Suppr超能文献

羊膜绒毛膜:从一种成熟方法中推断数据的新方法。

The chorioallantoic membrane: A novel approach to extrapolate data from a well-established method.

机构信息

Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Messina, Italy.

Department of Pharmacy-Drug Sciences, University of Bari "Aldo Moro", Bari, Italy.

出版信息

J Appl Toxicol. 2022 Jun;42(6):995-1003. doi: 10.1002/jat.4271. Epub 2021 Dec 7.

Abstract

The chorioallantoic membrane (CAM) of the chicken embryo is a highly vascularized extra-embryonic structure that has been widely used as an in vivo model for the evaluation of angiogenesis. This study was designed to optimize data extrapolation from the most exploited experimental protocol to improve its efficiency and the reliability of the obtainable results. In our study, we followed the most common procedure for CAM assay, employing retinoic acid and vascular endothelial growth factor as standards. CAMs were photographed at t , t , and t ; then, the main parameters of the predefined vascular network/area were evaluated. Subsequently, their variations in each CAM were calculated comparing them within the same CAM over the course of the whole treatment (t and t ), also comparing the treated CAMs respect to the untreated ones. Thus, we provide a novel approach aimed at extrapolating data from CAM assay that allows to (i) have a greater reliability and richness of data; (ii) better estimate the potential pro- and anti-angiogenic activity of new candidate drugs; (iii) save both eggs and time for the experiments.

摘要

鸡胚尿囊膜(CAM)是一种高度血管化的胚胎外结构,已被广泛用作评估血管生成的体内模型。本研究旨在优化最常用实验方案的数据外推,以提高其效率和可获得结果的可靠性。在我们的研究中,我们遵循 CAM 测定的最常见程序,使用视黄酸和血管内皮生长因子作为标准。在 t 、 t 、 t 时拍摄 CAM 照片;然后,评估预定血管网络/区域的主要参数。随后,在整个治疗过程中(t 和 t ),比较同一 CAM 中每个 CAM 的变化,也比较处理过的 CAM 与未处理的 CAM 的变化。因此,我们提供了一种新的方法来从 CAM 测定中推断数据,该方法可以:(i)具有更高的可靠性和数据丰富性;(ii)更好地估计新候选药物的潜在促血管生成和抗血管生成活性;(iii)节省实验用的鸡蛋和时间。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e15c/9300073/6107dc193e15/JAT-42-995-g003.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验