Fundamentals of Skin Bioimpedances

Formation of Anisotropic Conducting Interlayer for High-Resolution Epidermal Electromyography Using Mixed-Conducting Particulate Composite

Epidermal electrophysiology is a non-invasive method used in research and clinical practices to study the electrical activity of the brain, heart, nerves, and muscles. However, electrode/tissue interlayer materials such as ionically conducting pastes can negatively affect recordings by introducing lateral electrode-to-electrode ionic crosstalk and reducing spatial resolution. To overcome this issue, biocompatible, anisotropic-conducting interlayer composites (ACI) that establish an electrically anisotropic interface with the skin are developed, enabling the application of dense cutaneous sensor arrays. High-density, conformable electrodes are also microfabricated that adhere to the ACI and follow the curvilinear surface of the skin. The results show that ACI significantly enhances the spatial resolution of epidermal electromyography (EMG) recording compared to conductive paste, permitting the acquisition of single muscle action potentials with distinct spatial profiles. The high-density EMG in developing mice, non-human primates, and humans is validated. Overall, high spatial-resolution epidermal electrophysiology enabled by ACI has the potential to advance clinical diagnostics of motor system disorders and enhance data quality for human-computer interface applications.

Ultrasound Nanobubble Coupling Agent for Effective Noninvasive Deep-Layer Drug Delivery

Conventional coupling agents (such as polyvinylpyrrolidone, methylcellulose, and polyurethane) are unable to efficiently transport drugs through the skin's dual barriers (the epidermal cuticle barrier and the basement membrane barrier between the epidermis and dermis) when exposed to ultrasound, hindering deep and noninvasive transdermal drug delivery. In this study, nanobubbles prepared by the double emulsification method and aminated hyaluronic acid are crosslinked with aldehyde-based hyaluronic acid by dynamic covalent bonding through the Schiff base reaction to produce an innovative ultrasound-nanobubble coupling agent. By amplifying the cavitation effect of ultrasound, drugs can be efficiently transferred through the double barrier of the skin and delivered to deep layers. In an in vitro model of isolated porcine skin, this agent achieves an effective penetration depth of 728 µm with the parameters of ultrasound set at 2 W, 650 kHz, and 50% duty cycle for 20 min. Consequently, drugs can be efficiently delivered to deeper layers noninvasively. In summary, this ultrasound nanobubble coupling agent efficiently achieves deep-layer drug delivery by amplifying the ultrasonic cavitation effect and penetrating the double barriers, heralding a new era for noninvasive drug delivery platforms and disease treatment.

The Three-Lead EEG Sensor: Introducing an EEG-Assisted Depression Diagnosis System Based on Ant Lion Optimization

For depression diagnosis, traditional methods such as interviews and clinical scales have been widely leveraged in the past few decades, but they are subjective, time-consuming, and labor-consuming. With the development of affective computing and Artificial Intelligence (AI) technologies, Electroencephalogram (EEG)-based depression detection methods have emerged. However, previous research has virtually neglected practical application scenarios, as most studies have focused on analyzing and modeling EEG data. Furthermore, EEG data is typically obtained from specialized devices that are large, complex to operate, and poorly ubiquitous. To address these challenges, a wearable three-lead EEG sensor with flexible electrodes was developed to obtain prefrontal-lobe EEG data. Experimental measurements show that the EEG sensor achieves promising performance (background noise of no more than 0.91 μVpp, Signal-to-Noise Ratio (SNR) of 26--48 dB, and electrode-skin contact impedance of less than 1 K Ω). In addition, EEG data from 70 depressed patients and 108 healthy controls were collected using the EEG sensor, and the linear and nonlinear features were extracted. The features were then weighted and selected using the Ant Lion Optimization (ALO) algorithm to improve classification performance. The experimental results show that the k-NN classifier achieves a classification accuracy of 90.70%, specificity of 96.53%, and sensitivity of 81.79%, indicating the promising potential of the three-lead EEG sensor combined with the ALO algorithm and the k-NN classifier for EEG-assisted depression diagnosis.