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Hepatocellular carcinoma (HCC) ranks among the most prevalent malignancies globally. Although treatment strategies have improved, the prognosis for patients with advanced HCC remains unfavorable. Tumor-associated macrophages (TAMs) play a dual role, exhibiting both anti-tumor and pro-tumor functions. In this study, we analyzed single-cell RNA sequencing data from 10 HCC tumor cores and 8 adjacent non-tumor liver tissues available in the dataset GSE149614. Using dimensionality reduction and clustering approaches, we identified six major cell types and nine distinct TAM subtypes. We employed Monocle2 for cell trajectory analysis, hdWGCNA for co-expression network analysis, and CellChat to investigate functional communication between TAMs and other components of the tumor microenvironment. Furthermore, we estimated TAM abundance in TCGA-LIHC samples using CIBERSORT and observed that the relative proportions of specific TAM subtypes were significantly correlated with patient survival. To identify TAM-related genes influencing patient outcomes, we developed a high-dimensional, gene-based transformer survival model. This model achieved superior concordance index (C-index) values across multiple datasets, including TCGA-LIHC, OEP000321, and GSE14520, outperforming other methods. Our results emphasize the heterogeneity of tumor-associated macrophages in hepatocellular carcinoma and highlight the practicality of our deep learning framework in survival analysis.

The highly conserved histones in different species seem to represent a very ancient and universal innate host defense system against microorganisms in the biological world. Histones are the essential part of nuclear matter and act as a control switch for DNA transcription. However, histones are also found in the cytoplasm, cell membranes, and extracellular fluid, where they function as host defenses and promote inflammatory responses. In some cases, extracellular histones can act as damage-associated molecular patterns (DAMPs) and bind to pattern recognition receptors (PRRs), thereby triggering innate immune responses and causing initial organ damage. Histones and their fragments serve as antimicrobial peptides (AMPs) to directly eliminate bacteria, viruses, fungi, and parasites in vitro and in vivo . Histones are also involved in phagocytes-related innate immune response as components of neutrophil extracellular traps (NETs), neutrophil activators, and plasminogen receptors. In addition, as a considerable part of epigenetic regulation, histone modifications play a vital role in regulating the innate immune response and expression of corresponding defense genes. Here, we review the regulatory role of histones in innate immune response, which provides a new strategy for the development of antibiotics and the use of histones as therapeutic targets for inflammatory diseases, sepsis, autoimmune diseases, and COVID-19.

In this study, zeta potentials of graphitic carbon nitride (g-C3N4) nanoparticles were detailedly investigated under various electrolytes, solution pH, and humic acid (HA) concentration conditions. The hydrodynamic radius of g-C3N4 nanoparticles was measured to be 388.9 ± 24 nm, and the specific surface area of the g-C3N4 nanoparticles was measured to be 46.2 m2 g−1. The absolute values of g-C3N4 zeta potentials significantly decreased with the increasing ionic strength (IS) due to the charge screening. Compared to the monovalent cation, the zeta potentials of g-C3N4 were less negative with the presence of divalent cations. In addition, K+ was more effective than Na+ in decreasing the absolute values of g-C3N4 zeta potentials, and Ca2+ was more effective than Mg2+ in decreasing the absolute values of g-C3N4 zeta potentials. When NaCl and CaCl2 were used as the electrolytes, the zeta potentials of g-C3N4 became less negative with the decreasing pH conditions. When FeCl3 and AlCl3 were used as the electrolytes, the zeta potentials of g-C3N4 became more positive with increasing solution pH due to the changing species of Fe3+ and Al3+. The zeta potentials of g-C3N4 were significantly more negative with the presence of HA. The results from this work indicated electrolytes, solution pH, and HA concentration conditions play a complex role in zeta potentials of g-C3N4 nanoparticles in aqueous environment.

In order to improve the aging resistance of epoxy resin insulation materials and the cost-effectiveness and reliability of power apparatuses, a novel silicone-modified aging-resistant epoxy resin insulation material was developed. The modification was achieved via chemical grating, using dihydroxydiphenylsilane, 2-(3,4-epoxycyclohexyl)ethyltriethoxysilane and dicyclohexylamine. Accelerated thermal aging test, accelerated hydrothermal aging test and electrical tree test were carried out to compare the aging resistance of the novel, existing silicone-modified epoxy resin and unmodified epoxy resin under high temperature, humid environment and strong electric fields. The results show that compared with the unmodified resin, the novel material has advantages in thermal, hydrothermal aging resistance and electrical tree resistance. More specifically, after modification, the dielectric strength of the novel material after thermal aging test was improved by 12%; its partial discharge inception voltage (PDIV) after hydrothermal aging test was increased by 19.4% and the growth rate of electrical trees was 12.68% of that in unmodified resin. Compared with the existing silicone-modified epoxy resin, the novel silicone-modified epoxy resin sacrifices part of the hydrothermal properties, but showed better thermal stability, and the growth rate of electrical trees in the novel silicone-modified epoxy resin was 44.26% of that in the existing resin.

