Surface-enhanced Raman spectroscopy (SERS) for the diagnosis of acute myocardial infarction (AMI) using blood serum
Maira Naz1, Hira Shafique1, Muhammad Irfan Majeed1,2
1Department of Chemistry, University of Agriculture Faisalabad Faisalabad 38000 Pakistan irfan.majeed@uaf.edu.pk haqchemist@yahoo.com.
Insights
Early diagnosis of acute myocardial infarction (AMI) is crucial. Surface-enhanced Raman spectroscopy (SERS) with silver nanoparticles effectively differentiates AMI patients from healthy individuals using blood serum, aiding in cardiac troponin-I level prediction.
Area of Science:
- Biomedical Spectroscopy
- Nanomaterials in Diagnostics
- Chemometrics for Medical Analysis
Background:
- Acute myocardial infarction (AMI), or heart attack, is a leading cause of global mortality and morbidity.
- Early and accurate diagnosis of AMI is critical to reduce patient death rates.
- Cardiac troponin-I (cTnI) is a key biomarker for diagnosing AMI, but timely detection remains a challenge.
Purpose of the Study:
- To investigate the utility of surface-enhanced Raman spectroscopy (SERS) for the rapid diagnosis of AMI.
- To differentiate between blood serum samples from AMI patients and healthy individuals using SERS.
- To develop a quantitative model for predicting cTnI levels in AMI patients.
Main Methods:
- Utilized SERS with silver nanoparticles (AgNPs) as a substrate for analyzing blood serum samples.
- Analyzed samples from 49 confirmed AMI patients and 17 healthy individuals.
- Employed multivariate chemometric tools, including Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR), for spectral analysis.
Main Results:
- Identified distinct SERS spectral features in AMI-positive blood serum samples at specific wavenumbers (e.g., 534, 697, 1588 cm⁻¹).
- Demonstrated successful differentiation between healthy and AMI patient groups using SERS spectral data.
- Developed a PLSR model capable of predicting cTnI levels with low error (RMSEC: 2.98 ng/mL, RMSEP: 3.98 ng/mL).
Conclusions:
- SERS, in conjunction with AgNPs and chemometrics, offers a promising approach for the early and accurate diagnosis of AMI.
- The developed method shows potential for non-invasive, rapid detection and monitoring of cTnI levels in clinical settings.
- This technique could significantly improve patient outcomes by enabling timely medical intervention for acute myocardial infarction.
Abstract:
Acute myocardial infarction (AMI) is a serious medical condition generally known as heart attack, which is caused by the decreased or completely blocked blood flow to a part of the heart muscle. It is a significant cause of both mortality and morbidity throughout the world. Cardiac troponin-I (cTnI) is an important biomarker at different stages of AMI and is one of the most specific and widely used cardiac skeletal muscle proteins. Delays in medical treatment and inaccurate diagnosis might be the main cause of death of AMI patients. To overcome the death rate of AMI patients, early diagnosis of this disease is crucial. In the current study, surface-enhanced Raman spectroscopy (SERS) is employed for the characterization and diagnosis of this disease using blood serum samples from 49 clinically confirmed acute myocardial infarction (AMI) patients and 17 healthy persons. Silver nanoparticles (AgNPs) are used as the SERS substrate for the recognition of characteristic SERS spectral features, differentiating between healthy and AMI-positive samples. The acute myocardial infarction-positive blood serum samples reveal remarkable differences in spectral intensities at 534, 697, 744, 835, 927, 941, 988, 1221, 1303, 1403, 1481, 1541, 1588 and 1694 cm-1. For the differentiation and quantitative analysis of the SERS spectra, multivariate chemometric tools (including principal component analysis (PCA) and partial least squares regression (PLSR)) are employed. A PLSR model established on the basis of differentiating the SERS spectral features is found to be helpful in the prediction of the levels of cardiac troponin-I (cTnI) in the blood serum samples with the root mean square error of calibration (RMSEC) value of 2.98 ng mL-1 and root mean square errors of prediction (RMSEP) value of 3.98 ng mL-1 for S7.
