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Biosensors
Special Issues
Emerging Surface-Enhanced Raman Scattering Strategies and Applications for Biosensors
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Emerging Surface-Enhanced Raman Scattering Strategies and Applications for Biosensors

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Optical and Photonic Biosensors".

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 1711

Special Issue Editors

Prof. Dr. Haifeng Yang

E-Mail Website
Guest Editor
College of Chemistry and Materials Science, Shanghai Normal University, Shanghai 200234, China
Interests: Raman spectral electrochemistry; chemical imaging; chemical sensors and biosensors; SERS-probes for POCT applications
Special Issues, Collections and Topics in MDPI journals
Dr. Xinling Liu

E-Mail
Guest Editor
College of Chemistry and Materials Science, Shanghai Normal University, Shanghai 200234, China
Interests: chiral inorganic nanomaterials; surface-enhanced Raman scattering; chiroptical analysis; chiral discrimination
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Surface-enhanced Raman scattering (SERS) has been recognized as a sensitive and rapid tool used to provide rich vibrational spectroscopic information. However, there are still many issues that restrict the real application of SERS, especially in biosensing. For example, although various SERS substrates have been reported, the stability and reproducibility of substrates are concerning. To detect complex samples or those biomolecules with low-level Raman scattering cross-sections, it is challenging to realize molecule specificity and a high sensitivity. In addition, novel materials have been explored to construct SERS substrates, of which, the SERS mechanism remains to be clarified. In recent years, many efforts have been made to explore new applications of SERS in various bio-related areas (e.g., biological sample analysis, disease diagnosis, and nano–bio interactions) and achieve a high resolution in biological samples. Simultaneously, modern data processing methods (e.g., machine learning and algorithms) are also employed for SERS data analysis.

Over 40 years have passed since the discovery of SERS. Challenges and opportunities co-exist in the development of SERS techniques. Therefore, this Special Issue focuses on the emerging SERS strategies and applications in biosensing. The topics of this issue include, but are not limited to:

  • The SERS analysis of biomolecules, biomarkers, bio-related exogenous species (e.g., drugs, pesticide, and cosmetic ingredients), biological samples, and bio–nano interactions;
  • Novel SERS substrates for biosensing;
  • The reliable and reproducible preparation of SERS biosensors;
  • SERS strategies with improved selectivity and sensitivity for complex biological samples;
  • SERS imaging in biological samples;
  • SERS data processing methods;
  • Portable SERS biosensing devices.

Prof. Dr. Haifeng Yang
Dr. Xinling Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biosensors is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • SERS
  • biosensors
  • SERS imaging
  • biomarkers
  • disease diagnosis

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Published Papers (1 paper)

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Research

14 pages, 3596 KiB  
Article
Label-Free Surface-Enhanced Raman Spectroscopy with Machine Learning for the Diagnosis of Thyroid Cancer by Using Fine-Needle Aspiration Liquid Samples
by Lili Gao, Siyi Wu, Puwasit Wongwasuratthakul, Zhou Chen, Wei Cai, Qinyu Li and Linley Li Lin
Biosensors 2024, 14(8), 372; https://doi.org/10.3390/bios14080372 - 31 Jul 2024
Viewed by 1437
Abstract
The incidence of thyroid cancer is increasing worldwide. Fine-needle aspiration (FNA) cytology is widely applied with the use of extracted biological cell samples, but current FNA cytology is labor-intensive, time-consuming, and can lead to the risk of false-negative results. Surface-enhanced Raman spectroscopy (SERS) [...] Read more.
The incidence of thyroid cancer is increasing worldwide. Fine-needle aspiration (FNA) cytology is widely applied with the use of extracted biological cell samples, but current FNA cytology is labor-intensive, time-consuming, and can lead to the risk of false-negative results. Surface-enhanced Raman spectroscopy (SERS) combined with machine learning algorithms holds promise for cancer diagnosis. In this study, we develop a label-free SERS liquid biopsy method with machine learning for the rapid and accurate diagnosis of thyroid cancer by using thyroid FNA washout fluids. These liquid supernatants are mixed with silver nanoparticle colloids, and dispersed in quartz capillary for SERS measurements to discriminate between healthy and malignant samples. We collect Raman spectra of 36 thyroid FNA samples (18 malignant and 18 benign) and compare four classification models: Principal Component Analysis–Linear Discriminant Analysis (PCA-LDA), Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). The results show that the CNN algorithm is the most precise, with a high accuracy of 88.1%, sensitivity of 87.8%, and the area under the receiver operating characteristic curve of 0.953. Our approach is simple, convenient, and cost-effective. This study indicates that label-free SERS liquid biopsy assisted by deep learning models holds great promise for the early detection and screening of thyroid cancer. Full article
(This article belongs to the Special Issue Emerging Surface-Enhanced Raman Scattering Strategies and Applications for Biosensors)
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