Type 2 diabetes mellitus (T2DM) is a chronic disease characterized by hyperglycemia and dyslipidemia caused by impaired insulin secretion and resistance of the peripheral tissues. A major pathogenesis of T2DM is obesity-associated insulin resistance. Gynura divaricata (L.) DC. (GD) is a natural plant and has been reported to have numerous health-promoting effects on both animals and humans. In this study, we aimed to elucidate the regulatory mechanism of GD improving glucose and lipid metabolism in an obesity animal model induced by high-fat and high-sugar diet in combination with low dose of streptozocin and an insulin-resistant HepG2 cell model induced by dexamethasone. The study showed that the water extract of GD (GD extract A) could significantly reduce fasting serum glucose, reverse dyslipidemia and pancreatic damage, and regulate the body weight of mice. We also found that GD extract A had low toxicity in vivo and in vitro. Furthermore, GD extract A may increase glucose consumption in insulin-resistant HepG2 cells, markedly inhibit NF-κB activation, and decrease the impairment in signaling molecules of insulin pathway, such as IRS-1, AKT, and GLUT1. Overall, the results indicate that GD extract A is a promising candidate for the prevention and treatment of T2DM.
The Zuojin Pill consists of Coptidis Rhizoma (CR) and Euodiae Fructus (EF). It has been a classic prescription for the treatment of gastrointestinal diseases in China since ancient times. Alkaloids are considered to be its main pharmacologically active substances. The authors of the present study investigated the feasibility of preparing high purity total alkaloids (TAs) from CR and EF extracts separately and evaluated the effect for the treatment of bile reflux gastritis (BRG). Coptis chinensis Franch. and Evodia rutaecarpa (Juss.) Benth. were used in the study. An optimized method for the enrichment and purification of TAs with macroporous resin was established. Furthermore, qualitative analysis by using ultra-high performance liquid chromatography coupled with electrospray ionization and quadrupole-time of flight mass spectrometry (UHPLC–ESI–QTOF-MS) was explored to identify the components of purified TAs. Thirty-one compounds, thirty alkaloids and one phenolic compound, were identified or tentatively assigned by comparison with reference standards or literature data. A method of ultra-high performance liquid chromatography coupled with diode array detector (UHPLC–DAD) for quantitative analysis was also developed. The contents of nine alkaloids were determined. Moreover, a rat model of BRG was used to investigate the therapeutic effect of the combination of purified TAs from CR and EF. Gastric pathologic examination suggested that the alkaloids’ combination could markedly attenuate the pathological changes of gastric mucosa.
Flexible quantum dot light emitting diodes have attracted widespread attention due to their many advantages such as low cost, color tunability, and high luminance effciency. Among flexible electrodes, copper nanowires have attracted much attention due to their high electrical conductivity, simple fabrication process, and low cost. However, the oxidation and poor film quality of copper nanowires films are the barrier that restrict their practical application. In this paper, polyethylene terephthalate/copper nanowires/poly(p-phenylene benzobisoxazole) composite flexible electrode can solve the problems of oxidation and high surface roughness of copper nanowires, which improves the performance of flexible quantum dot light emitting diodes.
This paper proposes a data anomaly detection and correction algorithm for the tea plantation IoT system based on deep learning, aiming at the multi-cause and multi-feature characteristics of abnormal data. The algorithm is based on the Z-score standardization of the original data and the determination of sliding window size according to the sampling frequency. First, we construct a convolutional neural network (CNN) model to extract abnormal data. Second, based on the support vector machine (SVM) algorithm, the Gaussian radial basis function (RBF) and one-to-one (OVO) multiclassification method are used to classify the abnormal data. Then, after extracting the time points of abnormal data, a long short-term memory network is established for prediction with multifactor historical data. The predicted values are used to replace and correct the abnormal data. When multiple consecutive abnormal values are detected, a faulty sensor judgment is given, and the specific faulty sensor location is output. The results show that the accuracy rate and micro-specificity of abnormal data detection for the CNN-SVM model are 3–4% and 20–30% higher than those of the traditional CNN model, respectively. The anomaly detection and correction algorithm for tea plantation data established in this paper provides accurate performance.
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