A Review on Glial Fibrillary Acidic Protein Biosensors for Point-of-Care Stroke Diagnosis

Stroke is a major cause of mortality and disability worldwide, and rapid differentiation between IS, haemorrhagic stroke (HS), and SMs is essential for timely treatment. This narrative review examines the potential of GFAP as a biomarker for acute stroke diagnosis and evaluates biosensing strategies for its detection, with particular emphasis on PoC and pre-hospital applications, profiting from its coupling with microfluidic technologies. The biological role, temporal kinetics, and diagnostic and prognostic value of GFAP are first discussed in comparison with other stroke biomarkers. The suitability of cerebrospinal fluid (CSF), serum, plasma, whole blood, urine, saliva, and sweat as biological matrices is then assessed. Finally, electrochemical, optical, mechanical, and other biosensor platforms developed for GFAP detection are reviewed in terms of sensing principles, analytical performance, biological matrices, assay duration, and potential clinical applicability. Overall, despite the heterogeneity of the data available in the literature, the reviewed evidence supports the potential utility of GFAP for hyperacute stroke assessment, owing to its rapid elevation following ICH and its ability to differentiate HS from IS and SMs. However, variability in sampling time, analytical methods, biological matrices, and patient populations limits direct comparison between studies. Further development of integrated and multiplexed PoC biosensors, also benefiting from the wide possibilities of advanced microfluidics’ technologies, may facilitate rapid and accessible stroke diagnosis and patient stratification.

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Publication Details

Journal
Sensors
Published
2026-10-16
DOI
https://doi.org/10.3390/s26206579
Primary Topic
Biosensors and Analytical Detection
Type
article
Field-Weighted Citation Impact
0.00

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article

A Review on Glial Fibrillary Acidic Protein Biosensors for Point-of-Care Stroke Diagnosis

Vania C. Pinto, Susana Oliveira Catarino, Leonor C. Caetano
Sensors
Biosensors and Analytical Detection
article

A Review on Glial Fibrillary Acidic Protein Biosensors for Point-of-Care Stroke Diagnosis

Vania C. Pinto, Susana Oliveira Catarino, Leonor C. Caetano
article en

Abstract

Stroke is a major cause of mortality and disability worldwide, and rapid differentiation between IS, haemorrhagic stroke (HS), and SMs is essential for timely treatment. This narrative review examines the potential of GFAP as a biomarker for acute stroke diagnosis and evaluates biosensing strategies for its detection, with particular emphasis on PoC and pre-hospital applications, profiting from its coupling with microfluidic technologies. The biological role, temporal kinetics, and diagnostic and prognostic value of GFAP are first discussed in comparison with other stroke biomarkers. The suitability of cerebrospinal fluid (CSF), serum, plasma, whole blood, urine, saliva, and sweat as biological matrices is then assessed. Finally, electrochemical, optical, mechanical, and other biosensor platforms developed for GFAP detection are reviewed in terms of sensing principles, analytical performance, biological matrices, assay duration, and potential clinical applicability. Overall, despite the heterogeneity of the data available in the literature, the reviewed evidence supports the potential utility of GFAP for hyperacute stroke assessment, owing to its rapid elevation following ICH and its ability to differentiate HS from IS and SMs. However, variability in sampling time, analytical methods, biological matrices, and patient populations limits direct comparison between studies. Further development of integrated and multiplexed PoC biosensors, also benefiting from the wide possibilities of advanced microfluidics’ technologies, may facilitate rapid and accessible stroke diagnosis and patient stratification.

SensorsVol. 26(20)
Labbels (PT), University of Minho (PT)
Fundação para a Ciência e a Tecnologia
Good health and well-being
Openalex Percentile: Top 24%
Biosensors and Analytical Detection
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