Prednisone combined with Dihydroartemisinin attenuates systemic lupus erythematosus by regulating M1/M2 balance through the MAPK signaling pathway.

OBJECTIVE: Dihydroartemisinin (DHA) plays a very important role in various diseases. However, the precise involvement of DHA in systemic lupus erythematosus (SLE), relation to the equilibrium between M1 and M2 cells, remains uncertain. Therefore, we aimed to investigate the role of DHA in SLE and its effect on the M1/M2 cells balance. METHODS: SLE mice model was established by pristane induction. Flow cytometry was employed to measure the abundance of M1 and M2 cells within the peripheral blood of individuals diagnosed with SLE. The concentrations of various cytokines, namely TNF-α, IL-1ß, IL-4, IL-6, and IL-10, within the serum of SLE patients or SLE mice were assessed via ELISA. Immunofluorescence staining was utilized to detect the deposition of IgG and complement C3 in renal tissues of the mice. We conducted immunohistochemistry analysis to assess the expression levels of Collagen-I, a collagen protein, and α-SMA, a fibrosis marker protein, in the renal tissues of mice. Hematoxylin-eosin staining, Masson's trichrome staining, and Periodic acid Schiff staining were used to examine histological alterations. In this study, we employed qPCR and western blot techniques to assess the expression levels of key molecular markers, namely CD80 and CD86 for M1 cells, as well as CD206 and Arg-1 for M2 cells, within kidney tissue. Additionally, we investigated the involvement of the MAPK signaling pathway. The Venny 2.1 online software tool was employed to identify shared drug-disease targets, and subsequently, the Cytoscape 3.9.2 software was utilized to construct the "disease-target-ingredient" network diagram. Protein-protein interactions of the target proteins were analyzed using the String database, and the network proteins underwent enrichment analysis for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. RESULTS: The results showed that an increase in M1 cells and a decrease in M2 cells within the peripheral blood of individuals diagnosed with SLE. Further analysis revealed that prednisone (PDN) combined with DHA can alleviate kidney damage and regulate the balance of M1 and M2 cells in both glomerular mesangial cells (GMC) and kidney. The MAPK signaling pathway was found to be involved in SLE kidney damage and M1/M2 balance in the kidney. Furthermore, PDN and/or DHA were found to inhibit the MAPK signaling pathway in GMC and kidney. CONCLUSION: We demonstrated that PDN combined with DHA attenuates SLE by regulating M1/M2 balance through MAPK signaling pathway. These findings propose that the combination of PDN and DHA could serve as a promising therapeutic strategy for SLE, as it has the potential to mitigate kidney damage and reinstate the equilibrium of M1 and M2 cells.

Artificial intelligence for geoscience: Progress, challenges, and perspectives.

This paper explores the evolution of geoscientific inquiry, tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence (AI) and data collection techniques. Traditional models, which are grounded in physical and numerical frameworks, provide robust explanations by explicitly reconstructing underlying physical processes. However, their limitations in comprehensively capturing Earth's complexities and uncertainties pose challenges in optimization and real-world applicability. In contrast, contemporary data-driven models, particularly those utilizing machine learning (ML) and deep learning (DL), leverage extensive geoscience data to glean insights without requiring exhaustive theoretical knowledge. ML techniques have shown promise in addressing Earth science-related questions. Nevertheless, challenges such as data scarcity, computational demands, data privacy concerns, and the "black-box" nature of AI models hinder their seamless integration into geoscience. The integration of physics-based and data-driven methodologies into hybrid models presents an alternative paradigm. These models, which incorporate domain knowledge to guide AI methodologies, demonstrate enhanced efficiency and performance with reduced training data requirements. This review provides a comprehensive overview of geoscientific research paradigms, emphasizing untapped opportunities at the intersection of advanced AI techniques and geoscience. It examines major methodologies, showcases advances in large-scale models, and discusses the challenges and prospects that will shape the future landscape of AI in geoscience. The paper outlines a dynamic field ripe with possibilities, poised to unlock new understandings of Earth's complexities and further advance geoscience exploration.

Technologies and perspectives for achieving carbon neutrality.

Global development has been heavily reliant on the overexploitation of natural resources since the Industrial Revolution. With the extensive use of fossil fuels, deforestation, and other forms of land-use change, anthropogenic activities have contributed to the ever-increasing concentrations of greenhouse gases (GHGs) in the atmosphere, causing global climate change. In response to the worsening global climate change, achieving carbon neutrality by 2050 is the most pressing task on the planet. To this end, it is of utmost importance and a significant challenge to reform the current production systems to reduce GHG emissions and promote the capture of CO2 from the atmosphere. Herein, we review innovative technologies that offer solutions achieving carbon (C) neutrality and sustainable development, including those for renewable energy production, food system transformation, waste valorization, C sink conservation, and C-negative manufacturing. The wealth of knowledge disseminated in this review could inspire the global community and drive the further development of innovative technologies to mitigate climate change and sustainably support human activities.

Estimation of the Total Atmospheric Water Vapor Content and Land Surface Temperature Based on AATSR Thermal Data.

The total atmospheric water vapor content (TAWV) and land surfacetemperature (LST) play important roles in meteorology, hydrology, ecology and some otherdisciplines. In this paper, the ENVISAT/AATSR (The Advanced Along-Track ScanningRadiometer) thermal data are used to estimate the TAWV and LST over the Loess Plateauin China by using a practical split window algorithm. The distribution of the TAWV isaccord with that of the MODIS TAWV products, which indicates that the estimation of thetotal atmospheric water vapor content is reliable. Validations of the LST by comparingwith the ground measurements indicate that the maximum absolute derivation, themaximum relative error and the average relative error is 4.0K, 11.8% and 5.0%respectively, which shows that the retrievals are believable; this algorithm can provide anew way to estimate the LST from AATSR data.