Quantitative and direct serum albumin detection by label-free SERS using tunable hydroxyapatite nanostructure for prostate cancer detection

Lin, Yamin; Lin, Juqiang; Zheng, Mengmeng; Gong, Wei; Li, Huan; Shu, Zhengyu; Du, Weiwei; Gao, Siqi; Yu, Yun

doi:10.1016/j.aca.2022.340101

Cited by 9 publications

(9 citation statements)

References 32 publications

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“…SVM in combination with SERS has been used for the early detection of oral cancer among patients using serum and saliva samples and achieved an accuracy of 80% [ 227 ]. Prostate cancer has been extensively studied and successfully identified using different techniques, such as serum analysis combined with PCA-SVM [ 228 ]; detection of prostate specific antigens [ 229 , 230 , 231 , 232 ]; EVs combined with CNN [ 230 ]; miRNAs [ 233 ]; different multivariate techniques, e.g., PCA-LDA and PCA-SVM [ 234 ]; and urine profiling [ 235 ].…”

Section: Sers For Disease Diagnosismentioning

confidence: 99%

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

2023

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Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS’s full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.

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Section: Sers For Disease Diagnosismentioning

confidence: 99%

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

2023

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“…SERS‐based in vivo imaging is currently most commonly used in oncology research due to the enhanced permeation and retention effect, which allows SERS NPs to passively target and aggregate at tumor sites, thus enabling tumor imaging through SERS 157–159 . Although it is possible to recognize certain molecules in vivo such as DNA and proteins directly as Raman reporter molecules, so‐called label‐free SERS detection, the different molecular structures and the complexity of the human body environment make this direct recognition approach lack stability and is, therefore, less used in in vivo imaging 160–164 . Currently, the use of SERS nanosubstrates loaded with Raman reporter molecules with large Raman cross sections as SERS tags is the main approach for SERS detection, which provides a stable SERS signal intensity with high sensitivity and specificity 165–168 …”

Section: Sers‐based Biosensingmentioning

confidence: 99%

“…[157][158][159] Although it is possible to recognize certain molecules in vivo such as DNA and proteins directly as Raman reporter molecules, so-called label-free SERS detection, the different molecular structures and the complexity of the human body environment make this direct recognition approach lack stability and is, therefore, less used in in vivo imaging. [160][161][162][163][164] Currently, the use of SERS nanosubstrates loaded with Raman reporter molecules with large Raman cross sections as SERS tags is the main approach for SERS detection, which provides a stable SERS signal intensity with high sensitivity and specificity. [165][166][167][168] Qian et al pioneered the application of SERS tags for in vivo tumor detection and imaging.…”

Section: In Vivo Diagnosis and Imagingmentioning

confidence: 99%

Optical biosensing systems for a biological living body

Yan

Guo

Wang

et al. 2023

VIEW

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With the development of technology, biosensors are increasingly used in the biomedical field. Due to the high sensitivity of optical signals, good stability, high signal‐to‐noise ratio, and different spectral characteristics of different analytes, optical biosensors can achieve direct and real‐time detection of analytes with high specificity compared with traditional analytical techniques. In view of the rapid development of optical biosensors for in vivo applications and their promising future, we have attempted to summarize the working principles and recent advances in application in humans, with the aim of helping readers to gain an in‐depth and detailed understanding of optical biosensors developed in recent years for applications in the biological living body. In this review, we focus on some of the current widely used and promising optical biosensors, including colorimetric biosensors, fluorescence biosensors, and surface‐enhanced‐Raman‐scattering‐based biosensors. The working principles for each type of optical biosensor are concisely described and the application of the biosensor in the living body is summarized by category. We conclude by looking at the prospects of in vivo applications of optical biosensors.

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“…[197] Many researchers even have integrated multiple supervised learning algorithms into SERS sensors for improving the accuracy of disease diagnosis. [39,41,198,199] Wu et al combined amplification-free SERS biochip with classical least square-linear discriminant analysis (CLS-LDA) algorithm for tumor classification by analysis of multiple DNA mutational patterns. As displayed in Figure 11B, the CLS algorithm was applied to decompose the SERS spectra from 8 groups with mutation combinations and then obtained the estimated contribution of three SERS reporters corresponding to DNA mutations.…”

Section: Supervised Learningmentioning

confidence: 99%

“…prepared a melanoma diagnostic platform composed of SERS sensor and convolution neural network via monitoring the changes of culture medium composition caused by cell metabolic activity [197] . Many researchers even have integrated multiple supervised learning algorithms into SERS sensors for improving the accuracy of disease diagnosis [39,41,198,199] . Wu et al.…”

Section: Machine Learningmentioning

confidence: 99%

Recent Research Progress of Surface‐Enhanced Raman Scattering Dominated Analysis Strategies in Early Diagnosis of Diseases

Chen

Zheng

Tang

et al. 2023

Chemistry An Asian Journal

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Timely and powerful diagnostic means can achieve better therapeutic effects, reduce disease torment, and improve survival rate. As a powerful non‐invasive spectroscopy technology, surface‐enhanced Raman scattering (SERS) have testified to be a great potential candidate for extensive early clinical disease diagnosis. In recent years, the introduction of SERS label, combined with other analysis modes or artificial intelligence, and the emergence of miniaturized devices have made SERS technology more advantageous in early diagnosis of diseases. This review focuses on the research progress of SERS dominated analytical strategies in the field of early disease diagnosis in the past five years. The main content includes the application of label‐free SERS detection; the construction of label SERS methodologies for various disease markers; SERS dominated multimode early disease diagnosis strategies; integration of SERS and artificial intelligence; portable Raman equipment and SERS imaging; and opportunities and trends for SERS diagnostic technology in the future.

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Quantitative and direct serum albumin detection by label-free SERS using tunable hydroxyapatite nanostructure for prostate cancer detection

Cited by 9 publications

References 32 publications

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

Optical biosensing systems for a biological living body

Recent Research Progress of Surface‐Enhanced Raman Scattering Dominated Analysis Strategies in Early Diagnosis of Diseases

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