著者

Junping Kou Yun Ni Na Li Jingrong Wang Liang Liu Zhi-Hong Jiang

出版者

The Pharmaceutical Society of Japan

雑誌

Biological and Pharmaceutical Bulletin (ISSN:09186158)

巻号頁・発行日

vol.28, no.1, pp.176-180, 2005 (Released:2005-01-01)

参考文献数

29

被引用文献数

51 57

---

The ethanol extract of Chinese medicinal ants Polyrhachis lamelliden was evaluated for its analgesic and anti-inflammatory activities in mice. It was shown that the extract significantly inhibited acetic acid-induced writhing response and increased hot-plate pain threshold of mice at doses of 1.5 and 3.0 g crude drug/kg. Meanwhile, the extract significantly inhibited the increase in vascular permeability induced by acetic acid and in ear edema induced by xylene in mice. However, it had no obvious effect on leukocyte migration induced by carboxymethylcellulose sodium (CMC-Na). The ethanol extract suspended in water was partitioned with diethyl ether, ethyl acetate and n-butanol successively to yield four fractions including water fraction. Among these fractions, diethyl ether and ethyl acetate fractions were found to increase hot-plate pain threshold and to inhibit acetic acid-induced writhing response in mice. Water fractions markedly inhibited acetic acid-induced writhing response and reduced the dye leakage to the peritoneal cavity induced by acetic acid and ear edema induced by xylene. These results suggest that P. lamellidens presents remarkable analgesic and anti-inflammatory activity, which supported the traditional use of the medicinal ants in the treatment of various diseases associated with inflammation. The diethyl ether fraction has greater contribution to the overall analgesic activity, whereas the water fraction showed the greatest anti-inflammatory and peripheral analgesic activities.

著者

Ya-Fen Ye Chao Ying Yue-Xiang Jiang Chun-Na Li

出版者

Fuji Technology Press Ltd.

雑誌

Journal of Advanced Computational Intelligence and Intelligent Informatics (ISSN:13430130)

巻号頁・発行日

vol.21, no.6, pp.1017-1025, 2017-10-20 (Released:2018-11-20)

参考文献数

23

被引用文献数

3

---

In this study, we focus on the feature selection problem in regression, and propose a new version of L1 support vector regression (L1-SVR), known as L1-norm least squares support vector regression (L1-LSSVR). The alternating direction method of multipliers (ADMM), a method from the augmented Lagrangian family, is used to solve L1-LSSVR. The sparse solution of L1-LSSVR can realize feature selection effectively. Furthermore, L1-LSSVR is decomposed into a sequence of simpler problems by the ADMM algorithm, resulting in faster training speed. The experimental results demonstrate that L1-LSSVR is not only as effective as L1-SVR, LSSVR, and SVR in both feature selection and regression, but also much faster than L1-SVR and SVR.