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Programmed death receptor-1 (PD-1) blockade have achieved some efficacy but only in a fraction of patients with hepatocellular carcinoma (HCC). Programmed cell death 1 ligand 1 (PD-L1) binds to its receptor PD1 on T cells to dampen antigen-tumor immune responses. However, the mechanisms underlying PD-L1 regulation are not fully elucidated. Herein, we identify that tumoral Prdm1 overexpression inhibits cell growth in immune-deficient mouse models. Further, tumoral Prdm1 overexpression upregulates PD-L1 levels, dampening anti-tumor immunity in vivo, and neutralizes the anti-tumor efficacy of Prdm1 overexpression in immune-competent mouse models. Mechanistically, PRDM1 enhances USP22 transcription, thus reducing SPI1 protein degradation through deubiquitination, which enhances PD-L1 transcription. Functionally, PD-1 mAb treatment reinforces the efficacy of Prdm1 -overexpressing HCC immune-competent mouse models. Collectively, we demonstrate that the PRDM1-USP22-SPI1 axis regulates PD-L1 levels, resulting in infiltrated CD8 + T cell exhaustion. Furthermore, PRDM1 overexpression combined with PD-(L)1 mAb treatment provides a therapeutic strategy for HCC treatment. Members of the PRDI-BF1 and RIZ homology domain (PRDM) family have been involved in the regulation of several pathological conditions, including cancer. Here the authors show that PRDM1/BLIMP1 promotes immune evasion by regulating PD-L1 expression in hepatocellular carcinoma cells.

At present, the treatment of esophageal cancer (EC) is mainly surgical and drug treatment. However, due to drug resistance, these therapies can not effectively improve the prognosis of patients with the EC. Therefore, a multigene prognostic risk scoring system was constructed by bioinformatics analysis method to provide a theoretical basis for the prognosis and treatment decision of EC. The gene expression profiles and clinical data of esophageal cancer patients were gathered from the Cancer Genome Atlas TCGA database, and the differentially expressed genes (DEGs) were screened by R software. Genes with prognostic value were screened by Kaplan Meier analysis, followed by functional enrichment analysis. A cox regression model was used to construct the prognostic risk score model of DEGs. ROC curve and survival curve were utilized to evaluate the performance of the model. Univariate and multivariate Cox regression analysis was used to evaluate whether the model has an independent prognostic value. Network tool mirdip was used to find miRNAs that may regulate risk genes, and Cytoscape software was used to construct gene miRNA regulatory network. GSCA platform is used to analyze the relationship between gene expression and drug sensitivity. 41 DEGs related to prognosis were pre-liminarily screened by survival analysis. A prognostic risk scoring model composed of 8 DEGs (APOA2, COX6A2, CLCNKB, BHLHA15, HIST1H1E, FABP3, UBE2C and ERO1B) was built by Cox regression analysis. In this model, the prognosis of the high-risk score group was poor ( P  < 0.001). The ROC curve showed that (AUC = 0.862) the model had a good performance in predicting prognosis. In Cox regression analysis, the comprehensive risk score can be employed as an independent prognostic factor of the EC. HIST1H1E, UBE2C and ERO1B interacted with differentially expressed miRNAs. High expression of HIST1H1E was resistant to trametinib, selumetinib, RDEA119, docetaxel and 17-AAG, High expression of UBE2C was resistant to masitinib, and Low expression of ERO1B made the EC more sensitive to FK866. We constructed an EC risk score model composed of 8 DEGs and gene resistance analysis, which can provide reference for prognosis prediction, diagnosis and treatment of the EC patients.

