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A high-performance liquid chromatography—diode-array detection method was developed and validated to determine simultaneously eleven major alkaloids in Corydalis decumbens (Thunb.) Pers. The alkaloids detected were corlumidine, protopine, coptisine, tetrahydrojatrorrhizine, palmatine, berberine, sanguinarine, papaverine hydrochloride, tetrahydropalmatine, bicuculline, and corydaline. Chromatographic separation was achieved using a C-18 column with a mobile phase composed of A (0.2% acetic acid solution, adjusted with triethylamine to pH 5.0) and B (acetonitrile), with stepwise gradient elution. Ultraviolet diode-array detection was used; chromatograms were examined at the wavelength of 280 nm. The regression equations showed a good linear relationship between the peak area of each marker and concentration (r = 0.9994–0.9999). The recovery values ranged between 93.66% and 100.54%. The method was fully validated with respect to detection and quantification limits, precision, reproducibility, and accuracy. The described high-performance liquid chromatography (HPLC) method was successfully used for the differentiation and quantification of the eleven major alkaloids in C. decumbens (Thunb.) Pers. and can be considered an effective procedure for the analyses of this important class of natural compounds.

Open access
Journal of Behavioral Addictions
Authors: Ji-Bin Li, Anise M.S. Wu, Li-Fen Feng, Yang Deng, Jing-Hua Li, Yu-Xia Chen, Jin-Chen Mai, Phoenix K.H. Mo, and Joseph T.F. Lau


Background and aims

Problematic online social networking use is prevalent among adolescents, but consensus about the instruments and their optimal cut-off points is lacking. This study derived an optimal cut-off point for the validated Online Social Networking Addiction (OSNA) scale to identify probable OSNA cases among Chinese adolescents.


A survey recruited 4,951 adolescent online social networking users. Latent profile analysis (LPA) and receiver operating characteristic curve (ROC) analyses were applied to the validated 8-item OSNA scale to determine its optimal cut-off point.


The 3-class model was selected by multiple criteria, and validated in a randomly split-half subsample. Accordingly, participants were categorized into the low risk (36.4%), average risk (50.4%), and high risk (13.2%) groups. The highest risk group was regarded as “cases” and the rest as “non-cases”, serving as the reference standard in ROC analysis, which identified an optimal cut-off point of 23 (sensitivity: 97.2%, specificity: 95.2%). The cut-off point was used to classify participants into positive (probable case: 17:0%) and negative groups according to their OSNA scores. The positive group (probable cases) reported significantly longer duration and higher intensity of online social networking use, and higher prevalence of Internet addiction than the negative group.


The classification strategy and results are potentially useful for future research that measure problematic online social networking use and its impact on health among adolescents. The approach can facilitate research that requires cut-off points of screening tools but gold standards are unavailable.

Open access