Pneumonia is an acute respiratory infection caused by pathogens such as bacteria or viruses, and accurate early diagnosis is critical for reducing mortality. Chest X-ray (CXR) imaging serves as a conventional diagnostic tool. However, radiographic features of pneumonia often overlap with those of other pulmonary diseases and are subject to inter-observer variability. Traditional Convolutional Neural Network (CNN) models tend to capture redundant information during feature extraction, and single pre-trained models often exhibit limited generalization in multiclass classification tasks. This study proposes a multi-model ensemble learning framework based on multi-head attention mechanism. Firstly, the three pre-trained backbones—DenseNet-121, ResNet-50, and VGG-19—were fine-tuned through transfer learning by replacing their classification heads, adapting pooling layers, and optimizing the fully connected layers. Secondly, feature maps extracted from these tuned backbones were concatenated and fused using a multi-head attention mechanism; the fused representation was then refined by two consecutive multi-head attention layers and finally passed to a fully connected classifier to produce the ensemble prediction. Three task sets were constructed from a public Kaggle dataset: binary classification (normal vs. pneumonia), three-class classification (normal, COVID-19, viral pneumonia), and four-class classification (normal, lung opacity, viral pneumonia, COVID-19), achieving accuracies of 91.67%, 93.79%, and 90.60%, respectively. The results demonstrate that the proposed multi-head attention-based ensemble framework offers significant advantages for pneumonia multiclass classification, particularly by maintaining high recall and robustness in more complex scenarios such as four-class differentiation, indicating its potential as a clinical decision-support tool. Future work will involve expanding the dataset and evaluating the model’s generalizability across additional disease categories.

Background Focal adhesion plays an essential role in tumour invasiveness and metastasis. Hippo component YAP has been widely reported to be involved in many aspects of tumour biology. However, its role in focal adhesion regulation in breast cancer remains unexplored. Methods Tissue microarray was used to evaluate YAP expression in clinical breast cancer specimens by immunohistochemical staining. Cell migration and invasion abilities were measured by Transwell assay. A cell adhesion assay was used to measure the ability of cell adhesion to gelatin. The focal adhesion was visualized through immunofluorescence. Phosphorylated FAK and other proteins were detected by Western blot analysis. Gene expression profiling was used to screen differently expressed genes, and gene ontology enrichment was performed using DAVID software. The gene mRNA levels were measured by quantitative real-time PCR. The activity of the THBS1-promoter was evaluated by dual luciferase assay. Chromatin immunoprecipitation (ChIP) was used to verify whether YAP could bind to the THBS1-promoter region. The prediction of potential protein-interaction was performed with the String program. The ChIP sequence data of TEAD was obtained from the ENCODE database and analysed via the ChIP-seek tool. The gene expression dataset (GSE30480) of purified tumour cells from primary breast tumour tissues and metastatic lymph nodes was used in the gene set enrichment analysis. Prognostic analysis of the TCGA dataset was performed by the SurvExpress program. Gene expression correlation of the TCGA dataset was analysed via R2: Genomics Analysis and Visualization Platform. Results Our study provides evidence that YAP acts as a promoter of focal adhesion and tumour invasiveness via regulating FAK phosphorylation in breast cancer. Further experiments reveal that YAP could induce FAK phosphorylation through a TEAD-dependent manner. Using gene expression profiling and bioinformatics analysis, we identify the FAK upstream gene, thrombospondin 1, as a direct transcriptional target of YAP-TEAD. Silencing THBS1 could reverse the YAP-induced FAK activation and focal adhesion. Conclusion Our results unveil a new signal axis, YAP/THBS1/FAK, in the modulation of cell adhesion and invasiveness, and provides new insights into the crosstalk between Hippo signalling and focal adhesion.

