Low, Joyce Siew Yong and Teh, Huey Fang and Thevarajah, T. Malathi and Chang, Siow Wee and Khor, Sook Mei (2025) An AI-assisted microfluidic paper-based multiplexed surface-enhanced raman scattering (SERS) biosensor with electrophoretic removal and electrical modulation for accurate acute myocardial infarction (AMI) diagnosis and prognosis. Biosensors & Bioelectronics, 270. p. 116949. ISSN 0956-5663, DOI https://doi.org/10.1016/j.bios.2024.116949.
Full text not available from this repository.Abstract
SERS detects single molecules with exceptional sensitivity. To counter the issue of selectivity faced by point-ofcare, herein, an externally applied electric field that allows electrical modulation and electromigrates unbound SERS tags without multiple washing steps is successfully developed and demonstrated to improve the biosensor's selectivity and sensitivity in multiplexed detection of cTnI, HDL, and LDL in human serum at a low LoD. Ultrasensitive detectors can detect signals from non-specifically absorbed species, and these species can cover up overlapping analyte peaks, amplifying the effect of non-specific binding. Even though antifouling molecules can prevent non-specific adsorption at the sensor interface, this approach does not completely eliminate it. Our significant findings show that an electrically regulated device can electromigrate non-specifically bound species without cross-reacting with endogenous albumin proteins. Stability, repeatability, and reproducibility were good, with an RSD of 10%. Artificial intelligence was employed to interpret and analyze high-dimensional fingerprint SERS spectra using feature selection and dimensionality reduction for accurate acute myocardial infarction diagnosis and prognosis. These machine learning methods allow quantification of cTnI, HDL, and LDL biomarkers with low RMSE. Machine learning classifiers showed strong AUROC values of 0.950 +/- 0.111 and 0.884 +/- 0.139 for early and recurrent AMI detection, respectively. A high negative predictive value (NPV) of >= 99% indicates an effective early AMI rule-out. In short, this work demonstrated that a simple, low-cost, electrophoretic modulated biosensor with machine learning can diagnose, rule out, and predict recurring AMI.
| Item Type: | Article |
|---|---|
| Funders: | Impact Oriented Inter-disciplinary Research Grant (IIRG020A-2019) ; (IIRG020B-2019) ; (IIRG020C-2019) |
| Uncontrolled Keywords: | SERS; Acute myocardial infarction; Point-of-care; cardiac troponin; AI-Assisted |
| Subjects: | Q Science > QD Chemistry Q Science > QR Microbiology R Medicine > R Medicine (General) |
| Divisions: | Faculty of Medicine > Pathology Department Faculty of Science > Institute of Biological Sciences Faculty of Science > Department of Chemistry |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 24 Feb 2025 02:20 |
| Last Modified: | 24 Feb 2025 02:20 |
| URI: | http://eprints.um.edu.my/id/eprint/47281 |
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