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Deep learning-enhanced chemiluminescence vertical flow assay for high-sensitivity cardiac troponin I testing
Democratizing biomarker testing at the point-of-care requires innovations that match laboratory-grade sensitivity and precision in an accessible format. Here, we demonstrate high-sensitivity detection of cardiac troponin I (cTnI) through innovations in chemiluminescence-based sensing, imaging, and d...
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Published in: | arXiv.org 2024-12 |
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Main Authors: | , , , , , , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Online Access: | Get full text |
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Summary: | Democratizing biomarker testing at the point-of-care requires innovations that match laboratory-grade sensitivity and precision in an accessible format. Here, we demonstrate high-sensitivity detection of cardiac troponin I (cTnI) through innovations in chemiluminescence-based sensing, imaging, and deep learning-driven analysis. This chemiluminescence vertical flow assay (CL-VFA) enables rapid, low-cost, and precise quantification of cTnI, a key cardiac protein for assessing heart muscle damage and myocardial infarction. The CL-VFA integrates a user-friendly chemiluminescent paper-based sensor, a polymerized enzyme-based conjugate, a portable high-performance CL reader, and a neural network-based cTnI concentration inference algorithm. The CL-VFA measures cTnI over a broad dynamic range covering six orders of magnitude and operates with 50 uL of serum per test, delivering results in 25 min. This system achieves a detection limit of 0.16 pg/mL with an average coefficient of variation under 15%, surpassing traditional benchtop analyzers in sensitivity by an order of magnitude. In blinded validation, the computational CL-VFA accurately measured cTnI concentrations in patient samples, demonstrating a robust correlation against a clinical-grade FDA-cleared analyzer. These results highlight the potential of CL-VFA as a robust diagnostic tool for accessible, rapid cardiac biomarker testing that meets the needs of diverse healthcare settings, from emergency care to underserved regions. |
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ISSN: | 2331-8422 |