Plant diseases pose a significant threat to global food security, with severe implications for agricultural productivity. Early and accurate detection of these diseases is crucial, yet it remains a challenging task, significantly impacting crop yields and food supply chains. Despite the progress in artificial intelligence, particularly deep learning, challenges persist in real-world applications due to environmental noise, varying light conditions, and other complicating factors that hinder detection accuracy. This study introduces the AttCM-Alex model, a novel deep-learning framework designed to boost the detection and classification of plant diseases under challenging environmental conditions. By integrating convolutional operations with self-attention mechanisms, AttCM-Alex effectively addresses the variability in light intensity and image noise, ensuring robust performance. To simulate practical agricultural scenarios, the study employs bilinear interpolation for image dimension adjustment and introduces Salt-and-Pepper noise. Additionally, the model’s robustness was evaluated by varying image brightness levels by ±10%, ±20%, and ±30%. Experimental results demonstrate that AttCM-Alex significantly outperforms traditional models, particularly in scenarios involving fluctuating light conditions and noise interference. The model achieved a peak detection accuracy of 0.97 with a 30% increase in image brightness and maintained an accuracy of 0.93 even with a 30% decrease in brightness, highlighting its robustness and reliability. The findings affirm the AttCM-Alex model as a powerful tool for real-world agricultural applications, capable of enhancing disease detection systems’ accuracy and efficiency. This advancement not only supports better crop management practices but also contributes to sustainable agriculture and global food security.

Graph domain adaptation (GDA) aims to address the challenge of limited label data in the target graph domain. Existing methods such as UDAGCN, GRADE, DEAL, and COCO for different-level (node-level, graph-level) adaptation tasks exhibit variations in domain feature extraction, and most of them solely rely on representation alignment to transfer label information from a labeled source domain to an unlabeled target domain. However, this approach can be influenced by irrelevant information and usually ignores the conditional shift of the downstream predictor. To effectively address this issue, we introduce a target-oriented unsupervised graph domain adaptive framework for graph adaptation called TO-UGDA. Particularly, domain-invariant feature representations are extracted using graph information bottleneck. The discrepancy between two domains is minimized using an adversarial alignment strategy to obtain a unified feature distribution. Additionally, the meta pseudo-label is introduced to enhance downstream adaptation and improve the model’s generalizability. Through extensive experimentation on real-world graph datasets, it is proved that the proposed framework achieves excellent performance across various node-level and graph-level adaptation tasks.

The core challenge of Knowledge Base Question Answering (KBQA), as a bridge between natural language and structured knowledge, is to accurately map complex semantic queries into Graph Query Language (GQL). Compared with the traditional Text-to-SQL task, KBQA faces a dual challenge: the structural differences between GQL and SQL and the lack of high-order subgraph information in multi-hop inference of knowledge graphs. While existing approaches such as ChatKBQA have made progress, the limitation of subgraph scalability severely constrains multi-hop query performance. To this end, this study proposes Knowledge Graph Multi-hop Perceptron (KGMP) - a retrieval-generation framework fine-tuned based on open-source large language models, whose innovativeness is reflected in three aspects: 1. Dynamic Graph Traversal Mechanism: Through an iterative subgraph expansion strategy, KGMP effectively achieves dynamic traversal of problem oriented graphs with progressive reasoning. 2. Structured Interaction Protocol: Based on SparQL syntax, KGMP designs a lightweight interaction instruction set to build an efficient communication interface between LLM and knowledge graph. 3. Graph Structure Optimization Technique: Develop subgraph reordering algorithms and pruning strategies based on the reranker model to ensure that the subgraphs input to the LLM are both compact and semantically complete. By integrating KGMP as a retrieval module into the ChatKBQA framework and providing it with optimised multi-hop subgraph input, the experimental results show a performance improvement of 6.2% and 5.3% on the WebQSP and CWQ datasets, respectively. This study provides a new technical paradigm for deep collaboration between LLM and knowledge graph.

High-altitude pulmonary hypertension (HAPH) is a severe condition affecting highland residents, yet its molecular mechanisms remain incompletely understood. This study aimed to investigate the pathogenesis of HAPH through integrated metabolomic and proteomic analyses. We performed untargeted metabolomics and proteomics analyses on plasma samples from HAPH patients ( n =30) and matched healthy controls ( n =30). Differential expression analysis, pathway enrichment, and integrated multi-omics analysis were conducted. Key findings were validated using targeted proteomics (PRM). We identified 26 differentially expressed metabolites (12 upregulated, 14 downregulated) and 35 differentially expressed proteins (5 upregulated, 30 downregulated) in HAPH patients. Integrated pathway analysis revealed significant alterations in glycerophospholipid metabolism (PC(20:4/8Z,11Z), FC = 2.804, P.adjust = 0.047), immune response (IGLL1, FC = -1.557, P.adjust = 0.003), cytoskeletal organization (MYH10, FC = 7.574, P.adjust =0.189), and oxidative stress response pathways. PRM validation confirmed the differential expression of five key proteins: ACTG1, VNN1, CKB (upregulated), and APOF and CST3 (downregulated). Our integrated multi-omics analysis reveals a complex molecular network underlying HAPH pathogenesis, characterized by coordinated changes in lipid metabolism, immune function, and cellular structure. These findings provide new insights into HAPH mechanisms and identify potential therapeutic targets for intervention.