journal article·2020-12

Multifunctional bioreactive-nanoconstructs for sensitive and accurate MRI of cerebrospinal fluid pathology and intervention of Alzheimer’s disease

Chunsheng He, Taksim Ahmed, Azhar Z. Abbasi, Lily Yi Li, Warren D. Foltz, Ping Cai, Erin Knock, Paul E. Fraser, Andrew M. Rauth, Jeffrey T. Henderson, Xiao Yu Wu

Nano Today· Volume 35
Publisher page

Abstract

Abstract Lack of sensitive detection of early onset and progression of Alzheimer’s disease (AD) by non-invasive methods limits the development and implementation of therapeutic interventions. Given that cerebral oxidative stress and inflammation occur before diagnosable clinical symptoms, a multifunctional theranostic nanoconstruct (NC) system has been developed. It gains entry to the brain by a targeting moiety, binds selectively with soluble amyloid-β (Aβ) and Aβ plagues via a conjugated anti-Aβ antibody. The NC is activated by endogenous reactive oxygen species (ROS), enhancing magnetic resonance (MR) contrast signals in disease-affected areas. It exhibits superior performance in detecting cerebrospinal fluid (CSF) pathology in an AD mouse model by MR imaging. Intravenously injected NCs significantly amplify T1-weighted MR signals in the CSF by 1.51–2.24 fold, nearly proportional to cerebral concentrations of ROS and pro-inflammatory cytokine interleukin-1β (IL-1β). The NC-enhanced CSF MR signals demonstrate high detection sensitivity (88.9 %) and specificity (100 %) even at early-stage AD. Moreover, the NCs protected primary cortical neurons from oxidative stress in vitro and reduce cerebral ROS and IL-1β levels in AD mice by 36 %–83 %. This multifunctional NC-based technology may allow for early detection and treatment of AD prior to cognitive decline when therapies may prove more beneficial.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Alzheimer's disease research and treatments · S100 Proteins and Annexins · Nanoparticle-Based Drug Delivery

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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