1. Fast Viral Diagnostics: FTIR-Based Identification, Strain-Typing, and Structural Characterization of SARS-CoV-2.
- Author
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Lahiri P, Das S, Thakur S, Mehra R, Ranjan P, Wig N, Dar L, Bhattacharyya TK, Sengupta S, and Lahiri B
- Subjects
- Spectroscopy, Fourier Transform Infrared methods, Humans, Influenza A Virus, H1N1 Subtype isolation & purification, Influenza A Virus, H1N1 Subtype genetics, Spike Glycoprotein, Coronavirus chemistry, Machine Learning, Influenza A Virus, H3N2 Subtype isolation & purification, Influenza A Virus, H3N2 Subtype genetics, Neural Networks, Computer, SARS-CoV-2 isolation & purification, SARS-CoV-2 genetics, SARS-CoV-2 chemistry, COVID-19 diagnosis, COVID-19 virology
- Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has triggered an ongoing global pandemic, necessitating rapid and accurate diagnostic tools to monitor emerging variants and preparedness for the next outbreak. This study introduces a multidisciplinary approach combining Fourier Transform Infrared (FTIR) microspectroscopy and Machine learning to comprehensively characterize and strain-type SARS-CoV-2 variants. FTIR analysis of pharyngeal swabs from different pandemic waves revealed distinct vibrational profiles, particularly in nucleic acid and protein vibrations. The spectral wavenumber range between 1150 and 1240 cm
-1 was identified as the classification marker, distinguishing Healthy (noninfected) and infected samples. Machine learning algorithms, with neural networks exhibiting superior performance, successfully classified SARS-CoV-2 variants with a remarkable accuracy of 98.6%. Neural networks were also able to identify and differentiate a small cohort infected with influenza A variants, H1N1 and H3N2, from SARS-CoV-2-infected and Healthy samples. FTIR measurements further show distinct red shifts in vibrational energy and secondary structural alterations in the spike proteins of more transmissible forms of SARS-CoV-2 variants, providing experimental validation of the computational data. This integrated approach presents a promising avenue for rapid and reliable SARS-CoV-2 variant identification, enhancing our understanding of viral evolution and aiding in diagnostic advancements, particularly for an infectious disease with unknown etiology.- Published
- 2024
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