| Autor: |
Shan Hu, Benshun Tian, Tianyi Ma, Haiquan Kang, Yunjian Wu, Changyu Wu, Ying Yang, Feng Gu, Bing Gu |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Sensing and Bio-Sensing Research, Vol 42, Iss , Pp 100596- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2214-1804 |
| DOI: |
10.1016/j.sbsr.2023.100596 |
| Popis: |
In clinical practice, infections caused by Aspergillus species are common and associated with high mortality rates. However, isolating Aspergillus from clinical samples and cultures is challenging, and serological tests cannot differentiate between strains. Furthermore, the complex cell wall structure in Aspergillus species hinders DNA extraction and subsequent nucleic acid detection. Aspergillus fumigatus, Aspergillus flavus, and Aspergillus niger are common and frequently encountered pathogenic species. This study aimed to establish an efficient and rapid method for detecting Aspergillus species in clinical samples. Fe3O4@PEI.NH2 nanoparticles with large particle sizes were employed to capture Aspergillus from the samples. Flexible silver nanowires (AgNWs) were prepared and used as the surface-enhanced Raman scattering (SERS) substrate for Aspergillus detection. Through SERS spectroscopy, the identification of captured Aspergillus species was confirmed. Furthermore, the SERS spectrum underwent principal component analysis employing orthogonal partial least-squares discrimination analysis (OPLS-DA) as a multivariate analysis technique to distinguish between the various Aspergillus species effectively. After 10-fold cross-validation, the trained model achieved a testing accuracy of 99.15%, indicating excellent classification performance. By utilizing pre-prepared Fe3O4@PEI.NH2 and AgNWs, the detection process was completed within 60 min without disrupting the cell wall of Aspergillus. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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