A PVDF Receiver for Acoustic Monitoring of Microbubble-Mediated Ultrasound Brain Therapy

Ultrasound-Mediated Lysozyme Microbubbles Targeting NOX4 Knockdown Alleviate Cisplatin-Exposed Cochlear Hair Cell Ototoxicity

The nicotinamide adenine dinucleotide phosphate (NADPH) oxidase 4 (NOX4) protein plays an essential role in the cisplatin (CDDP)-induced generation of reactive oxygen species (ROS). In this study, we evaluated the suitability of ultrasound-mediated lysozyme microbubble (USMB) cavitation to enhance NOX4 siRNA transfection in vitro and ex vivo. Lysozyme-shelled microbubbles (LyzMBs) were constructed and designed for siNOX4 loading as siNOX4/LyzMBs. We investigated different siNOX4-based cell transfection approaches, including naked siNOX4, LyzMB-mixed siNOX4, and siNOX4-loaded LyzMBs, and compared their silencing effects in CDDP-treated HEI-OC1 cells and mouse organ of Corti explants. Transfection efficiencies were evaluated by quantifying the cellular uptake of cyanine 3 (Cy3) fluorescein-labeled siRNA. In vitro experiments showed that the high transfection efficacy (48.18%) of siNOX4 to HEI-OC1 cells mediated by US and siNOX4-loaded LyzMBs significantly inhibited CDDP-induced ROS generation to almost the basal level. The ex vivo CDDP-treated organ of Corti explants of mice showed an even more robust silencing effect of the NOX4 gene in the siNOX4/LyzMB groups treated with US sonication than without US sonication, with a marked abolition of CDDP-induced ROS generation and cytotoxicity. Loading of siNOX4 on LyzMBs can stabilize siNOX4 and prevent its degradation, thereby enhancing the transfection and silencing effects when combined with US sonication. This USMB-derived therapy modality for alleviating CDDP-induced ototoxicity may be suitable for future clinical applications.

Potential of the Novel Slot Blot Method with a PVDF Membrane for Protein Identification and Quantification in Kampo Medicines

Kampo is a Japanese traditional medicine modified from traditional Chinese medicine. Kampo medicines contain various traditional crude drugs with unknown compositions due to the presence of low-molecular-weight compounds and proteins. However, the proteins are generally rare and extracted with high-polarity solvents such as water, making their identification and quantification difficult. To develop methods for identifying and quantifying the proteins in Kampo medicines, in the current study we employ previous technology (e.g., column chromatography, electrophoresis, and membrane chromatography), focusing on membrane chromatography with a polyvinylidene difluoride (PVDF) membrane. Moreover, we consider slot blot analysis based on the principle of membrane chromatography, which is beneficial for analyzing the proteins in Kampo medicines as the volume of the samples is not limited. In this article, we assess a novel slot blot method developed in 2017 and using a PVDF membrane and special lysis buffer to quantify advanced glycation end products-modified proteins against other slot blots. We consider our slot blot analysis superior for identifying and quantifying proteins in Kampo medicines compared with other methods as the data obtained with our novel slot blot can be shown with both error bars and the statistically significant difference, and our operation step is simpler than those of other methods.

A Convolutional Neural Network for Beamforming and Image Reconstruction in Passive Cavitation Imaging

Convolutional neural networks (CNNs), initially developed for image processing applications, have recently received significant attention within the field of medical ultrasound imaging. In this study, passive cavitation imaging/mapping (PCI/PAM), which is used to map cavitation sources based on the correlation of signals across an array of receivers, is evaluated. Traditional reconstruction techniques in PCI, such as delay-and-sum, yield high spatial resolution at the cost of a substantial computational time. This results from the resource-intensive process of determining sensor weights for individual pixels in these methodologies. Consequently, the use of conventional algorithms for image reconstruction does not meet the speed requirements that are essential for real-time monitoring. Here, we show that a three-dimensional (3D) convolutional network can learn the image reconstruction algorithm for a 16×16 element matrix probe with a receive frequency ranging from 256 kHz up to 1.0 MHz. The network was trained and evaluated using simulated data representing point sources, resulting in the successful reconstruction of volumetric images with high sensitivity, especially for single isolated sources (100% in the test set). As the number of simultaneous sources increased, the network's ability to detect weaker intensity sources diminished, although it always correctly identified the main lobe. Notably, however, network inference was remarkably fast, completing the task in approximately 178 s for a dataset comprising 650 frames of 413 volume images with signal duration of 20μs. This processing speed is roughly thirty times faster than a parallelized implementation of the traditional time exposure acoustics algorithm on the same GPU device. This would open a new door for PCI application in the real-time monitoring of ultrasound ablation.