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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2025

Autores

Freitas, C (Autor) Outra; Eleutério, J (Autor) Outra; Soares, G (Autor) Outra; Enea, M (Autor) Outra; Nunes, D (Autor) Outra; Fortunato, E (Autor) Outra; Martins, R (Autor) Outra; Aguas, H (Autor) Outra; Eulalia Pereira (Autor) FCUP; Vieira, HLA (Autor) Outra; Ferreira, LS (Autor) Outra; Franco, R (Autor) Outra

Revista

Título: Biosensors-Basel

Vol. 15

Página Final: 136

ISSN: 2079-6374

Editora: MDPI

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-018-AYT

DOI: 10.3390/bios15030136

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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 18