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Abstract Nuclear regulation has potential in cancer therapy, with the nuclear pore complex (NPC) serving as a critical channel between the nucleus and cytoplasm, playing a role in regulating various biological processes and cancer. DNA methylation, an epigenetic modification mediated by DNA methyltransferases (DNMTs), influences gene expression and cell differentiation, and is crucial for the development and progression of tumor cells. Gliomas are the most common primary brain tumors, with glioblastoma being particularly aggressive, characterized by invasiveness, migration capability, and resistance to conventional treatments, resulting in poor prognosis. Our study revealed that the expression level of NUP37 affects the proliferation and invasion of glioma cells, and that the overexpression of DNMT1 can alleviate the adverse effects caused by NUP37 depletion. These findings suggest that NUP37 promotes the proliferation and invasion of glioma cells through its interaction with DNMT1.

Abstract CD8+T cells secreting granzyme A (GZMA) can induce pyroptosis in tumor cells by effectively cleaving gasdermin B (GSDMB), which is stimulated by interferon-γ (IFN-γ). However, the interaction between GZMA-expressing CD8+T cells and GSDMB-expressing tumor cells in colon cancer remains poorly understood. Our research employed multi-color immunohistochemistry (mIHC) staining and integrated clinical data to explore the spatial distribution and clinical relevance of GZMA- and IFN-γ-expressing CD8+ tumor-infiltrating lymphocytes (TILs), as well as GSDMB-expressing CK+ cells, within the tumor microenvironment (TME) of human colon cancer samples. Additionally, we utilizing single-cell RNA sequencing (scRNA-seq) data to examine the functional dynamics and interactions among these cell populations. scRNA-seq analysis of colorectal cancer (CRC) tissues revealed that CD8+TILs co-expressed GZMA and IFN-γ, but not other cell types. Our mIHC staining results indicated that a significant reduction in the infiltration of GZMA+IFN-γ+CD8+TILs in colon cancer patients (P

Abstract Ulcerative colitis (UC) is a persistent inflammatory condition that specifically targets the colon and rectum. Existing therapies fail to adequately address the clinical requirements of people suffering from this ailment. Despite the acknowledged potential of nanomedicines in the field of anti-inflammatory treatment, their widespread use in clinical settings is impeded by their expensive nature and the uncertainty surrounding their safety profiles. This study illustrates that two naturally occurring phytochemicals, Costunolide (COS) and Glycyrrhizic acid (GA), form carrier-free, multifunctional spherical nanoparticles (NPs) through noncovalent interactions, such as π–π stacking and hydrogen bonding. The COS-GA NPs displayed a synergistic anti-inflammatory effect, providing much more evidently improved therapeutic benefits for dextran sodium sulfate (DSS)-induced UC mice due to more effective reduction in inflammation and oxidative stress than did equal dosages of COS or GA used alone. In addition, COS-GA NPs have biocompatibility and biosafety properties unique to them. This study will serve as affirmation of the potential of COS-GA NPs as innovative natural anti-inflammatory and antioxidant activities and also such agents as drug discovery in UC, leading possibly to better outcomes in people living with this disabling condition.

Abstract Mobile edge computing (MEC) reduces the latency for end users to access applications deployed at the edge by offloading tasks to the edge. With the popularity of e-commerce and the expansion of business scale, server load continues to increase, and energy efficiency issues gradually become more prominent. Computation offloading has received widespread attention as a technology that effectively reduces server load. However, how to improve energy efficiency while ensuring computing requirements is an important challenge facing computation offloading. To solve this problem, using non-orthogonal multiple access (NOMA) to increase the efficiency of multi-access wireless transmission, MEC supporting NOMA is investigated in the research. Computing resources will be divided into separate sub-computing that will be handled via e-commerce terminals or transferred to edge sides by reutilizing radio resources, we put forward a Group Switching Matching Algorithm Based on Resource Unit Allocation (GSM-RUA) algorithm that is multi-dimensional. To this end, we first formulate this task allocation problem as a long-term stochastic optimization problem, which we then convert to three short-term deterministic sub-programming problems using Lyapunov optimization, namely, radio resource allocation in a large timescale, computation resource allocating and splitting in a small-time frame. Of the 3 short-term deterministic sub-programming problems, the first sub-programming problem can be remodeled into a 1 to n matching problem, which can be solved using the block-shift-matching-based radio resource allocation method. The latter two sub-programming problems are then transformed into two continuous convex problems by relaxation and then solved easily. We then use simulations to prove that our GSM-RUA algorithm is superior to the state-of-the-art resource management algorithms in terms of energy consumption, efficiency and complexity for e-commerce scenarios.

