[Preparation of baicalein using thermophilic and sugar-tolerant beta-glucosidase].

The reaction conditions of baicalin hydrolyzed into baicalein by a kind of thermophilic and sugar-tolerant beta-glucosidase were studied in this paper. The beta-glucosidase could catalyze baicalin into baicalein well in the acetic acid-sodium acetate buffer. The optimal enzyme activity was at 85 degrees C and pH 5.5. The enzyme was stable at the temperature less than 85 degrees C and pH range of 5-7.5. The maximum reaction rate V. and michaelis constant K. were 0.41 mmol x L(-1) x min(-1) and 3.31 mmol x L(-1) respectively. Different metal ions had different effects on the activity of enzyme. Na+ existing in acetic acid-sodium acetate buffer had an activation effect on enzyme. The enzyme activity was enhanced by the concentrations of glucose below 0.6 mol x L(-1), and was gradually inhibited when monosaccharide concentration was over 0.6 mol x L(-1). When the monosaccharide concentration reached 1.2 mol x L(-1), the inhibition rate of enzyme activity was about 50%, which showed good glucose tolerance. The good reaction conditions through the experiment have been determined as follows, the substrate: enzyme dose was 1 g: 0.2 mL, acetic acid-sodium acetate buffer pH 5.5, reaction temperature 85 degrees C, reaction time 10 h, and the enzymatic hydrolyzation ratio could reach 97%.

Desorption corona beam ionisation (DCBI) mass spectrometry for in-situ analysis of adsorbed phenol in cigarette acetate fiber filter.

The study of spatial distribution characteristics of the adsorbed compounds for absorbent materials has significant importance in understanding the behaviors of aerosols while they migrating in the absorbent materials. Herein, for the first time, desorption corona beam ionization-mass spectrometry (DCBI-MS) has proposed for direct in-situ analysis of adsorbed aerosol for absorbent materials. DCBI is a novel atmospheric pressure chemical ionization (APCI)-related technique developed by our group in recent years. It can facilitate accurately localizing sampling by forming a visible thin corona beam and avoid the risk of sample contamination and matrix interference compared with other similar techniques. The advantages of DCBI-MS allow rapid screening of the spatial distribution characteristics of the adsorbed compounds for absorbent materials. The distribution characteristic of phenol in cigarette filter tip filled with cellulose acetate fiber was studied as a model case for demonstrating the feasibility of the developed method. As a comparison, conventional HPLC was also used for the study of the distribution characteristic of phenol. The results revealed DCBI-MS had highly improved assay simplicity in spatial distribution characteristic analysis of phenol for the acetate fiber tip, therefore, exhibiting a great potential for convenient, rapid and cost-efficient analysis of the spatial distribution characteristic investigation of adsorbed compounds for adsorbent materials.

Adaptive variable-weighted support vector machine as optimized by particle swarm optimization algorithm with application of QSAR studies.

Representing a compound by a numerous structural descriptors becomes common in quantitative structure-activity relationship (QSAR) studies. As every descriptor carries molecular structure information more or less, it seems more advisable to investigate all the possible descriptor vectors rather than traditional variable selection when building a QSAR model. Based on particle swarm optimization (PSO) algorithm, a more flexible descriptor selection and model construction method variable-weighted support vector machine (VW-SVM) is proposed. The new strategy adopted in this paper is to weight all structural descriptors with continuous non-negative values rather than removing or reserving any ones arbitrarily. The manner of invoking PSO to seek non-negative weights of variables can be regarded as a process of searching optimized rescaling for every molecular structural descriptor. Moreover, PSO is employed to search the optimal parameters of VW-SVM model besides variable weights, enables the construction of a rational and adaptive parameter-free QSAR model according to the performance of the total model. Results obtained by investigating glycogen synthase kinase-3α inhibitors and carbonic anhydrase II inhibitors indicate VW-SVM can hold more useful structure information of compounds than other methods as optimally weighting all the descriptors, consequently leading to precisely QSAR models coupled with developed performance both in training and in prediction.