Science

Paper-based sensor enables rapid cardiac diagnosis in 15 minutes

Deep learning-enhanced paper-based vertical flow assay for sensitive troponin detection using nanoparticle amplification. Credit: Ozcan Lab @UCLA

In a significant advance in point-of-care medical diagnostics, a UCLA research team has introduced a deep learning-enhanced paper-based vertical flow assay (VFA) that can detect cardiac troponin I (cTnI) with high sensitivity. This innovative assay has the potential to democratize access to rapid and reliable cardiac diagnostics, especially in resource-limited settings.

Cardiovascular disease (CVD) remains the leading cause of death worldwide, killing more than 19 million people annually. Early detection of acute myocardial infarction (AMI), commonly known as a heart attack, is essential to improve patient outcomes and reduce mortality rates. However, the high costs and infrastructure requirements associated with traditional laboratory-based diagnostic equipment often limit access to quality care, especially in low- and middle-income areas.

To address this challenge, UCLA researchers developed a high-sensitivity vertical flow assay (hs-VFA) that combines the accuracy of traditional clinical testing with the convenience and affordability of point-of-care technology. . Their findings, detailed in a paper recently published in ACS Nano, show that this innovative platform can accurately quantify cTnI levels in as little as 15 minutes using small serum samples, making it an urgent Ideal for quick diagnosis in emergency situations or in remote areas.

The core of this platform lies in the integration of deep learning algorithms and cutting-edge nanoparticle amplification chemistry. The hs-VFA system uses time-lapse imaging and computational analysis to enhance the detection of cTnI, an important biomarker of cardiac injury, achieving detection limits as low as 0.2 picograms per milliliter (pg/mL) . This level of sensitivity significantly exceeds current point-of-care devices and meets the clinical requirement for a highly sensitive troponin test essential for early diagnosis of AMI.

“We are pleased to introduce this low-cost, portable solution that bridges the gap between central laboratory diagnostics and point-of-care testing,” said the study’s senior author and UCLA School of Engineering. said Professor Aydogan Ozkan, Wolgenau Professor for Innovation. . “Our paper-based platform, powered by deep learning, provides an effective alternative to the bulky and expensive equipment currently used in hospitals. It has the potential to bring advanced cardiac diagnostics to people who don’t have it.”

The hs-VFA system operates in two stages: an initial immunoassay stage followed by a signal amplification stage. In the immunoassay step, a gold nanoparticle conjugate is used to bind cTnI in serum. During the signal amplification step, gold ions are catalyzed by nanoparticles, resulting in a color change that is captured by a custom-designed portable reader. Deep learning algorithms then analyze these time-lapse images to increase the sensitivity and accuracy of cTnI detection.

In rigorous testing using both spiked and clinical serum samples, the hs-VFA demonstrated high accuracy with a coefficient of variation (CV) of less than 7%. It also showed strong correlation with gold standard laboratory analyzers. Importantly, hs-VFA also exhibited a wide dynamic range covering cTnI concentrations from 0.2 pg/mL to 100 nanograms per milliliter (ng/mL). This range is suitable not only for heart attack diagnosis but also for long-term monitoring of at-risk patients.

The cost-effectiveness of this platform is another key highlight. Paper-based assays cost less than $4 per test, while portable readers designed using a Raspberry Pi computer and off-the-shelf components cost about $170 each. This affordability is critical to expanding access to high-quality diagnostics in low-resource settings where traditional laboratory infrastructure may not be available.

“Our goal was to design a system that could be used not only in hospitals, but also in clinics, pharmacies, and even ambulances,” said Gyeo-Re, lead author of the study and a postdoctoral fellow at UCLA. Dr. Han said. “The ability to rapidly detect and quantify troponin levels in a variety of settings could enable faster and more effective treatment of heart attack patients, especially during the critical pre-hospital treatment phase.”

Beyond cardiac diagnostics, the researchers believe that the hs-VFA platform can also be adapted to other important low-abundance biomarkers, expanding its potential applications to various fields of medical diagnostics. The platform’s portability, simplicity, and affordability position it as a viable alternative to intensive clinical testing for many conditions, offering promise for improved health outcomes on a global scale.

This research was made possible through a collaboration between the UCLA Department of Electrical and Computer Engineering (Ozcan Lab), Department of Biological Engineering (Di Carlo Lab), and California NanoSystems Institute (CNSI).

More information: Gyeo-Re Han et al, Deep learning-enhanced paper-based vertical flow assay for sensitive troponin detection using nanoparticle amplification, ACS Nano (2024). DOI: 10.1021/acsnano.4c05153

Provided by UCLA Engineering Institute for Technology Advancement

Citation: Paper-based sensor provides rapid cardiac diagnosis in 15 minutes (October 6, 2024) from https://phys.org/news/2024-10-paper-based-sensor-rapid-cardiac.html Retrieved October 6, 2024

This document is subject to copyright. No part may be reproduced without written permission, except in fair dealing for personal study or research purposes. Content is provided for informational purposes only.

Related Articles

Leave a Reply

Your email address will not be published. Required fields are marked *

Back to top button