Lattice Boltzmann simulations on the tumbling to tank-treading transition: effects of membrane viscosity

Validation of a Microfluidic Device Prototype for Cancer Detection and Identification: Circulating Tumor Cells Classification Based on Cell Trajectory Analysis Leveraging Cell-Based Modeling and Machine Learning

Microfluidic devices (MDs) present a novel method for detecting circulating tumor cells (CTCs), enhancing the process through targeted techniques and visual inspection. However, current approaches often yield heterogeneous CTC populations, necessitating additional processing for comprehensive analysis and phenotype identification. These procedures are often expensive, time-consuming, and need to be performed by skilled technicians. In this study, we investigate the potential of a cost-effective and efficient hyperuniform micropost MD approach for CTC classification. Our approach combines mathematical modeling of fluid-structure interactions in a simulated microfluidic channel with machine learning techniques. Specifically, we developed a cell-based modeling framework to assess CTC dynamics in erythrocyte-laden plasma flow, generating a large dataset of CTC trajectories that account for two distinct CTC phenotypes. Convolutional neural network (CNN) and recurrent neural network (RNN) were then employed to analyze the dataset and classify these phenotypes. The results demonstrate the potential effectiveness of the hyperuniform micropost MD design and analysis approach in distinguishing between different CTC phenotypes based on cell trajectory, offering a promising avenue for early cancer detection.

Comment on "Dynamics and rheology of vesicles under confined Poiseuille flow" by Z. Gou, H. Zhang, A. Nait-Ouhra, M. Abbasi, A. Farutin and C. Misbah, Soft Matter, 2023, , 9101

In a recent paper, [Gou et al., Soft Matter, 2023, 19, 9101-9114] studied numerically the viscosity of a confined suspension of vesicles flowing in a channel as a function of vesicle concentration. In order to discuss the genericity of the observed behaviour, namely a nearly constant effective viscosity at low concentrations, we complement their study by a comparison with the few existing ones in the literature. In particular, we highlight that they fail to reproduce well established results for blood viscosity in microcirculation, thereby suggesting that the conclusions regarding the optimization of cell transport and oxygenation may not apply. We conclude with a quick discussion on potential improvements regarding numerical modeling, as long as physiological relevance is sought.

Validation of a Microfluidic Device Prototype for Cancer Detection and Identification: Circulating Tumor Cells Classification Based on Cell Trajectory Analysis Leveraging Cell-Based Modeling and Machine Learning

Microfluidic devices (MDs) present a novel method for detecting circulating tumor cells (CTCs), enhancing the process through targeted techniques and visual inspection. However, current approaches often yield heterogeneous CTC populations, necessitating additional processing for comprehensive analysis and phenotype identification. These procedures are often expensive, time-consuming, and need to be performed by skilled technicians. In this study, we investigate the potential of a cost-effective and efficient hyperuniform micropost MD approach for CTC classification. Our approach combines mathematical modeling of fluid-structure interactions in a simulated microfluidic channel with machine learning techniques. Specifically, we developed a cell-based modeling framework to assess CTC dynamics in erythrocyte-laden plasma flow, generating a large dataset of CTC trajectories that account for two distinct CTC phenotypes. Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) were then employed to analyze the dataset and classify these phenotypes. The results demonstrate the potential effectiveness of the hyperuniform micropost MD design and analysis approach in distinguishing between different CTC phenotypes based on cell trajectory, offering a promising avenue for early cancer detection.

Droplet dynamics in homogeneous isotropic turbulence with the immersed boundary-lattice Boltzmann method

We develop a numerical method for simulating the dynamics of a droplet immersed in a generic time-dependent velocity gradient field. This approach is grounded on the hybrid coupling between the lattice Boltzmann (LB) method, employed for the flow simulation, and the immersed boundary (IB) method, utilized to couple the droplet with the surrounding fluid. We show how to enrich the numerical scheme with a mesh regularization technique, allowing droplets to sustain large deformations. The resulting methodology is adapted to simulate the dynamics of droplets in homogeneous and isotropic turbulence, with the characteristic size of the droplet being smaller than the characteristic Kolmogorov scale of the outer turbulent flow. We report statistical results for droplet deformation and orientation collected from an ensemble of turbulent trajectories, as well as comparisons with theoretical models in the limit of small deformation.

