将血静性疾病的生物标志物集成到血液凝块形成的计算模型中:系统性审查
Mohamad Al Bannoud1,2, Tiago Dias Martins3, Silmara Aparecida de Lima Montalvão4,2
1Laboratory of Optimization, Design, and Advanced Control, School of Chemical Engineering, Universidade Estadual de Campinas, Campinas, São Paulo, Brazil.
Mathematical biosciences and engineering : MBE
|January 14, 2025
概括
血液凝固的计算模型缺乏整合常见的临床生物标志物,如D-二聚合物,PT和APTT,阻碍了针对出血和血栓性疾病的个性化药物.
科学领域:
- 生物医学工程 生物医学工程
- 计算生物学 计算生物学
- 血液学 血液学 血液学
背景情况:
- 个性化医疗需要具有可访问参数的计算模型,例如血液测试中的参数.
- 血液生物标志物对于评估血静平衡在血栓和出血障碍中至关重要.
- 将生物标志物集成到计算模型中,可以对患者特定的凝块形成进行分析.
研究的目的:
- 系统地审查临床相关生物标志物的整合到血块形成的计算模型中.
- 确定生物标志物集成的方法,差距和未来研究方向.
- 通过改进的计算建模,在血液静止方面推进个性化医疗.
主要方法:
- 根据PRISMA协议进行系统审查.
- 专注于十个临床显著的生物标志物 (D-二聚合物,纤维素,·威勒布兰德因子,XIII因子,P-选择素,PT,APTT,抗血素III,蛋白C,蛋白S).
- 包括53篇关于血栓生成,血液凝固或在流动下纤维素形成的数学模型的文章.
主要成果:
- 常见的生物标志物 (D-二聚体,PT,APTT) 很少和肤浅地被整合到计算模型中.
- 在不同研究的动力参数中存在显著的变化 (高达1000倍).
- 在包含常规临床测试用于预测血液凝结的模型中存在关键差距.
结论:
- 目前的计算模型不充分纳入负担得起的,常规的临床生物标志物,限制患者特定的预测.
- 这种差距阻碍了实际的临床应用,特别是对于患有血友病或蛋白C,S或抗血缺乏症的患者.
- 开发针对患者的特定模型,以考虑动力变异性,对于推进个性化静血护理至关重要.
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