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The digital economy is an important engine to promote sustainable economic growth. Exploring the mechanism by which the digital economy promotes economic development, industrial upgrading and environmental improvement is an issue worth studying. This paper takes China as an example for study and uses the data of 286 cities from 2011 to 2019. In the empirical analysis, the direction distance function (DDF) and the Global Malmquist-Luenberger (GML) productivity index methods are used to measure the green total factor productivity (GTFP), while Tobit, quantile regression, impulse response function and intermediary effect models are used to study the relationship among digital economy development, industrial structure upgrading and GTFP. The results show that: (1) The digital economy can significantly improve China’s GTFP; however, there are clear regional differences. (2) The higher the GTFP, the greater the promotion effect of the digital economy on the city’s GTFP. (3) From a dynamic long-term perspective, the digital economy has indeed positively promoted China’s GTFP. (4) The upgrading of industrial structures is an intermediary transmission mechanism for the digital economy to promote GTFP. This paper provides a good reference for driving green economic growth and promoting the environment.

I nnovation and green are the directions to promote the circular economy and environmental sustainability at the corporate level. This paper examines the impact of environmental regulation (pollution charge) on green technology innovation and the mediating role of corporate environmental responsibility. Our results indicate that: (1) Environmental regulations stimulate manufacturing enterprises’ environmental responsibility and green technology innovation. It is worth noting that corporate environmental responsibility strengthens the relationship between environmental regulation and green technology innovation. (2) Further investigation reveals that R&D expenditure and environmental investment have greatly strengthened the positive effect of environmental regulation on green technology innovation. (3) With more detailed disclosure about enterprises’ environment-related information, the more outstanding stimulation effects of environmental regulation. Discussions on the features of enterprise location have revealed that, if the goal of environmental protection is set too high or if the fiscal decentralization is too strong, implementation of environmental regulation would not achieve desirable results. Accordingly, we need to optimize the collection of environmental taxes, strengthen the enterprises’ environmental responsibility, and increase investment in R&D and environment protection. Meanwhile, the execution of environmental regulation should also take into account the institutional environment and governance features of the enterprise locations.

Background Many countries around the world highlight the health in all policies (HiAP). However, most of the related research focused on the influential factors and implementation strategies, with less concern on the evaluation of HiAP. In response to HiAP's call, the Chinese government has proposed health promotion policies (HPPs) in counties or districts, the evaluation of HPPs in sample counties or districts of Sichuan province in China is an essential basis for optimizing policy content, improving policy implementation, and ensuring health promotion's continuous and efficient operation. Methods This paper established an evaluation system for HPPs based on the PMC-Index model and then quantitatively analyzed 37 representative HPPs from the pilot areas in Sichuan province. In addition, a team of experts conducted a field assessment. Results The results showed that the average PMC index of 37 HPPs was 7.091, and correlation analysis showed that there was a significant correlation between the PMC index and expert score. Conclusions This study indicates that the overall consistency of HPPs was good and proves a connection between the formulation and implementation of HPPs.

Cerium dioxide (CeO2) was pretreated with reduction and reoxidation under different conditions in order to elucidate the role of surface Ce4+ and oxygen vacancies in the catalytic activity for direct synthesis of dimethyl carbonate (DMC) from CO2 and methanol. The corresponding catalysts were comprehensively characterized using N2 physisorption, XRD, TEM, XPS, TPD, and CO2-FTIR. The results indicated that reduction treatment promotes the conversion of Ce4+ to Ce3+ and improves the concentration of surface oxygen vacancies, while reoxidation treatment facilitates the conversion of Ce3+ to Ce4+ and decreases the concentration of surface oxygen vacancies. The catalytic activity was linear with the number of moderate acidic/basic sites. The surface Ce4+ rather than oxygen vacancies, as Lewis acid sites, promoted the adsorption of CO2 and the formation of active bidentate carbonates. The number of moderate basic sites and the catalytic activity were positively correlated with the surface concentration of Ce4+ but negatively correlated with the surface concentration of oxygen vacancies. The surface Ce4+ and lattice oxygen were active Lewis acid and base sites respectively for CeO2 catalyst, while surface oxygen vacancy and lattice oxygen were active Lewis acid and base sites, respectively, for metal-doped CeO2 catalysts. This may result from the different natures of oxygen vacancies in CeO2 and metal-doped CeO2 catalysts.

