TY - JOUR
T1 - Clinically oriented Alzheimer's biosensors
T2 - expanding the horizons towards point-of-care diagnostics and beyond
AU - Murti, Bayu Tri
AU - Putri, Athika Darumas
AU - Huang, Yi June
AU - Wei, Shih Min
AU - Peng, Chih Wei
AU - Yang, Po Kang
N1 - Funding Information:
B. T. M. and A. D. P. acknowledge the Taipei Medical University, Taiwan for providing the funding support of this project. This study was supported by grants from the Ministry of Science and Technology, under Grant No. 108-2636-E-038-003, 109-2636-E-038-004. This work was also nancially supported by the Higher Education Sprout Project by the Ministry of Education in Taiwan by Grant No. DP2-109-21121-01-N-02-03, DP2-110-21121-01-N-02-03, and in part by grants from Taipei Medical University (TMU 108-AE1-B40).
Publisher Copyright:
© The Royal Society of Chemistry 2021.
PY - 2021/6/8
Y1 - 2021/6/8
N2 - The development of minimally invasive and easy-to-use sensor devices is of current interest for ultrasensitive detection and signal recognition of Alzheimer's disease (AD) biomarkers. Over the years, tremendous effort has been made on diagnostic platforms specifically targeting neurological markers for AD in order to replace the conventional, laborious, and invasive sampling-based approaches. However, the sophistication of analytical outcomes, marker inaccessibility, and material validity strongly limit the current strategies towards effectively predicting AD. Recently, with the promising progress in biosensor technology, the realization of a clinically applicable sensing platform has become a potential option to enable early diagnosis of AD and other neurodegenerative diseases. In this review, various types of biosensors, which include electrochemical, fluorescent, plasmonic, photoelectrochemical, and field-effect transistor (FET)-based sensor configurations, with better clinical applicability and analytical performance towards AD are highlighted. Moreover, the feasibility of these sensors to achieve point-of-care (POC) diagnosis is also discussed. Furthermore, by grafting nanoscale materials into biosensor architecture, the remarkable enhancement in durability, functionality, and analytical outcome of sensor devices is presented. Finally, future perspectives on further translational and commercialization pathways of clinically driven biosensor devices for AD are discussed and summarized.
AB - The development of minimally invasive and easy-to-use sensor devices is of current interest for ultrasensitive detection and signal recognition of Alzheimer's disease (AD) biomarkers. Over the years, tremendous effort has been made on diagnostic platforms specifically targeting neurological markers for AD in order to replace the conventional, laborious, and invasive sampling-based approaches. However, the sophistication of analytical outcomes, marker inaccessibility, and material validity strongly limit the current strategies towards effectively predicting AD. Recently, with the promising progress in biosensor technology, the realization of a clinically applicable sensing platform has become a potential option to enable early diagnosis of AD and other neurodegenerative diseases. In this review, various types of biosensors, which include electrochemical, fluorescent, plasmonic, photoelectrochemical, and field-effect transistor (FET)-based sensor configurations, with better clinical applicability and analytical performance towards AD are highlighted. Moreover, the feasibility of these sensors to achieve point-of-care (POC) diagnosis is also discussed. Furthermore, by grafting nanoscale materials into biosensor architecture, the remarkable enhancement in durability, functionality, and analytical outcome of sensor devices is presented. Finally, future perspectives on further translational and commercialization pathways of clinically driven biosensor devices for AD are discussed and summarized.
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U2 - 10.1039/d1ra01553b
DO - 10.1039/d1ra01553b
M3 - Review article
AN - SCOPUS:85108279207
SN - 2046-2069
VL - 11
SP - 20403
EP - 20422
JO - RSC Advances
JF - RSC Advances
IS - 33
ER -