Unconjugated PLGA nanoparticles attenuate temperature-dependent β-amyloid aggregation and protect neurons against toxicity: implications for Alzheimer’s disease pathology
Bibliographic record
Abstract
Abstract Conversion of β-amyloid (Aβ) peptides from soluble random-coil to aggregated protein enriched with β-sheet-rich intermediates has been suggested to play a role in the degeneration of neurons and development of Alzheimer’s disease (AD) pathology. Aggregation of Aβ peptide can be prompted by a variety of environmental factors including temperature which can influence disease pathogenesis. Recently, we reported that FDA-approved unconjugated poly (d,l-lactide-co-glycolide) (PLGA) nanoparticles can have beneficial effects in cellular and animal models of AD by targeting different facets of the Aβ axis. In this study, using biochemical, structural and spectroscopic analyses, we evaluated the effects of native PLGA on temperature-dependent Aβ aggregation and its ability to protect cultured neurons from degeneration. Our results show that the rate of spontaneous Aβ1–42 aggregation increases with a rise in temperature from 27 to 40 °C and PLGA with 50:50 resomer potently inhibits Aβ aggregation at all temperatures, but the effect is more profound at 27 °C than at 40 °C. It appears that native PLGA, by interacting with the hydrophobic domain of Aβ1–42, prevents a conformational shift towards β-sheet structure, thus precluding the formation of Aβ aggregates. Additionally, PLGA triggers disassembly of matured Aβ1–42 fibers at a faster rate at 40 °C than at 27 °C. PLGA-treated Aβ samples can significantly enhance viability of cortical cultured neurons compared to neurons treated with Aβ alone by attenuating phosphorylation of tau protein. Injection of native PLGA is found to influence the breakdown/clearance of Aβ peptide in the brain. Collectively, these results suggest that PLGA nanoparticles can inhibit Aβ aggregation and trigger disassembly of Aβ aggregates at temperatures outside the physiological range and can protect neurons against Aβ-mediated toxicity thus validating its unique therapeutic potential in the treatment of AD pathology. Graphical Abstract
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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) | 0.001 | 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) | 0.000 | 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 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".