High Throughput Blood Analysis Based on Deep Learning Algorithm and Self‐Positioning Super‐Hydrophobic SERS Platform for Non‐Invasive Multi‐Disease Screening

Jia

et al. 2022

J Raman Spectroscopy

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Human blood contains a variety of biomolecules that are highly related to the occurrence, progression, and prognosis of diseases, including cancer. The development of rapid and accurate methods for the detection of these biomarkers with extremely low concentration is vital for improving cancer screening. Herein, a label‐free surface‐enhanced Raman scattering (SERS) technology was employed to detect human serum samples from healthy volunteers (n = 51) and breast cancer patients (n = 58) with the aim of exploring the differences in bio‐components between them. Moreover, the influence of measurement location during SERS assay was evaluated due to the occurrence of “coffee‐ring (CR)” effect associated with this work. Results showed that the SERS spectra obtained from the CR area can better reflect the overall biochemical information of serum samples and exhibited excellent diagnostic sensitivity (94.8%) and specificity (98%) for identifying breast cancer from healthy groups using principal component analysis and linear discriminant analysis (PCA‐LDA) algorithm. This work showed a preliminary study of the serum SERS method for breast cancer detection, providing a potential rapid and accurate liquid biopsy strategy for clinical cancer screening.

Section: Resultsmentioning

confidence: 90%

A comparative study based on serum SERS spectra in and on the coffee ring for high precision breast cancer detection

Jia

et al. 2022

J Raman Spectroscopy

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“…An aluminium (Al)-sheet-based super-hydrophobic platform was prepared using a simple, fast, low-cost approach based on chemical etching and chemical vapor deposition ( Figure 1 A). Unlike the prior scheme [ 24 ], in which a costly 0.8 cm-thick aluminium plate along with a complicated time-consuming mechanical polishing step are required, we employed a cheap 0.05 cm-thin aluminium sheet as the substrate and simplified the preparation process by omitting the polishing step while adjusting the parameters in the chemical etching step in this study. In detail, first, an Al sheet (17.0 cm × 17.0 cm × 0.05 cm) was pressed into 100 matrix-curved grooves (1.5 mm in depth and 8 mm in diameter) using a pressing machine for the sake of sample self-localization and high-throughput detection.…”

Section: Methodsmentioning

confidence: 99%

Highly Efficient Blood Protein Analysis Using Membrane Purification Technique and Super-Hydrophobic SERS Platform for Precise Screening and Staging of Nasopharyngeal Carcinoma

et al. 2022

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Early screening and precise staging are crucial for reducing mortality in patients with nasopharyngeal carcinoma (NPC). This study aimed to assess the performance of blood protein surface-enhanced Raman scattering (SERS) spectroscopy, combined with deep learning, for the precise detection of NPC. A highly efficient protein SERS analysis, based on a membrane purification technique and super-hydrophobic platform, was developed and applied to blood samples from 1164 subjects, including 225 healthy volunteers, 120 stage I, 249 stage II, 291 stage III, and 279 stage IV NPC patients. The proteins were rapidly purified from only 10 µL of blood plasma using the membrane purification technique. Then, the super-hydrophobic platform was prepared to pre-concentrate tiny amounts of proteins by forming a uniform deposition to provide repeatable SERS spectra. A total of 1164 high-quality protein SERS spectra were rapidly collected using a self-developed macro-Raman system. A convolutional neural network-based deep-learning algorithm was used to classify the spectra. An accuracy of 100% was achieved for distinguishing between the healthy and NPC groups, and accuracies of 96%, 96%, 100%, and 100% were found for the differential classification among the four NPC stages. This study demonstrated the great promise of SERS- and deep-learning-based blood protein testing for rapid, non-invasive, and precise screening and staging of NPC.

“… 219 An example in this direction comprised the combination of a portable SERS spectrometer with deep-learning as a point-of-care automatic device, providing early diagnosis of multiple diseases in a single run. 220 …”

Section: Hitting the Target With The Aid Of Artificial Intelligencementioning

confidence: 99%

Prospects of Surface-Enhanced Raman Spectroscopy for Biomarker Monitoring toward Precision Medicine

et al. 2022

Future precision medicine will be undoubtedly sustained by the detection of validated biomarkers that enable a precise classification of patients based on their predicted disease risk, prognosis, and response to a specific treatment. Up to now, genomics, transcriptomics, and immunohistochemistry have been the main clinically amenable tools at hand for identifying key diagnostic, prognostic, and predictive biomarkers. However, other molecular strategies, including metabolomics, are still in their infancy and require the development of new biomarker detection technologies, toward routine implementation into clinical diagnosis. In this context, surface-enhanced Raman scattering (SERS) spectroscopy has been recognized as a promising technology for clinical monitoring thanks to its high sensitivity and label-free operation, which should help accelerate the discovery of biomarkers and their corresponding screening in a simpler, faster, and less-expensive manner. Many studies have demonstrated the excellent performance of SERS in biomedical applications. However, such studies have also revealed several variables that should be considered for accurate SERS monitoring, in particular, when the signal is collected from biological sources (tissues, cells or biofluids). This Perspective is aimed at piecing together the puzzle of SERS in biomarker monitoring, with a view on future challenges and implications. We address the most relevant requirements of plasmonic substrates for biomedical applications, as well as the implementation of tools from artificial intelligence or biotechnology to guide the development of highly versatile sensors.