心肌损伤标记物对慢性肾脏病非透析患者评估心脏结构功能的预测
Journal of Kunming Medical University(2017)
Abstract
目的 探讨心肌损伤标记物血浆高敏心肌肌钙蛋白T (hs-cTnT)、N末端B型利钠肽原(NT-proB-NP)、肌酸激酶同工酶(CK-MB)的水平对不同时期的慢性肾脏病(CKD)非透析患者的预测价值.方法 选取CKD非透析患者共137例,检测心肌损伤标记物,做超声心动图等检查,由ROC曲线下面积评价诊断价值.结果 血浆hs-cTnT、NT-proBNP、CK-MB在CKD5期组最高,CKD3-4期组高于CKD1-2期组.左心室肥厚(LVH)和左室舒张功能不全(E/A<1)在CKD5期组和CKD3-4期组低于CKD1-2期组.有LVH和E/A<1患者hs-cTnT、NT-proBNP、CK-MB水平显著高于无LVH和E/A<1患者.用ROC曲线分析CKD患者心肌损伤标记物对LVH和E/A<1评估,敏感性和特异性最高是CK-MB,其次NT-proBNP,hs-cTnT,P<0.001.结论 CKD非透析患者心肌损伤标记物水平随着肾功能恶化而进行性升高,且与心脏结构功能密切相关,CK-MB在诊断LVH和E/A<1的准确性最高.
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