Detection of Helicobacter pylori Infection in Human Gastric Fluid Through Surface-Enhanced Raman Spectroscopy Coupled With Machine Learning Algorithms

Tang, Jia-Wei; Li, Fen; Liu, Xin; Wang, Jin-Ting; Xiong, Xue-Song; Lu, Xiang-Yu; Zhang, Xin-Yu; Si, Yu-Ting; Umar, Zeeshan; Tay, Alfred Chin Yen; Marshall, Barry J.; Yang, Wei-Xuan; Gu, Bing; Wang, Liang

doi:10.1016/j.labinv.2023.100310

Cited by 7 publications

(1 citation statement)

References 36 publications

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“…Raman spectroscopy improves early identification of Candida and contributes to early optimal antifungal therapy ( Chouthai et al, 2015 ). SERS analysis enables accurate detection of H. pylori in gastric fluid samples, establishing a novel noninvasive test for H. pylori ( Tang et al, 2024 ). Additionally, SERS is resilient against interference from moisture, a common challenge in biological assays, and facilitates real-time detection, which is critical for timely clinical interventions.…”

Section: Discussionmentioning

confidence: 99%

Rapid identification of bloodstream infection pathogens and drug resistance using Raman spectroscopy enhanced by convolutional neural networks

Kang,

Wang,

Sun

et al. 2024

Front. Microbiol.

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Bloodstream infections (BSIs) are a critical medical concern, characterized by elevated morbidity, mortality, extended hospital stays, substantial healthcare costs, and diagnostic challenges. The clinical outcomes for patients with BSI can be markedly improved through the prompt identification of the causative pathogens and their susceptibility to antibiotics and antimicrobial agents. Traditional BSI diagnosis via blood culture is often hindered by its lengthy incubation period and its limitations in detecting pathogenic bacteria and their resistance profiles. Surface-enhanced Raman scattering (SERS) has recently gained prominence as a rapid and effective technique for identifying pathogenic bacteria and assessing drug resistance. This method offers molecular fingerprinting with benefits such as rapidity, sensitivity, and non-destructiveness. The objective of this study was to integrate deep learning (DL) with SERS for the rapid identification of common pathogens and their resistance to drugs in BSIs. To assess the feasibility of combining DL with SERS for direct detection, erythrocyte lysis and differential centrifugation were employed to isolate bacteria from blood samples with positive blood cultures. A total of 12,046 and 11,968 SERS spectra were collected from the two methods using Raman spectroscopy and subsequently analyzed using DL algorithms. The findings reveal that convolutional neural networks (CNNs) exhibit considerable potential in identifying prevalent pathogens and their drug-resistant strains. The differential centrifugation technique outperformed erythrocyte lysis in bacterial isolation from blood, achieving a detection accuracy of 98.68% for pathogenic bacteria and an impressive 99.85% accuracy in identifying carbapenem-resistant Klebsiella pneumoniae. In summary, this research successfully developed an innovative approach by combining DL with SERS for the swift identification of pathogenic bacteria and their drug resistance in BSIs. This novel method holds the promise of significantly improving patient prognoses and optimizing healthcare efficiency. Its potential impact could be profound, potentially transforming the diagnostic and therapeutic landscape of BSIs.

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Section: Discussionmentioning

confidence: 99%

Rapid identification of bloodstream infection pathogens and drug resistance using Raman spectroscopy enhanced by convolutional neural networks

Kang,

Wang,

Sun

et al. 2024

Front. Microbiol.

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Label‐free surface‐enhanced Raman spectroscopy coupled with machine learning algorithms in pathogenic microbial identification: Current trends, challenges, and perspectives

Tang,

Yuan,

Wen

et al. 2024

Interdisciplinary Medicine

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Infectious diseases caused by microbial pathogens remain a primary contributor to global health burdens. Prompt control and effective prevention of these pathogens are critical for public health and medical diagnostics. Conventional microbial detection methods suffer from high complexity, low sensitivity, and poor selectivity. Therefore, developing rapid and reliable methods for microbial pathogen detection has become imperative. Surface‐enhanced Raman Spectroscopy (SERS), as an innovative non‐invasive diagnostic technique, holds significant promise in pathogenic microorganism detection due to its rapid, reliable, and cost‐effective advantages. This review comprehensively outlines the fundamental theories of Raman Spectroscopy (RS) with a focus on label‐free SERS strategy, reporting on the latest advancements of SERS technique in detecting bacteria, viruses, and fungi in clinical settings. Furthermore, we emphasize the application of machine learning algorithms in SERS spectral analysis. Finally, challenges faced by SERS application are probed, and the prospective development is discussed.

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Rapid discrimination between wild and cultivated Ophiocordyceps sinensis through comparative analysis of label-free SERS technique and mass spectrometry

Liu,

Tang,

et al. 2024

Current Research in Food Science

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Detection of Helicobacter pylori Infection in Human Gastric Fluid Through Surface-Enhanced Raman Spectroscopy Coupled With Machine Learning Algorithms

Cited by 7 publications

References 36 publications

Rapid identification of bloodstream infection pathogens and drug resistance using Raman spectroscopy enhanced by convolutional neural networks

Rapid identification of bloodstream infection pathogens and drug resistance using Raman spectroscopy enhanced by convolutional neural networks

Label‐free surface‐enhanced Raman spectroscopy coupled with machine learning algorithms in pathogenic microbial identification: Current trends, challenges, and perspectives

Rapid discrimination between wild and cultivated Ophiocordyceps sinensis through comparative analysis of label-free SERS technique and mass spectrometry

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