Wenbin Mao

Studying red blood cells (RBCs) under high-stress conditions is essential for understanding their mechanical response and the onset of sub-hemolytic damage. In this work, we present a comprehensive computational investigation of RBC behavior under high shear and extensional flow conditions. We begin with a systematic evaluation of RBC membrane models through a series of mechanical tests to identify formulations capable of capturing large deformations. Both one-component and two-component models are then employed to simulate RBC dynamics in pure shear flow and hyperbolic nozzle configurations, with results validated against experimental measurements of cell deformation. The developed framework demonstrates strong quantitative and qualitative agreement with experimental observations across a wide range of loading conditions, establishing a robust platform for modeling RBC mechanics under high stress. As a secondary contribution, the two-component model is used to examine bilayer–cytoskeleton bond dissociation as a mechanistic indicator of sub-hemolytic damage. RBCs are subjected to a numerically generated pure extensional field, and dissociation events are analyzed within a reduced-time framework. The results show that increasing extensional stress promotes earlier onset of dissociation in a non-dimensional sense and reveal a threshold-like transition in dissociation behavior around approximately 15 Pa, where significantly greater exposure is required to initiate bond failure. Furthermore, extensional stresses exhibit a substantially higher propensity for damage initiation compared to shear stresses at equivalent magnitudes. These findings provide a mechanistic perspective on stress-dependent damage initiation while highlighting the importance of extensional loading in RBC integrity.

With the increasing prevalence of cardiovascular diseases globally and limitations in current constitutive models for myocardium mechanics, this study aims to develop a deep learning (DL) model geared toward patient‐specific evaluation of left ventricular (LV) myocardium behavior. The primary aim is to bridge the gap between complex constitutive models and their practical clinical applications. We employed DL techniques to construct a model that operates under synthetic clinical metrics and pressure–volume loop data to predict the behavior of the LV myocardium for a whole cardiac cycle. This model generates a waveform of the active contraction parameter and estimates fiber angles at the endocardium and epicardium within a single forward pass. The outputs of our model can be used to infer the unknown parameters in finite element analysis of the myocardium. The DL model demonstrates consistency across various synthetic clinical data scenarios. While the model was tested using both idealized and a single patient‐derived geometry, it is important to note that the results have not yet been validated against real clinical data. Overall, the developed DL model offers an innovative approach to understanding and predicting myocardium mechanics in patient‐specific clinical settings. By reducing the barrier between the clinical applications and theoretical constitutive models, the proposed method has the potential to support more targeted cardiovascular interventions and diagnostics.

Accurate patient-specific constitutive parameters of the aortic wall are essential for biomechanical risk assessment of thoracic aortic aneurysms (TAA), yet their non-invasive determination remains a major challenge. In this study, we proposed a novel inverse finite element method (IFEM) based on our recently developed unified-fiber-distribution (UFD) model, to identify in vivo constitutive parameters of ascending TAA from patient-specific CT images. The UFD model considers the fibers (embedded in the matrix of the aortic tissues) as a unified distribution, which expects to be more physically consistent with the real fiber distribution than existing models (e.g., the well-known Gasser–Ogden–Holzapfel [GOH] model) that separate the fiber distribution into two/several fiber families. We performed ex vivo biaxial tensile testing on specimens from 30 TAA patients and demonstrated that the UFD model, using one fewer parameter, achieved statistically better accuracy than the GOH model (R² = 0.9773 ± 0.0012 vs. R² = 0.9647 ± 0.0039; p<0.001). We then applied the UFD-based IFEM, incorporating a modified backward displacement method for zero-pressure configuration recovery, to estimate in vivo constitutive parameters from multi-phase CT data of the same 30 patients. The in vivo CT-predicted parameters demonstrated good agreement with ex vivo ground truth values, with a mean relative error of 8.51% ± 0.62% across all four parameters and all patients, and sub-millimeter geometric agreement (0.77 ± 0.03 mm) between predicted and CT-derived diastolic configurations. These results demonstrate that the UFD-based IFEM can accurately and efficiently identify patient-specific constitutive parameters non-invasively, representing a significant step toward clinical translation of biomechanical risk assessment for thoracic aortic aneurysms.

Human intestine is a complex organ that performs critical roles in nutrient absorption, immune regulation, and host-microbiome interactions. Traditional 2D and 3D in vitro models, while useful for some applications, fall short in replicating the dynamic and multifaceted environment of the small intestine. In recent years, Intestine-on-Chip (IoC) technologies have emerged as promising platforms that integrate microfluidics, biomechanical cues, and tissue engineering to better simulate intestinal structure and function. This review provides a comprehensive overview of IoC devices, covering their underlying principles, historical development, design elements, and key functional capabilities, with special emphasis on the incorporation of mechanical strain and peristalsis-like motion. We also discuss the limitations of current models, including application of constant uniaxial strain, scale constraints, material challenges, biological complexity, and lack of standardization as well as prospective directions for advancing this field. By addressing these gaps, next-generation IoC systems can pave the way for more predictive disease models, advanced drug testing platforms, and personalized medicine applications.