Background Albuminuria is a hallmark of diabetic kidney disease (DKD) that promotes its progression, leading to renal fibrosis. Renal macrophage function is complex and influenced by macrophage metabolic status. However, the metabolic state of diabetic renal macrophages and the impact of albuminuria on the macrophage metabolic state are poorly understood. Methods Extracellular vesicles (EVs) from tubular epithelial cells (HK-2) were evaluated using transmission electron microscopy, nanoparticle tracking analysis and western blotting. Glycolytic enzyme expression in macrophages co-cultured with HSA-treated HK-2 cell-derived EVs was detected using RT-qPCR and western blotting. The potential role of EV-associated HIF-1α in the mediation of glycolysis was explored in HIF-1α siRNA pre-transfected macrophages co-cultured with HSA-treated HK-2 cell-derived EVs, and the extent of HIF-1α hydroxylation was measured using western blotting. Additionally, we injected db/db mice with EVs via the caudal vein twice a week for 4 weeks. Renal macrophages were isolated using CD11b microbeads, and immunohistofluorescence was applied to confirm the levels of glycolytic enzymes and HIF-1α in these macrophages. Results Glycolysis was activated in diabetic renal macrophages after co-culture with HSA-treated HK-2 cells. Moreover, HSA-treated HK-2 cell-derived EVs promoted macrophage glycolysis both in vivo and in vitro. Inhibition of glycolysis activation in macrophages using the glycolysis inhibitor 2-DG decreased the expression of both inflammatory and fibrotic genes. Mechanistically, EVs from HSA-stimulated HK-2 cells were found to accelerate macrophage glycolysis by stabilizing HIF-1α. We also found that several miRNAs and lncRNAs, which have been reported to stabilize HIF-1α expression, were increased in HSA-treated HK-2 cell-derived EVs. Conclusion Our study suggested that albuminuria induced renal macrophage glycolysis through tubular epithelial cell-derived EVs by stabilizing HIF-1α, indicating that regulation of macrophage glycolysis may offer a new treatment strategy for DKD patients, especially those with macroalbuminuria.

The goal of global carbon peak and neutrality gives an impetus to the utilization of clean energy (e.g., fuel cell) and carbon dioxide (CO2) at a large scale, where the oxygen reduction reaction (ORR) and CO2 reduction reaction (CO2RR) are the key reactions via the sustainable system, respectively. As a main precursor for fabricating affordable carbon‐based electrocatalysts with uniformly dispersed active centers and tailorable performances for ORR and CO2RR, metal organic frameworks (MOFs) have captured a surge of interest in recent years. Despite the facilitated development of MOF‐derived carbon‐based electrocatalysts by many investigations, it is still plagued by high overpotential and unsatisfied life span, which are greatly determined by the efficient and alterable confinement effect on synthesis and performance. In this review, firstly, the confined synthetic strategies (doping engineering, defect engineering, geometric engineering, etc.) of MOF‐derived carbon‐based electrocatalysts with multi‐sized active centers (atom, atomic clusters and nanoparticles (NPs)) are systematically summarized; secondly, the confinement effect on the interaction of ORR and CO2RR intermediates, as well as the catalytic durability and activity, was discussed from chemical and physical aspects. In the end, the review discusses the remaining challenges and emerging research topics in the future, including support upgradation and catalyst innovation, high selectivity and effective confinement synthesis, in situ and operando characterization techniques, theoretical investigation, and artificial intelligence (AI) assistant. The new understanding and insights into these aspects will guide the rational confinement concept of MOF‐derived carbon‐based electrocatalysts for ORR and CO2RR with optimized performances in terms of confinement engineering and are believed to be helpful for filling the existing gaps between scientific communities and practical use. The goal of global carbon peak and neutrality gives an impetus to the utilization of clean energy (e.g., fuel cell) and carbon dioxide (CO2) at a large scale, where the oxygen reduction reaction (ORR) and CO2 reduction reaction (CO2RR) are the key reactions via sustainable system, respectively. Metal organic frameworks (MOFs) have captured a surge of interest in recent years; however, it is still plagued by high overpotential and unsatisfied life span, which are greatly determined by the efficient and alterable confinement effect on synthesis and electrocatalytic performance. Herein, we reviewed the confinement effect on synthetic strategies and catalytic performance of MOF‐derived carbon‐based materials with diverse kinds of active centers, and reveal the structure–activity relationship of MOF‐derived carbon‐based materials when they are applied to ORR and CO2RR.

Graph neural networks (GNNs) have shown powerful capabilities in modeling and representing graph structural data across various graph learning tasks as an emerging deep learning approach. However, most existing GNNs focus on single-relational graphs and fail to fully utilize the rich and diverse relational information present in real-world graph data. In addition, deeper GNNs tend to suffer from overfitting and oversmoothing issues, leading to degraded model performance. To deeply excavate the multi-relational features in graph data and strengthen the modeling and representation abilities of GNNs, this paper proposes a multi-channel deep graph convolutional neural network method called DeepMCGCN. It constructs multiple relational subgraphs and adopts multiple GCN channels to learn the characteristics of different relational subgraphs separately. Cross-channel connections are utilized to obtain interactions between different relational subgraphs, which can learn node embeddings richer and more discriminative than single-channel GNNs. Meanwhile, it alleviates overfitting issues of deep models by optimizing convolution functions and adding residual connections between and within channels. The DeepMCGCN method is evaluated on three real-world datasets, and the experimental results show that its node classification performance outperforms that of single-channel GCN and other benchmark models, which improves the modeling and representation capabilities of the model.

