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Objective. Abnormal lipid metabolism is known to influence the malignant behavior of gastric cancer. However, the underlying mechanism remains elusive. In this study, we comprehensively analyzed the biological significance of genes involved in lipid metabolism in advanced gastric cancer (AGC). Methods. We obtained gene expression profiles from The Cancer Genome Atlas (TCGA) database for early and advanced gastric cancer samples and performed differential expression analysis to identify specific lipid metabolism-related genes in AGC. We then used consensus cluster analysis to classify AGC patients into molecular subtypes based on lipid metabolism and constructed a diagnostic model using least absolute shrinkage and selection operator- (LASSO-) Cox regression analysis and Gene Set Enrichment Analysis (GSEA). We evaluated the discriminative ability and clinical significance of the model using the Kaplan-Meier (KM) curve, ROC curve, DCA curve, and nomogram. We also estimated immune levels based on immune microenvironment expression, immune checkpoints, and immune cell infiltration and obtained hub genes by weighted gene co-expression network analysis (WGCNA) of differential genes from the two molecular subtypes. Results. We identified 6 lipid metabolism genes that were associated with the prognosis of AGC and used consistent clustering to classify AGC patients into two subgroups with significantly different overall survival and immune microenvironment. Our risk model successfully classified patients in the training and validation sets into high-risk and low-risk groups. The high-risk score predicted poor prognosis and indicated low degree of immune infiltration. Subgroup analysis showed that the risk model was an independent predictor of prognosis in AGC. Furthermore, our results indicated that most chemotherapeutic agents are more effective for AGC patients in the low-risk group than in the high-risk group, and risk scores for AGC are strongly correlated with drug sensitivity. Finally, we performed qRT-PCR experiments to verify the relevant results. Conclusion. Our findings suggest that lipid metabolism-related genes play an important role in predicting the prognosis of AGC and regulating immune invasion. These results have important implications for the development of targeted therapies for AGC patients. [ABSTRACT FROM AUTHOR]

Using the efficient, space-saving, and flexible supercritical carbon dioxide (sCO2) Brayton cycle is a promising approach for improving the performance of nuclear-powered ships. The purpose of this paper is to design and compare sCO2 cycle power systems suitable for nuclear-powered ships. Considering the characteristics of nuclear-powered ships, this paper uses different indicators to comprehensively evaluate the efficiency, cost, volume, and partial load performance of several nuclear-powered sCO2 cycles. Four load-following strategies are also designed and compared. The results show that the partial cooling cycle is most suitable for nuclear-powered ships because it offers both high thermal efficiency and low volume and cost, and can maintain relatively high thermal efficiency at partial loads. Additionally, the new load-following strategy that adjusts the turbine speed can keep the compressor away from the surge line, making the cycle more flexible and efficient compared to traditional inventory and turbine bypass strategies. [ABSTRACT FROM AUTHOR]