An acoustofluidic device for the automated separation of platelet-reduced plasma from whole blood.

Separating plasma from whole blood is an important sample processing technique required for fundamental biomedical research, medical diagnostics, and therapeutic applications. Traditional protocols for plasma isolation require multiple centrifugation steps or multiunit microfluidic processing to sequentially remove large red blood cells (RBCs) and white blood cells (WBCs), followed by the removal of small platelets. Here, we present an acoustofluidic platform capable of efficiently removing RBCs, WBCs, and platelets from whole blood in a single step. By leveraging differences in the acoustic impedances of fluids, our device generates significantly greater forces on suspended particles than conventional microfluidic approaches, enabling the removal of both large blood cells and smaller platelets in a single unit. As a result, undiluted human whole blood can be processed by our device to remove both blood cells and platelets (>90%) at low voltages (25 Vpp). The ability to successfully remove blood cells and platelets from plasma without altering the properties of the proteins and antibodies present creates numerous potential applications for our platform in biomedical research, as well as plasma-based diagnostics and therapeutics. Furthermore, the microfluidic nature of our device offers advantages such as portability, cost efficiency, and the ability to process small-volume samples.

A fully integrated wearable ultrasound system to monitor deep tissues in moving subjects.

Recent advances in wearable ultrasound technologies have demonstrated the potential for hands-free data acquisition, but technical barriers remain as these probes require wire connections, can lose track of moving targets and create data-interpretation challenges. Here we report a fully integrated autonomous wearable ultrasonic-system-on-patch (USoP). A miniaturized flexible control circuit is designed to interface with an ultrasound transducer array for signal pre-conditioning and wireless data communication. Machine learning is used to track moving tissue targets and assist the data interpretation. We demonstrate that the USoP allows continuous tracking of physiological signals from tissues as deep as 164 mm. On mobile subjects, the USoP can continuously monitor physiological signals, including central blood pressure, heart rate and cardiac output, for as long as 12 h. This result enables continuous autonomous surveillance of deep tissue signals toward the internet-of-medical-things.