In exploring persistent infections and malignancies, a distinctive subgroup of CD8+ T cells, progenitor exhausted CD8+ T (Tpex) cells, has been identified. These Tpex cells are notable for their remarkable self-renewal and rapid proliferation abilities. Recent strides in immunotherapy have demonstrated that Tpex cells expand and differentiate into responsive exhausted CD8+ T cells, thus underscoring their critical role in the immunotherapeutic retort. Clinical examinations have further clarified a robust positive correlation between the proportional abundance of Tpex cells and enhanced clinical prognosis. Tpex cells have found noteworthy applications in the formulation of inventive immunotherapeutic approaches against tumors. This review describes the functions of Tpex cells in the tumor milieu, particularly their potential utility in tumor immunotherapy. Precisely directing Tpex cells may be essential to achieving successful outcomes in immunotherapy against tumors.

Abstract Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a major class of natural products with diverse chemical structures and potent biological activities. A vast majority of RiPP gene clusters remain unexplored in microbial genomes, which is partially due to the lack of rapid and efficient heterologous expression systems for RiPP characterization and biosynthesis. Here, we report a unified biocatalysis (UniBioCat) system based on cell-free gene expression for rapid biosynthesis and engineering of RiPPs. We demonstrate UniBioCat by reconstituting a full biosynthetic pathway for de novo biosynthesis of salivaricin B, a lanthipeptide RiPP. Next, we delete several protease/peptidase genes from the source strain to enhance the performance of UniBioCat, which then can synthesize and screen salivaricin B variants with enhanced antimicrobial activity. Finally, we show that UniBioCat is generalizable by synthesizing and evaluating the bioactivity of ten uncharacterized lanthipeptides. We expect UniBioCat to accelerate the discovery, characterization, and synthesis of RiPPs.

Abstract Epidermal growth factor receptor (EGFR)-targeted therapy is an important treatment for RAS wild-type metastatic colorectal cancer (mCRC), but the resistance mechanism remains unclear. Here, the differential expression of circRNAs between Cetuximab sensitive and resistant cell lines was analyzed using whole-transcriptome sequencing. We identified that the expression of circHIF1A was significantly higher in LIM1215-R than in LIM1215. When treated with Cetuximab, downregulation of circHIF1A level weakened the proliferation and clonal formation ability of LIM1215-R, caused more cells to enter G0-G1 phase, and significantly reduced the basal respiration, ATP production, and maximal respiration, as well as the glycolytic capacity and glycolytic reserve. The response rate and prognosis of circHIF1A-positive patients were inferior to those of negative patients. Mechanistically, circHIF1A can upregulate the level of hypoxia-inducible factor 1 A (HIF1A) by competitively binding to miR-361-5p, inducing the overexpression of enzymes such as glucose transporter 1 (GLUT1) and lactate dehydrogenase A (LDHA). In a xenograft model, inhibition of circHIF1A expression increased the sensitivity to Cetuximab treatment. In conclusion, circHIF1A can promote HIF1α-mediated glycometabolism alteration to induce Cetuximab resistance in CRC. It has the potential to become a screening indicator for the Cetuximab beneficial population in mCRC and a new therapeutic target for enhancing treatment efficacy.

Abstract Emerging tumor immunotherapy methods encompass bispecific antibodies (BSABs), immune checkpoint inhibitors (ICIs), and adoptive cell immunotherapy. BSABs belong to the antibody family that can specifically recognize two different antigens or epitopes on the same antigen. These antibodies demonstrate superior clinical efficacy than monoclonal antibodies, indicating their role as a promising tumor immunotherapy option. Immune checkpoints are also important in tumor immunotherapy. Programmed cell death protein-1 (PD-1) is a widely acknowledged immune checkpoint target with effective anti-tumor activity. PD-1 inhibitors have demonstrated notable therapeutic efficacy in treating hematological and solid tumors; however, more than 50% of patients undergoing this treatment exhibit a poor response. However, ICI-based combination therapies (ICI combination therapies) have been demonstrated to synergistically increase anti-tumor effects and immune response rates. In this review, we compare the clinical efficacy and side effects of BSABs and ICI combination therapies in real-world tumor immunotherapy, aiming to provide evidence-based approaches for clinical research and personalized tumor diagnosis and treatment.

