Toxic effects of colloidal nanosilver in zebrafish embryos
Bibliographic record
Abstract
A variety of consumer products containing silver nanoparticles (Ag NPs) are currently marketed. However, their safety for humans and for the environment has not yet been established and no standard method to assess their toxicity is currently available. The objective of this work was to develop an effective method to test Ag NP toxicity and to evaluate the effects of ion release and Ag NP size on a vertebrate model. To this aim, the zebrafish animal model was exposed to a solution of commercial nanosilver. While the exposure of embryos still surrounded by the chorion did not allow a definite estimation of the toxic effects exerted by the compound, the exposure for 48 h of 3-day-old zebrafish hatched embryos afforded a reliable evaluation of the effects of Ag NPs. The effects of the exposure were detected especially at molecular level; in fact, some selected genes expressed differentially after the exposure. The Ag NP toxic performance was due to the combined effect of Ag(+) ion release and Ag NP size. However, the effect of NP size was particularly detectable at the lowest concentration of nanosilver tested (0.01 mg l(-1)) and depended on the solubilization media. The results obtained indicate that in vivo toxicity studies of nanosilver should be performed with ad hoc methods (in this case using hatched embryos) that might be different depending on the type of nanosilver. Moreover, the addition of this compound to commercial products should take into consideration the Ag NP solubilization media.
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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) | 0.001 | 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".