Gupta Saloni, Kundu Snehasis
Department of Mathematics, NIT Jamshedpur, Jharkhand 831014, India.
Comput Biol Med. 2025 Sep 3;197(Pt A):111003. doi: 10.1016/j.compbiomed.2025.111003.
The behavior of blood viscosity is influenced by several physical factors, particularly hematocrit levels and vessel diameter. For a fixed hematocrit, apparent blood viscosity decreases with tube diameters in the range of 9μm to 1000μm, a phenomenon known as the Fåhræus-Lindqvist (FL) effect. Almost all existing models of the apparent blood viscosity are empirically proposed describing that viscosity exponentially increases with hematocrit. To predict apparent blood viscosity, this study proposes a new model derived from an iterative approach based on Einstein's complete formula by considering different blood samples with varying RBC levels. To predict model parameters, this work proposes the informational entropy approach based on a probabilistic concept, which is new in this field. The model generalizes several existing models and successfully predicts the FL effects. We validate its accuracy against a wide range of experimental data sets (from infants to adults, along with the original data set of Fåhræus-Lindqvist) and perform a comparative analysis with existing models. To illustrate the model performance the error analysis has been carried out. The results show that our model offers a better prediction across a wide range of considered data sets and existing models. The model is tested for predicting RBC transport efficiency and analyzing vessel oxygen transport rates and the results are found satisfactory. Apart from these, a new entropy theory based computational methodology is proposed allowing flexibility in adapting the model to different data sets. The suggested hybrid methodology can be used for future similar research.
血液粘度的行为受多种物理因素影响，尤其是血细胞比容水平和血管直径。对于固定的血细胞比容，在9μm至1000μm范围内，表观血液粘度随管径减小而降低，这一现象被称为法赫劳斯-林德奎斯特（FL）效应。几乎所有现有的表观血液粘度模型都是基于经验提出的，描述粘度随血细胞比容呈指数增加。为了预测表观血液粘度，本研究提出了一种新模型，该模型基于爱因斯坦完整公式，通过考虑不同红细胞水平的不同血液样本，采用迭代方法推导得出。为了预测模型参数，本研究基于概率概念提出了信息熵方法，这在该领域尚属新颖。该模型概括了几个现有模型，并成功预测了FL效应。我们针对广泛的实验数据集（从婴儿到成人，以及法赫劳斯-林德奎斯特的原始数据集）验证了其准确性，并与现有模型进行了对比分析。为了说明模型性能，我们进行了误差分析。结果表明，在广泛考虑的数据集和现有模型中，我们的模型具有更好的预测效果。该模型经过测试用于预测红细胞运输效率和分析血管氧气运输速率，结果令人满意。除此之外，我们还提出了一种基于熵理论的新计算方法，使模型能够灵活适应不同数据集。所建议的混合方法可用于未来类似的研究。
