The Effect of Silver Nanoparticles on the Scavenger Receptor-Scara1on Microglia
Bibliographic record
Abstract
Alzheimer's disease is one of the most prevalent neurodegenerative diseases in the world.The neuropathological hallmark of Alzheimer's disease is extracellular deposit of amyloid-β (Aβ) in the brain.Microglial cells are able to remove of Aβ aggregates by receptor-dependent endocytosis [1,3].Nanotechnology is one of the fastest developing science discipline and nanoparticles (NPs), due to their strong absorption properties are widely used in industry, and also in medical diagnosis and treatment.It was documented that NPs can prevent the formation of Aß-aggregates whereby reducing their neurotoxicity and likely can impact on the Aß-uptake by microglia [2,[4][5][6][7].It is supposed that NPs can increase number of emerging phagocytosis bubbles and the Aβ uptake due to the co-transport phenomenon, or in contrary reduce the number of lipid rafts available and therefore inhibit of Aβ transport by some kind of competition.Moreover, by activating different paths to cell signaling, NPs can probably change the expression of the amyloid β-receptors on microglia cell membranes.The goal of our study was to verify whether silver nanoparticles (AgNPs, 20 nm, BSA coated) can change the ability of microglial scavenger receptor 1 (Scara1) for the Aß (1-42) uptake and influence gene or protein expression of these receptors in mouse BV-2 cells.The results from flow cytometry indicate that both Aβ and AgNPs are taken up by microglial cells using the same receptor: AgNPs (50 µg/ml) can decrease the uptake of Aβ by about 80% compared to the control group and Scara1 inhibitor (poliinosinic acid) diminish both AgNPs and Aß peptide uptake.Real-time PCR analysis showed that AgNPs did not change the Scara1 gene expression.The Western blotting (measuring the whole receptor content) revealed a slight decrease in the protein receptor level after treatment of cells with AgNPs (50 µg/ml).On the other hand, the content of the receptor on the cell surface, measured cytometrically, was greatly diminished in the presence of AgNPs.In summary, AgNPs clearly blocked the receptor and so they may play rather disadvantageous role in Aβ removal.Results from the Project "The influence of nanoparticles on beta-amyloid removal by microglia cells".
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".