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An In Vitro Organ Culture Model of the Murine Intervertebral Disc
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结合概率有限元模型和人工神经网络,研究人类脊椎间盘中的营养水平
Liang-Dong Zheng1, Wei Li1, Zu-Xiang He1
1Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai 200092, China.
Clinical biomechanics (Bristol, Avon)
|October 4, 2024
概括
磁盘高度,含水量和端板厚度对于工程磁盘中的营养物质运输至关重要. 人工神经网络准确地预测营养水平,有助于磁盘设计.
科学领域:
- 生物医学工程 生物医学工程
- 组织工程是组织工程.
- 计算生物学 计算生物学
背景情况:
- 椎间盘中营养物质的运输受到扩散距离和扩散性的影响.
- 缺乏对这些因素的概率分析和评估磁盘营养水平的工具.
研究的目的:
- 以概率分析磁盘形态和含水量参数对营养物质运输的影响.
- 开发和验证人工神经网络,用于预测工程磁盘中的营养度.
主要方法:
- 生成了500个具有不同参数的磁盘样本进行分析.
- 计算了斯皮尔曼相关系数,以评估参数和营养水平之间的关系.
- 训练有素的人工神经网络使用来自概率有限元模型的数据.
主要成果:
- 磁盘高度与核脉氧度负相关 (没有负载/负载:r=-0.83/-0.76).
- 环状纤维的水含量与葡萄糖度正相关 (r=0.68/0.73).
- 末端板的厚度显著影响了软骨末端板的乳酸度 (r=0.94/0.95).
- 人工神经网络实现了高的预测准确性 (R2>0.95,MAPE<5%).
结论:
- 盘的高度,纤维状圆环的含水量和端板的厚度是营养水平的关键调节者.
- 精确控制这些参数对于设计有效的组织工程磁盘至关重要.
- 人工神经网络显示出作为评估工程组织中营养水平的工具的希望.
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