Institute for Breath Research, Leopold-Franzens-Universität, Innrain 66, A-6020 Innsbruck, Austria.
Institute of Chemistry, Jan Kochanowski University, 25-369 Kielce, Poland.
Molecules. 2022 Apr 7;27(8):2381. doi: 10.3390/molecules27082381.
Researchers looking for biomarkers from different sources, such as breath, urine, or blood, frequently search for specific patterns of volatile organic compounds (VOCs), often using pattern recognition or machine learning techniques. However, they are not generally aware that these patterns change depending on the source they use. Therefore, we have created a simple model to demonstrate that the distribution patterns of VOCs in fat, mixed venous blood, alveolar air, and end-tidal breath are different. Our approach follows well-established models for the description of dynamic real-time breath concentration profiles. We start with a uniform distribution of end-tidal concentrations of selected VOCs and calculate the corresponding target concentrations. For this, we only need partition coefficients, mass balance, and the assumption of an equilibrium state, which avoids the need to know the volatiles' metabolic rates and production rates within the different compartments.
研究人员从不同来源(如呼吸、尿液或血液)寻找生物标志物时,经常会寻找挥发性有机化合物 (VOC) 的特定模式,通常使用模式识别或机器学习技术。然而,他们通常没有意识到这些模式会根据他们使用的来源而发生变化。因此,我们创建了一个简单的模型来证明脂肪、混合静脉血、肺泡气和呼气末呼吸中 VOC 的分布模式是不同的。我们的方法遵循描述动态实时呼吸浓度曲线的既定模型。我们从选定 VOC 的呼气末浓度的均匀分布开始,并计算相应的目标浓度。为此,我们只需要分配系数、质量平衡和平衡状态的假设,这避免了需要知道不同隔室中挥发性物质的代谢率和产生率。