An evaluation of the ultrasonic extraction process and the antioxidant activities of hydroxysafflor yellow A (HSYA) and anhydrosafflor yellow B (AHSYB) from safflower are presented herein. Using response surface methodology (RSM), based on a four-factor-three-level Box–Behnken design (BBD), the extraction parameters, namely, temperature, extraction time, solvent-to-material ratio, and extraction power, were optimized for maximizing the yields of HSYA and AHSYB. The maximum yield was obtained at a temperature of 66 °C with an extraction time of 36 min, solvent-to-material ratio of 16 mL/g, and the extraction power of 150 W, which was adjusted according to the actual conditions. The HSYA and AHSYB contents were determined using high performance liquid chromatography (HPLC). The yield and the comprehensive evaluation value of HSYA and AHSYB were calculated. The antioxidant activities of the extracts were determined using a ferric reducing antioxidant power (FRAP) kit and 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging activity. The results suggested that the safflower extracts possessed obvious ferric reducing and DPPH radical scavenging activities. The antioxidant activity increased with increasing concentration. The results suggested that optimizing the conditions of ultrasonic extraction using RSM can significantly increase the yields of HSYA and AHSYB from safflower. The safflower extracts showed better antioxidant activity. This study can encourage future research on cardiovascular and cerebrovascular diseases.

Background: Stomach adenocarcinoma (STAD) is a significant global health problem. It is urgent to identify reliable predictors and establish a potential prognostic model.Methods: RNA-sequencing expression data of patients with STAD were downloaded from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) database. Gene expression profiling and survival analysis were performed to investigate differentially expressed genes (DEGs) with significant clinical prognosis value. Overall survival (OS) analysis and univariable and multivariable Cox regression analyses were performed to establish the prognostic model. Protein–protein interaction (PPI) network, functional enrichment analysis, and differential expression investigation were also performed to further explore the potential mechanism of the prognostic genes in STAD. Finally, nomogram establishment was undertaken by performing multivariate Cox regression analysis, and calibration plots were generated to validate the nomogram.Results: A total of 229 overlapping DEGs were identified. Following Kaplan–Meier survival analysis and univariate and multivariate Cox regression analysis, 11 genes significantly associated with prognosis were screened and five of these genes, including COL10A1, MFAP2, CTHRC1, P4HA3, and FAP, were used to establish the risk model. The results showed that patients with high-risk scores have a poor prognosis, compared with those with low-risk scores (p = 0.0025 for the training dataset and p = 0.045 for the validation dataset). Subsequently, a nomogram (including TNM stage, age, gender, histologic grade, and risk score) was created. In addition, differential expression and immunohistochemistry stain of the five core genes in STAD and normal tissues were verified.Conclusion: We develop a prognostic-related model based on five core genes, which may serve as an independent risk factor for survival prediction in patients with STAD.