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Our current understanding of how sugar metabolism affects inflammatory pathways in macrophages is incomplete. Here, we show that glycogen metabolism is an important event that controls macrophage-mediated inflammatory responses. IFN-γ/LPS treatment stimulates macrophages to synthesize glycogen, which is then channeled through glycogenolysis to generate G6P and further through the pentose phosphate pathway to yield abundant NADPH, ensuring high levels of reduced glutathione for inflammatory macrophage survival. Meanwhile, glycogen metabolism also increases UDPG levels and the receptor P2Y 14 in macrophages. The UDPG/P2Y 14 signaling pathway not only upregulates the expression of STAT1 via activating RARβ but also promotes STAT1 phosphorylation by downregulating phosphatase TC45. Blockade of this glycogen metabolic pathway disrupts acute inflammatory responses in multiple mouse models. Glycogen metabolism also regulates inflammatory responses in patients with sepsis. These findings show that glycogen metabolism in macrophages is an important regulator and indicate strategies that might be used to treat acute inflammatory diseases. Glycogen can be metabolized via glycogenolysis and the pentose phosphate pathway as well as into the production of UDP glucose, which when secreted can bind the P2Y 14 receptor. Here the authors show how these glycogen metabolism pathways contribute to proinflammatory macrophage activation and susceptibility to sepsis.

An imbalance in the differentiation potential of bone marrow mesenchymal stem cells (BMSCs) is an important pathogenic mechanism underlying osteoporosis (OP). N6-methyladenosine (m 6 A) is the most common post-transcriptional modification in eukaryotic cells. The role of the Wilms’ tumor 1-associated protein (WTAP), a member of the m 6 A functional protein family, in regulating BMSCs differentiation remains unknown. We used patient-derived and mouse model-derived samples, qRT-PCR, western blot assays, ALP activity assay, ALP, and Alizarin Red staining to determine the changes in mRNA and protein levels of genes and proteins associated with BMSCs differentiation. Histological analysis and micro-CT were used to evaluate developmental changes in the bone. The results determined that WTAP promoted osteogenic differentiation and inhibited adipogenic differentiation of BMSCs. We used co-immunoprecipitation (co-IP), RNA immunoprecipitation (RIP), methylated RNA immunoprecipitation (MeRIP), RNA pulldown, and dual-luciferase assay to explore the direct mechanism. Mechanistically, the expression of WTAP increased during osteogenic differentiation and significantly promoted pri-miR-181a and pri-miR-181c methylation, which was recognized by YTHDC1, and increased the maturation to miR-181a and miR-181c. MiR-181a and miR-181c inhibited the mRNA expression of SFRP1, promoting the osteogenic differentiation of BMSCs. Our results demonstrated that the WTAP/YTHDC1/miR-181a and miR-181c/SFRP1 axis regulated the differentiation fate of BMSCs, suggesting that it might be a potential therapeutic target for osteoporosis.

Heavy metals are highly nephrotoxic and may increase the risk of chronic kidney disease. However, the potential roles of inflammatory responses and dietary patterns in the relationship between heavy metals and kidney disease remain underexplored. This study examines the heavy metal exposure-inflammatory response-kidney function decline pathway and the modulating role of dietary patterns in heavy metal-induced kidney damage. Results from generalized linear models, restricted cubic spline models, and weighted quantile regression models show that exposure to individual heavy metals in blood and urine (except urinary mercury) is associated with a decrease in estimated glomerular filtration rate (eGFR) and an increase in urine albumin/creatinine ratio (UACR) and Systemic Inflammatory Response Index (SIRI) (except for blood mercury, urinary lead, and urinary mercury). Among blood heavy metals, lead (0.502) and cadmium (0.640) have the greatest impact on eGFR and UACR, respectively. Among urinary heavy metals, cadmium (0.463) and lead (0.902) have the greatest impact on eGFR and UACR, respectively. Moreover, exposure to multiple heavy metals in blood is linked to lower eGFR and higher UACR and SIRI, while exposure to multiple heavy metals in urine is associated with decreased eGFR, UACR, and SIRI. SIRI and Dietary Inflammatory Index (DII) are significantly associated with decreased eGFR and increased UACR, whereas Healthy Eating Index 2020 (HEI2020) is significantly associated with increased eGFR and decreased UACR. Mediation analysis indicates that individual or combined exposure to blood or urinary heavy metals affects kidney function via SIRI, with stronger mediation effects for individual heavy metal exposure than for combined exposure. Moderation analysis shows that DII exacerbates the nephrotoxicity of blood or urinary heavy metals, while HEI2020 has an ameliorating effect.