Abstract Fluoropyrimidine-based combination chemotherapy plus targeted therapy is the standard initial treatment for unresectable metastatic colorectal cancer (mCRC), but the prognosis remains poor. This phase 3 trial (ClinicalTrials.gov: NCT03950154) assessed the efficacy and adverse events (AEs) of the combination of PD-1 blockade-activated DC-CIK (PD1-T) cells with XELOX plus bevacizumab as a first-line therapy in patients with mCRC. A total of 202 participants were enrolled and randomly assigned in a 1:1 ratio to receive either first-line XELOX plus bevacizumab (the control group, n = 102) or the same regimen plus autologous PD1-T cell immunotherapy (the immunotherapy group, n = 100) every 21 days for up to 6 cycles, followed by maintenance treatment with capecitabine and bevacizumab. The main endpoint of the trial was progression-free survival (PFS). The median follow-up was 19.5 months. Median PFS was 14.8 months (95% CI, 11.6–18.0) for the immunotherapy group compared with 9.9 months (8.0–11.8) for the control group (hazard ratio [HR], 0.60 [95% CI, 0.40–0.88]; p = 0.009). Median overall survival (OS) was not reached for the immunotherapy group and 25.6 months (95% CI, 18.3–32.8) for the control group (HR, 0.57 [95% CI, 0.33–0.98]; p = 0.043). Grade 3 or higher AEs occurred in 20.0% of patients in the immunotherapy group and 23.5% in the control groups, with no toxicity-associated deaths reported. The addition of PD1-T cells to first-line XELOX plus bevacizumab demonstrates significant clinical improvement of PFS and OS with well tolerability in patients with previously untreated mCRC.

Due to the high heterogeneity of ovarian cancer (OC), it occupies the main cause of cancer-related death among women. As the most aggressive and frequent subtype of OC, high-grade serous cancer (HGSC) represents around 70 % of all patients. With the booming progress of single-cell RNA sequencing (scRNA-seq), unique and subtle changes among different cell states have been identified including novel risk genes and pathways. Here, our present study aims to identify differentially correlated core genes between normal and tumor status through HGSC scRNA-seq data analysis. R package high-dimension Weighted Gene Co-expression Network Analysis (hdWGCNA) was implemented for building gene interaction networks based on HGSC scRNA-seq data. DiffCorr was integrated for identifying differentially correlated genes between tumor and their adjacent normal counterparts. Software Cytoscape was implemented for constructing and visualizing biological networks. Real-time qPCR (RT-qPCR) was utilized to confirm expression pattern of new genes. We introduced ScHGSC-IGDC (Identifying Genes with Differential Correlations of HGSC based on scRNA-seq analysis), an in silico framework for identifying core genes in the development of HGSC. We detected thirty-four modules in the network. Scores of new genes with opposite correlations with others such as NDUFS5, TMSB4X, SERPINE2 and ITPR2 were identified. Further survival and literature validation emphasized their great values in the HGSC management. Meanwhile, RT-qPCR verified expression pattern of NDUFS5, TMSB4X, SERPINE2 and ITPR2 in human OC cell lines and tissues. Our research offered novel perspectives on the gene modulatory mechanisms from single cell resolution, guiding network based algorithms in cancer etiology field.

