Native PLGA nanoparticles attenuate Aβ-seed induced tau aggregation under in vitro conditions: potential implication in Alzheimer’s disease pathology
Bibliographic record
Abstract
Abstract Evidence suggests that beta-amyloid (Aβ)-induced phosphorylation/aggregation of tau protein plays a critical role in the degeneration of neurons and development of Alzheimer’s disease (AD), the most common cause of dementia affecting the elderly population. Many studies have pursued a variety of small molecules, including nanoparticles conjugated with drugs to interfere with Aβ and/or tau aggregation/toxicity as an effective strategy for AD treatment. We reported earlier that FDA approved PLGA nanoparticles without any drug can attenuate Aβ aggregation/toxicity in cellular/animal models of AD. In this study, we evaluated the effects of native PLGA on Aβ seed-induced aggregation of tau protein using a variety of biophysical, structural and spectroscopic approaches. Our results show that Aβ1-42 seeds enhanced aggregation of tau protein in the presence and absence of heparin and the effect was attenuated by native PLGA nanoparticles. Interestingly, PLGA inhibited aggregation of both 4R and 3R tau isoforms involved in the formation of neurofibrillary tangles in AD brains. Furthermore, Aβ seed-induced tau aggregation in the presence of arachidonic acid was suppressed by native PLGA. Collectively, our results suggest that native PLGA nanoparticles can inhibit the Aβ seed-induced aggregation of different tau protein isoforms highlighting their therapeutic implication in the treatment of AD.
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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".