Dynamics and rheology of vesicles under confined Poiseuille flow

The rheological behavior and dynamics of a vesicle suspension, serving as a simplified model for red blood cells, are explored within a Poiseuille flow under the Stokes limit. Investigating vesicle response has led to the identification of novel solutions that complement previously documented forms like the parachute and slipper shapes. This study has brought to light the existence of alternative configurations, including a fully off-centered form and a multilobe structure. The study unveils the presence of two distinct branches associated with the slipper shape. One branch arises as a consequence of a supercritical bifurcation from the symmetric parachute shape, while the other emerges from a saddle-node bifurcation. Notably, the findings are represented through diagrams that display data collapsing harmoniously based on a combination of independent dimensionless parameters. Delving into the rheological implications, a remarkable observation emerges: the normalized viscosity (i.e. similar to intrinsic viscosity) exhibits a non-monotonic trend as a function of vesicle concentration. Initially, the normalized viscosity diminishes as the concentration increases, followed by a subsequent rise at higher concentrations. Noteworthy is the presence of a minimum value in the normalized viscosity at lower concentrations, aligning well with the concentrations observed in microcirculation scenarios. The intricate behavior of the normalized viscosity can be attributed to a delicate spatial arrangement within the suspension. Importantly, this trend echoes the observations made in a linear shear flow scenario, thereby underscoring the universality of the rheological behavior for confined suspensions.

Effect of droplet deformability on shear thinning in a cylindrical channel

Droplets suspended in fluids flowing through microchannels are often encountered in different contexts and scales, from oil extraction down to microfluidics. They are usually flexible and deform as a product of the interplay between flexibility, hydrodynamics, and interaction with confining walls. Deformability adds distinct characteristics to the nature of the flow of these droplets. We simulate deformable droplets suspended in a fluid at a high volume fraction flowing through a cylindrical wetting channel. We find a discontinuous shear thinning transition, which depends on the droplet deformability. The capillary number is the main dimensionless parameter that controls the transition. Previous results have focused on two-dimensional configurations. Here we show that, in three dimensions, even the velocity profile is different. To perform this study, we improve and extend to three dimensions a multicomponent lattice Boltzmann method which prevents the coalescence between the droplets.

Effect of constitutive law on the erythrocyte membrane response to large strains

Three constitutive laws, that is the Skalak, neo-Hookean and Yeoh laws, commonly employed for describing the erythrocyte membrane mechanics are theoretically analyzed and numerically investigated to assess their accuracy for capturing erythrocyte deformation characteristics and morphology. Particular emphasis is given to the nonlinear deformation regime, where it is known that the discrepancies between constitutive laws are most prominent. Hence, the experiments of optical tweezers and micropipette aspiration are considered here, for which relationships between the individual shear elastic moduli of the constitutive laws can also be established through analysis of the tension-deformation relationship. All constitutive laws were found to adequately predict the axial and transverse deformations of a red blood cell subjected to stretching with optical tweezers for a constant shear elastic modulus value. As opposed to Skalak law, the neo-Hookean and Yeoh laws replicated the erythrocyte membrane folding, that has been experimentally observed, with the trade-off of sustaining significant area variations. For the micropipette aspiration, the suction pressure-aspiration length relationship could be excellently predicted for a fixed shear elastic modulus value only when Yeoh law was considered. Importantly, the neo-Hookean and Yeoh laws reproduced the membrane wrinkling at suction pressures close to those experimentally measured. None of the constitutive laws suffered from membrane area compressibility in the micropipette aspiration case.