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Updated: Aug 6, 2025
Author Spotlight: Advancing the Analysis of Plasma Extracellular Vesicle Proteome for Cardiovascular Biomarker Studies
Published on: January 31, 2025
SERS spectroscopy with machine learning to analyze human plasma derived sEVs for coronary artery disease diagnosis
Xi Huang1, Bo Liu2,3, Shenghan Guo4,5
1Department of Electrical and Computer Engineering University of Nebraska Lincoln Lincoln Nebraska USA.
Insights
This study presents a novel method for early coronary artery disease (CAD) detection using plasma small extracellular vesicles (sEVs) and machine learning. Support Vector Machine (SVM) achieved high accuracy in identifying CAD stages noninvasively.
Area of Science:
- Biomedical Engineering
- Cardiovascular Medicine
- Nanotechnology
Background:
- Coronary artery disease (CAD) is a leading global cause of mortality.
- Early and accurate detection of CAD is crucial for timely intervention and improved patient outcomes.
- Current diagnostic methods may have limitations in early-stage detection.
Purpose of the Study:
- To develop a noninvasive diagnostic strategy for early coronary artery disease (CAD) detection.
- To investigate the potential of plasma small extracellular vesicles (sEVs) as biomarkers for CAD.
- To apply machine learning algorithms for accurate CAD classification and prediction.
Main Methods:
- Isolation of human plasma small extracellular vesicles (sEVs) from four patient groups: healthy controls, stable plaque, non-ST-elevation myocardial infarction, and ST-elevation myocardial infarction.
- Surface-enhanced Raman scattering (SERS) measurements of sEVs.
- Application of five machine learning approaches (Quadratic Discriminant Analysis, Support Vector Machine (SVM), K-Nearest Neighbor, Artificial Neural Network) for data analysis and prediction.
Main Results:
- Support Vector Machine (SVM) demonstrated the highest predictive performance among the tested algorithms.
- SVM achieved 86.4% accuracy for early CAD detection and 92.3% for overall prediction.
- SVM exhibited high sensitivity (97.69%) and specificity (95.7%) in classifying CAD stages.
Conclusions:
- Plasma-derived small extracellular vesicles (sEVs) hold significant potential as biomarkers for noninvasive CAD diagnosis.
- The combination of SERS and SVM offers a promising, safe, and highly accurate strategy for early CAD detection.
- This approach could facilitate timely intervention and improve management of cardiovascular disease.
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.
