A microfluidic model to study the effects of arrhythmic flows on endothelial cells.

Atrial fibrillation (AF) is the most common type of cardiac arrhythmia and an important contributor to morbidity and mortality. Endothelial dysfunction has been postulated to be an important contributing factor in cardiovascular events in patients with AF. However, how vascular endothelial cells respond to arrhythmic flow is not fully understood, mainly due to the limitation of current in vitro systems to mimic arrhythmic flow conditions. To address this limitation, we developed a microfluidic system to study the effect of arrhythmic flow on the mechanobiology of human aortic endothelial cells (HAECs). The system utilises a computer-controlled piezoelectric pump for generating arrhythmic flow with a unique ability to control the variability in both the frequency and amplitude of pulse waves. The flow rate is modulated to reflect physiological or pathophysiological shear stress levels on endothelial cells. This enabled us to systematically dissect the importance of variability in the frequency and amplitude of pulses and shear stress level on endothelial cell mechanobiology. Our results indicated that arrhythmic flow at physiological shear stress level promotes endothelial cell spreading and reduces the plasma membrane-to-cytoplasmic distribution of ß-catenin. In contrast, arrhythmic flow at low and atherogenic shear stress levels does not promote endothelial cell spreading or redistribution of ß-catenin. Interestingly, under both shear stress levels, arrhythmic flow induces inflammation by promoting monocyte adhesion via an increase in ICAM-1 expression. Collectively, our microfluidic system provides opportunities to study the effect of arrhythmic flows on vascular endothelial mechanobiology in a systematic and reproducible manner.

Recent developments in modeling, imaging, and monitoring of cardiovascular diseases using machine learning.

Cardiovascular diseases are the leading cause of mortality, morbidity, and hospitalization around the world. Recent technological advances have facilitated analyzing, visualizing, and monitoring cardiovascular diseases using emerging computational fluid dynamics, blood flow imaging, and wearable sensing technologies. Yet, computational cost, limited spatiotemporal resolution, and obstacles for thorough data analysis have hindered the utility of such techniques to curb cardiovascular diseases. We herein discuss how leveraging machine learning techniques, and in particular deep learning methods, could overcome these limitations and offer promise for translation. We discuss the remarkable capacity of recently developed machine learning techniques to accelerate flow modeling, enhance the resolution while reduce the noise and scanning time of current blood flow imaging techniques, and accurate detection of cardiovascular diseases using a plethora of data collected by wearable sensors.

Investigating the mechanotransduction of transient shear stress mediated by Piezo1 ion channel using a 3D printed dynamic gravity pump.

Microfluidic systems are widely used for studying the mechanotransduction of flow-induced shear stress in mechanosensitive cells. However, these studies are generally performed under constant flow rates, mainly, due to the deficiency of existing pumps for generating transient flows. To address this limitation, we have developed a unique dynamic gravity pump to generate transient flows in microfluidics. The pump utilises a motorised 3D-printed cam-lever mechanism to change the inlet pressure of the system in repeated cycles. 3D printing technology facilitates the rapid and low-cost prototyping of the pump. Customised transient flow patterns can be generated by modulating the profile, size, and rotational speed of the cam, location of the hinge along the lever, and the height of the syringe. Using this unique dynamic gravity pump, we investigated the mechanotransduction of shear stress in HEK293 cells stably expressing Piezo1 mechanosensitive ion channel under transient flows. The controllable, simple, low-cost, compact, and modular design of the pump makes it suitable for studying the mechanobiology of shear sensitive cells under transient flows.

Generation of programmable dynamic flow patterns in microfluidics using audio signals.

Customised audio signals, such as musical notes, can be readily generated by audio software on smartphones and played over audio speakers. Audio speakers translate electrical signals into the mechanical motion of the speaker cone. Coupling the inlet tube to the speaker cone causes the harmonic oscillation of the tube, which in turn changes the velocity profile and flow rate. We employ this strategy for generating programmable dynamic flow patterns in microfluidics. We show the generation of customised rib and vortex patterns through the application of multi-tone audio signals in water-based and whole blood samples. We demonstrate the precise capability to control the number and extent of the ribs and vortices by simply setting the frequency ratio of two- and three-tone audio signals. We exemplify potential applications of tube oscillation for studying the functional responses of circulating immune cells under pathophysiological shear rates. The system is programmable, compact, low-cost, biocompatible, and durable. These features make it suitable for a variety of applications across chemistry, biology, and physics.