The Yulong porphyry copper belt in eastern Tibet is located in the middle of Tethys–Himalayan metallogenic mega-province, which is one of the three major porphyry copper metallogenic mega-provinces. The Yulong copper belt belongs to the super porphyry copper belt and represents one of the most important copper mineralization prospecting areas in China. A significant quantity of research data shows that this study area belongs to the environment of intracontinental collision and compression, with a complex geological structure, magmatic rock development and excellent metallogenic geological background. However, because this area is located in an alpine and high-altitude area, it is difficult to carry out any traditional field geological surveys, and the existing studies of both prospecting and prediction are relatively weak. This study focused on information extraction for alteration minerals in the Yulong metallogenic belt and its surroundings based on multispectral data and hyperspectral data, establishing a spectral library of alteration minerals in this area. Based on Sentinel-1A radar data and Landsat-8 OLI color synthesis data, the linear structure of the study area was interpreted. On this basis, the information extraction results relating to alteration minerals obtained from multi-source remote sensing data, linear structure interpretation results and the geochemical exploration data of the study area were superimposed to comprehensively analyze the metallogenic geological conditions and mineralization characteristics in the area, establish remote sensing prospecting indicators there and optimize the potential areas for prospecting, providing technical support for the next step of prospecting and exploration in the area.

Amidst growing skepticism towards globalization and rising digital trade, this study investigates the impact of Restrictions on Cross-Border Data Flows (RCDF) on Domestic Value Chains (DVCs) stability. As global value chains participation declines, the stability of DVCs—integral to internal economic dynamics—becomes crucial. This study situates within a framework exploring the role of innovation and RCDF in the increasingly interconnected global trade. Using a panel data fixed effect model, our analysis provides insights into the varying effects of RCDF on DVCs stability across countries with diverse economic structures and technological advancement levels. This approach allows for a nuanced understanding of the interplay between digital trade policies, value chain stability, and innovation. RCDF tend to disrupt DVCs by negatively impacting innovation, which necessitates proactive policy measures to mitigate these effects. In contrast, low-income countries experience a less detrimental impact; RCDF may even aid in integrating their DVCs into Global Value Chains, enhancing economic stability. It underscores the need for dynamic, adaptable policies and global collaboration to harmonize digital trade standards, thus offering guidance for policy-making in the context of an interconnected global economy.

Strategic emerging minerals such as lithium, beryllium, niobium and tantalum are the most important rare metals currently, especially with the increasing demand of emerging industries on rare metals in China. The Jiajika deposit with a complete Li-Be-Nb-Ta metallogenic series is the largest pegmatite type rare metal deposit in China at present. In this paper, systematic researches of geochronology and petrogeochemistry were carried out to understand the genetic relationships between mine- ralization and magma evolution in the Jiajika deposit, which might be helpful to further rare-element prospecting in Songpan-Garze area. Zircon LA-ICP-MS U-Pb dating yields a concordia age of 217±1.1 Ma and a weighted mean 206 Pb/ 238 U age of 217±0.84 Ma for the aplite from the No. 308 pegmatite. Cassiterite LA-MC-ICPMS dating yields concordant ages of 211±4.6 Ma for the No. 308 pegmatite vein and 198±4.4 Ma for the No. 133 pegmatite vein, indicating that the rare metal mineralization mainly occurred in the Late Indosinian Period, further suggesting that the granites, aplites and pegmatites in Jiajika formed during a relatively stable stage after the intense orogeny of the Indosinian cycle. The rare metal-bearing granitic rocks and pegmatites show a clear linear relationship between A/CNK and A/NK and are enriched in total alkalis and depleted in CaO, FeO, MnO, MgO, Ba and Sr. All barren rocks and mineralized rocks feature similar rare earth element and trace element geochemical patterns. Thus, these characteristics indicate that the aplites and pegmatites represent the highly differentiated products of the two-mica granite (MaG) in this area, which is the most likely parent magma. During the evolution of magma, strong alkali metasomatism occurred between the melt phase and the volatile-rich fluid phase; as a result, large-scale rare metal mineralization occurred in certain structural zones of the pegmatite veins in the Jiajika deposit.

