H型高血压患者HDL亚组分的改变及对内皮细胞抗炎功能的影响
Chinese Journal of Arteriosclerosis(2022)
Abstract
目的 观察H型高血压患者血同型半胱氨酸水平(Hcy)与高密度脂蛋白(HDL)亚组分的相关性,并研究HDL亚组分对内皮细胞抗炎功能的影响.方法 选取健康人群133例、单纯高血压患者76例、H型高血压患者85例,测定空腹血糖(FBG)、血清肌酐(SCr)、尿酸(UA)、总胆固醇(TC)、甘油三酯(TG)、低密度脂蛋白胆固醇(LDLC)、高密度脂蛋白胆固醇(HDLC)、HDL2、HDL3和Hcy等指标.分别留取各组血清各6例,应用快速蛋白液相色谱法(FPLC)提取大颗粒HDL(L-HDL),观察L-HDL对肿瘤坏死因子α(TNF-α)诱导的人脐静脉内皮细胞(HUVEC)炎症的改善作用.结果 健康对照组、单纯高血压组和H型高血压组UA、FBG、TC、LDLC水平差异无统计学意义(P>0.05).对比单纯高血压患者和健康对照组,H型高血压患者具有更低的HDLC、HDL2水平及更高的HDL3水平(P<0.001);血Hcy水平与血HDLC、HDL2呈显著负相关(P<0.01),但是与HDL3无显著相关性(P=0.083).健康对照组、单纯高血压组L-HDL均可下调TNF-α诱导的血管内皮细胞表达血管细胞黏附分子1(VCAM-1),而H型高血压组L-HDL并不能明显减低VCAM-1表达.结论 H型高血压患者可能通过高Hcy影响HDL的亚组分分布以及L-HDL对内皮细胞的抗炎功能.
More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