The effects of non-Newtonian blood modeling and pulsatility on hemodynamics in the food and drug administration’s benchmark nozzle model

BACKGROUND: Computational fluid dynamics (CFD) is an important tool for predicting cardiovascular device performance. The FDA developed a benchmark nozzle model in which experimental and CFD data were compared, however, the studies were limited by steady flows and Newtonian models. OBJECTIVE: Newtonian and non-Newtonian blood models will be compared under steady and pulsatile flows to evaluate their influence on hemodynamics in the FDA nozzle. METHODS: CFD simulations were validated against the FDA data for steady flow with a Newtonian model. Further simulations were performed using Newtonian and non-Newtonian models under both steady and pulsatile flows. RESULTS: CFD results were within the experimental standard deviations at nearly all locations and Reynolds numbers. The model differences were most evident at Re = 500, in the recirculation regions, and during diastole. The non-Newtonian model predicted blunter upstream velocity profiles, higher velocities in the throat, and differences in the recirculation flow patterns. The non-Newtonian model also predicted a greater pressure drop at Re = 500 with minimal differences observed at higher Reynolds numbers. CONCLUSIONS: An improved modeling framework and validation procedure were used to further investigate hemodynamics in geometries relevant to cardiovascular devices and found that accounting for blood’s non-Newtonian and pulsatile behavior can lead to large differences in predictions in hemodynamic parameters.

Applications of deep learning to the assessment of red blood cell deformability

BACKGROUND: Measurement of abnormal Red Blood Cell (RBC) deformability is a main indicator of Sickle Cell Anemia (SCA) and requires standardized quantification methods. Ektacytometry is commonly used to estimate the fraction of Sickled Cells (SCs) by measuring the deformability of RBCs from laser diffraction patterns under varying shear stress. In addition to estimations from model comparisons, use of maximum Elongation Index differences (ΔEImax) at different laser intensity levels was recently proposed for the estimation of SC fractions. OBJECTIVE: Implement a convolutional neural network to accurately estimate rigid-cell fraction and RBC concentration from laser diffraction patterns without using a theoretical model and eliminating the ektacytometer dependency for deformability measurements. METHODS: RBCs were collected from control patients. Rigid-cell fraction experiments were performed using varying concentrations of glutaraldehyde. Serial dilutions were used for varying the concentration of RBC. A convolutional neural network was constructed using Python and TensorFlow. RESULTS: Our measurements and model predictions show that a linear relationship between ΔEImax and rigid-cell fraction exists only for rigid-cell fractions less than 0.2. Our proposed neural network architecture can be used successfully for both RBC concentration and rigid-cell fraction estimations without a need for a theoretical model.

Spectral analysis of blood flow oscillations to assess the plantar skin blood flow regulation in response to preconditioning local vibrations

BACKGROUND: Local vibration has been shown promise in improving skin blood flow and wound healing. However, the underlying mechanism of local vibration as a preconditioning intervention to alter plantar skin blood flow after walking is unclear. OBJECTIVE: The objective was to use wavelet analysis of skin blood flow oscillations to investigate the effect of preconditioning local vibration on plantar tissues after walking. METHODS: A double-blind, repeated measures design was tested in 10 healthy participants. The protocol included 10-min baseline, 10-min local vibrations (100Hz or sham), 10-min walking, and 10-min recovery periods. Skin blood flow was measured over the first metatarsal head of the right foot during the baseline and recovery periods. Wavelet amplitudes after walking were expressed as the ratio of the wavelet amplitude before walking. RESULTS: The results showed the significant difference in the metabolic (vibration 10.06 ± 1.97, sham 5.78 ± 1.53, p < 0.01) and neurogenic (vibration 7.45 ± 1.54, sham 4.78 ± 1.22, p < 0.01) controls. There were no significant differences in the myogenic, respiratory and cardiac controls between the preconditioning local vibration and sham conditions. CONCLUSIONS: Our results showed that preconditioning local vibration altered the normalization rates of plantar skin blood flow after walking by stimulating the metabolic and neurogenic controls.