ArticleBioengineering & translational medicine2023
SERS spectroscopy with machine learning to analyze human plasma derived sEVs for coronary artery disease diagnosis and prognosis.
Article in Bioengineering & translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Advances in Extracellular Vesicle-Based Surface-Enhanced Raman Spectroscopy for Cancer Diagnosis.Biosensors · 2026Review
- Gold- or Silver-Nanoparticle SERS Platforms for Plasma-Based Diagnostics and AI-Driven Analysis.Sensors (Basel, Switzerland) · 2026Review
- Quantum machine learning-based electrokinetic mining for the identification of nanoparticles and exosomes with minimal training data.Bioactive materials · 2025Article
- Metabolic and proteomic signatures differentiate inflammatory phenotypes from cancer and predict treatment response in patient sera.Bioengineering & translational medicine · 2025Article
- Machine learning-assisted point-of-care diagnostics for cardiovascular healthcare.Bioengineering & translational medicine · 2025Review
- Differentiated mesenchymal stem cells-derived exosomes immobilized in decellularized sciatic nerve hydrogels for peripheral nerve repair.Journal of controlled release : official journal of the Controlled Release Society · 2024Article
- Cancer diagnosis using label-free SERS-based exosome analysis.Theranostics · 2024Review
- Non-Contrasted CT Radiomics for SAH Prognosis Prediction.Bioengineering (Basel, Switzerland) · 2023Article
- Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends.Analytical and bioanalytical chemistry · 2023Review
- SERS spectroscopy with machine learning to analyze human plasma derived sEVs for coronary artery disease diagnosis and prognosis.Bioengineering & translational medicine · 2023Article
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Authors and funding
12 authors.
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Abstract
Coronary artery disease (CAD) is one of the major cardiovascular diseases and represents the leading causes of global mortality. Developing new diagnostic and therapeutic approaches for CAD treatment are critically needed, especially for an early accurate CAD detection and further timely intervention. In this study, we successfully isolated human plasma small extracellular vesicles (sEVs) from four stages of CAD patients, that is, healthy control, stable plaque, non-ST-elevation myocardial infarction, and ST-elevation myocardial infarction. Surface-enhanced Raman scattering (SERS) measurement in conjunction with five machine learning approaches, including Quadratic Discriminant Analysis, Support Vector Machine (SVM), K-Nearest Neighbor, Artificial Neural network, were then applied for the classification and prediction of the sEV samples. Among these five approaches, the overall accuracy of SVM shows the best predication results on both early CAD detection (86.4%) and overall prediction (92.3%). SVM also possesses the highest sensitivity (97.69%) and specificity (95.7%). Thus, our study demonstrates a promising strategy for noninvasive, safe, and high accurate diagnosis for CAD early detection.
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