Significance of native PLGA nanoparticles in the treatment of Alzheimer's disease pathology
Bibliographic record
Abstract
Alzheimer's disease (AD) is believed to be triggered by increased levels/aggregation of β-amyloid (Aβ) peptides. At present, there is no effective disease-modifying treatment for AD. Here, we evaluated the therapeutic potential of FDA-approved native poly(d,l-lactide-co-glycolide) (PLGA) nanoparticles on Aβ aggregation and in cellular/animal models of AD. Our results showed that native PLGA can not only suppress the spontaneous aggregation but can also trigger disassembly of preformed Aβ aggregates. Spectroscopic studies, molecular dynamics simulations and biochemical analyses revealed that PLGA, by interacting with the hydrophobic domain of Aβ1-42, prevents a conformational shift towards the β-sheet structure, thus precluding the formation and/or triggering disassembly of Aβ aggregates. PLGA-treated Aβ samples can enhance neuronal viability by reducing phosphorylation of tau protein and its associated signaling mechanisms. Administration of PLGA can interact with Aβ aggregates and attenuate memory deficits as well as Aβ levels/deposits in the 5xFAD mouse model of AD. PLGA can also protect iPSC-derived neurons from AD patients against Aβ toxicity by decreasing tau phosphorylation. These findings provide unambiguous evidence that native PLGA, by targeting different facets of the Aβ axis, can have beneficial effects in mouse neurons/animal models as well as on iPSC-derived AD neurons - thus signifying its 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) | 0.000 | 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".