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Peptoid Nanosheet-Based Sensing System for the Diagnosis and Surveillance of Amnestic Mild Cognitive Impairment and Alzheimer’s Disease
- Source :
- ACS Chemical Neuroscience. 12:4257-4264
- Publication Year :
- 2021
- Publisher :
- American Chemical Society (ACS), 2021.
-
Abstract
- Alzheimer's disease (AD) is one of the most common neurodegenerative diseases characterized by progressive cognitive decline. Early diagnosis and dynamic monitoring are essential to the treatment and care of AD but challenging. Here we develop a noninvasive, blood-based AD detection method based on surface plasmonic resonance imaging (SPRi) technique. The functionalized sensing SPRi chips were constructed with self-assembled loop-displaying peptoid nanosheets to improve the detection sensitivity of plasma amyloid β42 (Aβ42). We analyze the plasma from 30 clinically diagnosed AD patients, 29 amnestic cognitive impairment (aMCI) patients, and 30 control individuals and demonstrate that this sensing system can significantly distinguish the three groups with high sensitivity and specificity. In the follow-up studies of the aMCI patients, we find that decrease in the binding signals in the patients correlates with the disease progression into AD whereas the almost unchanged signals correlate with stable disease remaining at aMCI status. These results show the capability of the peptoid-nanosheet-based SRPi sensing system for the early diagnosis and dynamic monitoring of AD.
- Subjects :
- Oncology
Surface plasmonic resonance
medicine.medical_specialty
Amyloid
Physiology
business.industry
Cognitive Neuroscience
Peptoid nanosheet
Disease progression
Cell Biology
General Medicine
Disease
Neuropsychological Tests
Biochemistry
Peptoids
Stable Disease
Alzheimer Disease
Internal medicine
Disease Progression
Humans
Medicine
Cognitive Dysfunction
business
Cognitive impairment
Sensing system
Subjects
Details
- ISSN :
- 19487193
- Volume :
- 12
- Database :
- OpenAIRE
- Journal :
- ACS Chemical Neuroscience
- Accession number :
- edsair.doi.dedup.....917e3ba81592c33b93d5975fbbc35b16