Sublethal Effects of Silver Nanoparticles and Dissolved Silver in Freshwater Mussels
Bibliographic record
Abstract
The increasing application of silver nanoparticles (nAg) in various consumer products has raised concerns regarding toxicological impacts in the environment. It is unclear at present whether the toxicity of nAg is mainly the result of the release of ionic Ag(+) in mussels. The freshwater mussel Elliptio complanata was exposed to increasing concentrations of 20-nm nAg, 80-nm nAg, and dissolved Ag(+) for 48 h at 15°C. The following biomarkers were used to determine the mode of action of nAg-induced adverse effects: metallothioneins (MT) (ionic Ag(+) release), lipid peroxidation (LPO) (ionic Ag(+) and nanosurface interactions), heat-shock proteins (HSP) (size-related effects), protein-ubiquitin levels (size-related effects), and DNA strand breaks (ionic Ag(+) and size effects). Results revealed that the response pattern of 80 nm nAg was more closely related to ionic Ag(+) than 20 nm nAg, suggesting a more important release of dissolved Ag from 80 nm nAg. Data showed that all forms of Ag were able to increase the levels of MT and LPO, which suggests the presence of ionic Ag(+) leads to oxidative stress. However, nanoparticles were also able to induce changes in protein-ubiquitin and to a lesser extent actinomyosin-ATPase, MT, and DNA strand breaks in the digestive gland in a manner different from Ag(+), which permitted discrimination of the forms of Ag. Moreover, LPO was closely associated with DNA strand breaks in the digestive gland and was not entirely explained by induction of MT, suggesting another type of toxic interaction. It was concluded that the presence of nAg not only increases the toxic loadings of released Ag ions but also generates other and perhaps cumulative effects of nanoparticle-induced toxicity related to size and surface properties.
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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 | 0.000 | 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 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".