Extraction of pectins from renewable grapefruit (Citrus paradisi) peels using deep eutectic solvents and analysis of their structural and physicochemical properties.

This study presents an innovative attempt to extract high-quality pectins from grapefruit (Citrus paradisi) peels by using deep eutectic solvents (DESs) as extraction agents. The maximum yield of betaine-citric acid (BC)-extracted pectin (BC-P) reached 36.47 % under the optimum process conditions: an L/S ratio of 25 mL/g, a pH of 2.0, and a temperature of 85 °C for 120 min. The yield of BC-P was significantly higher than HCl-extracted pectin (HCl-P, 8.76 %) under a pH of 2.0. In addition, the structural, physicochemical, and emulsifying properties of the purified pectins (BC-P and HCl-P) and commercial pectin (CP) were comparatively analyzed. Results showed that BC-P exhibited higher RG-I value, more arabinan side-chains, bigger Mw and Mn value than HCl-P. Moreover, the viscosity, G' and G'' of BC-P were significantly higher than those of HCl-P and CP. More importantly, BC-P demonstrated better emulsifying activity and stability compared to HCl-P and CP. When the concentration of BC-P was increased to 1.50 %, a stable emulsion containing a 50 % soybean oil fraction could be obtained. Our results confirmed that DESs can be considered as high-effective agents for pectin extraction. Pectins extracted from grapefruit peels can be as a promising natural emulsifiers that can be used in the food industry.

Identification and Validation of Constructing the Prognostic Model With Four DNA Methylation-Driven Genes in Pancreatic Cancer.

Background: Pancreatic cancer (PC) is a highly aggressive gastrointestinal tumor and has a poor prognosis. Evaluating the prognosis validly is urgent for PC patients. In this study, we utilized the RNA-sequencing (RNA-seq) profiles and DNA methylation expression data comprehensively to develop and validate a prognostic signature in patients with PC. Methods: The integrated analysis of RNA-seq, DNA methylation expression profiles, and relevant clinical information was performed to select four DNA methylation-driven genes. Then, a prognostic signature was established by the univariate, multivariate Cox, and least absolute shrinkage and selection operator (LASSO) regression analyses in The Cancer Genome Atlas (TCGA) dataset. GSE62452 cohort was utilized for external validation. Finally, a nomogram model was set up and evaluated by calibration curves. Results: Nine DNA methylation-driven genes that were related to overall survival (OS) were identified. After multivariate Cox and LASSO regression analyses, four of these genes (RIC3, MBOAT2, SEZ6L, and OAS2) were selected to establish the predictive signature. The PC patients were stratified into two groups according to the median risk score, of which the low-risk group displayed a prominently favorable OS compared with the high-risk group, whether in the training (p < 0.001) or validation (p < 0.01) cohort. Then, the univariate and multivariate Cox regression analyses showed that age, grade, risk score, and the number of positive lymph nodes were significantly associated with OS in PC patients. Therefore, we used these clinical variables to construct a nomogram; and its performance in predicting the 1-, 2-, and 3-year OS of patients with PC was assessed via calibration curves. Conclusion: A prognostic risk score signature was built with the four alternative DNA methylation-driven genes. Furthermore, in combination with the risk score, age, grade, and the number of positive lymph nodes, a nomogram was established for conveniently predicting the individualized prognosis of PC patients.