Native PEG–PLGA Attenuates β-Amyloid Aggregation and Toxicity under <i>In Vitro</i> Conditions
Bibliographic record
Abstract
Self-aggregation of amyloid-β (Aβ) peptide plays a key role in the pathogenesis of Alzheimer’s disease (AD), the most prevalent cause of dementia affecting the elderly population. The development of an effective treatment for AD pathology remains elusive due to the presence of the blood-brain barrier (BBB) and the heterogeneous nature of disease progression. Recently, we reported that FDA-approved native poly( d, l -lactic- co -glycolic acid) (PLGA) nanoparticles without any conjugated/encapsulated agent can attenuate Aβ aggregation/toxicity in cellular and animal models of AD. Given the limitation associated with the fast clearance of the native PLGA by the reticuloendothelial system (RES), in the present study, we synthesized PEGylated native PLGA nanoparticles (PEG–PLGA-1) to reduce their clearance via the RES and evaluated their effects on Aβ aggregation/toxicity after biochemical and structural characterization. Determined with Thioflavin T kinetic assay, dynamic light scattering and fluorescence imaging, it was revealed that the native PEG–PLGA-1, which exhibits increased stability, not only inhibits the aggregation of Aβ peptides, but also triggers the disassembly of Aβ aggregates. Additionally, we showed that PEG–PLGA-1 are nontoxic and can significantly enhance the viability of mouse primary cortical cultured neurons against Aβ-mediated toxicity. Collectively, these results suggest that native PEG–PLGA-1 nanoparticles can inhibit Aβ aggregation and trigger disassembly of Aβ aggregates and can protect neurons against Aβ-mediated toxicity, thus suggesting their unique therapeutic potential in the treatment of AD pathology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".