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解开网络:通过in silico医学驱动的排气挥发性有机化合物的标准化采样和分析的路线图

Robin Curnow¹, Carl A Whitfield², Waqar Ahmed^1,3

¹Division of Immunology, Immunity to Infection and Respiratory Medicine, University of Manchester, Manchester, United Kingdom.

American journal of physiology. Lung cellular and molecular physiology

|September 9, 2025

概括

标准化呼吸挥发性有机化合物 (VOC) 分析对于临床生物标志物至关重要. 计算建模提供了一个有希望的路线图,以克服呼吸学研究中的标准化挑战.

科学领域:

生物医学工程生物医学工程
分析化学分析化学
计算生物学计算生物学

背景情况:

人体呼吸中的挥发性有机化合物 (VOC) 显示出作为疾病生物标志物的潜力.
由于采样和分析缺乏标准化,呼吸中VOC生物标志物的临床实施受到阻碍.
标准化对于验证多中心研究中的潜在生物标志物至关重要.

研究的目的:

突出 in silico 方法在标准化呼吸学研究中的作用.
讨论计算建模在准和验证疾病生物标志物的潜力.
提出一张路线图,用于将建模方法应用于呼吸学标准化.

主要方法:

审查关于呼吸 VOC 分析和标准化挑战的现有文献.
探索计算建模,数学和机器学习方法.
从类似的标准化工作中分析例子,比如分数呼出的氧化.

主要成果:

在Silico方法对于解决呼吸学中的标准化差距至关重要.
计算模型可以机械地将VOC与生物标志物验证的潜在代谢过程联系起来.
标准化的进展需要优化采样协议,并考虑混因素.

结论:

计算建模提供了一种可行的策略,以实现呼吸学中的标准化.
拟议的路线图整合了呼吸生物标志物开发的现有和新型建模方法.
通过建模进行标准化将加速呼吸中VOC生物标志物的临床转化.

关键词:

呼吸测试呼吸测试呼吸测试在形医学中.机器学习是机器学习.数学建模的数学建模挥发性有机化合物挥发性有机化合物

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