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.
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.
