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. Moreover, the extracts of the Paulownia flower have been proven safe for animals. These promote the development of new products and technologies using Paulownia flowers, with intellectual property rights. The review presents the current developments on the chemical composition, biological and pharmacological activities, and economic values of Paulownia flowers, and aims to provide a reference for their further utilization.

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). The results showed that the insulin sensitivity of ZAG-treated mice was significantly improved. The body weight, WAT weight, and intramuscular fat were significantly decreased in the HFZ group compared with the HFD group. The lipolytic enzymes, including phosphorylation of hormone-sensitive lipase and adipose triglyceride lipase, were significantly upregulated in the skeletal muscle of mice that received the ZAG treatment compared with the HFD group. Insulin signaling proteins, such as phosphorylation of insulin receptor substrate 1 and cell membrane glucose transporter type 4, were also significantly increased in the skeletal muscle of the ZAG-treated group. Furthermore, a metabolic rate study showed that ZAG overexpression increases the respiratory exchange ratio and heat production. In vitro, ZAG treatment promotes glucose uptake and decreases intracellular lipids in C2C12 myotubes. Taken together, these data showed that overexpression of ZAG alleviates HFD-induced insulin resistance in mice, along with decreasing the lipid content of skeletal muscle.

[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. RMSECVs of the models for the constituents studied were 2.572, 0.442, 2.794 and 9.438, respectively. RMSEPs for the were 4.598, 8.657, 0.4586, and 5.106, respectively. The method is fast, and satisfactory results were obtained. The proposed method can be used for determining the active constituents in Chinese herbal medicine.

[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%, 1.10%, 4.41%, 0.68%, 1.44%, 1.88%, 1.29% and 0.03% respectively, and the percentage recovery is 90.0%-111.0%. The method is convenient and accurate. It can be used to determine the trace elements simultaneousy in Dioscorea opposita thumb.

[Radial basis function networks and IR spectrometry applied for identification of official rhubarb samples].

The Fourier transform infrared spectrometry (FTIRS) and radial basis function neural network (RBF-NN) have been applied to develop classification models for identifying official and unofficial rhubarb samples. The original data were compressed from 775 variables to 49 variables by using wavelet transformation method. The compressed spectra with reduced variables maintain the characteristics of the IR spectra and speed up the network training process. The effects of network parameters including error goal and spread constant, were investigated. The rate of correct classification is up to 97.78% at optimized conditions. Results show that the combination of IRS and ANN can be used as fast and convenient tool for identification of Chinese herbal samples.

[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. 52 rhubarb samples in which 25 official and 27 unofficial rhubarb samples are included have been used to network modeling. The effects of neuron number in hidden layer and momentum parameter on classification have been investigated. Results showed that about 98% rhubarb samples could be identified correctly when optimized parameters were used. This method can be useful for quality control in rhubarb-contained Chinese medicine production.

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 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. The results obtained by using Takagi-Sugeno fuzzy system were better than those by commonly used BP-networks. With a proper network training parameter, 100% correctness can be obtained by using Takagi-Sugeno fuzzy system. This method is more accurate than the common methods, and is more scientific than traditional methods. So it is applied to identifying rhubarb easily and rapidly.

[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. The measured spectra were compressed by wavelet transformation (WT) and then the ANN classification models were trained with the reduced-variables spectral data. The rate of correct classification was over 96%. The effects of neurons in hidden layer and the momentum were also discussed. Owing to its fast and nondestructive properties, NIRS is a promising approach to identifying Chinese medicinal herbs.