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5-Fluorouracil (5-FU) is an effective anticancer drug widely used in the world. To improve therapy efficiency and reduce side effects, it is very important to frequently detect the concentration of 5-FU in blood samples of patients. In this work, a new type of lateral flow immunochromatographic assay (LFIA) based on surface-enhanced Raman scattering (SERS) for ultrasensitive and specific detection of 5-FU in blood samples was developed. Au@Ag/Au nanoparticles (NPs) employing Au particles as the core and Ag/Au alloy as the shell were synthesized, characterized and used as the substrate in SERS-LFIA due to their high SERS enhancement and biocompatibility. The immunoprobe was made in the form of AuMBA@Ag/Au-Ab in which mercaptobenzoic acid (MBA, a common Raman active reporter) was embedded in the core–shell layer and the monoclonal antibody (mAb) against 5-FU was immobilized on the surface. The performance of SERS-LFIA was similar to that in colloidal gold based-LFIA, and the entire assay time was within 20 min. According to the color intensity on the testing (T) lines of LFIA strips visualized by eyes, the contents of 5-FU in the samples could be qualitatively or semi-quantitatively identified. Furthermore, by measuring the characteristic Raman intensities of MBA on T lines, quantitative detection of 5-FU in the samples were achieved. The IC50 and limit of detection (LOD) of the LFIA for 5-FU were found to be 20.9 pg mL−1 and 4.4 pg mL−1, respectively. There was no cross-reactivity (CR) of the LFIA with nine relative compounds, and the CR with cytosine, tegafur and carmofur were less than 4.5%. The recoveries of 5-FU from spiked blood samples were in the range of 78.6~86.4% with the relative standard deviation (RSD) of 2.69~4.42%. Five blood samples containing 5-FU collected from the Cancer Hospital were measured by SERS-LFIA, and the results were confirmed by LC-MS/MS. It was proven that the proposed method was able to simply and rapidly detect 5-FU in blood samples with high sensitivity, specificity, accuracy and precision.
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Liu et al. (2022) studied this question.
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