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IntroductionHepatocellular carcinoma (HCC), representing more than 80% of primary liver cancer cases, lacks satisfactory etiology and diagnostic methods. This study aimed to elucidate the role of programmed cell death-associated genes (CDRGs) in HCC by constructing a diagnostic model using single-cell RNA sequencing (scRNA-seq) and RNA sequencing (RNA-seq) data.MethodsSix categories of CDRGs, including apoptosis, necroptosis, autophagy, pyroptosis, ferroptosis, and cuproptosis, were collected. RNA-seq data from blood-derived exosomes were sourced from the exoRBase database, RNA-seq data from cancer tissues from the TCGA database, and scRNA-seq data from the GEO database. Subsequently, we intersected the differentially expressed genes (DEGs) of the HCC cohort from exoRBase and TCGA databases with CDRGs, as well as DEGs obtained from single-cell datasets. Candidate biomarker genes were then screened using clinical indicators and a machine learning approach, resulting in the construction of a seven-gene diagnostic model for HCC. Additionally, scRNA-seq and spatial transcriptome sequencing (stRNA-seq) data of HCC from the Mendeley data portal were used to investigate the underlying mechanisms of these seven key genes and their association with immune checkpoint blockade (ICB) therapy. Finally, we validated the expression of key molecules in tissues and blood-derived exosomes through quantitative Polymerase Chain Reaction (qPCR) and immunohistochemistry experiments.ResultsCollectively, we obtained a total of 50 samples and 104,288 single cells. Following the meticulous screening, we established a seven-gene diagnostic model for HCC, demonstrating high diagnostic efficacy in both the exoRBase HCC cohort (training set: AUC = 1; testing set: AUC = 0.847) and TCGA HCC cohort (training set: AUC = 1; testing set: AUC = 0.976). Subsequent analysis revealed that HCC cluster 3 exhibited a higher stemness index and could serve as the starting point for the differentiation trajectory of HCC cells, also displaying more abundant interactions with other cell types in the microenvironment. Notably, key genes TRIB3 and NQO1 displayed elevated expression levels in HCC cells. Experimental validation further confirmed their elevated expression in both tumor tissues and blood-derived exosomes of cancer patients. Additionally, stRNA analysis not only substantiated these findings but also suggested that patients with high TRIB3 and NQO1 expression might respond more favorably to ICB therapy.ConclusionsThe seven-gene diagnostic model demonstrated remarkable accuracy in HCC screening, with TRIB3 emerging as a promising diagnostic tool and therapeutic target for HCC.

BackgroundUpper tract urothelial carcinoma (UTUC) and bladder urothelial carcinoma (BLCA) both originate from uroepithelial tissue, sharing remarkably similar clinical manifestations and therapeutic modalities. However, emerging evidence suggests that identical treatment regimens may lead to less favorable outcomes in UTUC compared to BLCA. Therefore, it is imperative to explore molecular processes of UTUC and identify biological differences between UTUC and BLCA.MethodsIn this study, we performed a comprehensive analysis using single-cell RNA sequencing (scRNA-seq) on three UTUC cases and four normal ureteral tissues. These data were combined with publicly available datasets from previous BLCA studies and RNA sequencing (RNA-seq) data for both cancer types. This pooled analysis allowed us to delineate the transcriptional differences among distinct cell subsets within the microenvironment, thus identifying critical factors contributing to UTUC progression and phenotypic differences between UTUC and BLCA.ResultsscRNA-seq analysis revealed seemingly similar but transcriptionally distinct cellular identities within the UTUC and BLCA ecosystems. Notably, we observed striking differences in acquired immunological landscapes and varied cellular functional phenotypes between these two cancers. In addition, we uncovered the immunomodulatory functions of vein endothelial cells (ECs) in UTUC, and intercellular network analysis demonstrated that fibroblasts play important roles in the microenvironment. Further intersection analysis showed that MARCKS promote UTUC progression, and immunohistochemistry (IHC) staining revealed that the diverse expression patterns of MARCKS in UTUC, BLCA and normal ureter tissues.ConclusionThis study expands our multidimensional understanding of the similarities and distinctions between UTUC and BLCA. Our findings lay the foundation for further investigations to develop diagnostic and therapeutic targets for UTUC.

Abstract Organic electroactive materials are increasingly recognized as promising cathode materials for aqueous zinc–ion batteries (AZIBs), owing to their structural diversity and renewable nature. Despite this, the electrochemistry of these organic cathodes in AZIBs is still less than optimal, particularly in aspects such as output voltage, cyclability, and rate performance. In this review, we provide an overview of the evolutionary history of organic cathodes in AZIBs and elucidate their charge‐storage mechanisms. We then delve into the strategies to overcome the prevailing challenges faced by aqueous Zn−organic batteries, including low achievable capacity and output voltage, poor cycling stability, and rate performance. Design strategies to enhance cell performance include tailoring molecular structure, engineering electrode microstructure, and modulation of electrolyte composition. Finally, we highlight that future research directions should cover performance evaluation under practical conditions and the recycling and reuse of organic electrode materials.

Abstract There are many nanoscale pores in deep low permeability coal seams. The flow of coalbed methane (gas) in nanoscale pores belongs to the gas flow in porous media with medium and high Knudsen numbers. Its flow mechanism is one of the key unsolved scientific problems. In order to explore the gas transport law in nanopores of coal, a gas transport model based on microscopic boundary restriction was adopted to describe the gas flow law, and its rationality was verified by experimental data. The Field Emission Scanning Electron Microscope was used to scan the nanopores of customized anodized aluminum membrane. Then, the Maximally Stable Extremal Regions (MSER) algorithm of MATLAB and binarization algorithm were employed to quantify the pore structure parameters (equivalent pore size and porosity) of the membrane nanoscale pores. Finally, PMI micro-flow permeability tester was used to carry out different rarefied degree gas penetration experiments through anodized aluminum membranes, and the adopted gas transport model was verified. The results show that the binarization method is more accurate to characterize sample whose theoretical pore sizes are 20–35 nm, while MSER characterizes samples whose theoretical pore sizes are 110–150 nm and 200–300 nm more accurately. In other words, binarization method is more accurate for characterizing mesopores, while MSER algorithm is more accurate for macropores. The results have important reference value for more accurate extraction of nanopore parameters of porous media. Compared with traditional gas transport model, the adopted model considering the microscopic boundary restriction in this paper is closer to the experimental results. Moreover, it is suitable for describing the gas flow law in multiscale nanopores. The study provided important guiding significance for ascertaining the gas migration law in low permeability coal seam, improving the prediction accuracy of gas extraction and taking effective measures to increase production. The research results can further enrich the theoretical system of gas transport in coal, which is conducive to the efficient gas extraction, and is of great significance to promote the realization of carbon peak and carbon neutrality.