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Efferocytosis, the process by which phagocytes like macrophages and dendritic cells clear apoptotic cells, is crucial for maintaining tissue homeostasis. However, its function in bladder cancer (BLCA) remains unclear and warrants further exploration. This study seeks to establish a prognostic and treatment response signature based on efferocytosis-related genes (EFRGs) for bladder cancer patients. BLCA-related datasets were sourced from the Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/ ) and the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/ ). A comprehensive analysis was performed on 28 prognostic EFRGs. Clustering analysis was carried out using ConsensusClusterPlus. Prognostic differentially expressed genes (DEGs) were identified based on expression variations across the subtypes. A prognostic model was subsequently developed using least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression. Lastly, a thorough analysis was conducted to explore the relationship between risk scores and the tumor immune microenvironment, somatic mutations, as well as responses to immunotherapy and chemotherapy. Consensus clustering revealed two efferocytosis subtypes, Cluster A and Cluster B, and identified 61 prognostic DEGs between them. A risk scoring model, incorporating four key DEGs—SERPINE2, DPYSL3, CTSE, and KRT16—was constructed and validated. This model successfully stratified patients into high-risk and low-risk groups, with high-risk patients showing worse prognosis, increased immune infiltration, and higher immune checkpoint gene expression. The risk scores also provide insights into patient responsiveness to treatment. In conclusion, we identified four key genes—SERPINE2, DPYSL3, CTSE, and KRT16—that can be used to develop a prognostic model for bladder cancer. These findings may provide valuable molecular targets for the clinical diagnosis and therapeutic strategies of bladder cancer.

Background The tea plant ( Camellia sinensis (L.) O. Kuntze) is one of the most economically important woody crops. Plastic greenhouse covering cultivation has been widely used in tea areas of northern China. Chlorophyll is not only the crucial pigment for green tea, but also plays an important role in the growth and development of tea plants. Currently, little is known about the effect of plastic greenhouse covering cultivation on chlorophyll in tea leaves. Results To investigate the effect of plastic greenhouse covering cultivation on chlorophyll in tea leaves, color difference values, chlorophyll contents, gene expression, enzyme activities and photosynthetic parameters were analyzed in our study. Sensory evaluation showed the color of appearance, liquor and infused leaves of greenhouse tea was greener than field tea. Color difference analysis for tea liquor revealed that the value of ∆L, ∆b and b/a of greenhouse tea was significantly higher than field tea. Significant increase in chlorophyll content, intracellular CO 2 , stomatal conductance, transpiration rate, and net photosynthetic rate was observed in greenhouse tea leaves. The gene expression and activities of chlorophyll-metabolism-related enzymes in tea leaves were also activated by greenhouse covering. Conclusion The higher contents of chlorophyll a, chlorophyll b and total chlorophyll in greenhouse tea samples were primarily due to higher gene expression and activities of chlorophyll-metabolism-related enzymes especially, chlorophyll a synthetase (chlG), pheophorbide a oxygenase (PAO) and chlorophyllide a oxygenase (CAO) in tea leaves covered by greenhouse. In general, our results revealed the molecular basis of chlorophyll metabolism in tea leaves caused by plastic greenhouse covering cultivation, which had great significance in production of greenhouse tea.

Background Mounting evidence highlights the critical role of mitochondrial dysfunction, driven by mitochondrial-related genes (MTRGs), in the development, progression, and therapeutic response of cancer. However, a comprehensive analysis linking specific Mitochondria-Related Gene Signature (MTRGS) to Bladder Cancer (BLCA) prognosis and immunotherapy efficacy remains largely unexplored. Therefore, this study aims to investigate the role of MTRGs in BLCA, construct and validate a novel MTRGs-based prognostic signature, and explore its potential for guiding personalized treatment strategies. Materials and methods Leveraging transcriptomic and clinical data from The Cancer Genome Atlas (TCGA-BLCA) cohort, we constructed a mitochondrial-related risk score model using LASSO, univariate and multivariate Cox regression analyses. This model was subsequently validated in an independent Gene Expression Omnibus (GEO) dataset. We then employed integrated bioinformatics approaches (implemented in R with online databases) to characterize features of the tumor microenvironment (TME), immune cell infiltration, Gene Set Enrichment Analysis (GSEA), tumor mutational burden (TMB), and drug sensitivity across different risk groups. Additionally, using data from public databases, we further verified our findings through single-cell RNA sequencing (scRNA-seq) analyses. Results Using 104 mitochondria-related differentially expressed genes (MTR-DEGs), unsupervised non-negative matrix factorization (NMF) clustering stratified BLCA patients into three molecular subtypes (Clusters 1–3). Survival analysis revealed that patients in Cluster 3 had significantly longer overall survival than those in Clusters 1 and 2. Our mitochondrial-related risk model incorporating six core genes ( MAP1B , PYCR1 , HSD3B1 , KLK6 , AKR1B15 , and TAT) - exhibited robust prognostic capability (3-years AUC = 0.695 in TCGA-BLCA, 0.798 in GEO-GSE32894, 0.703 in GSE13507). The risk model revealed distinct immune infiltration patterns between high- and low-risk groups. Furthermore, Tumor Immune Dysfunction and Exclusion (TIDE) and immunophenotype score (IPS) analyses demonstrated that integrating risk scores with stromal/immune signatures significantly enhanced immunotherapy benefit prediction across BLCA risk-subgroups. Crucially, the model demonstrated predictive power for therapy response: low-risk patients showed potential benefit from immune checkpoint inhibitors, while high-risk patients exhibited heightened sensitivity to specific chemotherapy agents or targeted therapies (e.g., Tozasertib, Gemcitabine) and may require intensified regimens. Conclusion This validated mitochondrial risk model delivers a clinically actionable biomarker for BLCA prognosis stratification and guides personalized therapeutic selection, enabling precision treatment intensification.

