The spread of COVID-19 over the past three years is largely due to the continuous mutation of the virus, which has significantly impeded global efforts to prevent and control this epidemic. Specifically, mutations in the amino acid sequence of the surface spike (S) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have directly impacted its biological functions, leading to enhanced transmission and triggering an immune escape effect. Therefore, prompt identification of these mutations is crucial for formulating targeted treatment plans and implementing precise prevention and control measures. In this study, the label-free surface-enhanced Raman scattering (SERS) technology combined with machine learning (ML) algorithms provide a potential solution for accurate identification of SARS-CoV-2 variants. We establish a SERS spectral database of SARS-CoV-2 variants and demonstrate that a diagnostic classifier using a logistic regression (LR) algorithm can provide accurate results within 10 min. Our classifier achieves 100% accuracy for Beta (B.1.351/501Y.V2), Delta (B.1.617), Wuhan (COVID-19) and Omicron (BA.1) variants. In addition, our method achieves 100% accuracy in blind tests of positive and negative human nasal swabs based on the LR model. This method enables detection and classification of variants in complex biological samples. Therefore, ML-based SERS technology is expected to accurately discriminate various SARS-CoV-2 variants and may be used for rapid diagnosis and therapeutic decision-making.
Li Chengcheng,Lu Jinhui,Xiang Chao,Zhang Enhui,Tian Xiaofang,Zhang Ling,Li Tingting,Li Chengyao
Three-metal-core-shell nanoparticles (Au@Pt@PdNPs) providing excellent peroxidase-like activity were applied in lateral flow immunoassay (LFIA), designated as Au@Pt@Pd-LFIA, for detecting the nucleocapsid protein (NP) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). An Au@Pt@Pd-LFIA was developed for quantitatively testing of SARS-CoV-2 NP with a range 0.12–31.25 ng/mL. The limit of detection (LOD) of Au@Pt@Pd-LFIA strip was 0.06 ng/mL, which was 16-fold or eightfold more sensitive than that of the gold lateral flow immunoassay (Au-LFIA) and the gold flower flow immunoassay (AF-LFIA) strips, respectively. For detection of clinical samples from nasal swabs using test strips, Au@Pt@Pd-LFIA had 84.09% sensitivity, 100% specificity, and 92.55% accuracy. In terms of detection time, the testing of Au@Pt@Pd-LFIA strip was 16 min similar to Au-LFIA (15 min) and AF-LFIA (10 min), but much shorter than ELISA (2 h). In conclusion, Au@Pt@Pd-LFIA is a sensitive, rapid, and simple test for quantitative detection of SARS-CoV-2 NP in nasal swab samples. Graphical Abstract
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