Healthy regulation of Tibetan Brassica rapa L. polysaccharides on alleviating hyperlipidemia: A rodent study

Hyperlipidemia is a common metabolic disorder, which can lead to obesity, hypertension, diabetes, atherosclerosis and other diseases. Studies have shown that polysaccharides absorbed by the intestinal tract can regulate blood lipids and facilitate the growth of intestinal flora. This article aims to investigate whether Tibetan turnip polysaccharide (TTP) plays a protective role in blood lipid and intestinal health via hepatic and intestinal axes. Here we show that TTP helps to reduce the size of adipocytes and the accumulation of liver fat, playing a dose-dependent effect on ADPN levels, suggesting an effect on lipid metabolism regulation. Meantime, TTP intervention results in the downregulation of intercellular cell adhesion molecule-1 (ICAM-1), vascular cell adhesion molecule-1 (VCAM-1) and serum inflammatory factors (interleukin-6 (IL-6), interleukin-1β (IL-1β) and tumor necrosis factor-α (TNF-α)), implying that TTP suppresses the progression of inflammation in the body. The expression of key enzymes associated with cholesterol and triglyceride synthesis, such as 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR), cholesterol 7α-hydroxylase (CYP7A1), peroxisome proliferator-activated receptors γ (PPARγ), acetyl-CoA carboxylase (ACC), fatty acid synthetase (FAS) and sterol-regulatory element binding proteins-1c (SREBP-1c), can be modulated by TTP. Furthermore, TTP also alleviates the damage to intestinal tissues caused by high-fat diet, restores the integrity of the intestinal barrier, improves the composition and abundance of the intestinal flora and increases the levels of SCFAs. This study provides a theoretical basis for the regulation of body rhythm by functional foods and potential intervention in patients with hyperlipidemia

Searching for Barium Stars from the LAMOST Spectra Using the Machine-learning Method: I

Barium stars are chemically peculiar stars that exhibit enhancement of s -process elements. Chemical abundance analysis of barium stars can provide crucial clues for the study of the chemical evolution of the Galaxy. The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has released more than 6 million low-resolution spectra of FGK-type stars by Data Release 9, which can significantly increase the sample size of barium stars. In this paper, we used machine-learning algorithms to search for barium stars from low-resolution spectra of LAMOST. We have applied the Light Gradient Boosting Machine (LGBM) algorithm to build classifiers of barium stars based on different features, and build predictors for determining [Ba/Fe] and [Sr/Fe] of barium candidates. The classification with features in the whole spectrum performs best: for the sample with strontium enhancement, Precision = 97.81% and Recall = 96.05%; for the sample with barium enhancement, Precision = 96.03% and Recall = 97.70%. In prediction, [Ba/Fe] estimated from Ba ii line at 4554 Å has smaller dispersion than that from Ba ii line at 4934 Å: MAE _4554 Å = 0.07, σ _4554 Å = 0.12. [Sr/Fe] estimated from Sr ii line at 4077 Å performs better than that from Sr ii line at 4215 Å: MAE _4077 Å = 0.09, σ _4077 Å = 0.16. A comparison of the LGBM and other popular algorithms shows that LGBM is accurate and efficient in classifying barium stars. This work demonstrated that machine learning can be used as an effective means to identify chemically peculiar stars and determine their elemental abundance