Hepatocellular carcinoma (HCC) represents a substantial global health burden. Tumorinfiltrating B lymphocytes (TIL-Bs) contribute to tumor progression and significantly impact the efficacy of tumor therapy. However, the characteristics of TIL-Bs in HCC and their effect on HCC therapy remain elusive. Single-cell RNA sequencing (scRNAseq) was applied to investigate the heterogeneity, cellular differentiation and cell-cell communication of TIL-Bs in HCC. Further, the Cancer Genome Atlas-liver hepatocellular carcinoma (TCGA-LIHC) and liver cancer institutes (LCI) cohorts were applied to construct and validate the plasma cell marker-based prognostic risk model. The relationship between the prognostic risk model and the responsiveness of immunotherapy and chemotherapy in patients with HCC were estimated by OncoPredict and tumor immune dysfunction and exclusion (TIDE) algorithm. Finally, we established nomogram and calibration curves to evaluate the precision of the risk score in predicating survival probability. Our data identified five subtypes of TIL-Bs in HCC, each exhibiting varying levels of infiltration in tumor tissues. The interactions between TIL-Bs and other cell types contributed to shaping distinct tumor microenvironments (TME). Moreover, we found that TIL-Bs subtypes had disparate prognostic values in HCC patients. The prognostic risk model demonstrated exceptional predictive accuracy for overall survival and exhibited varying sensitivities to immunotherapy and chemotherapy among patients with HCC. Our data demonstrated that the risk score stood as an independent prognostic predictor and the nomogram results further affirmed its strong prognostic capability. This study reveals the heterogeneity of TIL-Bs and provides a prognostic risk model based on plasma cell markers in HCC, which could prove valuable in predicting prognosis and guiding the choice of suitable therapies for patients with HCC.

Background Immune-based therapies are commonly employed to combat hepatocellular carcinoma (HCC). However, the presence of immune-regulating elements, especially regulatory T cells (Tregs), can dramatically impact the treatment efficacy. A deeper examination of the immune-regulation mechanisms linked to these inhibitory factors and their impact on HCC patient outcomes is warranted. Methods We employed multicolor fluorescence immunohistochemistry (mIHC) to stain Foxp3, cytokeratin, and nuclei on an HCC tissue microarray (TMA). Leveraging liver cancer transcriptome data from TCGA, we built a prognostic model focused on Treg-associated gene sets and represented it with a nomogram. We then sourced liver cancer single-cell RNA sequencing data (GSE140228) from the GEO database, selectively focusing on Treg subsets, and conducted further analyses, including cell-to-cell communication and pseudo-time trajectory examination. Results Our mIHC results revealed a more substantial presence of Foxp3 + Tregs in HCC samples than in adjacent normal tissue samples ( P < .001). An increased presence of Foxp3 + Tregs in HCC samples correlated with unfavorable patient outcomes ( HR = 1.722, 95% CI :1.023-2.899, P = .041). The multi-factorial prognosis model we built from TCGA liver cancer data highlighted Tregs as a standalone risk determinant for predicting outcomes ( HR = 3.84, 95% CI: 2.52-5.83, P < .001). Re-analyzing the scRNA-seq dataset (GSE140228) showcased distinctive gene expression patterns in Tregs from varying tissues. Interactions between Tregs and other CD4 + T cell types were predominantly governed by the CXCL13/CXCR3 signaling pathway. Communication pathways between Tregs and macrophages primarily involved MIF-CD74/CXCR4, LGALS9/CD45, and PTPRC/MRC1. Additionally, macrophages could influence Tregs via HLA-class II and CD4 interactions. Conclusion An elevated presence of Tregs in HCC samples correlated with negative patient outcomes. Elucidating the interplay between Tregs and other immune cells in HCC could provide insights into the modulatory role of Tregs within HCC tissues.

Cell metabolite adenosine can induce extensive and persistent immunosuppression by binding to adenosine receptors on immune cells. Seriously, the hypoxia‐driven adenosinergic axis aggravates adenosine accumulation via dephosphorizing immune‐activating adenosine triphosphate (ATP) released during immunogenic cell death (ICD). Different from direct adenosine clearance or adenosine receptor blockade or directly using ecto‐enzyme (CD39/CD73) antagonist, it is hoped to use an innovative small science engineering to regulate the upstream hypoxia/HIF‐1α signal of the hypoxia‐adenosinergic axis, thereby reducing the immunosuppressive extracellular adenosine and enhancing ICD‐triggered antitumor immunity. PM@Mn is constructed by gradually integrating metformin and MnO2 on polydopamine (PDA) nanoparticles. PM@Mn can effectively suppress hypoxia‐adenosinergic axis via combining catalytic oxygen production with reduced endogenous oxygen consumption. Such motif of hypoxia relief suppresses the metabolism of ATP to adenosine via down‐regulating the expression of HIF‐1α, CD39, and CD73. Meanwhile, PDA in PM@Mn can induce local tumor ablation and trigger the “vaccine effect” of ICD under near‐infrared radiation. In a mouse breast cancer model with low immunogenicity, our strategy can effectively reduce adenosine accumulation, PM@Mn group exhibits 4.51‐fold cytotoxic T lymphocyte infiltration and tumor inhibition rate of 75.4%. This study provides a new strategy to advance ICD‐triggered antitumor immunity through supressing hypoxia‐adenosinergic axis.

