Organotypic and primary neural cultures as models to assess effects of different gold nanostructures on glia and neurons
Bibliographic record
Abstract
Gold nanoparticles (AuNP) have unique physicochemical properties and have been used as delivery vehicles, contrast agents, and therapeutic compounds. Although the effects of AuNPs on peripheral tissues and immortalized cell lines have been extensively characterized, their effects on the central nervous system (CNS) are predominantly unknown. The main objective of the current study was to evaluate how AuNPs of varying sizes (1–100 nm), shapes (clusters, spheres, rods, flowers), and surfaces impact synaptic structures in the hippocampus, a brain structure often affected in neurodegeneration. Using a combination of organotypic hippocampal, as well as, primary neuronal, glial, and astrocytic cultures, we examined AuNPs impact on hippocampal dendritic spine density, internalization in various neural cells, and lysosomal status in astrocytes. Considering that neurons interact with astrocytes, and that lysosomes play a role in dendritic spine status, transcription factor TFEB and abundance of lysosomal marker, LAMP1 were evaluated. Both biomarkers were significantly increased in astrocytes exposed to AuNPs, suggesting that AuNPs not only enter lysosomes, but also increase lysosome biogenesis. Results from our studies show that AuNPs with poly(ethylene glycol) (AuNPs-PEG) or glutathione (AuNP-GSH) surfaces do not substantially decrease hippocampal dendritic spine density. Conversely, AuNPs coated with the detergent, CTAB, significantly decreased total spine density. Interestingly small gold nanoclusters (Au15(SG)13) with GSH reduced spine density, whereas larger gold nanoclusters (Au25(SG)18) with the same ligand did not. Thus, assessment of dendritic morphology, spine densities can reveal subtler changes of neural cells than cell death when exposed to nanoparticles, including AuNPs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".