Estimating Thrombus Elasticity by Shear Wave Elastography to Evaluate Ultrasound Thrombolysis for Thrombus With Different Stiffness.

OBJECTIVE: There is uncertainty about deep vein thrombosis standard treatment as thrombus stiffness alters each case. Here, we investigated thrombus' stiffness of different compositions and ages using shear wave elastography (SWE). We then studied the effectiveness of ultrasound-thrombolysis on different thrombus compositions. METHODS: Shear waves generated through mechanical shaker and traveled along thrombus of different hematocrit (HCT) levels, whereas 18-MHz ultrasound array used to detect wave propagation. Thrombus' stiffness was identified by the shear wave speed (SWS). In thrombolysis, a 3.2 MHz focused transducer was applied to different thrombus compositions using different powers. The thrombolysis rate was defined as the percentage of weight loss. RESULTS: The estimated average SWS of 20%, 40%, and 60% HCT thrombus were 0.75 m/s, 0.44 m/s, and 0.32 m/s, respectively. For Thrombolysis, the percentage weight loss at 8 MPa Negative pressure for the same HCT groups were 23.1%, 35.29%, and 39.66% respectively. CONCLUSION: SWS is inversely related to HCT level and positively related to thrombus age. High HCT thrombus had higher weight loss compared to low HCT. However, the difference between 20% and 40% HCT was more significant than between 40% and 60% HCT in both studies. Our results suggest that thrombus with higher SWS require more power to achieve the same thrombolysis rate as thrombus with lower SWS. SIGNIFICANCE: Characterizing thrombus elastic property undergoing thrombolysis enables evaluation of ultrasound efficacy for fractionating thrombus and reveals the appropriate ultrasound parameters selection to achieve a certain thrombolysis rate in the case of a specific thrombus stiffness.

3D Printing and processing of miniaturized transducers with near-pristine piezoelectric ceramics for localized cavitation.

The performance of ultrasonic transducers is largely determined by the piezoelectric properties and geometries of their active elements. Due to the brittle nature of piezoceramics, existing processing tools for piezoelectric elements only achieve simple geometries, including flat disks, cylinders, cubes and rings. While advances in additive manufacturing give rise to free-form fabrication of piezoceramics, the resultant transducers suffer from high porosity, weak piezoelectric responses, and limited geometrical flexibility. We introduce optimized piezoceramic printing and processing strategies to produce highly responsive piezoelectric microtransducers that operate at ultrasonic frequencies. The 3D printed dense piezoelectric elements achieve high piezoelectric coefficients and complex architectures. The resulting piezoelectric charge constant, d33, and coupling factor, kt, of the 3D printed piezoceramic reach 583 pC/N and 0.57, approaching the properties of pristine ceramics. The integrated printing of transducer packaging materials and 3D printed piezoceramics with microarchitectures create opportunities for miniaturized piezoelectric ultrasound transducers capable of acoustic focusing and localized cavitation within millimeter-sized channels, leading to miniaturized ultrasonic devices that enable a wide range of biomedical applications.

A Computational Model of Mitochondria Motility in Axons.

Mitochondria play a critical role in regulating cellular processes including ATP production, intracellular calcium signaling and generation of reactive oxidative species (ROS). Neurons rely on mitochondrial function to perform a range of complex processes, and mitochondrial dysfunctions have been shown to have an impact in pathologies of the nervous system. Yet, neurons contain a finite number of mitochondria, and their location is known to change in response to a number of factors including age and cellular activity, thereby impacting neuronal response. In this paper, we introduce a novel computational model of mitochondria motility that focuses on their movements along the axon. We describe the biological processes involved and the main parameters of the model. We use the model to investigate how some of these parameters affect the ability of mitochondria to position themselves in regions of high energy demand. Finally, we discuss the significance of our work and its downstream applications in further understanding pathologies of the nervous system such as Alzheimer's disease, and help identify potential novel therapeutic targets.