Exhausted CD8+T cells represent a distinct cellular lineage that emerges during both chronic infections and cancers. Recent studies have shown that persistent antigen exposure can drive the differentiation of precursor exhausted CD8+T cells, termed Tpex cells, which are characterized as TCF-1+PD-1+CD8+T cells. Elevated Tpex cell frequencies in the tumor microenvironment (TME) are associated with improved overall survival (OS) in cancer patients and heightened responsiveness to anti-PD-1 therapy. In our present study, we utilized multi-color immunohistochemistry (mIHC) to determine the localization and clinical implications of tumor-infiltrating Tpex cells within the TME of human colorectal cancer (CRC) tissues. We also conducted a multi-omics integrative analysis using single-cell RNA sequencing (scRNA-seq) data derived from both the murine MC38 tumor model and human CRC tissues. This analysis helped delineate the transcriptional and functional attributes of Tpex cells within the CRC TME. Furthermore, we employed spatial transcriptome sequencing data from CRC patients to investigate the interactions between Tpex cells and other immune cell subsets within the TME. In conclusion, our study not only established a method for Tpex cell detection using mIHC technology but also confirmed that assessing Tpex cells within the CRC TME could be indicative of patients’ survival. We further uncovered the transcriptional and functional characteristics of Tpex cells in the TME and ascertained their pivotal role in the efficacy of immunotherapy against CRC.

Understanding the biodiversity–productivity relationship (BPR) is crucial for biodiversity conservation and ecosystem management. While it is known that diversity enhances forest productivity, the underlying mechanisms at the local neighborhood level remain poorly understood. We established a 9.6 ha dynamic forest plot to study how neighborhood diversity, intraspecific competition, and interspecific competition influence tree growth across spatial scales using linear mixed-effects models. Our analysis reveals a significant positive correlation between neighborhood species richness (NSR) and relative growth rate (RGR). Notably, intraspecific competition, measured by conspecific neighborhood density and resource competition, negatively impacts RGR at finer scales, indicating intense competition among conspecifics for limited resources. In contrast, interspecific competition, measured by heterospecific density and resource competition, has a negligible impact on RGR. The relative importance of diversity and intra/interspecific competition in influencing tree growth varies with scale. At fine scales, intraspecific competition dominates negatively, while at larger scales, the positive effect of NSR on RGR increases, contributing to a positive BPR. These findings highlight the intricate interplay between local interactions and spatial scale in modulating tree growth, emphasizing the importance of considering biotic interactions and spatial variability in studying BPR.

The presence of burrowing mammals can have extensive effects on plants and soils, creating bare soil patches in alpine meadows and potentially altering plant-soil carbon (C) and nitrogen (N). This study focuses on the plateau pika (Ochotona curzoniae) to examine the responses of plant-soil C and N to a small burrowing mammal from quadrat scale to plot scale. The density of active burrow entrances in disturbed plots was used as an indicator of the disturbance intensity of plateau pikas. The study found that the below-ground biomass (BGB) and its C and N, as well as soil C and N concentrations were significantly lower in bare soil areas than in vegetated areas and undisturbed plots. This shows that the quadrat scale limited the estimation of the C and N sequestration potential. Therefore, further research on the plot scale found that the disturbance by plateau pika significantly reduced plant biomass and BGB carbon stock. However, plateau pika did not affect soil C and N stocks or ecosystem C and N stocks. These findings suggest the bare soil patches formed by plateau pika caused plant and soil heterogeneity but had a trade-off effect on plant-soil C and N stocks at the plot scale. Nevertheless, moderate disturbance intensity increased the C and N sequestration potential in grassland ecosystems. These results provide a possible way to estimate how disturbance by small burrowing mammals affects C and N cycling in grassland ecosystems while accurately assessing the effects of small burrowing mammal densities on C and N in grassland ecosystems.

