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Towards Rapid and Low-Cost Stroke Detection Using SERS and Machine Learning

Title
Towards Rapid and Low-Cost Stroke Detection Using SERS and Machine Learning
Type
Article in International Scientific Journal
Year
2025
Authors
Freitas, C
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Eleutério, J
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Soares, G
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Enea, M
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Nunes, D
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Fortunato, E
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Martins, R
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Aguas, H
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Vieira, HLA
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Ferreira, LS
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Franco, R
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Journal
Title: Biosensors-BaselImported from Authenticus Search for Journal Publications
Vol. 15
Final page: 136
ISSN: 2079-6374
Publisher: MDPI
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Publicação em ISI Web of Knowledge ISI Web of Knowledge - 0 Citations
Publicação em Scopus Scopus - 0 Citations
Other information
Authenticus ID: P-018-AYT
Abstract (EN): Stroke affects approximately 12 million individuals annually, necessitating swift diagnosis to avert fatal outcomes. Current hospital imaging protocols often delay treatment, underscoring the need for portable diagnostic solutions. We have investigated silver nanostars (AgNS) incubated with human plasma, deposited on a simple aluminum foil substrate, and utilizing Surface-Enhanced Raman Spectroscopy (SERS) combined with machine learning (ML) to provide a proof-of-concept for rapid differentiation of stroke types. These are the seminal steps for the development of low-cost pre-hospital diagnostics at point-of-care, with potential for improving patient outcomes. The proposed SERS assay aims to classify plasma from stroke patients, differentiating hemorrhagic from ischemic stroke. Silver nanostars were incubated with plasma and spiked with glial fibrillary acidic protein (GFAP), a biomarker elevated in hemorrhagic stroke. SERS spectra were analyzed using ML to distinguish between hemorrhagic and ischemic stroke, mimicked by different concentrations of GFAP. Key innovations include optimized AgNS-plasma incubates formation, controlled plasma-to-AgNS ratios, and a low-cost aluminum foil substrate, enabling results within 15 min. Differential analysis revealed stroke-specific protein profiles, while ML improved classification accuracy through ensemble modeling and feature engineering. The integrated ML model achieved rapid and precise stroke predictions within seconds, demonstrating the assay's potential for immediate clinical decision-making.
Language: English
Type (Professor's evaluation): Scientific
No. of pages: 18
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