An efficient method for the preparative isolation and purification of alkaloids from Gelsemium by using high speed counter-current chromatography and preparative HPLC.

We established an efficient method using high-speed countercurrent chromatography (HSCCC) combined with preparative high-performance liquid chromatography (prep-HPLC) for isolating and purifying Gelsemium elegans (G. elegans) alkaloids. First, the two-phase solvent system composed of 1% triethylamine aqueous solution/n-hexane/ethyl acetate/ethanol (volume ratio 4:2:3:2) was employed to separate the crude extract (350 mg) using HSCCC. Subsequently, the mixture that resulted from HSCCC was further separated by Prep-HPLC, resulting in seven pure compounds including: 14-hydroxygelsenicine (1, 12.1 mg), sempervirine (2, 20.8 mg), 19-(R)-hydroxydihydrogelelsevirine (3, 10.1 mg), koumine (4, 50.5 mg), gelsemine (5, 32.2 mg), gelselvirine (6, 50.5 mg), and 11-hydroxyhumanmantenine (7, 12.5 mg). The purity of these seven compounds were 97.4, 98.9, 98.5, 99, 99.5, 96.8, and 85.5%, as determined by HPLC. The chemical structures of the seven compounds were analyzed and confirmed by electrospray ionization mass spectrometry (ESI-MS), 1H-nuclear magnetic resonance (1H NMR), and 13 C-nuclear magnetic resonance (13 C NMR) spectra. The results indicate that the HSCCC-prep-HPLC method can effectively separate the major alkaloids from the purified G. elegans, holding promising prospects for potential applications in the separation and identification of other traditional Chinese medicines.

Characteristics of pine wood nematode disease in Nankang District, Ganzhou, Jiangxi Province, China.

Pine wood nematode (PWN) disease is one of the major disasters in forests of southern China, causing substantial forest resources and ecological and economic losses. Based on field surveys and WFV image data from the GF-1 satellite, we constructed a spatial identification model of PWN disease with the random forest model to explore the relative influences of topography, human activities and stand factors on the occurrence of diseases and predict their spatial distribution. We then used the spatial autocorrelation analysis to assess the distribution characteristics of PWN disease at the regional scale. The results showed that the random forest model constructed in this study was effective in identifying pine nematode diseases (AUC value=0.99, overall accuracy=0.96). The norma-lized difference greenness index (NDGI), the distance to the highway, and normalized vegetation index (NDVI) were important factors in explaining the spatial variations of PWN disease occurrence. There was a positive spatial correlation in the occurrence of PWN disease (not randomly distributed but with obvious spatial aggregation characteristics). The high occurrence areas of pine wood nematode disease concentrated in Chitu Township, Zhufang Township and Shibatang Township, low occurrence areas concentrated in the vicinity of Rongjiang Street. The areas far away from the highway, low in elevation, and close to county roads were suffered to PWN disease. The results could serve the regional monitoring of pine nematode disease occurrence and provide practical guidance for PWN disease management.