The flapping of wings is a common locomotion strategy for small organisms in both aerial and aquatic environments. Cuvierina atlantica, a marine pteropod, employs a distinctive “overlap-and-fling” motion to propel itself underwater. To examine the hydrodynamics underlying this behavior, we perform high-resolution fluid–structure interaction simulations using the lattice Boltzmann method coupled with the immersed boundary method. The body geometry and wing kinematics are accurately reconstructed from high-speed experimental videos and validated through direct overlay comparisons. The simulations show that C. atlantica relies primarily on lift-based propulsion, with both leading-edge and trailing-edge vortices contributing to thrust generation. The overlap-and-fling motion enhances circulation, promoting the development of strong vortices and sustained low-pressure regions along the wing edges, while wing–wing interaction increases vertical lift by more than 20%. Parametric analyses demonstrate that higher fluid viscosity elevates total hydrodynamic force but markedly reduces the lift-to-drag ratio and overall propulsion efficiency due to stronger viscous effects. Increasing flapping frequency boosts force production but leads to saturation in lift-to-drag and pressure-to-shear ratios, associated with increasingly chaotic wake structures. When interpreted using the Strouhal number, efficiency in the neutrally buoyant case exhibits an optimal value near 0.6. However, under gravity, this relationship breaks down, as weight-induced posture adjustments modify pitching dynamics and alter the phase relation between force generation and wake evolution. Across all conditions, higher chordwise Reynolds numbers generate numerous small vortices that enhance energy dissipation and reduce swimming efficiency.

Objective: This study investigates the effects of hemodynamic factors on blood cell suspension flows and their properties in curved microvessels. A parametric study is employed to compare these properties between curved and straight vessels.Methods: A 3D fluid solver coupled with a cell membrane modeling framework via the immersed boundary method was used to simulate cell‐resolved blood flow in straight and curved vessels featuring a 90° bend with moderate curvature.Results: Blood flow in curved vessels shows lower and higher shear rates in the inner and outer bulk regions, respectively, compared to straight vessels. Asymmetry in hematocrit profiles is linked to less dense suspensions, smaller diameters, and higher Capillary numbers, while the maximum velocity location remains consistent with straight vessels. At physiological shear rates, moderate curvatures, and large diameters, curvature has minimal impact on apparent viscosity. However, diffusivity is elevated at the center of curved vessels compared to straight ones.Conclusions: This study reveals new insights into blood suspension flows in curved microvessels with a 90° bend, highlighting key differences from straight vessels under certain hemodynamic conditions. These findings lay the groundwork for future research on realistic microvessel geometries and their implications.

Technological breakthroughs have enhanced our understanding of myocardial mechanics and physiological responses to detect early disease indicators. Using constitutive models to represent myocardium structure is critical for understanding the intricacies of such complex tissues. Several models have been developed to depict both passive response and active contraction of myocardium, however they require careful adjustment of material parameters for patient-specific scenarios and substantial time and computing resources. Thus, most models are unsuitable for employment outside of research. Deep learning (DL) has sparked interest in data-driven computational modeling for complex system analysis. We developed a DL model for assessing and forecasting the behavior of an active contraction model of the left ventricular (LV) myocardium under a patient-specific clinical setting. Our original technique analyzes a context in which clinical measures are limited: as model input, just a handful of clinical parameters and a pressure-volume (PV) loop are required. This technique aims to bridge the gap between theoretical calculations and clinical applications by allowing doctors to use traditional metrics without administering additional data and processing resources. Our DL model's main objectives are to produce a waveform of active contraction property that properly portrays patient-specific data during a cardiac cycle and to estimate fiber angles at the endocardium and epicardium. Our model accurately represented the mechanical response of the LV myocardium for various PV curves, and it applies to both idealized and patient-specific geometries. Integrating artificial intelligence with constitutive-based models allows for the autonomous selection of hidden model parameters and facilitates their application in clinical settings.

Due to the inherent complexity of biological applications that more often than not include fluids and structures interacting together, the development of computational fluid–structure interaction models is necessary to achieve a quantitative understanding of their structure and function in both health and disease. The functions of biological structures usually include their interactions with the surrounding fluids. Hence, we contend that the use of fluid–structure interaction models in computational studies of biological systems is practical, if not necessary. The ultimate goal is to develop computational models to predict human biological processes. These models are meant to guide us through the multitude of possible diseases affecting our organs and lead to more effective methods for disease diagnosis, risk stratification, and therapy. This review paper summarizes computational models that use smoothed-particle hydrodynamics to simulate the fluid–structure interactions in complex biological systems.