Gold Nanoparticles in Neurological Diseases: A Review of Neuroprotection
Bibliographic record
Abstract
This review explores the diverse applications of gold nanoparticles (AuNPs) in neurological diseases, with a specific focus on Alzheimer's disease (AD), Parkinson's disease (PD), and stroke. The introduction highlights the pivotal role of neuroinflammation in these disorders and introduces the unique properties of AuNPs. The review's core examines the mechanisms by which AuNPs exert neuroprotection and anti-neuro-inflammatory effects, elucidating various pathways through which they manifest these properties. The potential therapeutic applications of AuNPs in AD are discussed, shedding light on promising avenues for therapy. This review also explores the prospects of utilizing AuNPs in PD interventions, presenting a hopeful outlook for future treatments. Additionally, the review delves into the potential of AuNPs in providing neuroprotection after strokes, emphasizing their significance in mitigating cerebrovascular accidents' aftermath. Experimental findings from cellular and animal models are consolidated to provide a comprehensive overview of AuNPs' effectiveness, offering insights into their impact at both the cellular and in vivo levels. This review enhances our understanding of AuNPs' applications in neurological diseases and lays the groundwork for innovative therapeutic strategies in neurology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".