Cerebral Autoregulation and Response to Intravenous Thrombolysis for Acute Ischemic Stroke
Scientific reports(2020)SCI 3区
Neurology Department | Department of Cardiovascular Sciences
Abstract
We hypothesized that knowledge of cerebral autoregulation (CA) status during recanalization therapies could guide further studies aimed at neuroprotection targeting penumbral tissue, especially in patients that do not respond to therapy. Thus, we assessed CA status of patients with acute ischemic stroke (AIS) during intravenous r-tPA therapy and associated CA with response to therapy. AIS patients eligible for intravenous r-tPA therapy were recruited. Cerebral blood flow velocities (transcranial Doppler) from middle cerebral artery and blood pressure (Finometer) were recorded to calculate the autoregulation index (ARI, as surrogate for CA). National Institute of Health Stroke Score was assessed and used to define responders to therapy (improvement of ≥ 4 points on NIHSS measured 24–48 h after therapy). CA was considered impaired if ARI < 4. In 38 patients studied, compared to responders, non-responders had significantly lower ARI values (affected hemisphere: 5.0 vs. 3.6; unaffected hemisphere: 5.4 vs. 4.4, p = 0.03) and more likely to have impaired CA (32% vs. 62%, p = 0.02) during thrombolysis. In conclusion, CA during thrombolysis was impaired in patients who did not respond to therapy. This variable should be investigated as a predictor of the response to therapy and to subsequent neurological outcome.
MoreTranslated text
Key words
Biomedical engineering,Stroke,Science,Humanities and Social Sciences,multidisciplinary
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
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
2007
被引用404 | 浏览
2005
被引用141 | 浏览
2009
被引用48 | 浏览
1998
被引用468 | 浏览
2011
被引用137 | 浏览
2013
被引用36 | 浏览
2012
被引用88 | 浏览
2016
被引用359 | 浏览
2016
被引用43 | 浏览
2017
被引用15 | 浏览
2016
被引用29 | 浏览
2017
被引用86 | 浏览
2017
被引用52 | 浏览
2016
被引用20 | 浏览
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
去 AI 文献库 对话