Deep learning-enhanced paper-based vertical flow assay for high-sensitivity troponin detection using nanoparticle amplification
arxiv(2024)
摘要
Successful integration of point-of-care testing (POCT) into clinical settings
requires improved assay sensitivity and precision to match laboratory
standards. Here, we show how innovations in amplified biosensing, imaging, and
data processing, coupled with deep learning, can help improve POCT. To
demonstrate the performance of our approach, we present a rapid and
cost-effective paper-based high-sensitivity vertical flow assay (hs-VFA) for
quantitative measurement of cardiac troponin I (cTnI), a biomarker widely used
for measuring acute cardiac damage and assessing cardiovascular risk. The
hs-VFA includes a colorimetric paper-based sensor, a portable reader with
time-lapse imaging, and computational algorithms for digital assay validation
and outlier detection. Operating at the level of a rapid at-home test, the
hs-VFA enabled the accurate quantification of cTnI using 50 uL of serum within
15 min per test and achieved a detection limit of 0.2 pg/mL, enabled by gold
ion amplification chemistry and time-lapse imaging. It also achieved high
precision with a coefficient of variation of < 7
range, covering cTnI concentrations over six orders of magnitude, up to 100
ng/mL, satisfying clinical requirements. In blinded testing, this computational
hs-VFA platform accurately quantified cTnI levels in patient samples and showed
a strong correlation with the ground truth values obtained by a benchtop
clinical analyzer. This nanoparticle amplification-based computational hs-VFA
platform can democratize access to high-sensitivity point-of-care diagnostics
and provide a cost-effective alternative to laboratory-based biomarker testing.
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