The emergence of artificial intelligence as a transformative force in the field of information technology has exerted a significant impact on the development of renewable energy. In-depth analysis of the impact of AI on renewable energy development is crucial for promoting energy transition and facilitating sustainable development. This research utilizes a dataset comprising 30 provincial panels spanning from 2010 to 2023. This study found that AI technology can promote renewable energy development, a conclusion that still holds after robustness and endogeneity tests. An examination of the mechanism reveals that AI technology facilitates the advancement of renewable energy through the enhancement of trade openness and the concentration of manufacturing activities. The analysis of the moderating effect indicates that environmental regulation and environmental protection expenditures positively moderated the relationship between AI technology and renewable energy development and climate policy uncertainty negatively moderated the relationship between AI technology and renewable energy development. Further analysis revealed that AI technology has the potential to substantially improve the development of local renewable energy resources while also facilitating the advancement of renewable energy in adjacent areas, exhibiting spatial spillover effects. This study verifies the positive effects of AI technology on renewable energy development and enriches existing research perspectives in the field of energy economics.

Originating from “rocking‐chair concept”, lithium‐ion batteries (LIBs) have become one of the most important electrochemical energy storage technologies, which have largely impacted our daily life. The utilization of electrolyte additives in small quantities (≤5% by wt or vol) has been long viewed as an economical and efficient approach to regulate the properties of electrolyte and electrode–electrolyte interphases and consequently improve the cycling performance of LIBs. Among all the kinds of electrolyte additives, sulfur‐containing compounds have gained significant attention due to their unique features in building stable electrode–electrolyte interphases and protect battery cells from overcharging. In this work, advances and progresses of sulfur‐containing additives used in LIBs are overviewed, with special attention paid to the working mechanisms of these electrolyte additives. Particularly, four representative sulfur‐containing compounds (i.e., 1,3‐propane sultone, prop‐1‐ene‐1,3‐sultone, 1,3,2‐dioxathiolane‐2,2‐dioxide, and ethylene sulfite) are comparatively discussed concerning their impact on electrode–electrolyte interphases and cell performances. Future work on the development of sulfur‐containing compounds as functional electrolyte additives is also provided. The present review is anticipated to be not only a base document to access the status quo in this research domain but also a guideline to select specialized additives and electrolytes for practical applications. The review presents the advances and progresses in implementing sulfur‐containing compounds as electrolyte additives for lithium‐ion batteries, aiming to access the status quo in this intriguing research domain and provide a useful guideline to design functional additives for battery applications.

Amorphous molybdenum sulfide (MoS x ) has been regarded as a promising hydrogen evolution reaction (HER) catalyst due to its mild preparation conditions and low-cost precursor materials. In this work, we report a simple strategy combining the growth of amorphous MoS x on the surface of metal organic frameworks (ZIF-67) and annealing treatment to prepare Co-doped MoS x nanopolyhedrons (denoted as CoMoS x NPs). The CoMoS x NPs exhibit excellent HER activity in acid condition with an overpotential of 188 mV at a current density of 10 mA cm −2 (η 10 ), and a relatively stable overpotential after 2000 cyclic voltammetry (CV) cycles testing. The excellent HER performance of the CoMoS x NPs can be attributed to the doping of Co element adjust the electronic structure and increase the conductivity of catalyst, and the nanopolyhedrons structure which can expose more active sites for HER electrocatalytic. This study offers a low-cost and simple strategy to prepare high-activity HER catalyst, which holds great promises in developing advanced electrocatalysts for energy storage.

Background Macrophages play a crucial role in the development of cardiac fibrosis (CF). Although our previous studies have shown that glycogen metabolism plays an important role in macrophage inflammatory phenotype, the role and mechanism of modifying macrophage phenotype by regulating glycogen metabolism and thereby improving CF have not been reported. Methods Here, we took glycogen synthetase kinase 3β (GSK3β) as the target and used its inhibitor NaW to enhance macrophage glycogen metabolism, transform M2 phenotype into anti-fibrotic M1 phenotype, inhibit fibroblast activation into myofibroblasts, and ultimately achieve the purpose of CF treatment. Results NaW increases the pH of macrophage lysosome through transmembrane protein 175 (TMEM175) and caused the release of Ca 2+ through the lysosomal Ca 2+ channel mucolipin-2 (Mcoln2). At the same time, the released Ca 2+  activates TFEB, which promotes glucose uptake by M2 and further enhances glycogen metabolism. NaW transforms the M2 phenotype into the anti-fibrotic M1 phenotype, inhibits fibroblasts from activating myofibroblasts, and ultimately achieves the purpose of treating CF. Conclusion Our data indicate the possibility of modifying macrophage phenotype by regulating macrophage glycogen metabolism, suggesting a potential macrophage-based immunotherapy against CF. Graphical Abstract

