Detection of β-amyloid aggregates/plaques in 5xFAD mice by labelled native PLGA nanoparticles: implication in the diagnosis of Alzheimer’s disease
Bibliographic record
Abstract
Evidence suggests that increased level/aggregation of β-amyloid (Aβ) peptide, together with enhanced phosphorylation/aggregation of tau protein, play a critical role in the development of Alzheimer's disease (AD), the leading cause of dementia in the elderly. At present, AD diagnosis is based primarily on cognitive assessment, neuroimaging, and immunological assays to detect altered levels/deposition of Aβ peptides and tau protein. While measurement of Aβ and tau in the cerebrospinal fluid/blood can indicate disease status, neuroimaging of aggregated Aβ and tau protein in the brain using positron emission tomography (PET) enable to monitor the pathological changes in AD patients. With advancements in nanomedicine, several nanoparticles, apart from drug-delivery, have been used as a diagnostic agent to identify more accurately changes in AD patients. Recently, we reported that FDA approved native PLGA nanoparticles can interact with Aβ to inhibit its aggregation/toxicity in cellular and animal models of AD. Here, we reveal that fluorescence labelled native PLGA following acute intracerebellar injection can identify majority of the immunostained Aβ as well as Congo red labelled neuritic plaques in the cortex of 5xFAD mice. Labelling of plaques by PLGA is apparent at 1 h, peak around 3 h and then start declining by 24 h after injection. No fluorescent PLGA was detected in the cerebellum of 5xFAD mice or in any brain regions of wild-type control mice following injection. These results provide the very first evidence that native PLGA nanoparticles can be used as a novel nano-theragnostic agent in the treatment as well as diagnosis 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".