Diagnosis of Mild Cognitive Impairment and Alzheimer’s Disease by the Plasma and Serum Amyloid-beta 42 Assay through Highly Sensitive Peptoid Nanosheet Sensor

Abstract

Alzheimer's disease (AD) is the most common neurodegenerative disorder with a continuous pathophysiological process starting from the preclinical and mild cognitive impairment (MCI) phases to the dementia phase. Early diagnosis is prerequisite for the early intervention of AD but meanwhile challenging. Amyloid-beta 1−42 (Aβ 42 ) plays a crucial part in AD pathology. Positron-emission tomography (PET) imaging of Aβ 42 in the brain and the measurement of Aβ 42 in the cerebrospinal fluid (CSF) have been adopted for the auxiliary diagnosis of AD, but their widespread clinical application has been limited due to the radiation and the high-cost of PET and the invasive lumbar puncture for collecting CSF. Noninvasive and cost-effective blood-based assay is desirable for the early diagnosis of AD. Here, a label-free assay for the quantification of blood Aβ 42 was developed using the high-throughput surface plasmon resonance imaging method with the aid of an antibody-mimetic peptoid nanosheet equipping Aβ 42 -recognizing loops. We demonstrated that this nanosheet-based sensor system could distinguish the plasma and sera from normal individuals and patients suffering AD and amnestic MCI with high sensitivity and specificity, preceding the diagnostic performance of the Aβ 42 -recognizing molecule and the antibody specific to Aβ 42 . This work provides a label-free, cost-effective, highly sensitive, and high-throughput blood-based assay for early detection of AD.

Functional MRI-Specific Alterations in Salience Network in Mild Cognitive Impairment: An ALE Meta-Analysis

“…Mild cognitive impairment (MCI) is universally considered as the predementia phase of Alzheimer's disease (AD) (Gao et al, 2020 ). About one in five community-dwelling elders aged 65 years and above suffer from MCI (Livingston et al, 2017 ).…”

Plasma d-glutamate levels for detecting mild cognitive impairment and Alzheimer’s disease: Machine learning approaches

“…Our ROC analysis revealed that the d -glutamate level for all patients with MCI versus healthy controls determined an optimal cut-off value (1374.49), with a good sensitivity (0.905) and specificity (0.613; AUC = 0.816), whereas the ROC analysis revealed that the d -glutamate level for all patients with AD versus healthy controls determined an optimal cut-off value (971.56), with a moderate sensitivity (0.632) and a good specificity (0.968; AUC = 0.839). Studies have investigated peripheral blood biomarkers to predict MCI and AD (Gao et al, 2020; Su et al, 2019; Xie et al, 2020). A recent meta-analysis reported that patients with MCI or AD had lower brain-derived neurotrophic factor (BDNF) levels than did healthy controls.…”

The Mechanism by Which LiCl Attenuates the Impaired Ability of Learning and Memory of APP/PS1 Double Transgenic Mice May Involve Elevating the Expression of a7 Nicotinic Acetylcholine Receptors via Regulation of the Wnt/β-catenin Pathway

“…AD is associated with a specific onset and course of cognitive and functional decline during ageing, together with aggregation of a particular Aβ with hyperphosphorylated tau protein that causes loss of synapses and neurodegeneration [54,55]. Aβ, the level of which is associated with the extent of cognitive decline [56], is measured in the cerebrospinal fluid and brain for purposes of auxiliary diagnosis [57].…”