinopathy in China is unclear. A national prevalence survey of diabetic complications was conducted. 50,564 participants with gradable non-mydriatic fundus photographs were enrolled. The prevalence rates (95% confidence intervals) of diabetic retinopathy and vision-threatening diabetic retinopathy were 16.3% (15.3%–17.2%) and 3.2% (2.9%–3.5%), significantly higher in the northern than in the southern regions. The differences in prevalence between those who had not attained a given metabolic goal and those who had were more pronounced for Hemoglobin A1c than for blood pressure and low-density lipoprotein cholesterol. The participants with vision-threatening diabetic retinopathy had significantly higher proportions of visual impairment and blindness than those with non-vision-threatening diabetic retinopathy. The likelihoods of diabetic retinopathy and vision-threatening diabetic retinopathy were also associated with education levels, household income, and multiple dietary intakes. Here, we show multi-level factors associated with the presence and the severity of diabetic retinopathy. Current data on the national distribution of diabetic retinopathy (DR) is lacking. Here, the authors show the national distribution, associated multi-level factors, and visual impairment of DR and vision-threatening DR in Chinese adults with diabetes.

There is no consensus about the impact of urbanization on energy efficiency. We seek to fill this gap in literature using data from 78 countries for the period of 1995 through 2012. Extending the Stochastic Impacts by Regression on Population, Affluence, and Technology model, we identify the impact of urbanization on energy consumption and efficiency. Results of generalized method of moments estimation indicate that the process of urbanization leads to substantial increases in both the actual and the optimal energy consumption, but a decrease in efficiency of energy use. In addition, we find that the extent to which energy inefficiency correlates with urbanization is greater in countries with higher gross domestic product per capita.

The efficient co‐production of H2 and CH4 via anaerobic digestion (AD) requires separate stages, as it cannot yet be achieved in one step. Lactic acid bacteria (LAB) (Limosilactobacillus) release H2 and acetate by enhancing hydrolysis, potentially increasing CH4 production with simultaneous H2 accumulation. This study investigated the enhanced effect of one‐step co‐production of H2 and CH4 in AD by LAB and elucidated its enhancement mechanisms. The results showed that 236.3 times increase in H2 production and 7.1 times increase in CH4 production are achieved, resulting in profits of 469.39 USD. Model substrates lignocellulosic straw, sodium acetate, and H2 confirmes LAB work on the hydrolysis stage and subsequent sustainable volatile fatty acid production during the first 6 days of AD. In this stage, the enrichment of Limosilactobacillus carrying bglB and xynB, the glycolysis pathway, and the high activity of protease, acetate kinase, and [FeFe] hydrogenase, jointly achieved rapid acetate and H2 accumulation, driving hydrogenotrophic methanogenesis dominated. From day 7 to 24, with enriched Methanosarcina, and increased methenyltetrahydromethanopterin hydrogenase activity, continuously produced acetate led to the mainly acetoclastic methanogenesis shift from hydrogenotrophic methanogenesis. The power generation capacity of LAB‐enhanced AD is 333.33 times that of China's 24,000 m3 biogas plant. This study proposes a novel strategy for one‐step co‐production of hydrogen and methane through the addition of lactic acid bacteria (Limosilactobacillus), overcoming the limitations of conventional two‐stage anaerobic digestion processes. Mechanistic analysis confirms the strengthening effect of Limosilactobacillus in the hydrolysis phase, and preliminary economic analysis indicates significant potential for the application of this strategy.

Climate change and human activities are the primary drivers influencing changes in runoff dynamics. However, current understanding of future hydrological processes under scenarios of gradual climate change and escalating human activities remains uncertain, particularly in tropical regions affected by deforestation. Based on this, we employed the SWAT model coupled with the near future (2021–2040) and middle future (2041–2060) global climate models (GCMs) under four shared socioeconomic pathways (SSP1-2.6 (SSP1 + RCP2.6), SSP2-4.5 (SSP2 + RCP4.5), SSP3-7.0 (SSP3 + RCP7.0), and SSP5-8.5 (SSP5 + RCP8.5)) from the CMIP6 and the CA-Markov model to evaluate the runoff response to future environmental changes in the Dingan River Basin (DRB). The quantification of the impacts of climate change and land use change on future runoff changes was conducted. The results revealed a non-significant increasing trend in precipitation during the historical period (1999–2018). Furthermore, all three future scenarios (SSP1-2.6, SSP3-7.0, and SSP5-8.5) exhibited an upward trend in precipitation from 2021 to 2060. Notably, the SSP5-8.5 scenario demonstrated a highly significant increase ( P  < 0.01), while the SSP2-4.5 scenario displayed a non-significant decreasing trend. The future precipitation pattern exhibits a decrease during spring and winter, while showing an increase in summer and autumn. The temperature exhibited a significant increase ( P  < 0.05) across the four future scenarios, with amplitudes of 0.24 °C/(10 years), 0.36 °C/(10 years), 0.36 °C/(10 years), and 0.50 °C/(10 years) for SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 respectively. The future trend of land use change entails a continuous increase in cultivated land and a corresponding decrease in artificial forest land. By 2032, the area of cultivated land is projected to witness a growth of 4.10%, while artificial forest coverage will experience a decline of 4.45%. Furthermore, by 2046, the extent of cultivated land is anticipated to expand by 4.41%, accompanied by a reduction in artificial forest cover amounting to 5.39%. The average annual runoff during the historical period was 53.35 m³/s, and the Mann-Kendall (MK) trend test showed that it exhibited a non-significant increasing trend. Compared with the historical period, the comprehensive impact of climate change and land use will cause changes in the runoff by 0.49%, 1.98%, − 3.13%, and 3.65% for the scenarios of SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 in the near future, and − 3.24%, 1.30%, − 3.75% and 18.24% in the middle future respectively. The intra-annual variations in future runoff suggest an earlier peak and a more concentrated distribution of runoff during the wet season (May to October). Compared to historical periods, the total runoff in the wet season under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios increased by 6.53%, 8.91%, 7.17%, and 7.39%, respectively. The research findings offer significant insights into the future hydrological processes in tropical regions, while also serving as a valuable reference for watershed water resource management and disaster control.

