Passive Isothermal Flexible Sensor Enabled by Smart Thermal-Regulating Aerogels.

Environmentally induced sensor temperature fluctuations can distort the outputs of a sensor, reducing their stability during long-term health monitoring. Here, a passive isothermal flexible sensor is proposed by using hierarchical cellulose aerogel (HCA) as the top tribonegative layer, which allows the sensor to adapt dynamic thermal environments through both radiative cooling and heat insulation. The radiative cooling effect can cool down the temperatures of a sensor in summer, while the hollow microfibers in HCA provide ultralow thermal conductivity to reduce internal heat loss in winter.

Non-Invasive Detection of Early-Stage Fatty Liver Disease via an On-Skin Impedance Sensor and Attention-Based Deep Learning.

Early-stage nonalcoholic fatty liver disease (NAFLD) is a silent condition, with most cases going undiagnosed, potentially progressing to liver cirrhosis and cancer. A non-invasive and cost-effective detection method for early-stage NAFLD detection is a public health priority but challenging. In this study, an adhesive, soft on-skin sensor with low electrode-skin contact impedance for early-stage NAFLD detection is fabricated.

Machine learning-directed electrical impedance tomography to predict metabolically vulnerable plaques.

The characterization of atherosclerotic plaques to predict their vulnerability to rupture remains a diagnostic challenge. Despite existing imaging modalities, none have proven their abilities to identify metabolically active oxidized low-density lipoprotein (oxLDL), a marker of plaque vulnerability. To this end, we developed a machine learning-directed electrochemical impedance spectroscopy (EIS) platform to analyze oxLDL-rich plaques, with immunohistology serving as the ground truth.