WeChat Mini Program
Old Version Features

CPZ诱导精神分裂症样模型小鼠海马脱髓鞘改变与行为学变化及其相关性的研究

Journal of Chongqing Medical University(2014)

Cited 1|Views13
Abstract
目的:研究双环己酮草酰二腙(cuprizone,CPZ)诱导C57BL/6小鼠建立的精神分裂症样模型小鼠海马内有髓神经纤维体积改变及其与行为学改变之间的关系.方法:19只雄性6周龄小鼠,随机分为实验组(9只)和对照组(10只),实验组用含0.2%(质量分数百分比,w/w)CPZ的混合鼠饲料饲喂6周,建立精神分裂症样小鼠模型;对照组用标准鼠饲料饲喂6周.运用旷场实验、高架十字迷宫实验、Morris水迷宫实验、转棒实验和探孔实验测试行为学改变;髓鞘碱性蛋白(myelin basic protein,MBP)免疫组化染色及透射电镜定性观察髓鞘结构变化;体视学方法测量并计算海马总体积及海马内有髓神经纤维体积;统计学方法分析行为学改变与海马内有髓神经纤维体积改变之间的关系.结果:与对照组相比,实验组小鼠体质量下降,差异具有统计学意义(P<0.05);行为学实验结果表明:实验组小鼠存在异常的焦虑行为(P=0.018)及空间认知能力障碍(P=0.037),但运动能力(P=0.443)、探索习性(P=-0.306)及学习记忆能力未见受损(P=0.462);MBP免疫组化染色发现海马区域免疫阳性染色变浅;透射电镜定性观察发现海马内有髓神经纤维存在脱髓鞘改变;体视学定量测定发现,实验组小鼠海马总体积未发生显著性改变(P=0.955),而海马内有髓神经纤维体积减小(P=0.009);统计学相关分析发现,闭臂路程百分比和海马内有髓神经纤维体积呈正相关(rs=0.83,P=0.003),开臂路程百分比和海马内有髓神经纤维体积呈负相关(rs=-0.65,P=0.043).结论:进一步证实CPZ模型小鼠可以出现类似精神分裂症样症状的行为学改变及海马内有髓神经纤维存在脱髓鞘改变,并且发现海马内有髓神经纤维体积和行为学表现间具有一定的相关性.
More
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined