International journal of nanomedicine · 2022
A SERS Platform for Rapid Detection of Drug Resistance of Non-Candida albicans Using Fe3O4@PEI and Triangular Silver Nanoplates.
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Gu Feng, Hu Shan, Wu Yunjian, Wu Changyu, Yang Ying, Gu Bing, Du Hong
Аннотация
PURPOSE: Candida infection has a high mortality rate, and the increasing prevalence of non-Candida albicans drug resistance in recent years poses a potential threat to human health. Non-Candida albicans has long culture cycles, and its firm cell walls making it difficult to isolate DNA for sequencing.
MATERIALS AND METHODS: Fe3O4@PEI (PEI, polyvinyl imine) was mixed with clinical samples to form Fe3O4@PEI@non-Candida albicans and enriched them with magnets. Triangular silver nanoplates enhanced the surface-enhanced Raman scattering (SERS) signal. SERS was used to detect the fingerprint spectrum of non-Candida albicans. Then, orthogonal partial least squares discriminant analysis (OPLS-DA) was used to analyze the drug resistance of non-Candida albicans.
RESULTS: SERS combined with OPLS-DA could well analyze the drug resistance of non-Candida albicans. Through 10-fold-cross validation, the accuracy of training and test data is greater than 99%, indicating that the model has good classification ability. We used SERS for the first time to detect the drug resistance of non-Candida albicans directly.
CONCLUSION: This approach can be utilized without causing damage to the cell wall and can be accomplished in as little as 90 minutes. It can provide timely guidance for the treatment of patients with good clinical application potential.
MATERIALS AND METHODS: Fe3O4@PEI (PEI, polyvinyl imine) was mixed with clinical samples to form Fe3O4@PEI@non-Candida albicans and enriched them with magnets. Triangular silver nanoplates enhanced the surface-enhanced Raman scattering (SERS) signal. SERS was used to detect the fingerprint spectrum of non-Candida albicans. Then, orthogonal partial least squares discriminant analysis (OPLS-DA) was used to analyze the drug resistance of non-Candida albicans.
RESULTS: SERS combined with OPLS-DA could well analyze the drug resistance of non-Candida albicans. Through 10-fold-cross validation, the accuracy of training and test data is greater than 99%, indicating that the model has good classification ability. We used SERS for the first time to detect the drug resistance of non-Candida albicans directly.
CONCLUSION: This approach can be utilized without causing damage to the cell wall and can be accomplished in as little as 90 minutes. It can provide timely guidance for the treatment of patients with good clinical application potential.