The combination of coal precise mining and information technology in the new century is one of the important directions for the future development of coal mining. Taking the fully mechanized top coal caving condition of a thick coal seam in the 90,101 working face of Baoshan Yujing Coal Mine in Shanyin City, Shanxi Province as an example, the intelligent identification method of section coal pillar stability was studied. The load transfer law of overlying strata in the upper part of coal pillar was analyzed, and the coal pillar values of each index were obtained by using an empirical formula, mean impact value-genetic algorithm-BP neural network (MIV-GA-BP) simulation experiment, and finite difference algorithm. The Delphi index evaluation system was used to calculate the optimal value of the coal pillar. The results showed that the non-contact cantilevered triangle on the two wings of the coal pillar in the goaf reduced the stress on the coal pillar; according to the width of the coal pillar at 10 m, 14 m, 16 m, and 20 m, combined with the relationship between the plastic zone and the core zone of coal pillar, and the relationship between the stress field and the ultimate strength of coal pillar, the numerical simulation value of the coal pillar was determined. The MIV (mean impact value) characteristics screened out the influencing factors of coal pillar width in the section near the horizontal fully mechanized top coal caving face order of importance; the relative error between the predicted value and the expected value of the MIV-GA-BP simulation experiment was less than 5%, which has good stability for the multi-factor nonlinear coupling prediction problem; and the optimal value of the coal pillar was 16.03 m by the intelligent identification method of the coal pillar. When the 16 m pillar was used, the surrounding rock deformation of the roadway was small, and the control effect was good. The research results provide a theoretical basis and reference for the parameter determination of similar projects.

Leaching mining of ion-adsorption rare earths degrades soil organic matter and hampers vegetation recovery. High-resolution UAV remote sensing enables large-scale monitoring of reclamation, yet vegetation detection accuracy is constrained by key challenges. Conventional three-channel detection struggles with terrain complexity, illumination variation, and shadow effects. Fixed UAV altitude and missing topographic data further cause resolution inconsistencies, posing major challenges for accurate vegetation detection in reclaimed land. To enhance multi-spectral vegetation detection, the model input is expanded from the traditional three channels to six channels, enabling full utilization of multi-spectral information. Furthermore, the Channel Attention and Global Pooling SPPF (CAGP-SPPF) module is introduced for multi-scale feature extraction, integrating global pooling and channel attention to capture multi-channel semantic information. In addition, the C2f_DynamicConv module replaces conventional convolutions in the neck network to strengthen high-dimensional feature transmission and reduce information loss, thereby improving detection accuracy. On the self-constructed reclaimed vegetation dataset, MRV-YOLO outperformed YOLOv8, with mAP@0.5 and mAP@0.5:0.95 increasing by 4.6% and 10.8%, respectively. Compared with RT-DETR, YOLOv3, YOLOv5, YOLOv6, YOLOv7, yolov7-tiny, YOLOv8-AS, YOLOv10, and YOLOv11, mAP@0.5 improved by 6.8%, 9.7%, 5.3%, 6.5%, 6.4%, 8.9%, 4.6%, 2.1%, and 5.4%, respectively. The results demonstrate that multichannel inputs incorporating near-infrared and dual red-edge bands significantly enhance detection accuracy for reclaimed vegetation in rare earth mining areas, providing technical support for ecological restoration monitoring.

Background Phosphatase and tensin homolog deleted on chromosome 10 (PTEN) signaling has been known to play a critical role in maintaining cellular and tissue homeostasis, which also has an essential role in the inflammatory response. However, it remains unidentified whether and how the macrophage PTEN may govern the innate immune signaling stimulator of interferon genes (STING) mediated inflammation and hepatocyte necroptosis in APAP-induced liver injury (AILI). Methods Myeloid-specific PTEN knockout (PTEN M−KO ) and floxed PTEN (PTEN FL/FL ) mice were treated with APAP (400 mg/kg) or PBS. In a parallel in vitro study, bone marrow-derived macrophages (BMMs) were isolated from these conditional knockout mice and transfected with CRISPR/Cas9-mediated Notch1 knockout (KO) or CRISPR/Cas9-mediated STING activation vector followed by LPS (100 ng/ml) stimulation. Results Here, we report that myeloid-specific PTEN knockout (PTEN M−KO ) mice were resistant to oxidative stress-induced hepatocellular injury with reduced macrophage/neutrophil accumulation and proinflammatory mediators in AILI. PTEN M−KO increased the interaction of nuclear Notch intracellular domain (NICD) and nuclear factor (erythroid-derived 2)-like 2 (NRF2) in the macrophage nucleus, reducing reactive oxygen species (ROS) generation. Mechanistically, it is worth noting that macrophage NICD and NRF2 co-localize within the nucleus under inflammatory conditions. Additionally, Notch1 promotes the interaction of immunoglobulin kappa J region (RBPjκ) with NRF2. Disruption of the Notch1 signal in PTEN deletion macrophages, reduced RBPjκ and NRF2 binding, and activated STING signaling. Moreover, PTEN M−KO macrophages with STING activated led to ROS generation and TNF-α release, resulting in hepatocyte necroptosis upon co-culture with primary hepatocytes. Conclusions Our findings demonstrate that the macrophage PTEN-NICD/NRF2-STING axis is critical to regulating oxidative stress-induced liver inflammation and necroptosis in AILI and implies the therapeutic potential for managing sterile liver inflammation. -_SzJnLNvYmjVKnhJWJTYK Video Abstract Graphical Abstract

