Author(s): Yu J, Liu C, Zhang X, Han J
Abstract Share this page
Abstract Recent studies have demonstrated that acupuncture is feasible to treat vascular dementia (VD). The aim of this study was to present behavioral evidence that acupuncture could improve the learning and memory of multi-infarct dementia rats. The pattern of multi-infarct dementia in rats was made by injecting homogeneous emboli into the internal carotid artery. Of them the rats which showed damage in learning and memory (n = 43) were randomly allocated to 3 groups: impaired group (n = 14), acupuncture group (n = 15) and placebo-acupuncture group (n = 14). Moreover, normal group (n = 15) and sham-operated group (n = 15) were set as control groups. The acupuncture group was given acupuncture, which consisted of Tanzhong (CV17), Zhongwan (CV12), Qihai (CV6), Zusanli (ST36) and Xuehai (SP10). Morris water maze test was employed to assess spatial discriminational ability per group respectively and to analyze the curative effects of acupuncture. Compared to the impaired and placebo-acupuncture groups, no cognition impairment was found in the normal and sham-operated groups, and the statistic analysis showed that there were significant differences between normal and impaired groups in ANOVA. Shortened mean escape latency was detected in the acupuncture group compared with the impaired group during the same trial days. Search strategy changed from random pattern adopted by impaired and placebo-acupuncture rats to tendency or linear pattern popular in normal group. The present results suggested that the acupuncture exerted a protective effect on cognitive impairment caused by cerebral multi-infarction in rats, and acupuncture has a specificity of cure. Acupuncture as a potential clinic method in treating VD should be developed and investigated in the future.
This article was published in Physiol Behav
and referenced in Alternative & Integrative Medicine
- Md Bahadur Badsha
Integration of omics into metabolic flux distribution by complementary elementary mode analysis for large-scale metabolic networks