Microneedle "Smart Patch" Could Provide Quick, Easy Diagnosis of Parkinson's, Alzheimer's Diseases
Providing a result in just six minutes, this interstitial fluid sensor is as easy to apply as a Band-Aid.
A team of researchers at Swansea University and the Polytechnic Institute of Porto have developed a microneedle-based "smart patch" wearable which, they say, could be used to detect and diagnose neurodegenerative diseases like Parkinson's and Alzheimer's — by monitoring "proinflammatory biomarkers."
"The skin is the largest organ in the body – it contains more ISF [Interstitial Fluid] than the total volume of blood," explains co-corresponding author Sanjiv Sharma, PhD, of the work's focus on a skin-worn device. "This fluid is an ultrafiltrate of blood and holds biomarkers that complement other biofluids such as sweat, saliva, and urine. It can be sampled in a minimally invasive manner and used either for point of care testing or real time using microneedle devices."
It's just such a device that the team has developed, a compact patch featuring an array of microneedles that a patient can easily apply at home — and which breaks the skin barrier without causing any discomfort.
"We employed microneedle array-based biosensing patches as wearable transdermal sensors to detect the proinflammatory cytokine IL-6," Sharma explains. "IL-6 is present in the skin ISF with other cytokines and is implicated in many clinical states including neurodegenerative diseases and fatal pneumonia from SARS-CoV-2. We have been able to detect IL-6 at concentrations as low as 1 pg/mL in synthetic skin ISF, indicating its utility for routine point of care, bloodless measurements in simpler settings, worldwide.
"The devices we developed are scalable, and the resulting sensor has a short measurement time [of] six minutes with high accuracy and a low limit of detection. This new diagnostic tool, for screening of inflammatory biomarkers in point of care testing, will see the skin act as a window to the body and vital organs such as the brain."
The team's work has been published in the journal ACS Omega under open-access terms.
Main article image courtesy of Swansea University.