The Influence of Silver Nanoparticle Form on the Toxicity in Freshwater Mussels
Bibliographic record
Abstract
The contribution of the form of silver nanomaterials (nAg) towards toxicity in aquatic organisms is not well understood. The purpose of this study was to examine the toxicity of various structures (sphere, cube and prism) of nAg in Dreissena bugensis mussels. Mussels were exposed to increasing concentrations of polyvinyl-coated nAg of the same size for 96 h at 15 °C. They were then analyzed for biophysical changes in the cytoplasm (viscosity, protein aggregation and lipids), neuro-activity (fractal kinetics of acetylcholinesterase (AChE)), oxidative stress (labile zinc (Zn) and lipid peroxidation) and inflammation (arachidonate cyclooxygenase). Although some decreasing effects in protein aggregation were observed, viscosity was more strongly decreased in mussels exposed to spheric and prismatic nAg. The activity of AChE was significantly decreased in the following form-dependent manner: prismatic > cubic > spheric nAg. The fractal dimension of AChE reactions was reduced by all geometries of nAg, while dissolved Ag had no effects. For nanoparticles with the same coating and relative size, spheric nAg produced more significant changes towards the fractal dimension of AChE, while prismatic nAg increased both protein aggregation and viscosity, whereas cubic nAg decreased protein aggregation in the cytoplasm. It is concluded that the geometries of nanoparticles could influence toxicity in aquatic organisms.
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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".