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The extraction efficiency, physicochemical properties, and structural characteristics of β-glucan extracted from highland barley branby sequential microwave-ultrasound-assisted extraction were explored. At the same heating rate, microwave enhanced α-amylase activity. The extraction efficiency of highland barley β-glucan was correlated with ultrasound power and treatment time. An ultrasound power of 600 W for 30 min and microwave heating at 60 ℃ for 30 min resulted in the maximum β-glucan yield of (6.30 ± 0.38)%. The results of physicochemical properties showed that with increasing sonication time up to 40 min, the solubility and foaming capacity of barley β-glucan increased significantly (P < 0.05), while the turbidity and emulsifying capacity decreased significantly (P < 0.05). The results of particle size distribution showed that with increasing sonication time, the relative molecular mass of barley β-glucan decreased, and ultrasound changed the rheological behavior of β-glucan, decreasingits viscosity and resulting in shear thinning. The infrared spectroscopic results showed that sonication did not change the functional groups of β-glucan, but it caused partial breakage of the glycosidic bonds. The microscopic results showed that ultrasonic treatment led to a looser structure of barley β-glucan, which was conducive to improving its extraction efficiency. Therefore, the microwave-ultrasound-assisted extraction of β-glucan from highland barley provides a basis for developing new food types and functional products.

Obesity is a metabolic syndrome characterized by abnormal lipid deposition and energy imbalance. CD38 is a single-chain transmembrane glycoprotein widely expressed in a variety of cell types. The roles of skeletal muscle and brown fat in CD38 deficiency under HFD-induced obesity remain unknown. In this study, we established obesity model with HFD and examined the changes in metabolites with metabonomics. Our results showed that CD38 expression was increased in muscle and brown fat after HFD treatment. Moreover, the results of metabonomics showed that CD38 deficiency significantly altered the metabolites in energy metabolism, cofactor generation, and redox homeostasis. Furthermore, CD38 deficiency reduced the expressions of NADPH oxidase 2 and FASN in mRNA level. We found that the expressions of Sirt1, Sirt3, and PGC1[alpha] were upregulated in CD38-deficient muscle tissue. In brown fat, the Sirt1-3, cell death inducing DFFA-like effector A, ELOVL3, and Di[O.sub.2] expressions were increased in CD38-deficient mice. Our results showed the uncoupling protein 1 expression was upregulated. And [NAD.sup.+] supplementation increased the expression of Sirt1 and PGC1[alpha] after palmitic acid treatment. Taken together, our results demonstrated that the protection of CD38 deficiency on HFD-induced obesity was related to the inhibition of oxidative stress and increasing energy expenditure via activating [NAD.sup.+]/Sirtuins signaling pathways in muscle and brown fat. Keywords: CD38, Sirt1, energy expenditure, PGC1[alpha], Introduction Obesity is a chronic metabolic disease caused by the interaction of genetic and environmental factors. Over the past ~40 years, the prevalence of obesity has increased in pandemic dimensions [...]

Byline: Jing-Yun. Xu, Tian-Xiang. Yu, Xiao-Ming. Guan, Bo. Ding, Mu-Lan. Ren, Yang. Shen Introduction: Laparoendoscopic single-site inguinal lymphadenectomy (LESS-IL), a minimally invasive technique, has been reported in patients with vulvar [...]

Brain radiomics can reflect the characteristics of brain pathophysiology. However, the value of T1-weighted images, quantitative susceptibility mapping, and R2* mapping in the diagnosis of Parkinson's disease (PD) was underestimated in previous studies. In this prospective study to establish a model for PD diagnosis based on brain imaging information, we collected high-resolution T1-weighted images, R2* mapping, and quantitative susceptibility imaging data from 171 patients with PD and 179 healthy controls recruited from August 2014 to August 2019. According to the inclusion time, 123 PD patients and 121 healthy controls were assigned to train the diagnostic model, while the remaining 106 subjects were assigned to the external validation dataset. We extracted 1408 radiomics features, and then used data-driven feature selection to identify informative features that were significant for discriminating patients with PD from normal controls on the training dataset. The informative features so identified were then used to construct a diagnostic model for PD. The constructed model contained 36 informative radiomics features, mainly representing abnormal subcortical iron distribution (especially in the substantia nigra), structural disorganization (e.g., in the inferior temporal, paracentral, precuneus, insula, and precentral gyri), and texture misalignment in the subcortical nuclei (e.g., caudate, globus pallidus, and thalamus). The predictive accuracy of the established model was 81.1 ± 8.0% in the training dataset. On the external validation dataset, the established model showed predictive accuracy of 78.5 ± 2.1%. In the tests of identifying early and drug-naïve PD patients from healthy controls, the accuracies of the model constructed on the same 36 informative features were 80.3 ± 7.1% and 79.1 ± 6.5%, respectively, while the accuracies were 80.4 ± 6.3% and 82.9 ± 5.8% for diagnosing middle-to-late PD and those receiving drug management, respectively. The accuracies for predicting tremor-dominant and non-tremor-dominant PD were 79.8 ± 6.9% and 79.1 ± 6.5%, respectively. In conclusion, the multiple-tissue-specific brain radiomics model constructed from magnetic resonance imaging has the ability to discriminate PD and exhibits the advantages for improving PD diagnosis.