BackgroundUlcerative colitis (UC) is a chronic and debilitating inflammatory bowel disease that impairs quality of life. Cuproptosis, a recently discovered form of cell death, has been linked to many inflammatory diseases, including UC. This study aimed to examine the biological and clinical significance of cuproptosis-related genes in UC.MethodsThree gene expression profiles of UC were obtained from the Gene Expression Omnibus (GEO) database to form the combined dataset. Differential analysis was performed based on the combined dataset to identify differentially expressed genes, which were intersected with cuproptosis-related genes to obtain differentially expressed cuproptosis-related genes (DECRGs). Machine learning was conducted based on DECRGs to identify signature genes. The prediction model of UC was established using signature genes, and the molecular subtypes related to cuproptosis of UC were identified. Functional enrichment analysis and immune infiltration analysis were used to evaluate the biological characteristics and immune infiltration landscape of signature genes and molecular subtypes.ResultsSeven signature genes (ABCB1, AQP1, BACE1, CA3, COX5A, DAPK2, and LDHD) were identified through the machine learning algorithms, and the nomogram built from these genes had excellent predictive performance. The 298 UC samples were divided into two subtypes through consensus cluster analysis. The results of the functional enrichment analysis and immune infiltration analysis revealed significant differences in gene expression patterns, biological functions, and enrichment pathways between the cuproptosis-related molecular subtypes of UC. The immune infiltration analysis also showed that the immune cell infiltration in cluster A was significantly higher than that of cluster B, and six of the characteristic genes (excluding BACE1) had higher expression levels in subtype B than in subtype A.ConclusionsThis study identified several promising signature genes and developed a nomogram with strong predictive capabilities. The identification of distinct subtypes of UC enhances our current understanding of UC’s underlying pathogenesis and provides a foundation for personalized diagnosis and treatment in the future.

No abstract

Combination flooding, which performs well on both sweep efficiency and displacement efficiency, is being extensively utilized in Daqing Oilfield. Surfactant-polymer (SP) flooding and alkaline-surfactant-polymer (ASP) flooding are two of the most used combination flooding techniques. According to the findings, alkaline lowers the oil-water interfacial tension and reduces the other component’s adsorption. Nevertheless, alkaline also has negative effects like scaling and cost increasing. Therefore, the use of alkaline in combination flooding is still disputed. In order to clarify the role of alkaline on combination flooding, SP and ASP flooding comparative tests are conducted with typical formula in Daqing Oilfield. A 3000-centimeter long model is built to simulate long distance migration for the property change study. During the flooding, fluid samples are obtained along the model, and the properties of the samples such as viscosity, interfacial tension (IFT), and components concentration are measured. According to the findings, the 3.60 percent higher recovery rate caused by ASP flooding compared to SP flooding is primarily due to the near inlet area. In ASP flooding, alkaline reduces polymer and surfactant near inlet dynamic retention by 20.16% and 13.43%, respectively, when compared to SP flooding. Besides, alkaline also maintains viscosity at a higher level and IFT at a lower level in the first third of the well spacing in ASP flooding. Consequently, the effects of alkaline are noticeable in the near inlet area and fade as distance increase.