Robotic Sensory Cilia Monitors Internal Biomarkers to Detect and Assess Airway Diseases

Continuous monitoring of airway conditions is vital for timely interventions, particularly when airway stents are used to relieve central airway obstructions in lung cancer and other respiratory diseases. Mucus conditions are key biomarkers that indicate inflammation and the status of stent patency, but they are difficult to monitor effectively. Current techniques, which rely on computed tomography imaging and bronchoscope inspections, present risks such as radiation exposure and lack the ability to provide continuous, real-time feedback outside of hospital settings. Researchers have now developed a system of artificial cilia to monitor mucus conditions in human airways, providing a more efficient way to detect infections, airway obstructions, or the severity of conditions like cystic fibrosis (CF), chronic obstructive pulmonary diseases (COPD), and lung cancer.

In their paper published in PNAS, a team of researchers from Vanderbilt University School of Engineering (Nashville, TN, USA) has introduced a novel technology designed to mimic the sensing abilities of biological cilia to detect crucial mucus characteristics such as viscosity and layer thickness, both important biomarkers of disease severity. Their sensing mechanism for mucus viscosity uses external magnetic fields to activate a magnetic artificial cilium, which senses changes in its shape through a flexible strain-gauge.

In addition, the team has developed an artificial cilium with capacitance sensing capabilities to measure mucus layer thickness, offering unique features such as self-calibration, adjustable sensitivity, and an expanded range. These functions are powered by external magnetic fields generated by a wearable magnetic actuation system. The researchers tested this method by deploying the sensors either independently or alongside an airway stent within an artificial trachea and in sheep trachea models. The sensors wirelessly transmit data to a smartphone or cloud platform for further analysis and disease diagnosis.

“The proposed sensing mechanisms and devices pave the way for real-time monitoring of mucus conditions, facilitating early disease detection and providing stent patency alerts, thereby allowing timely interventions and personalized care,” according to the study.

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