Search

Oxidative damage is an important cause of aging. The antioxidant and anti-aging activities of Longan polysaccharides, especially purified Longan polysaccharides, have not been thoroughly investigated. Therefore, this study aimed to investigate the antioxidant and anti-aging activities and mechanisms of crude polysaccharides and purified polysaccharides from Longan. A purified acidic Longan polysaccharide LP-A was separated from Longan crude polysaccharide LP. Subsequently, its structural characterization, anti-aging activity and mechanism were studied. The results showed that LP-A was an acidic heteropolysaccharide with an average molecular weight (Mw) of 4.606 × 10 4  Da which was composed of nine monosaccharides. The scavenging rate of ABTS free radical in vitro reached 99 %. In the nematode life experiment, 0.3 mg/mL LP group and LP-A group could prolong the average lifespan of nematodes by 9.31 % and 25.80 %, respectively. Under oxidative stress stimulation, LP-A group could prolong the survival time of nematodes by 69.57 %. In terms of mechanism, Longan polysaccharide can regulate insulin / insulin-like growth factor (IIS) signaling pathway, increase the activity of antioxidant enzymes, reduce lipid peroxidation, enhance the body's resistance to stress damage, and effectively prolong the lifespan of nematodes. In conclusion, LP-A has better anti-aging activity than crude polysaccharide LP, which has great potential for developing as an anti-aging drug., Competing Interests: Declaration of competing interest The authors declare no conflicts of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Currently, extraction process optimization is generally based on a few features, regardless of their different changing trends and the panoramic view of the extraction process. Comprehensive evaluation and understanding is hard to establish due to the small number of experiments. Here, machine learning-assisted optimization is demonstrated for better understanding the complex extraction process based on data from an orthogonal experimental design (OED). From two perspectives of panoramic characteristics and specific characteristics, several observations are adopted to evaluate the performance of the extraction process, including quantitative 1H NMR, HPLC fingerprint, molecular weight, yield of dry extract and content of components. The close relationship between influencing factors and the extraction performance is described by grey relation analysis. With the help of radial basis function neural network (RBFNN), a nonlinear fitting regression equation is developed for every observation and influencing factor. A genetic algorithm is then introduced for multi-objective optimization and Pareto fronts are obtained. To select the best combination of water extraction process and ethanol extraction process, a list of the combinations of Pareto front points from those extraction processes is formed and ranked using CRITIC-TOPSIS. Finally, the ideal extraction is characterized by molecular weight, monosaccharide composition and UHPLC-MS/MS. With the verification between OED experiments and machine learning, the changing rates of all observations range from 1.33% to 30.11%, which confirms that machine learning-assisted optimization gives better performance than conventional OED. Molecular weight could range from 61.5~594.9 kDa with some are over measuring range, furthermore mannose and glucose are the most abuntant monosaccharides of the polysaccharide from ideal extraction. 160 components are identified via UHPLC-MS/MS as well. In conclusion, ML is a powerful tool for predicting and understanding extraction processes, thus accelerating the development of eco-friendly extraction processes. [ABSTRACT FROM AUTHOR]