The main purpose of this article is to link the environment, economy, electricity, and society and put forward a new point of view. The current research mainly explores the relationship between the environment, economy, and society and lacks a discussion on electricity. Using a new research framework, this article examines the relationship between energy intensity, energy consumption structure, population density, urbanization rate, and carbon intensity based on relevant data from 2000 to 2017 in China. In the empirical research, according to the cluster analysis, China’s 30 provinces are divided into three regions according to the electrification rate standard. The cross-sectional dependence test method is used to verify the cross-sectional dependence of the data, and the second-generation panel unit root test method is used. Exploring the relationship between the variables, this article finally uses the convergence analysis method to explore the degree of influence of each variable on the carbon intensity. The empirical results show that there are both short-term effects and long-term relationships in various regions, and the influencing factors of each region are different. It further shows that the carbon intensity of the four panels shows convergence, β absolute convergence, and β conditional convergence, but the main influencing factors in different regions are different. Finally, based on the results of empirical research, policy recommendations for reducing carbon intensity in different regions are put forward.

TiO2 is an ideal photocatalyst candidate except for its large bandgap and fast charge recombination. A novel laminated junction composed of defect‐controlled and sulfur‐doped TiO2 with carbon substrate (LDC‐S‐TiO2/C) is synthesized using the 2D transition metal carbides (MXenes) as a template to enhance light absorption and improve charge separation. The prepared LDC‐S‐TiO2/C catalyst delivers a high photocatalytic H2 evolution rate of 333 µmol g−1 h−1 with a high apparent quantum yield of 7.36% at 400 nm and it is also active even at 600 nm, resulting into a 48 time activity compared with L‐TiO2/C under visible light irradiation. Further theoretical modeling calculation indicates that such novel approach also reduces activation energy of hydrogen production apart from broadening the absorption wavelength, facilitating charge separation, and creating a large surface area substrate. This synergic effect can also be applied to other photocatalysts' modification. The study provides a novel approach for synthesis defective metal oxides based hybrids and broaden the applications of MXene family. A laminated hybrid junction of sulfur‐doped TiO2 and carbon substrate via a sulfur impregnation into Ti3C2 MXenes and the subsequent oxidation processes is demonstrated. The defects design on carbon is helpful for the enhancement of photocatalytic activity and a novel method is demonstrated to synthesize highly active photocatalysts for solar energy conversion.

The epidural-related maternal fever (ERMF) induced by patient-controlled epidural analgesia (PCEA) remains unpredictable. Our objective is to develop ERMF prediction models using real-world data, aiming to identify pertinent contributing factors and support obstetricians in making personalized clinical decisions. Women who used patient-controlled epidural analgesia between October 2021 and March 2023 at a tertiary hospital in Jiangsu Province were retrospectively documented. The primary outcome was the occurrence of maternal fever associated with epidural use. We developed six machine learning (ML) models and assessed the area under curve (AUC) for characteristics of subjects' performance, calibration curves, and decision curve analyses. A total of 1,492 women were enrolled, with 24.3% experiencing ERMF (362 cases). The AUC ratios between the logistic regression (LR) model and the stochastic gradient descent (SGD) models showed statistical significance (p < 0.05), while the differences between the other models were not statistically significant. In comparison to the SVM model, the LR model exhibited better calibration (Brier score: 0.193; calibration slope: 0.715; calibration intercept: 0.062). Consequently, the LR model was selected as the prediction model. Furthermore, the LR-based nomogram identified eight significant predictors of ERMF, including neutrophil percentage, first stage of labor, amniotic fluid contamination during membrane rupture, artificial rupture of membranes, chorioamnionitis, post-analgesic antimicrobials, pre-analgesic oxytocin, post-analgesic oxytocin, and dinoprostone suppositories. Optimally applying logistic regression models can enable rapid and straightforward identification of ERMF risk and the implementation of rational therapeutic measures, in contrast to machine learning models.

