FTIR-based molecular fingerprinting for the rapid classification of dengue and chikungunya from human sera using machine learning: an observational studyResearch in context
Summary: Background: Dengue and chikungunya are arboviral diseases transmitted by Aedes mosquitoes, co-endemic in southeast Asia and India. Accurate and rapid diagnosis is crucial for effective outbreak management, but conventional diagnostic methods (ELISA, RT-PCR) are limited by cross-reactivity...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | The Lancet Regional Health - Southeast Asia |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772368225001015 |
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| Summary: | Summary: Background: Dengue and chikungunya are arboviral diseases transmitted by Aedes mosquitoes, co-endemic in southeast Asia and India. Accurate and rapid diagnosis is crucial for effective outbreak management, but conventional diagnostic methods (ELISA, RT-PCR) are limited by cross-reactivity and the need for specialized infrastructure. Vibrational spectroscopy offers a novel, label-free alternative for detecting host molecular changes directly from serum. Methods: We conducted an observational study to evaluate the diagnostic potential of Fourier Transform Infrared (FTIR) and Raman micro-spectroscopy combined with machine learning for the classification of dengue and chikungunya from human serum. Serum samples from confirmed dengue (N = 142), chikungunya (N = 120), and healthy controls (N = 40) were analysed. Vibrational spectra were acquired using FTIR and Raman techniques, followed by spectral deconvolution and machine learning-based classification using Support Vector Machine (SVM), Neural Network (NN), and Random Forest (RF) models. Findings: FTIR analysis revealed distinctive group-specific vibrational signatures, particularly in the Amide I and III regions, where dengue-infected sera exhibited a marked increase in β-sheet content and loss of α-helical structures. Raman spectroscopy further identified differences in nucleic acid backbone vibrations and protein conformations. The SVM, RF, and NN models, trained on FTIR data, achieved near-perfect classification (AUC = 1.000; CA-score ≥0.989), outperforming traditional diagnostic methods. Additionally, t-SNE and Silhouette analyses demonstrated superior clustering performance with FTIR, with clear separation of Chikungunya samples (average Silhouette score 0.385) compared to Raman, where clustering was less distinct. Interpretation: Vibrational spectroscopy, particularly FTIR integrated with machine learning, offers a robust, rapid, and scalable diagnostic platform for distinguishing arboviral infections in regions with high co-infection rates. By capturing host biomolecular changes directly from serum, this method minimizes cross-reactivity and enhances diagnostic speed compared to ELISA and RT-PCR. Its deployment in point-of-care settings could significantly improve arboviral surveillance and clinical management, especially in resource-limited regions. Funding: This study was funded by the Department of Health Research- Indian Council of Medical Research (DHR-ICMR) Grant-In-Aid grant number GIA/2020/000346 and CoEs Phase II, IIT/SRIC/IDK-PHASE-II/2024/01. |
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| ISSN: | 2772-3682 |