Background Oral cancer (OC) is a common and dangerous malignant tumor with a low survival rate. However, the micro level mechanism has not been explained in detail. Methods Gene and miRNA expression micro array data were extracted from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) and miRNAs (DE miRNAs) were identified by R software. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of genes and genomes (KEGG) pathway analysis were used to assess the potential molecular mechanisms of DEGs. Cytoscape software was utilized to construct protein–protein interaction (PPI) network and miRNA-gene network. Central genes were screened out with the participation of gene degree, molecular complex detection (MCODE) plugin, and miRNA-gene network. Then, the identified genes were checked by The Cancer Genome Atlas (TCGA) gene expression profile, Kaplan-Meier data, Oncomine, and the Human Protein Atlas database. Receiver operating characteristic (ROC) curve was drawn to predict the diagnostic efficiency of crucial gene level in normal and tumor tissues. Univariate and multivariate Cox regression were used to analyze the effect of dominant genes and clinical characteristics on the overall survival rate of OC patients. Results Gene expression data of gene expression profiling chip(GSE9844, GSE30784, and GSE74530) were obtained from GEO database, including 199 tumor and 63 non-tumor samples. We identified 298 gene mutations, including 200 upregulated and 98 downregulated genes. GO functional annotation analysis showed that DEGs were enriched in extracellular structure and extracellular matrix containing collagen. In addition, KEGG pathway enrichment analysis demonstrated that the DEGs were significantly enriched in IL-17 signaling pathway and PI3K-Akt signaling pathway. Then, we detected three most relevant modules in PPI network. Central genes (CXCL8, DDX60, EIF2AK2, GBP1, IFI44, IFI44L, IFIT1, IL6, MMP9,CXCL1, CCL20, RSAD2, and RTP4) were screened out with the participation of MCODE plugin, gene degree, and miRNA-gene network. TCGA gene expression profile and Kaplan-Meier analysis showed that high expression of CXCL8, DDX60, IL6, and RTP4 was associated with poor prognosis in OC patients, while patients with high expression of IFI44L and RSAD2 had a better prognosis. The elevated expression of CXCL8, DDX60, IFI44L, RSAD2, and RTP44 in OC was verified by using Oncomine database. ROC curve showed that the mRNA levels of these five genes had a helpful diagnostic effect on tumor tissue. The Human Protein Atlas database showed that the protein expressions of DDX60, IFI44L, RSAD2, and RTP44 in tumor tissues were higher than those in normal tissues. Finally, univariate and multivariate Cox regression showed that DDX60, IFI44L, RSAD2, and RTP44 were independent prognostic indicators of OC. Conclusion This study revealed the potential biomarkers and relevant pathways of OC from publicly available GEO database, and provided a theoretical basis for elucidating the diagnosis, treatment, and prognosis of OC.

The study aims to explore the spatial association network characteristics of biased green innovation in the manufacturing sector and its core drivers. This study constructs a Malmquist-Luenberger decomposition index model to identify the input and output biases of green technological innovation (GIIM and GIOM) in the manufacturing industry. This study uses a modified gravity model and social network analysis method to conduct a robust assessment of GIIM spatial association network of 30 provinces in China from 2012 to 2021. The results show: (1) The GIIM association network structure is stable and has good accessibility, with close connections between provinces and blocks, and significant spillover effects between provinces. (2) The regional network shows a \"core-periphery\" spatial variation, with the core area expanding and the peripheral area shrinking. (3) The digital transformation characteristics of the network components and the intensity of environmental regulation have a significant impact on GIIM.