417 the Influence of Left Atrial and Ventricular Volumes on Exercise Capacity in Endurance Athletes
Heart Lung and Circulation(2020)SCI 4区
Baker Heart And Diabetes Institute
Abstract
“Athlete’s heart” describes a symmetrical enlargement of all four cardiac chambers in response to exercise training. However, the uniformity of chamber dilation and the degree to which each contributes to enhanced fitness has not been interrogated. 214 current and former endurance athletes (75% men), aged 16-80 years, underwent a resting echocardiogram and maximal bicycle cardiopulmonary exercise test. Left ventricular volumes (LVEDV) and left atrial volumes (LAESV) were measured using 3D echocardiography and 2D biplane methods, respectively. LVEDV was significantly related to peak VO2 (r=0.643 p<0.001) whereas there was no association between LAESV and exercise capacity (r=0.013 p=0.844). LA/LV ratio (median 0.54) was negatively related to peak VO2 (r=-0.513, p<0.001). Furthermore, a higher LA/LV ratio was observed in athletes with a history of atrial fibrillation (AF, n=19, 0.86±39) compared to those without AF (0.55±14, p<0.001). Athletic remodelling affects the left atrium and ventricle differently, with the latter increasing proportionately to exercise capacity. On the other hand, left atrial enlargement appears to be somewhat maladaptive, with no influence on exercise capacity and associated with the development of AF. The novel ratio of LA/LV volumes warrants further evaluation as a potential predictor of AF.
MoreTranslated text
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
GPU is busy, summary generation fails
Rerequest