Multiplexed computational sensing with a point‐of‐care serodiagnosis assay to simultaneously quantify three biomarkers of acute cardiac injury is demonstrated. This point‐of‐care sensor includes a paper‐based fluorescence vertical flow assay (fxVFA) processed by a low‐cost mobile reader, which quantifies the target biomarkers through trained neural networks, all within <15 min of test time using 50 µL of serum sample per patient. This fxVFA platform is validated using human serum samples to quantify three cardiac biomarkers, i.e., myoglobin, creatine kinase‐MB, and heart‐type fatty acid binding protein, achieving less than 0.52 ng mL−1limit‐of‐detection for all three biomarkers with minimal cross‐reactivity. Biomarker concentration quantification using the fxVFA that is coupled to neural network‐based inference is blindly tested using 46 individually activated cartridges, which shows a high correlation with the ground truth concentrations for all three biomarkers achieving >0.9 linearity and <15% coefficient of variation. The competitive performance of this multiplexed computational fxVFA along with its inexpensive paper‐based design and handheld footprint makes it a promising point‐of‐care sensor platform that can expand access to diagnostics in resource‐limited settings.
Precise monitoring of specific biomarkers in biological fluids with accurate biodiagnostic sensors is critical for early diagnosis of diseases and subsequent treatment planning. In this work, we demonstrated an innovative biodiagnostic sensor, portable reusable accurate diagnostics with nanostar antennas (PRADA), for multiplexed biomarker detection in small volumes (~50 μl) enabled in a microfluidic platform. Here, PRADA simultaneously detected two biomarkers of myocardial infarction, cardiac troponin I (cTnI), which is well accepted for cardiac disorders, and neuropeptide Y (NPY), which controls cardiac sympathetic drive. In PRADA immunoassay, magnetic beads captured the biomarkers in human serum samples, and gold nanostars (GNSs) “antennas” labeled with peptide biorecognition elements and Raman tags detected the biomarkers via surface‐enhanced Raman spectroscopy (SERS). The peptide‐conjugated GNS‐SERS barcodes were leveraged to achieve high sensitivity, with a limit of detection (LOD) of 0.0055 ng/ml of cTnI, and a LOD of 0.12 ng/ml of NPY comparable with commercially available test kits. The innovation of PRADA was also in the regeneration and reuse of the same sensor chip for ~14 cycles. We validated PRADA by testing cTnI in 11 de‐identified cardiac patient samples of various demographics within a 95% confidence interval and high precision profile. We envision low‐cost PRADA will have tremendous translational impact and be amenable to resource‐limited settings for accurate treatment planning in patients.
more » « less- Award ID(s):
- 1634856
- NSF-PAR ID:
- 10455808
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Bioengineering & Translational Medicine
- Volume:
- 5
- Issue:
- 3
- ISSN:
- 2380-6761
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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References A. J. Steckl, P. Ray, (2018), doi:10.1021/acssensors.8b00726.
Y. Lei, D. Butler, M. C. Lucking, F. Zhang, T. Xia, K. Fujisawa, T. Granzier-Nakajima, R. Cruz-Silva, M. Endo, H. Terrones, M. Terrones, A. Ebrahimi,
Sci. Adv. 6 , 4250–4257 (2020).V. Kammarchedu, D. Butler, A. Ebrahimi,
Anal. Chim. Acta .1232 , 340447 (2022).H. Yoon, J. Nah, H. Kim, S. Ko, M. Sharifuzzaman, S. C. Barman, X. Xuan, J. Kim, J. Y. Park,
Sensors Actuators B Chem. 311 , 127866 (2020).T. Wu, A. Alharbi, R. Kiani, D. Shahrjerdi,
Adv. Mater. 31 , 1–12 (2019).