Background Patients with type 2 diabetes (T2DM) have an increasing need for personalized and Precise management as medical technology advances. Artificial intelligence (AI) technologies on mobile devices are being developed gradually in a variety of healthcare fields. As an AI field, knowledge graph (KG) is being developed to extract and store structured knowledge from massive data sets. It has great prospects for T2DM medical information retrieval, clinical decision-making, and individual intelligent question and answering (QA), but has yet to be thoroughly researched in T2DM intervention. Therefore, we designed an artificial intelligence-based health education accurately linking system (AI-HEALS) to evaluate if the AI-HEALS-based intervention could help patients with T2DM improve their self-management abilities and blood glucose control in primary healthcare. Methods This is a nested mixed-method study that includes a community-based cluster-randomized control trial and personal in-depth interviews. Individuals with T2DM between the ages of 18 and 75 will be recruited from 40-45 community health centers in Beijing, China. Participants will either receive standard diabetes primary care (SDPC) (control, 3 months) or SDPC plus AI-HEALS online health education program (intervention, 3 months). The AI-HEALS runs in the WeChat service platform, which includes a KBQA, a system of physiological indicators and lifestyle recording and monitoring, medication and blood glucose monitoring reminders, and automated, personalized message sending. Data on sociodemography, medical examination, blood glucose, and self-management behavior will be collected at baseline, as well as 1,3,6,12, and 18 months later. The primary outcome is to reduce HbA1c levels. Secondary outcomes include changes in self-management behavior, social cognition, psychology, T2DM skills, and health literacy. Furthermore, the cost-effectiveness of the AI-HEALS-based intervention will be evaluated. Discussion KBQA system is an innovative and cost-effective technology for health education and promotion for T2DM patients, but it is not yet widely used in the T2DM interventions. This trial will provide evidence on the efficacy of AI and mHealth-based personalized interventions in primary care for improving T2DM outcomes and self-management behaviors. Trial registration Biomedical Ethics Committee of Peking University: IRB00001052-22,058, 2022/06/06; Clinical Trials: ChiCTR2300068952, 02/03/2023.

This article presents the results of the China National Diabetes and Metabolic Disorders study, which was performed in 2007–2008 to estimate the prevalence of diabetes among Chinese adults. The results indicate that diabetes has become a major public health problem in China. The results of the China National Diabetes and Metabolic Disorders study indicate that diabetes has become a major public health problem in China. Cardiovascular disease has become the leading cause of death in China, a development that has followed rapid economic growth, an increase in life expectancy, and changes in lifestyle. 1 Diabetes is a major risk factor for cardiovascular disease, and the prevalence of diabetes is high and is increasing in China. 2 – 4 A national survey conducted in 1994, involving 224,251 Chinese residents, 25 to 64 years of age, from 19 provinces, showed that the prevalences of diabetes and impaired glucose tolerance were 2.5% and 3.2%, respectively. 2 These estimates were higher by a factor of approximately 3 than those reported in 1980. 3 In . . .

Surface strain engineering is a promising strategy to design various electrocatalysts for sustainable energy storage and conversion. However, achieving the multifunctional activity of the catalyst via the adjustment of strain engineering remains a major challenge. Herein, an excellent trifunctional electrocatalyst (Ru/RuO2@NCS) is prepared by anchoring lattice mismatch strained core/shell Ru/RuO2 nanocrystals on nitrogen‐doped carbon nanosheets. Core/shell Ru/RuO2 nanocrystals with ~5 atomic layers of RuO2 shells eliminate the ligand effect and produce ~2% of the surface compressive strain, which can boost the trifunctional activity (oxygen evolution reaction [OER], oxygen reduction reaction [ORR], and hydrogen evolution reaction [HER]) of the catalyst. When equipped in rechargeable Zn‐air batteries, the Ru/RuO2@NCS endows them with high power (137.1 mW cm−2) and energy (714.9 Wh kgZn−1) density and excellent cycle stability. Moreover, the as‐fabricated Zn‐air batteries can drive a water splitting electrolyzer assembled with Ru/RuO2@NCS and achieve a current density of 10 mA cm−2 only requires a low potential ~1.51 V. Density functional theory calculations reveal that the compressive strained RuO2 could reduce the reaction barrier and improve the binding of rate‐determining intermediates (*OH, *O, *OOH, and *H), leading to the enhanced catalytic activity and stability. This work can provide a novel avenue for the rational design of multifunctional catalysts in future clean energy fields. Lattice strain were induced by the in‐situ heteroepitaxial growth of lattice‐mismatched RuO2 shell layer grows coherently on the stretchable Ru core. RuO2 activated by compressive strain simultaneously enhances the electrocatalytic OER, ORR, and HER. Zinc‐air batteries and water splitting devices assembled using the strained RuO2 as electrode electrocatalysts also shows excellent operation performance.