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Rapidly dividing tumors, embryos, and wounded tissues require more oxygen than usual, lowering the oxygen concentration in the blood. At low oxygen or hypoxic conditions, an oxygen-sensitive transcription factor called the hypoxia-inducible factor 1 or HIF1 is activated. HIF1 is a dimeric protein of alpha (ɑ) and beta (β) subunits.  Under optimal oxygen conditions, HIF1β is present in the nucleus while HIF1ɑ remains in the cytosol. HIF1ɑ is hydroxylated by prolyl...
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预测毛细血管网络血动力学 in silico通过机器学习.

Saman Ebrahimi1, Prosenjit Bagchi1

  • 1Mechanical and Aerospace Engineering Department, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.

PNAS nexus
|May 10, 2024
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概括

机器学习模型准确地预测了微循环中的血液流动和红细胞分布. 这一突破使大血管网络中的详细血液动力学分析成为可能,克服了计算的局限性.

科学领域:

  • 生物医学工程 生物医学工程
  • 计算流体动力学的流体动力学.
  • 生理学 生理学 生理学

背景情况:

  • 微循环血流是复杂的,偏离了简单的模型.
  • 目前的成像缺乏对生理分析至关重要的3D速度和RBC度数据.
  • 现有的计算模型对于大规模的微血管网络来说过于资源密集.

研究的目的:

  • 开发机器学习模型,用于准确的3D血流和红细胞度分析.
  • 预测关键的血液动力学变量,如壁切应力 (WSS) 和无细胞层 (CFL).
  • 在微血管网络中实现大规模的器官级血液动力学分析.

主要方法:

  • 利用人工神经网络和U-net架构.
  • 在高保真计算流体动力学数据上训练模型.
  • 根据已确定的计算结果验证的ML模型预测.

主要成果:

  • 实现了高度准确的3D血液速度和红细胞度概况.
  • 成功预测了WSS和CFL,与真实数据的良好一致.
  • 与传统方法相比,计算时间减少了几个数量级.

结论:

关键词:
血液动力学 血液动力学高准确度建模的模型.机器学习是机器学习.微循环是一种微循环.红细胞是红血细胞的组成部分.

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  • 机器学习为详细的微血管分析提供了一个计算效率高的替代方案.
  • 这种方法使得以前无法实现的器官规模血液动力学预测成为可能.
  • 开发的ML模型对于推进血液流量调节和疾病研究至关重要.