Ultrafast Microscopy Imaging of Acoustic Cluster Therapy Bubbles: Activation and Oscillation

Acoustic Cluster Therapy (ACT®) is a platform for improving drug delivery and has had promising pre-clinical results. A clinical trial is ongoing. ACT® is based on microclusters of microbubbles–microdroplets that, when sonicated, form a large ACT® bubble. The aim of this study was to obtain new knowledge on the dynamic formation and oscillations of ACT® bubbles by ultrafast optical imaging in a microchannel. The high-speed recordings revealed the microbubble–microdroplet fusion, and the gas in the microbubble acted as a vaporization seed for the microdroplet. Subsequently, the bubble grew by gas diffusion from the surrounding medium and became a large ACT® bubble with a diameter of 5–50 μm. A second ultrasound exposure at lower frequency caused the ACT® bubble to oscillate. The recorded oscillations were compared with simulations using the modified Rayleigh–Plesset equation. A term accounting for the physical boundary imposed by the microchannel wall was included. The recorded oscillation amplitudes were approximately 1–2 µm, hence similar to oscillations of smaller contrast agent microbubbles. These findings, together with our previously reported promising pre-clinical therapeutic results, suggest that these oscillations covering a large part of the vessel wall because of the large bubble volume can substantially improve therapeutic outcome.publishedVersio

Focal areas of increased lipid concentration on the coating of microbubbles during short tone-burst ultrasound insonification

Acoustic behavior of lipid-coated microbubbles has been widely studied, which has led to several numerical microbubble dynamics models that incorporate lipid coating behavior, such as buckling and rupture. In this study we investigated the relationship between micro-bubble acoustic and lipid coating behavior on a nanosecond scale by using fluorescently labeled lipids. It is hypothesized that a local increased concentration of lipids, appearing as a focal area of increased fluorescence intensity (hot spot) in the fluorescence image, is related to buckling and folding of the lipid layer thereby highly influencing the microbubble acoustic behavior. To test this hypothesis, the lipid microbubble coating was fluorescently labeled. The vibration of the microbubble (n= 177; 2.3-10.3 μm in diameter) upon insonification at an ultrasound frequency of 0.5 or 1 MHz at 25 or 50 kPa acoustic pressure was recorded with the UPMC Cam, an ultra-high-speed fluorescence camera, operated at ∼4-5 million frames per second. During short tone-burst excitation, hot spots on the microbubble coating occurred at relative vibration amplitudes > 0.3 irrespective of frequency and acoustic pressure. Around resonance, the majority of the microbubbles formed hot spots. When the microbubble also deflated acoustically, hot spot formation was likely irreversible. Although compression-only behavior (defined as substantially more microbubble compression than expansion) and subharmonic responses were observed in those microbubbles that formed hot spots, both phenomena were also found in microbubbles that did not form hot spots during insonification. In conclusion, this study reveals hot spot formation of the lipid monolayer in the microbubble's compression phase. However, our experimental results show that there is no direct relationship between hot spot formation of the lipid coating and microbubble acoustic behaviors such as compression-only and the generation of a subharmonic response. Hence, our hypothesis that hot spots are related to acoustic buckling could not be verified

Predictive Model for Conducting Electromagnetic Interference by Bidirectional Excitation Controller

Bidirectional excitation controller is used in the excitation system of brushed DC motor. There are many monitoring sensors and weak current switches nearby. Therefore, it is necessary to study the conduction interference of the excitation controller. Firstly, based on the working principle of bidirectional excitation controller, the propagation path model and corresponding equivalent circuit of bidirectional excitation controller are established. Then, the parasitic capacitance parameters between the switch tube and the heat sink were extracted by ANSYS Q3D software, and the dynamic model of IGBT was established by using ANSYS Simplorer software. Based on ANSYS software, the prediction model of the equipment conducted electromagnetic interference was obtained. Finally an excitation controller conducting interference test platform was built, and the predicted results were compared with the measured interference results of the experimental platform to verify the accuracy of the prediction model

Dynamic Behavior of Microbubbles during Long Ultrasound Tone-Burst Excitation: Mechanistic Insights into Ultrasound-Microbubble Mediated Therapeutics Using High-Speed Imaging and Cavitation Detection

Kerala’s COVID-19 response has gained international attention for its egalitarian approach. Ophira Gamliel reflects on how studying the Malayalam language provides insight into the culture that enables this success

Forecasting the crowd: An effective and efficient neural network for citywide crowd information prediction at a fine spatio-temporal scale

Modelling and forecasting citywide crowd information (e.g., crowd volume of a region, the inflow of crowds into a region, outflow of crowds from a region) at a fine spatio-temporal scale is crucial for urban and transport planning, city management, public safety, and traffic management. However, this is a challenging task due to its complex spatial and temporal dependences. This paper proposes an effective and efficient model to reduce the training time cost while maintaining predictive accuracy in forecasting citywide crowd information at a fine spatio-temporal scale. Our model integrates Gated Recurrent Unit (GRU), convolutional neural network (CNN), and k-nearest neighbors (k-NN) to jointly capture the spatial and temporal dependences between two regions in a city. The evaluation with two different datasets in two different cities shows that compared to the state-of-the-art baselines, our model has better predictive accuracy (reducing the mean absolute errors MAEs by 20.99% on average) and a lower training time cost (reducing the time cost to only 26.16% on average of that of the baselines). Our model also has better abilities in making accurate predictions with low time cost under the influences of large-scale special events (when massive crowds of people are gathering in a short time) and for regions with high and irregular crowd changes. In summary, our model is an effective, efficient, and reliable method for forecasting citywide crowd information at a fine spatio-temporal scale, and has a high potential for many applications, such as city management, public safety, and transportation