Mimosine functionalized gold nanoparticles (Mimo-AuNPs) suppress β-amyloid aggregation and neuronal toxicity
Bibliographic record
Abstract
Evidence suggests that increased level/aggregation of beta-amyloid (Aβ) peptides initiate neurodegeneration and subsequent development of Alzheimer's disease (AD). At present, there is no effective treatment for AD. In this study, we reported the effects of gold nanoparticles surface-functionalized with a plant-based amino acid mimosine (Mimo-AuNPs), which is found to cross the blood-brain barrier, on the Aβ fibrillization process and toxicity. Thioflavin T kinetic assays, fluorescence imaging and electron microscopy data showed that Mimo-AuNPs were able to suppress the spontaneous and seed-induced Aβ1-42 aggregation. Spectroscopic studies, molecular docking and biochemical analyses further revealed that Mimo-AuNPs stabilize Aβ1-42 to remain in its monomeric state by interacting with the hydrophobic domain of Aβ1-42 (i.e., Lys16 to Ala21) there by preventing a conformational shift towards the β-sheet structure. Additionally, Mimo-AuNPs were found to trigger the disassembly of matured Aβ1-42 fibers and increased neuronal viability by reducing phosphorylation of tau protein and the production of oxyradicals. Collectively, these results reveal that the surface-functionalization of gold nanoparticles with mimosine can attenuate Aβ fibrillization and neuronal toxicity. Thus, we propose Mimo-AuNPs may be used as a potential treatment strategy towards AD-related pathologies.
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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".