斑马鱼模型评估侧柏叶提取物的保湿功效
Zebrafish Modeling to Evaluate the Moisturizing Efficacy of Platycladus Orientalis Leaf Extracts
陈正鹤 1龚芳莲 1蒋日琼 1吴志强 1李凯婷 1翟丹 2王著显 2吴煜凡 2刘莉 2朱红霞2
作者信息
- 1. 霸王(广州)有限公司,广东广州 510440
- 2. 南方医科大学中医药学院,广东广州 510515
- 折叠
摘要
目的 观察侧柏叶提取物的保湿功效.方法 采用体外称重法测定侧柏叶提取物的保湿率;斑马鱼保湿模型进一步研究用侧柏叶提取物高中低剂量处理斑马鱼尾部后面积收缩率的变化,并与空白对照组、模型对照组以及阳性对照组相比来评估侧柏叶提取物的保湿功效.结果 5％侧柏叶水提物与5％透明质酸的保湿率差异无统计学意义(P＞0.05),1％侧柏叶醇提物与1％透明质酸的保湿率差异无统计学意义(P＞0.05);0.2、0.4、0.8 mg·mL-1侧柏叶水提物组与模型对照组相比斑马鱼尾部面积收缩率明显升高,且差异均有统计学意义(P＜0.05).结论 结合体外称重法结果与斑马鱼尾部面积收缩率结果,侧柏叶水提物具有较好的保湿功效,可作为保湿原料进一步开发.
Abstract
OBJECTIVE To observe the moisturizing effects of Platycladus Orientalis leaf extracts.METHODS In vitro weighing method was used to determine the moisturization rate of Platycladus Orientalis leaf extracts.The zebrafish moisturizing model was used to further investigate the changes in the area shrinkage rate of zebrafish tails treated with high,medium.and low doses of Platycladus Orientalis leaf extracts and to assess the moisturizing efficacy of Platycladus Orientalis leaf extracts in comparison with blank control,model control and positive control.RESULTS There was no statistically significant difference in moisturizing rate between 5％aqueous extract of Platycladus Orientalis leaf and 5％hyaluronic acid(P＞0.05),and there was no statistically significant difference in moisturizing rate between 1％alcoholic extract of Platycladus Orientalis leaf and 1％hyaluronic acid(P＞0.05).The 0.2,0.4,0.8 mg·mL-1 aqueous extracts of Platycladus Orientalis leaf groups showed significantly higher contraction rate of zebrafish tail area compared to the model control group,and the differences were statistically significant(P＜0.05).CONCLUSION Based on the results of the in vitro weighing method and the shrinkage rate of zebrafish tail area,the aqueous extract of Platycladus Orientalis leaf has good moisturizing efficacy and can be further developed as a moisturizing raw material.
关键词
侧柏叶/保湿/斑马鱼模型/保湿率Key words
Platycladus Orientalis leaf/moisturization/zebrafish model/moisturization rate引用本文复制引用
出版年
2024
NETL
NSTL
万方数据