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Development of a rapid UPLC-MS/MS method for the determination of toddalolactone in mouse blood and its application in pharmacokinetics

Zhou Jing, Wang Hongzhe, Miao Caiyun, Yao Yunxi, Ma Jianshe

Acta Chromatographica

2022

Vol. 34, no. 1

18--23

A rapid and simple UPLC-MS/MS method was developed to determine toddalolactone in mouse blood and applied to measure the pharmacokinetics of toddalolactone in mice. Blood samples were first preprocessed by ethyl acetate liquid-liquid extraction. Oxypeucedanin hydrate (internal standard, IS) and toddalolactone were gradient eluted from a UPLC BEH C18 column using a mobile phase consisting of acetonitrile and water (0.1% formic acid). Using electrospray ionization (ESI) as the ionization source, multiple reaction monitoring was used to detect the precursor and product ions of m/z 309.2 and 205.2, respectively, for toddalolactone and of m/z 305.1 and 203.0 for IS, respectively, for quantitative detection. A calibration curve was run over the concentration range of 5–4,000 ng/mL (r > 0.995). The matrix effects ranged from 93.5 to 98.4%, and the recovery was higher than 77.3%. The precision was less than 13%, and the accuracy ranged from 90.9 to 108.4%. The developed UPLC-MS/MS method was successfully used for measuring the pharmacokinetics of toddalolactone in mice after oral (20 mg/kg) and intravenous administration (5 mg/kg), and the absolute bioavailability of toddalolactone was 22.4%.

The analysis of transformer's condition based on Bayes-discriminant method with fault-tree fuzzy evaluation

Li Dianyang, Wang Shanyuan, Zhang Yujie, Feng Jian, Wang Hongzhe, Qin Ling

Diagnostyka

2020

Vol. 21, No. 2

3--12

China's electric power construction is renewing Increasingly, and the network is complex and changeable where the automation is getting higher. In this paper, Fuzzy evaluation system is established according to fault tree, and the estimation of transformer’s state is judged by analytic hierarchy process. Bayes-discriminant and discriminant formula are used to discriminate transformer’s attributes, which are based on historical data. The machine identification of transformer faults combines the fuzzy evaluation and Bayes-discriminant. It’s accuracy can be improved by correcting parameters. This method can effectively avoid subjective interference caused by artificial weights. The example shows that this method could be applied to judge health status of electric power equipment and this method can play an early-warning role in the operation of monitoring system.