Combining Acoustic Bioprinting with AI-Assisted Raman Spectroscopy for High-Throughput Identification of Bacteria in Blood.

Identifying pathogens in complex samples such as blood, urine, and wastewater is critical to detect infection and inform optimal treatment. Surface-enhanced Raman spectroscopy (SERS) and machine learning (ML) can distinguish among multiple pathogen species, but processing complex fluid samples to sensitively and specifically detect pathogens remains an outstanding challenge. Here, we develop an acoustic bioprinter to digitize samples into millions of droplets, each containing just a few cells, which are identified with SERS and ML. We demonstrate rapid printing of 2 pL droplets from solutions containing S. epidermidis, E. coli, and blood; when they are mixed with gold nanorods (GNRs), SERS enhancements of up to 1500× are achieved.We then train a ML model and achieve ≥99% classification accuracy from cellularly pure samples and ≥87% accuracy from cellularly mixed samples. We also obtain ≥90% accuracy from droplets with pathogen:blood cell ratios <1. Our combined bioprinting and SERS platform could accelerate rapid, sensitive pathogen detection in clinical, environmental, and industrial settings.

A tool for monitoring cell type-specific focused ultrasound neuromodulation and control of chronic epilepsy.

Focused ultrasound (FUS) is a powerful tool for noninvasive modulation of deep brain activity with promising therapeutic potential for refractory epilepsy; however, tools for examining FUS effects on specific cell types within the deep brain do not yet exist. Consequently, how cell types within heterogeneous networks can be modulated and whether parameters can be identified to bias these networks in the context of complex behaviors remains unknown. To address this, we developed a fiber Photometry Coupled focused Ultrasound System (PhoCUS) for simultaneously monitoring FUS effects on neural activity of subcortical genetically targeted cell types in freely behaving animals. We identified a parameter set that selectively increases activity of parvalbumin interneurons while suppressing excitatory neurons in the hippocampus. A net inhibitory effect localized to the hippocampus was further confirmed through whole brain metabolic imaging. Finally, these inhibitory selective parameters achieved significant spike suppression in the kainate model of chronic temporal lobe epilepsy, opening the door for future noninvasive therapies.

Lamb Wave Multitouch Ultrasonic Touchscreen.

Touchscreen sensors are widely used in many devices such as smart phones, tablets, and laptops with diverse applications. We present the design, analysis, and implementation of an ultrasonic touchscreen system that utilizes the interaction of transient Lamb waves with objects in contact with the screen. It attempts to improve on the existing ultrasound technologies, with the potential of addressing some of the weaknesses of the dominant technologies, such as the capacitive or resistive ones. Compared with the existing ultrasonic and acoustic modalities, among other advantages, it provides the capability of detecting several simultaneous touch points and also a more robust performance. The localization algorithm, given the hardware design, can detect several touch points with a very limited number of measurements (one or two). This in turn can significantly reduce the manufacturing cost.

Design of Tunable Ultrasonic Receivers for Efficient Powering of Implantable Medical Devices With Reconfigurable Power Loads.

Miniaturized ultrasonic receivers are designed for efficient powering of implantable medical devices with reconfigurable power loads. Design parameters that affect the efficiency of these receivers under highly variable load conditions, including piezoelectric material, geometry, and operation frequency, are investigated. Measurements were performed to characterize electrical impedance and acoustic-to-electrical efficiency of ultrasonic receivers for off-resonance operation. Finally, we propose, analyze, and demonstrate adaptive matching and frequency tuning techniques using two different reconfigurable matching networks for typical implant loads from 10 [Formula: see text] to 1 mW. Both simulations and measurements show a significant increase in total implant efficiency (up to 50 percentage points) over this load power range when operating off-resonance with the proposed matching networks.