Tijjani Adam
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Preferred name
Tijjani Adam
Official Name
Tijjani, Adam
Alternative Name
Adam, T.
Adama, Tijjani
Adam, Tijjani
Adam, Tijjan
Tijjani, A.
Main Affiliation
Scopus Author ID
55074964600
Researcher ID
AAH-5534-2019

Research Output
Now showing 1 - 2 of 2
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Cyclic and differential pulse voltammetric measurements on fibrils formation of alpha synuclein in Parkinson's disease by a gold interdigitated tetraelectrodes

2024-01-01 , Adam Hussaini , Subash Chandra Bose Gopinath , Krishnan Hemavathi , Tijjani Adam , Mohammed M. , Perumal V. , Fakhri M.A. , Salim E.T. , Raman P. , Subramaniam, Sreeramanan , Chen Y. , Sasidharan S.

Parkinson's disease is a neurodegenerative disorder characterized by the aggregation and deposition of alpha-synuclein protein, which are pathological hallmarks. To understand the fibril formation of alpha-synuclein in Parkinson's disease, this study uses cyclic and differential pulse voltammetric measurements. These measurements analyze the electrochemical properties and behavior of alpha-synuclein during its fibril formation process. By applying a potential sweep or pulse to the alpha-synuclein sample, it is possible to gain insights into its redox activity and structural changes during fibril formation. This could lead to the development of therapeutic strategies to prevent or disrupt this pathological event in Parkinson's disease. To detect Parkinson's disease, a 15 nm sized gold conjugated antibody was used as the probe and seeded on gold interdigitated tetraelectrodes (AuIDTE). Alpha synuclein variations (fibriled and non-fibriled) were detected using phosphate-buffer saline and glycine buffer based on cyclic voltammetry and differential pulse voltammetry techniques. Discriminated by Tau protein measurement that was employed as a control. The linear regression for detecting alpha synuclein aggregation using differential pulse voltammetry was R2 = 0.9987 [y = 9E-06x - 4E-07], with a limit of detection of 10 aM, on a linear range of 1 aM-1 pM. Cyclic voltammetry determined the limit of detection of aggregated alpha synuclein to be 100 aM, with a linear relationship of R2 = 0.9939 [y = 7E-06x - 2E-06]. The sensor has excellent analytical performance in terms of detection limit, sensitivity, selectivity, repeatability, and stability.

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Publication

An update on pathogenesis and clinical scenario for Parkinson’s disease: diagnosis and treatment

2023 , Adam Hussaini , Subash Chandra Bose Gopinath , Nor Azizah Parmin , Tijjani Adam , Mohd Khairuddin Md Arshad , Husein Irzaman , Uda Hashim

In severe cases, Parkinson’s disease causes uncontrolled movements known as motor symptoms such as dystonia, rigidity, bradykinesia, and tremors. Parkinson’s disease also causes non-motor symptoms such as insomnia, constipation, depression and hysteria. Disruption of dopaminergic and non-dopaminergic neural networks in the substantia nigra pars compacta is a major cause of motor symptoms in Parkinson’s disease. Furthermore, due to the difficulty of clinical diagnosis of Parkinson’s disease, it is often misdiagnosed, highlighting the need for better methods of detection. Treatment of Parkinson’s disease is also complicated due to the difficulties of medications passing across the blood–brain barrier. Moreover, the conventional methods fail to solve the aforementioned issues. As a result, new methods are needed to detect and treat Parkinson's disease. Improved diagnosis and treatment of Parkinson's disease can help avoid some of its devastating symptoms. This review explores how nanotechnology platforms, such as nanobiosensors and nanomedicine, have improved Parkinson’s disease detection and treatment. Nanobiosensors integrate science and engineering principles to detect Parkinson’s disease. The main advantages are their low cost, portability, and quick and precise analysis. Moreover, nanotechnology can transport medications in the form of nanoparticles across the blood–brain barrier. However, because nanobiosensors are a novel technology, their use in biological systems is limited. Nanobiosensors have the potential to disrupt cell metabolism and homeostasis, changing cellular molecular profiles and making it difficult to distinguish sensor-induced artifacts from fundamental biological phenomena. In the treatment of Parkinson’s disease, nanoparticles, on the other hand, produce neurotoxicity, which is a challenge in the treatment of Parkinson’s disease. Techniques must be developed to distinguish sensor-induced artifacts from fundamental biological phenomena and to reduce the neurotoxicity caused by nanoparticles.

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