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In a significant advancement for point-of-care medical diagnostics, a team of researchers from UCLA has introduced a deep learning-enhanced, paper-based vertical flow assay (VFA) capable of detecting cardiac troponin I (cTnI) with high sensitivity. The innovative assay holds the potential to democratize access to rapid and reliable cardiac diagnostics, particularly in resource-limited settings.

Cardiovascular diseases (CVDs) remain the leading cause of death worldwide, accounting for over 19 million fatalities annually. Early detection of acute myocardial infarction (AMI), commonly known as a heart attack, is essential for improving patient outcomes and reducing mortality rates. However, the high costs and infrastructure requirements associated with traditional laboratory-based diagnostic equipment often limit access to high-quality care, particularly in low- and middle-income regions.

To address this challenge, UCLA researchers developed a high-sensitivity vertical flow assay (hs-VFA) that combines the precision of traditional laboratory testing with the convenience and affordability of point-of-care technologies. Their findings, detailed in a recently published paper in ACS Nano, demonstrate that this innovative platform can accurately quantify cTnI levels in just 15 minutes using a small sample of serum, making it ideal for rapid diagnostics in emergency settings or remote locations.

"We are excited to introduce this low-cost, portable solution that bridges the gap between central laboratory diagnostics and point-of-care testing," said Professor Aydogan Ozcan, the senior author of the study and the Volgenau Chair for Engineering Innovation at UCLA. "Our paper-based platform, powered by deep learning, offers an effective alternative to the bulky, expensive instruments currently used in hospitals. It holds the promise of bringing advanced cardiac diagnostics to underserved populations globally."

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