Lipoprotein-Based Nanoparticles Rescue the Memory Loss of Mice with Alzheimer’s Disease by Accelerating the Clearance of Amyloid-Beta
Bibliographic record
Abstract
Amyloid-beta (Aβ) accumulation in the brain is believed to play a central role in Alzheimer's disease (AD) pathogenesis, and the common late-onset form of AD is characterized by an overall impairment in Aβ clearance. Therefore, development of nanomedicine that can facilitate Aβ clearance represents a promising strategy for AD intervention. However, previous work of this kind was concentrated at the molecular level, and the disease-modifying effectiveness of such nanomedicine has not been investigated in clinically relevant biological systems. Here, we hypothesized that a biologically inspired nanostructure, apolipoprotein E3-reconstituted high density lipoprotein (ApoE3-rHDL), which presents high binding affinity to Aβ, might serve as a novel nanomedicine for disease modification in AD by accelerating Aβ clearance. Surface plasmon resonance, transmission electron microscopy, and co-immunoprecipitation analysis showed that ApoE3-rHDL demonstrated high binding affinity to both Aβ monomer and oligomer. It also accelerated the microglial, astroglial, and liver cell degradation of Aβ by facilitating the lysosomal transport. One hour after intravenous administration, about 0.4% ID/g of ApoE3-rHDL gained access to the brain. Four-week daily treatment with ApoE3-rHDL decreased Aβ deposition, attenuated microgliosis, ameliorated neurologic changes, and rescued memory deficits in an AD animal model. The findings here provided the direct evidence of a biomimetic nanostructure crossing the blood-brain barrier, capturing Aβ and facilitating its degradation by glial cells, indicating that ApoE3-rHDL might serve as a novel nanomedicine for disease modification in AD by accelerating Aβ clearance, which also justified the concept that nanostructures with Aβ-binding affinity might provide a novel nanoplatform for AD therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".