Optimization of Tertiary Alkaloids Separation from Corydalis yanhusuo by Macroporous Resins

Corydalis yanhusuo is used widely for the treatment of gastralgia, costalgia and dysmenorrhea in Chinese medicine. The alkaloid is the main active ingredient of C. yanhusuo. Response surface methodology was applied to optimize the separation and purification process for alkaloids by AB-8 resin-packed chromatogram column. The optimal conditions were found to be as follows: height-diameter ratio of AB-8 resin-packed chromatogram column, 10.50; concentration and pH of feed sample solution, 1.12 mg mL–1 and 7.16, respectively. The gradient elution program was 30 % ethanol for 2 BV (bed volume) followed by 80 % of ethanol for 5 BV at flow rate of 3 mL min–1. After the AB-8 resin treatment, the contents of alkaloids and tetrahydropalmatine were increased respectively from 25.20 % and 2.12 % to 58.25 % and 6.58 %, the recovery of alkaloids and tetrahydropalmatine were 85.40 % and 65.21 %, respectively. The results indicated that the optimization of alkaloid separation from C. yanhusuo by macroporous resins is feasible and efficient

Similarity-Based Classification in Partially Labeled Networks

We propose a similarity-based method, using the similarity between nodes, to address the problem of classification in partially labeled networks. The basic assumption is that two nodes are more likely to be categorized into the same class if they are more similar. In this paper, we introduce ten similarity indices, including five local ones and five global ones. Empirical results on the co-purchase network of political books show that the similarity-based method can give high accurate classification even when the labeled nodes are sparse which is one of the difficulties in classification. Furthermore, we find that when the target network has many labeled nodes, the local indices can perform as good as those global indices do, while when the data is sparce the global indices perform better. Besides, the similarity-based method can to some extent overcome the unconsistency problem which is another difficulty in classification.Comment: 13 pages,3 figures,1 tabl

{μ-2-[4-(Benzothia­zol-2-yl)benz­yl]-2-aza­propane-1,3-dithiol­ato-1:2κ4 S,S′:S,S′}bis­[tricarbonyl­iron(I)]

The title compound, [Fe2(C16H14N2S3)(CO)6], was prepared as the biomimetic model for the active site of iron-only hydrogenase. The structure is similar to the diiron subsite of the iron-only hydrogenase active site, and contains a diiron-aza­dithiol­ate moiety in which a boat six-membered ring is fused with a chair six-membered ring. The substituted benzyl group attached to the bridging N atom resides in an equatorial position. The sum of the C—N—C angles around this N atom [331.9 (12)°] indicates sp 3 hybridization

Dehydro­abietic acid

The title compound [systematic name: (1R,4aS,10aR)-7-iso­prop­yl-1,4a-dimethyl-1,2,3,4,4a,9,10,10a-octa­hydro­phen­anthrene-1-carboxylic acid], C20H28O2, has been isolated from disproportionated rosin which is obtained by isomerizing gum rosin with a Pd-C catalyst.. Two crystallographically independent mol­ecules exist in the asymmetric unit. In each mol­ecule, there are three six-membered rings, which adopt planar, half-chair and chair conformations. The two cyclo­hexane rings form a trans ring junction with the two methyl groups in axial positions. The crystal structure is stabilized by inter­molecular O—H⋯O hydrogen bonds

Inhibitory Effect of Phthalic Acid on Tyrosinase: The Mixed-Type Inhibition and Docking Simulations

Tyrosinase inhibition studies are needed due to the medicinal applications such as hyperpigmentation. For probing effective inhibitors of tyrosinase, a combination of computational prediction and enzymatic assay via kinetics was important. We predicted the 3D structure of tyrosinase, used a docking algorithm to simulate binding between tyrosinase and phthalic acid (PA), and studied the reversible inhibition of tyrosinase by PA. PA inhibited tyrosinase in a mixed-type manner with a Ki = 65.84 ± 1.10 mM. Measurements of intrinsic and ANS-binding fluorescences showed that PA induced changes in the active site structure via indirect binding. Simulation was successful (binding energies for Dock6.3 = −27.22 and AutoDock4.2 = −0.97 kcal/mol), suggesting that PA interacts with LEU73 residue that is predicted commonly by both programs. The present study suggested that the strategy of predicting tyrosinase inhibition based on hydroxyl groups and orientation may prove useful for screening of potential tyrosinase inhibitors