High Sensitivity Cardiac Troponin I Detection via MP-Locked Aptamer and Multimeric DNAzyme-Coupled Hyperbranched
Sayantan Tripathy1,2, Sahil Sharma3, Ng Ka Wai1
1Department of Biomedical Engineering, Texas A&M University, College Station, Texas, USA.
Small (Weinheim an Der Bergstrasse, Germany)
|February 24, 2026
Summary
This study introduces a novel biosensing platform for rapid and sensitive detection of cardiac troponin I (cTnI), crucial for diagnosing myocardial infarction. The system combines aptamers, DNA amplification, and machine learning for accurate results in human and canine samples.
Area of Science:
- Biomedical Engineering
- Nanotechnology
- Analytical Chemistry
Background:
- Early diagnosis of myocardial infarction (MI) relies on sensitive cardiac troponin I (cTnI) detection.
- Point-of-care diagnostics are needed for timely MI assessment.
- Existing methods may lack the required sensitivity or speed for rapid clinical decisions.
Purpose of the Study:
- To develop a novel colorimetric biosensing platform for high-sensitivity cardiac troponin I (cTnI) detection.
- To integrate this biosensor with machine learning for enhanced diagnostic performance.
- To evaluate the platform's efficacy in detecting cTnI in human and canine serum samples.
Main Methods:
- Utilized magnetic particle (MP) anchored locked aptamers with complementary strand stabilization.
- Employed hyperbranched hybridization chain reaction (HCR) and DNAzyme nanocomplex for enzyme-free signal amplification.
- Integrated the biosensing output with machine learning models for classification and performance assessment.
Main Results:
- Achieved a low detection limit of 0.25 ng/L for cTnI with a wide dynamic range (0.5-50,000 ng/L).
- Demonstrated high sensitivity and specificity with classification accuracies of 90.91% (human) and 83.33% (canine).
- Confirmed platform robustness through blind testing with human serum samples, yielding ~90% accuracy.
Conclusions:
- The developed platform enables rapid (25-30 min) and sensitive cTnI detection.
- The integrated biosensing and machine learning approach shows significant potential for pre-clinical myocardial infarction diagnosis across species.
- This technology offers a promising tool for point-of-care cardiac diagnostics.