Background Chronic pain (CP) and subacute pain (SAP) represent major public health challenges, frequently coexisting with sleep disorders (SD). However, the association between pain duration and SD remains poorly characterized. Methods: This cross‐sectional study analyzed data from the National Health and Nutrition Examination Survey (NHANES, 2009–2010). CP, SAP, and SD were assessed through structured interviews conducted by trained personnel using the Computer‐Assisted Personal Interviewing (CAPI) system. Multivariable logistic regression models were employed to evaluate the relationship between pain duration (CP vs. SAP) and SD, while subgroup analyses explored potential effect modifications by analgesic use and pain intensity. Results Among 1,109 participants, after adjusting for confounders, individuals with CP exhibited an 85% higher likelihood of SD compared to those with SAP (OR = 1.85, 95% CI: 1.39–2.47, p < 0.001). Pain that worsened during the day (OR = 1.95, 95% CI: 1.35–2.80, p < 0.001), persisted at rest (OR = 2.38, 95% CI: 1.65–3.43, p < 0.001), failed to be alleviated by exercise (OR = 1.59, 95% CI: 1.20–2.11, p = 0.001), and awakened from (OR = 2.70, 95% CI: 2.06–3.55, p < 0.001) was significantly associated with SD. Subgroup analyses revealed no significant interaction effects of analgesic use or pain intensity on the CP/SAP‐SD association (p for interaction > 0.05). Conclusion These findings suggest that pain (both subacute and chronic) may be an independent risk factor for SD, supporting the need for early intervention for SAP. These findings suggest that pain (both subacute and chronic) may be an independent risk factor for SD, supporting the need for early intervention for SAP.

ObjectiveSETD2, the sole histone H3K36 trimethyltransferase, is frequently mutated or deleted in human cancer, including pancreatic ductal adenocarcinoma (PDAC). However, whether SETD2/H3K36me3 alteration results in PDAC remains largely unknown.DesignTCGA(PAAD) public database and PDAC tissue array with SETD2/H3K36me3 staining were used to investigate the clinical relevance of SETD2 in PDAC. Furthermore, to define the role of SETD2 in the carcinogenesis of PDAC, we crossed conditional Setd2 knockout mice (Pdx cre Setd2 flox/flox) together with Kras G12D mice. Moreover, to examine the role of SETD2 after ductal metaplasia, Crisp/cas9 was used to deplete Setd2 in PDAC cells. RNA-seq and H3K36me3 ChIP-seq were performed to uncover the mechanism.ResultsSETD2 mutant/low expression was correlated with poor prognosis in patients with PDAC. Next, we found that Setd2 acted as a putative tumour suppressor in Kras-driven pancreatic carcinogenesis. Mechanistically, Setd2 loss in acinar cells facilitated Kras-induced acinar-to-ductal reprogramming, mainly through epigenetic dysregulation of Fbxw7. Moreover, Setd2 ablation in pancreatic cancer cells enhanced epithelia–mesenchymal transition (EMT) through impaired epigenetic regulation of Ctnna1. In addition, Setd2 deficiency led to sustained Akt activation via inherent extracellular matrix (ECM) production, which would favour their metastasis.ConclusionTogether, our findings highlight the function of SETD2 during pancreatic carcinogenesis, which would advance our understanding of epigenetic dysregulation in PDAC. Moreover, it may also pave the way for development of targeted, patients-tailored therapies for PDAC patients with SETD2 deficiency.

Background Neutrophil extracellular traps (NETs) and oxidative stress (OS) may be involved in sepsis-associated acute kidney injury (SA-AKI). The aim of this study was to identify potential regulators which modulate NETs and OS in SA-AKI, and to find potential therapeutic agents. Methods and Materials SA-AKI-related datasets GSE255281 and GSE225192 were downloaded from Gene Expression Omnibus. Molecular subtypes associated with NETs were identified by unsupervised clustering. The OS-related genes were obtained by weighted gene co-expression network analysis. Differentially expressed genes were screened by “limma” package in R. Least absolute shrinkage and selection operator algorithm was applied to identify the hub genes. Additionally, the biological functions of the hub genes were analyzed with single sample gene set enrichment analysis. NetworkAnalyst database was searched to screen the drugs targeting the hub targets. qRT-PCR was used to analyze the expression of key genes in the peripheral blood mononuclear cells (PBMCs) of the patients with SA-AKI and healthy controls. HK-2 cells and human umbilical vein endothelial cells (HUVECs) were induced by lipopolysaccharide (LPS) to construct a SA-AKI model, and the effects of estradiol and (+)-JQ1 on HK-2 cells and HUVECs were evaluated by CCK-8 assays, flow cytometry and OS indices. Results Based on NETs-related genes, SA-AKI samples could be divide into two subgroups, and the differentially expressed genes between two subgroups were associated with OS. In silico analyses identified 13 hub targets. The expression of ECT2 and CHRDL1 in PBMCs of SA-AKI patients was significantly lower than that in control group, and the expressions of PTAFR, CSF3 and FOS were significantly higher. Estradiol and (+)-JQ1, which targeted more of the hub targets with good binding affinity, could increase the viability of HK-2 cells and HUVECs induced by LPS and inhibit apoptosis and OS. Conclusion Formation of NETs, contributes to OS and pathogenesis of SA-AKI. Estradiol and (+)-JQ1, targeting multiple regulators in the formation of NETs, may be potential therapeutic agents for the treatment of SA-AKI. Clinical trial number Not applicable.