Chemical composition, health benefits and future prospects of Paulownia flowers: A review.

The plants of the genus Paulownia (Scrophulariaceae) have been gaining attention for wood production, and their flowers, which are a seasonal by-product, have been traditionally used in medicinal products. The phytochemistry and pharmacology of Paulownia flowers contribute to their economic uses in medicines, foods, animal feeds, and cosmetics. The chemical composition of Paulownia flowers is mostly flavonoids, phenylpropanoids, terpenoids, volatile components, polysaccharides, lignans, and iridoids, which exhibit various health benefits, such as antioxidant, anti-inflammatory, antibacterial, antiviral, anticancer, hypoglycemic, hypolipidemic, neuroprotective and immunoregulation activities.

ZAG alleviates HFD-induced insulin resistance accompanied with decreased lipid depot in skeletal muscle in mice.

Over the past two decades, intramuscular lipids have been viewed as a cause of insulin resistance due to their ability to suppress insulin-stimulated glucose uptake in skeletal muscle. Zinc-α2-glycoprotein (ZAG) is an adipokine involved in lipolysis of white adipose tissue (WAT). To investigate the action of ZAG on insulin resistance induced by a high-fat diet (HFD), which affects the intramuscular fat, mice were divided into three groups, normal diet, HFD, and ZAG treatment under HFD (HFZ).

[Quantitative prediction of active constituents in rhubarb by near infrared spectroscopy and radial basis function neural networks].

Near infrared spectroscopy (NIRS) and artificial neural networks were used for the quantitative prediction of four active constituents in rhubarb: anthraquinones, anthraquinone glucosides, stilbene glucosides, Tannins and related compounds. The near infrared spectra of the samples were acquired in 1100-2500 nm from powdered rhubarb samples. Four calibration models using radial basis function neural networks (RBFNN) were set up to correlate the spectra with the values determined by HPLC.

[Determination of nine trace elements in Dioscorea opposita thumb by flame atomic absorption spectrophotometry].

The contents of the potassium, calcium, sodium, magnesium, copper, zinc, iron, manganese, strontium and nickel in Dioscorea opposita thumb were determined. The relative standard deviation(RSD) of the first eight contents is 0.43%, 1.

[Identification of official rhubarb samples based on IR spectra and neural networks].

The Fourier transform infrared (IR) spectrometry and neural networks have been used to identification of official and un-official rhubarb samples in the present work. The IR spectra were compressed by using wavelet transform and then were normalized prior to network training. Spectra with 700 data points were compressed to 44 variables, therefore, the training process of neural networks were speed up.

[Identification of rhubarb samples based on IR spectra by using Takagi-Sugeno fuzzy systems].

Takagi-Sugeno fuzzy system is composed of several back-propagation neural networks (BP-NNs), and has some fuzzy logic properties. In this paper, the Takagi-Sugeno fuzzy logic system is applied to identifying official and unofficial rhubarb samples based on their infrared spectra. The effects of the number of hidden neurons and the momentum parameters on the prediction were investigated.

Two new constituents from Rheum sublanceolatum.

The chemical investigation of Rheum sublanceolatum has led to the isolation of two new constituents, the structures of which were identified on the basis of physical and spectroscopic analysis as 2-acetyl-3-methyl-6,8-dihydroxy-2,3,4,4-tetrahydronaphthalene-1-one-6-O-beta-D-glucopyranoside (1) and 2,5-dimethyl-7-hydroxychromone-7-O-beta-D-(6'-O-galloyl)-glucopyranoside (2).

[Identification of official rhubarb samples based on NIR spectra and neural networks].

Rhubarb is one of the most widely used Chinese medicinal herbs in China. Fast and accurate identification of official and unofficial rhubarb samples is most critical for quality control of Chinese medicine production. In the present paper near-infrared reflectance spectrometry (NIRS) and artificial neural network (ANN) were combined to develop classification models for identifying 52 official and unofficial rhubarb samples.