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本文引用的文献

1
Left Ventricular Assist Device Inflow Cannula Angle and Thrombosis Risk.左心室辅助装置流入管角度与血栓形成风险。
Circ Heart Fail. 2018 Apr;11(4):e004325. doi: 10.1161/CIRCHEARTFAILURE.117.004325.
2
Introducing the pro-coagulant contact system in the numerical assessment of device-related thrombosis.引入促凝接触系统对器械相关性血栓形成进行数值评估。
Biomech Model Mechanobiol. 2018 Jun;17(3):815-826. doi: 10.1007/s10237-017-0994-3. Epub 2018 Jan 4.
3
Elevated hematocrit enhances platelet accumulation following vascular injury.血细胞比容升高会增强血管损伤后的血小板聚集。
Blood. 2017 May 4;129(18):2537-2546. doi: 10.1182/blood-2016-10-746479. Epub 2017 Mar 1.
4
Multi-Constituent Simulation of Thrombus Deposition.多成分血栓沉积模拟。
Sci Rep. 2017 Feb 20;7:42720. doi: 10.1038/srep42720.
5
Heart Disease and Stroke Statistics-2017 Update: A Report From the American Heart Association.《2017年心脏病和中风统计数据更新:美国心脏协会报告》
Circulation. 2017 Mar 7;135(10):e146-e603. doi: 10.1161/CIR.0000000000000485. Epub 2017 Jan 25.
6
Development of a platelet adhesion transport equation for a computational thrombosis model.用于计算血栓形成模型的血小板黏附运输方程的开发。
J Biomech. 2017 Jan 4;50:114-120. doi: 10.1016/j.jbiomech.2016.11.012. Epub 2016 Nov 10.
7
Development of a computational model for macroscopic predictions of device-induced thrombosis.用于设备诱导血栓形成宏观预测的计算模型的开发。
Biomech Model Mechanobiol. 2016 Dec;15(6):1713-1731. doi: 10.1007/s10237-016-0793-2. Epub 2016 May 12.
8
A computational model based on fibrin accumulation for the prediction of stasis thrombosis following flow-diverting treatment in cerebral aneurysms.一种基于纤维蛋白积聚的计算模型,用于预测脑动脉瘤血流分流治疗后的淤滞性血栓形成。
Med Biol Eng Comput. 2017 Jan;55(1):89-99. doi: 10.1007/s11517-016-1501-1. Epub 2016 Apr 22.
9
Mathematical modeling of thrombus formation in idealized models of aortic dissection: initial findings and potential applications.主动脉夹层理想化模型中血栓形成的数学建模:初步研究结果及潜在应用
J Math Biol. 2016 Nov;73(5):1205-1226. doi: 10.1007/s00285-016-0986-4. Epub 2016 Mar 23.
10
Computational simulation of platelet interactions in the initiation of stent thrombosis due to stent malapposition.因支架贴壁不良导致支架内血栓形成起始阶段血小板相互作用的计算模拟
Phys Biol. 2016 Jan 20;13(1):016001. doi: 10.1088/1478-3975/13/1/016001.

体外实时磁共振成像血栓定量。

In vitro real-time magnetic resonance imaging for quantification of thrombosis.

机构信息

Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, USA.

Huck Institutes of Life Science, The Pennsylvania State University, University Park, PA, USA.

出版信息

MAGMA. 2021 Apr;34(2):285-295. doi: 10.1007/s10334-020-00872-2. Epub 2020 Jul 29.

DOI:10.1007/s10334-020-00872-2
PMID:32729094
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7854797/
Abstract

OBJECTIVES

Thrombosis is a leading cause of failure for cardiovascular devices. While computational simulations are a powerful tool to predict thrombosis and evaluate the risk for medical devices, limited experimental data are available to validate the simulations. The aim of the current study is to provide experimental data of a growing thrombus for device-induced thrombosis.

MATERIALS AND METHODS

Thrombosis within a backward-facing step (BFS), or sudden expansion was investigated, using bovine and human blood circulated through the BFS model for 30 min, with a constant inflow rate of 0.76 L/min. Real-time three-dimensional flow-compensated magnetic resonance imaging (MRI), supported with Magnevist, a contrast agent improving thrombus delineation, was applied to quantify thrombus deposition and growth within the model.

RESULTS

The study showed that the BFS model induced a flow recirculation region, which facilitated thrombosis. By 30 min, in comparison to bovine blood, human blood resulted in smaller thrombus formation, in terms of the length (13.3 ± 0.6 vs. 18.1 ± 1.3 mm), height (2.3 ± 0.1 vs. 2.6 ± 0.04 mm), surface area exposed to blood (0.67 ± 0.03 vs 1.05 ± 0.08 cm), and volume (0.069 ± 0.004 vs. 0.093 ± 0.007 cm), with p < 0.01. Normalization of the thrombus measurements, which excluded the flow recirculation effects, suggested that the thrombus sizes increased during the first 15 min and stabilized after 20 min. Blood properties, including viscosity, hematocrit, and platelet count affected thrombosis.

CONCLUSION

For the first time, contrast agent-supported real-time MRI was performed to investigate thrombus deposition and growth within a sudden expansion. This study provides experimental data for device-induced thrombosis, which is valuable for validation of computational thrombosis simulations.

摘要

目的

血栓形成是心血管器械失效的主要原因。虽然计算模拟是预测血栓形成和评估医疗器械风险的有力工具,但可用的实验数据有限,无法验证模拟结果。本研究的目的是提供用于医疗器械诱导血栓形成的生长血栓的实验数据。

材料与方法

通过在反向流步(BFS)或突然扩张中循环牛血和人血 30 分钟,以 0.76 L/min 的恒定流入率,使用 BFS 模型来研究血栓形成。实时三维流动补偿磁共振成像(MRI),辅之以对比剂 Magnevist,可改善血栓的描绘,用于定量模型内血栓的沉积和生长。

结果

该研究表明,BFS 模型会形成一个回流区,从而促进血栓形成。与牛血相比,人血在 30 分钟时形成的血栓较小,在长度(13.3±0.6 毫米比 18.1±1.3 毫米)、高度(2.3±0.1 毫米比 2.6±0.04 毫米)、暴露于血液的表面积(0.67±0.03 平方厘米比 1.05±0.08 平方厘米)和体积(0.069±0.004 立方厘米比 0.093±0.007 立方厘米)方面均较小,p 值均<0.01。排除回流效应后对血栓测量值进行归一化,表明血栓大小在前 15 分钟内增加,20 分钟后稳定。血液特性,包括粘度、血细胞比容和血小板计数,均会影响血栓形成。

结论

本研究首次采用对比剂支持的实时 MRI 研究突然扩张内的血栓沉积和生长。本研究为医疗器械诱导的血栓形成提供了实验数据,这对于计算血栓形成模拟的验证具有重要价值。