Accurately identifying a specific faulty monomer in a battery pack in the early stages of battery failure is essential to preventing safety accidents and minimizing property damage. While there are existing lithium-ion power battery fault diagnosis methods used in laboratory settings, their effectiveness in real-world vehicle conditions is limited. To address this, fault diagnosis methods for real-vehicle conditions should incorporate fault characteristic parameters based on external battery fault characterization, enabling the accurate identification of different fault types. However, these methods are constrained when confronted with complex fault types. To overcome these limitations, this paper proposes a battery fault diagnosis method that combines multidimensional fault features. By merging different fault feature parameters and mapping them to a high-dimensional space, the method utilizes a local outlier factor (LOF) algorithm to detect anomalous values, enabling fault diagnosis in complex working conditions. This method improves the detection time by an average of 22 min compared to the extended RMSE method and maintains strong robustness while correctly detecting faults compared to other conventional methods.

The rehabilitation of wind turbine foundations after damage is increasingly common. However, limited research exists on the deformation of wind turbine foundations after rehabilitation. Artificial intelligence methods can be used to analyze future deformation state and predict post-rehabilitation deformation of foundations. This paper focuses on analyzing the stability of damaged wind turbine foundations after rehabilitation, as well as establishing and evaluating machine learning models. Specifically, Decision Tree (DT), Extreme Gradient Boosting (XGB), Support Vector Regression (SVR), and Long Short-Term Memory Network (LSTM) models are utilized to predict the vertical displacement of the rehabilitated foundation. Hence, the stability of the rehabilitated foundation is discussed in correlation with the measured wind speed, based on the foundation vertical displacement data. During the development of the machine learning model, the most suitable combination of hyperparameters is determined. The prediction performance of the SVR and LSTM models, which exhibit good performance, is compared to further evaluate their effectiveness. Furthermore, the models are analyzed and validated. The results indicate that the vertical displacements of the rehabilitated foundations gradually get close to a state of steady fluctuation over time. The SVR model is identified as the most effective in predicting the vertical displacements of wind turbine foundations after rehabilitation. This study aims to analyze and predict the vertical displacement of wind turbine foundations after rehabilitation based on extensive field monitoring data and powerful machine learning models.

One of the most important feature in the Kondo physics is the universal scaling behavior. In this study, we analyze the transport behavior of the spin thermocurrent driven by a small temperature bias and under a weak magnetic field. We conclude that the spin thermocurrent exhibits a universal scaling behavior, similar to the spin susceptibility. The validity of our conclusion is also checked by using the numerically exact hierarchical equations of motion approach. From our fitting result of the scaling function, it is found that the Kondo temperature can be determined by the maximum of the spin thermocurrent at $T/T_K = 0.383$ .

Vegetation on dunes regulates the water supply from the dunes to the inter-dune lowland, which is a crucial factor affecting lake water dynamics in the inter-dune lowland. Previous researchers have paid insufficient attention to the water regulation function of dunes on a landscape- and regional scale. To fill this gap, both remote sensing technology and field observations were used to analyze the variations in the lake area and their influence factors, such as vegetation coverage and precipitation in the lake watershed, on a multi-year scale (2000–2020) and one-year scale (2021), respectively. The results showed that precipitation is the main factor influencing the changes in lake water, and artificial sand vegetation can regulate the changes in lake water. On the multi-year scale, with the coverage of artificial sand-fixing vegetation increasing on sand dunes in the lake watershed, the areas of the lakes were gradually decreasing. On the one-year scale, with dune vegetation coverage increased, the water supply from dunes to lakes showed a decreasing trend. This model can provide a possibility for estimating and predicting the influence of water supply from dunes to lakes that is affected by sand-fixing vegetation. The findings have significant theoretical and practical utility for the rational utilization of water resources in sandy land, as well as for assisting in the selection of an optimized construction mode for desert control projects.