Rapid identification and antibiotic susceptibility test of pathogens in blood based on magnetic separation and surface-enhanced Raman scattering

Li, Jia; Wang, Chongwen; Shi, Luoluo; Shao, Liting; Fu, Peiwen; Wang, Keli; Xiao, Rui; Wang, Shengqi; Gu, Bing

doi:10.1007/s00604-019-3571-x

Cited by 57 publications

(36 citation statements)

References 42 publications

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“… 29 , 30 This highlights the advantages of SERS in detecting pathogenic fungi. Superior to others’ former selection of materials, 17 , 21 we selected and prepared materials of Fe 3 O 4 @PEI (300–500 nm) and AgNPs + based on the characteristics of large diameter and negative charge of fungi. Compared with the latest method of detecting fungi SERS, our method does not require pure cultures 18 and does not require lysis of the fungal cell wall.…”

Section: Discussionmentioning

confidence: 99%

“…The supernatant was discarded, and the AgNPs + were resuspended and rotated for 15 minutes. 21 We then concentrated the particles with a magnet in a small volume. The Raman spectrum of the precipitate was detected similarly as above and compared to the standard spectrum.…”

Section: Methodsmentioning

confidence: 99%

“…22 Figure 1A shows the production process of Fe 3 O 4 @PEI. We improved the method of Liu et al 19,21 and synthesized Fe 3 O 4 microspheres sized 300-500 nm. In an ultrasonic bath, PEI is mixed with Fe 3 O 4 .…”

mentioning

confidence: 99%

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Rapid Detection Method for Pathogenic Candida Captured by Magnetic Nanoparticles and Identified Using SERS via AgNPs+

Hu¹,

Kang²,

Gu³

et al. 2021

IJN

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Candidemia infection is common in the clinic and has a high mortality rate. Candida albicans, Candida tropicalis, and Candida krusei are very important and common pathogenic species. Candida is difficult to isolate from clinical samples and culture, and immunological detection cannot distinguish these related strains. Furthermore, Candida has a complex cell wall, which causes difficulties in the extraction of DNA for nucleic acid detection. The purpose of this study was to establish a protocol for the direct identification of Candida from serum. Materials and Methods: We synthesized Fe 3 O 4 @PEI (where PEI stands for polyethylenimine) magnetic nanoparticles to capture Candida and prepared positively charged silver nanoparticles (AgNPs +) as the substrate for surface-enhanced Raman scattering (SERS). Candida was directly identified from serum by SERS detection. Results: Orthogonal partial least squares discriminant analysis (OPLS-DA) was used as the multivariate analysis tool. Principal component analysis confirmed that this method can clearly distinguish common Candida. After 10-fold cross-validation, the accuracy of training data in this model was 100% and the accuracy of test data was 99.8%, indicating that the model has good classification ability. Conclusion: The detection could be completed within 40 minutes using Fe 3 O 4 @PEI and AgNPs + prepared in advance. This is the first time that Fe 3 O 4 @PEI was used in the detection of Candida by SERS. We report the first rapid method to identify fungi directly from serum without breaking the cell wall to extract DNA from the fungi.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Rapid Detection Method for Pathogenic Candida Captured by Magnetic Nanoparticles and Identified Using SERS via AgNPs+

Hu¹,

Kang²,

Gu³

et al. 2021

IJN

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“…Therefore, the SERS technique requires advanced data processing algorithms to capture these minor differences. A vast majority of publications have reported that machine learning techniques can be employed to discriminate antibioticresistant and susceptible bacteria by using data obtained from SERS [15][16][17][18] .…”

Section: Introductionmentioning

confidence: 99%

Drug-resistant Staphylococcus Aureus Bacteria Detection with the Combination of Surface-enhanced Raman Spectroscopy and Deep Learning Techniques

Ciloglu

Çalışkan

Saridag

et al. 2021

Preprint

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Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door - antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with other traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared with other traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data. The proposed method is a label-free, easy implemented, and reliable technique with high sensitivity for clinical use.

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“…Rapid and sensitive methods that could timely diagnose this pathogen are the key to reduce the spread of infection and guarantee food safety at the source. The traditional microbiological culture method is considered as the standard method for bacterial detection ( Hu et al, 2016 ; Li et al, 2019 ). However, it is labor intensive and time consuming, usually needing more than 24 h of culture and analysis.…”

Section: Introductionmentioning

confidence: 99%

Rapid, Quantitative, High-Sensitive Detection of Escherichia coli O157:H7 by Gold-Shell Silica-Core Nanospheres-Based Surface-Enhanced Raman Scattering Lateral Flow Immunoassay

Shi

Xiao

et al. 2020

Front. Microbiol.

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Escherichia coli O157:H7 is regarded as one of the most harmful pathogenic microorganisms related to foodborne diseases. This paper proposes a rapiddetection biosensor for the sensitive and quantitative analysis of E. coli O157:H7 in biological samples by surface-enhanced Raman scattering (SERS)-based lateral flow immunoassay (LFIA). A novel gold-shell silica-core (SiO 2 /Au) nanosphere (NP) with monodispersity, good stability, and excellent SERS activity was utilized to prepare highperformance tags for the SERS-based LFIA system. The SiO 2 /Au SERS tags, which were modified with two layers of Raman reporter molecules and monoclonal antibodies, effectively bind with E. coli O157:H7 and form sandwich immune complexes on the test lines. E. coli O157:H7 was quantitatively detected easily by detecting the Raman intensity of the test lines. Under optimal conditions, the limit of detection (LOD) of the SiO 2 /Au-based SERS-LIFA strips for the target bacteria was 50 cells/mL in PBS solution, indicating these strips are 2,000 times more sensitive than colloidal Au-based LFIA strips. Moreover, the proposed assay demonstrated high applicability in E. coli O157:H7 detection in biological samples, including tap water, milk, human urine, lettuce extract and beef, with a low LOD of 100 cells/mL. Results indicate that the proposed SERS-based LFIA strip is applicable for the sensitive and quantitative determination of E. coli O157:H7.

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Rapid identification and antibiotic susceptibility test of pathogens in blood based on magnetic separation and surface-enhanced Raman scattering

Cited by 57 publications

References 42 publications

Rapid Detection Method for Pathogenic Candida Captured by Magnetic Nanoparticles and Identified Using SERS via AgNPs+

Rapid Detection Method for Pathogenic Candida Captured by Magnetic Nanoparticles and Identified Using SERS via AgNPs+

Drug-resistant Staphylococcus Aureus Bacteria Detection with the Combination of Surface-enhanced Raman Spectroscopy and Deep Learning Techniques

Rapid, Quantitative, High-Sensitive Detection of Escherichia coli O157:H7 by Gold-Shell Silica-Core Nanospheres-Based Surface-Enhanced Raman Scattering Lateral Flow Immunoassay

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