强骨抗萎膏通过调控MFN2及抑制内质网应激改善失重状态下大鼠骨丢失
Journal of Yunnan University of Chinese Medicine(2023)
北京中医药大学
Abstract
目的 观察强骨抗萎膏对尾吊大鼠骨细胞凋亡和内质网应激的影响,探讨强骨抗萎膏防治模拟失重状态下骨丢失的机制.方法 60 只SD大鼠分为对照组、模拟失重组、强骨抗萎膏组、阿仑膦酸钠组,骨疏康颗粒组,每组 12 只.采用尾部悬吊模拟失重状态,尾吊 28 d;对照组和模拟失重组给予每日等体积生理盐水灌胃,其它 3 组复制模型,分别每日给予中药强骨抗萎膏 2.4 g/kg,阿仑膦酸钠 0.007 g/kg,骨疏康颗粒 0.26 g/kg灌胃.持续给药 4 周后处死大鼠,TUNEL法检测骨细胞凋亡情况;Western blot法检测葡萄糖调节蛋白 78(GRP78)和线粒体融合蛋白 2(MFN2)表达,RT-PCR和Western blot检测骨组织中蛋白激酶R样内质网激酶(PERK),肌醇依赖性激酶 1α(IRE1α)、活化转录因子 6(ATF6)及C/EBP同源蛋白(CHOP)的基因和蛋白表达.结果 TUNEL结果显示模拟失重组骨细胞凋亡较对照组增加,强骨抗萎膏可抑制骨细胞凋亡.与对照组大鼠比较,模拟失重组PERK基因表达量明显增高(P<0.05),强骨抗萎膏组较模拟失重组PERK基因表达量下降(P<0.05);与对照组比较,模拟失重组的GRP78、PERK、CHOP和ATF6 蛋白含量明显增高(P<0.05),MFN2 蛋白表达降低(P<0.05),强骨抗萎膏组较模拟失重组GRP78、PERK、CHOP和ATF6 蛋白含量均明显降低(P<0.05),MFN2 表达量升高.结论 强骨抗萎膏可以改善模拟失重大鼠骨细胞凋亡,通过抑制骨组织内质网应激相关因子GRP78、PERK、ATF6 和CHOP,上调MFN2 表达,从而改善失重条件下的骨丢失.
MoreKey words
weightlessness,bone loss,endoplasmic reticulum stress,MFN2,Qiang Gu Kang Wei Extraction
求助PDF
上传PDF
View via Publisher
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