relation: https://khub.utp.edu.my/scholars/18980/
title: Analysis on Parkinson's disease through Faradaic Detection
creator: Adam, H.
creator: Gopinath, S.C.B.
creator: Arshad, M.K.M.
creator: Fakhri, M.A.
creator: Salim, E.T.
creator: Perumal, V.
creator: Gunny, A.A.N.
creator: Adam, T.
description: Parkinson's disease is a neurological condition affecting the motor system, causing dopaminergic neuron death in the substantia nigra, leading to reduced dopamine levels and motor function deficits. Given the complex nature of Parkinson's disease, relying solely on motor symptoms for diagnosis may not be sufficient. To address this limitation, researchers have turned their attention to the identification and quantification of biomarkers that can serve as indicators of Parkinson's disease. Faradaic detection is a promising method for Parkinson's disease research, using electrochemical processes to detect and quantify biomarker like alpha synuclein. This approach helps monitor surface resistance, binding processes, and electrolyte resistance in the system. Quantification of biomarkers plays a crucial role in detecting early stages of the disease and tracking its progression. Through Faradaic detection methods, this study was able to measure the levels of specific biomarkers that are associated with Parkinson's disease. This approach allows for a more sensitive and accurate detection of Parkinson's disease, even in its early stages, which can improve the chances of early intervention and effective treatment. © 2023 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Adam, H. and Gopinath, S.C.B. and Arshad, M.K.M. and Fakhri, M.A. and Salim, E.T. and Perumal, V. and Gunny, A.A.N. and Adam, T.  (2023) Analysis on Parkinson's disease through Faradaic Detection.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182745074&doi=10.1109%2fSENNANO57767.2023.10352562&partnerID=40&md5=c88554ebb93ac9dafa14ddf4f52a388b
relation: 10.1109/SENNANO57767.2023.10352562
identifier: 10.1109/SENNANO57767.2023.10352562