Light olefins, as important bulk raw materials in the petrochemical industry, play an irreplaceable role in the development of the manufacturing industry and the economy. The epoxides of light olefins are important intermediates for the synthesis of polymers, drugs, and fine chemicals, and their green, efficient, and safe synthesis has attracted much attention. This review focuses on the research progress of light olefin epoxidation and elucidates traditional epoxidation methods, such as the chlorohydrin method. Although these processes have mature processes, they have drawbacks, including equipment corrosion, environmental pollution, poor safety, and high waste emissions. Special emphasis is placed on catalytic epoxidation systems using oxygen or organic peroxides as oxygen sources. For homogeneous catalytic systems, certain metal complexes exhibit high activity and selectivity yet are difficult to separate and recycle. Moreover, heterogeneous catalytic systems have become a research hotspot due to their advantages of easy separation and reusability, with supported metal catalysts being a prime example. Meanwhile, the effects of reaction temperature, pressure, solvent, etc., on epoxidation are explored. The specific reaction mechanisms are also studied and analyzed. Current research challenges, including enhancing catalyst stability and reducing costs, are summarized. In the future, developing highly efficient, green, and economically viable epoxidation technologies for large-scale industrial applications represents an important research direction in this field.

Breast cancer (BC) is now the most common type of cancer in women. Disulfidptosis is a new regulation of cell death (RCD). RCD dysregulation is causally linked to cancer. However, the comprehensive relationship between disulfidptosis and BC remains unknown. This study aimed to explore the predictive value of disulfidptosis-related genes (DRGs) in BC and their relationship with the TME. This study obtained 11 disulfidptosis genes (DGs) from previous research by Gan et al. RNA sequencing data of BC were downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database (GEO) databases. First, we examined the effect of DG gene mutations and copy number changes on the overall survival of breast cancer samples. We then used the expression profile data of 11 DGs and survival data for consensus clustering, and BC patients were divided into two clusters. Survival analysis, gene set variation analysis (GSVA) and ss GSEA were used to compare the differences between them. Subsequently, DRGs were identified between the clusters used to perform Cox regression and least absolute shrinkage and selection operator regression (LASSO) analyses to construct a prognosis model. Finally, the immune cell infiltration pattern, immunotherapy response, and drug sensitivity of the two subtypes were analyzed. CCK-8 and a colony assay obtained by knocking down genes and gene sequencing were used to validate the model. Two DG clusters were identified based on the expression of 11DGs. Then, 225 DRGs were identified between them. RS, composed of six genes, showed a significant relationship with survival, immune cell infiltration, clinical characteristics, immune checkpoints, immunotherapy response, and drug sensitivity. Low-RS shows a better prognosis and higher immunotherapy response than high-RS. A nomogram with perfect stability constructed using signature and clinical characteristics can predict the survival of each patient. CCK-8 and colony assay obtained by knocking down genes have demonstrated that the knockdown of high-risk genes in the RS model significantly inhibited cell proliferation. This study elucidates the potential relationship between disulfidptosis-related genes and breast cancer and provides new guidance for treating breast cancer.

Highlights The latest advancements in the application of machine learning (ML) for the screening of solid-state battery materials are reviewed. The achievements of various ML algorithms in predicting different performances of the battery management system are discussed. Future challenges and perspectives of artificial intelligence in solid-state battery are discussed. Solid-state batteries are widely recognized as the next-generation energy storage devices with high specific energy, high safety, and high environmental adaptability. However, the research and development of solid-state batteries are resource-intensive and time-consuming due to their complex chemical environment, rendering performance prediction arduous and delaying large-scale industrialization. Artificial intelligence serves as an accelerator for solid-state battery development by enabling efficient material screening and performance prediction. This review will systematically examine how the latest progress in using machine learning (ML) algorithms can be used to mine extensive material databases and accelerate the discovery of high-performance cathode, anode, and electrolyte materials suitable for solid-state batteries. Furthermore, the use of ML technology to accurately estimate and predict key performance indicators in the solid-state battery management system will be discussed, among which are state of charge, state of health, remaining useful life, and battery capacity. Finally, we will summarize the main challenges encountered in the current research, such as data quality issues and poor code portability, and propose possible solutions and development paths. These will provide clear guidance for future research and technological reiteration.