Serum Exosome SERS Assay Based on TiN‐Ag@Ag Sol Composite Substrate and Its Application in the Diagnosis of Gastric Cancer DOI
Huan Wang,

Zhengang Wu,

Yingna Wei

et al.

Journal of Raman Spectroscopy, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 30, 2024

ABSTRACT Gastric cancer (GC) is a highly lethal malignancy, seriously threatening people's physical health. Accurate screening of gastric could improve the survival rate patients. Therefore, exploring noninvasive and efficient methods for great significance. In past few years, exosomes have received much attention their potential in disease diagnosis treatment. Here, aim this study was to explore detection serum via surface‐enhanced Raman spectroscopy (SERS) technique based on TiN‐Ag@Ag sol composite substrate, its application evaluated. Exosomes were extracted from 31 GC patients healthy controls (HC) using an exosome kit. This used various machine learning algorithms such as principal component analysis linear discriminant (PCA‐LDA), partial least squares (PLS‐DA), support vector (SVM), k‐nearest neighbor (KNN) algorithm analyze SERS spectra, order distinguish between HC GC. The results show that performs best classification. These indicate combination provides new technological approach screening. offers proposal universal applicability identification with samples clinical diagnosis.

Language: Английский

Artificial intelligence-based plasma exosome label-free SERS profiling strategy for early lung cancer detection DOI
Dechan Lu,

Zhikun Shangguan,

Zhehao Su

et al.

Analytical and Bioanalytical Chemistry, Journal Year: 2024, Volume and Issue: 416(23), P. 5089 - 5096

Published: July 17, 2024

Language: Английский

Citations

9
Surface-Enhanced Raman Scattering (SERS) for exosome detection DOI

Biqing Chen,

Xiaohong Qiu

Clinica Chimica Acta, Journal Year: 2025, Volume and Issue: 568, P. 120148 - 120148

Published: Jan. 20, 2025

Language: Английский

Citations

0
Label-free Detection of Urine Extracellular Vesicles from Duchenne Muscular Dystrophy Patients Using Surface-Enhanced Raman Spectroscopy Combined with Machine Learning Models DOI Creative Commons

Archana Rajavel,

Jayasree Kumar,

E. S. Narayanan

et al.

ACS Omega, Journal Year: 2025, Volume and Issue: unknown

Published: March 24, 2025

Duchenne muscular dystrophy (DMD) is a neuromuscular disease that affects males in the pediatric age group. Currently, there no painless, cost-effective prognostic method available to monitor DMD progression. The main hypothesis of this study was biochemical composition extracellular vesicles (EVs) isolated from urine patients can be distinctly differentiated healthy controls using surface-enhanced Raman Spectroscopy (SERS) combined with machine learning models. This differentiation expected provide noninvasive, rapid, and accurate diagnostic tool for early detection, staging, monitoring by identifying molecular signatures captured SERS leveraging analytical power algorithms. We collected fasting morning samples 52 17 EVs Total Exosome Isolation kit. substrates are prepared silver nanoparticles, which were employed capture fingerprints uniformity reproducibility, achieving relative standard deviation values 7.3% 8.9%. observed alterations phenylalanine α-helical proteins compared controls. These spectral data analyzed PCA, Support Vector Machines, k-Nearest Neighbor (KNN) algorithms identify distinct patterns stage based on composition. Our integrated approach demonstrated 60% sensitivity 100% specificity distinguishing controls, highlighting potential KNN accurate, rapid diagnosis DMD. offers promising avenue detection personalized treatment strategies, ultimately improving patient outcomes quality life.

Language: Английский

Citations

0
基于柔性基底的表面增强拉曼光谱应用研究进展 DOI

王楠 Wang Nan,

刘艺 Liu Yi,

张竣 Zhang Jun

et al.

Chinese Journal of Lasers, Journal Year: 2024, Volume and Issue: 51(21), P. 2107401 - 2107401

Published: Jan. 1, 2024

Citations

2
Integration of Nanoengineering with Artificial Intelligence and Machine Learning in Surface‐Enhanced Raman Spectroscopy (SERS) for the Development of Advanced Biosensing Platforms DOI Creative Commons
Farbod Ebrahimi, Anjali Kumari, Kristen Dellinger

et al.

Advanced Sensor Research, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 20, 2024

Abstract Surface‐enhanced Raman spectroscopy (SERS) has emerged as a powerful tool for biomedical diagnosis, combining heightened sensitivity with molecular precision. The integration of artificial intelligence (AI) and machine learning (ML) further elevated its capabilities, refining data interpretation, pattern prediction, bolstering diagnostic accuracy. This review chronicles advancements in SERS diagnostics, emphasizing the collaboration between ML innovative nanostructures, substrates, nanoprobes enhancement. breakthroughs are highlighted SERS‐based point‐of‐care techniques nuanced detection key biomarkers, from nucleic acids to proteins metabolites. article also addresses prevailing challenges, such need standardized methodologies optimized platforms. Moreover, potential portable systems is discussed clinical deployment, well current efforts challenges trials. In essence, this positions fusion nanoengineering, AI, ML, frontier next‐generation diagnostics.

Language: Английский

Citations

2
Serum Exosome SERS Assay Based on TiN‐Ag@Ag Sol Composite Substrate and Its Application in the Diagnosis of Gastric Cancer DOI
Huan Wang,

Zhengang Wu,

Yingna Wei

et al.

Journal of Raman Spectroscopy, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 30, 2024

ABSTRACT Gastric cancer (GC) is a highly lethal malignancy, seriously threatening people's physical health. Accurate screening of gastric could improve the survival rate patients. Therefore, exploring noninvasive and efficient methods for great significance. In past few years, exosomes have received much attention their potential in disease diagnosis treatment. Here, aim this study was to explore detection serum via surface‐enhanced Raman spectroscopy (SERS) technique based on TiN‐Ag@Ag sol composite substrate, its application evaluated. Exosomes were extracted from 31 GC patients healthy controls (HC) using an exosome kit. This used various machine learning algorithms such as principal component analysis linear discriminant (PCA‐LDA), partial least squares (PLS‐DA), support vector (SVM), k‐nearest neighbor (KNN) algorithm analyze SERS spectra, order distinguish between HC GC. The results show that performs best classification. These indicate combination provides new technological approach screening. offers proposal universal applicability identification with samples clinical diagnosis.

Language: Английский

Citations

0