Zn1-xFexO (x =0.011, 0.029, 0.048, 0.10, 0.20) thin films have been grown on Al2O3 (0001) substrates by magnetron co-sputtering technique. X-ray diffraction analysis revealed that Fe atoms were successfully incorporated into ZnO host matrix, and the films exhibited wurzite structure up to x = 0.20 with reduced c-axis lattice constant compared to that of pure ZnO. There are not any secondary phases, and Fe2+ as well as Fe3+ substitutes for Zn2+ of ZnO host. With increasing x, the optical band gap is found to shift slightly to lower energies (66 meV for x = 0.20) than that of the pure ZnO. Magnetization measurement indicated that all samples were ferromagnetic at room temperature. An explanation for the dependence of the optical and magnetic properties of the samples on the value of x is also given.

Fault tree analysis (FTA) is a powerful technique that can be used to analyze the faults related to a large-scale complicated system. In the conventional FTA, it can be used to assess the effects of combinations of failures on system behavior but the ambiguities and uncertainties of basic events cannot be handled effectively. Therefore, employing Bayesian theory helps probabilistic estimation of basic events and subsequently the top event. This study presents an integrated approach to Bayesian theory and FTA for handling uncertainty in the fault diagnosis of heavy CNC machines. In this context, making use of a directed graph to describe the fault, and then expressed as reachability matrix, the fault tree and fault information are obtained. In order to set the determinate conditions at every node of fault tree combining FTA with rule reasoning, the minimal cut sets are determined by representing the fault tree as a top-down logical equation. Finally, Bayesian method is integrated into the fault tree diagnostic method to calculate the posterior probability triggered by each fault tree to locate where on the fault tree and ensure high efficiency of fault diagnosis. The proposed methodology is applied to the fault diagnosis of fuzzy probabilistic analysis in the Φ 160 CNC boring and milling machine. The results indicate that the proposed approach is very reliable and effective in fault diagnosis considering uncertainty reduction or handling.

Surface plasmon resonance (SPR) is one of the most sensitive label-free detection methods and has been used in a wide range of chemical and biochemical sensing. Upon using a 200 nm top layer of dielectric film with a high value of the real part ε ′ of the dielectric function, on top of an SPR sensor in the Kretschmann configuration, the sensitivity is improved. The refractive index effect of dielectric film on sensitivity is usually ignored. Dielectric films with different refractive indices were prepared by radio frequency magnetron (RF) sputtering and measured with spectroscopic ellipsometry (SE). The imaginary part ε ′′ of the top nanolayer permittivity needs to be small enough in order to reduce the losses and get sharper dips. The stability of the sensor is also improved because the nanolayer is protecting the Ag film from interacting with the environment. The response curves of the Ag/ZnO chips were obtained by using SPR sensor. Theoretical analysis of the sensitivity of the SPR sensors with different ZnO film refractive indices is presented and studied. Both experimental and simulation results show that the Ag/ZnO films exhibit an enhanced SPR over the pure Ag film with a narrower full width at half maximum (FWHM). It shows that the top ZnO layer is effective in enhancing the surface plasmon resonance and thus its sensitivity.

This study investigates therapeutic efficacy of photothermal therapy (PTT) in an orthotropic xenograft model of bone metastasis of breast cancer. The near-infrared (NIR) irradiation on Multi-Walled Carbon Nanotubes (MWNTs) resulted in a rapid heat generation which increased with the MWNTs concentration up to 100 μg/ml. MWNTs alone exhibited no toxicity, but inclusion of MWNTs dramatically decreased cell viability when combined with laser irradiation. Thermographic observation revealed that treatment with 10 μg MWNTs followed by NIR laser irradiation resulted in a rapid increase in temperature up to 73.4±11.98 °C in an intraosseous model of bone metastasis of breast cancer. In addition, MWNTs plus NIR laser irradiation caused a remarkably greater suppression of tumor growth compared with treatment with either MWNTs injection or NIR irradiation alone, significantly reducing the amount of tumor-induced bone destruction. All these demonstrate the efficacy of PTT with MWNTs for bone metastasis of breast cancer.

Silicon carbon nitride (SiCN) nanowires, nanorods and nanotubes have gained much attention due to their excellent field emission and photoluminescence properties. These nanostructures were usually grown using catalysts at high temperature (800–1000 °C). In this paper, synthesis of SiCN nanostructures at a temperature less than 500°C is reported. Various kinds of SiCN nanostructures were synthesized using microwave plasma chemical vapor deposition method. Gas mixtures of CH4, H2 and N2 were used as precursors and Si chips were inserted in the sample holder at symmetrical positions around the specimen as additional Si sources. Metallic gallium was used as the liquid medium in a mechanism similar to vapor-liquid-solid. Morphologies of the resultant were characterized by field emission scanning electron microscopy. Energy dispersive spectrometry and X-ray photoelectron spectroscopy were used to characterize their compositions and bonding states.

Graphitic carbon (GC) is prepared using an ion-exchange resin as carbon source at 600 °C. A Co salt is selected as the graphitization catalyst and is pre-exchanged onto the resin during the ion-exchange process. The GC is characterized by transmission electron microscopy, x-ray diffraction, Raman spectroscopy, and thermogravimetry. Analysis of the crystallization shows that graphitization can occur at a temperature of as low as 600 °C, compared to the usual temperature of above 2000 °C in industry and above 1000 °C in literature. Different carbon structures have been found for different pretreatments of the resin and different heat treatment temperatures. This energy-saving method is an important breakthrough for the economic mass production of GC.