Proteomic evaluation of citrate-coated silver nanoparticles toxicity in Daphnia magna
Bibliographic record
Abstract
Recent decades have seen a strong increase in the promise and uses of nanotechnology. This is correlated with their growing release in the environment and there is concern that nanomaterials may endanger ecosystems. Silver nanoparticles (AgNPs) have some of the most varied applications, making their release into the environment unavoidable. In order to assess their potential toxicity in aquatic environments, the acute toxicity of citrate-coated AgNPs to Daphnia magna was measured and compared to that of AgNO3. AgNPs were found to be ten times less toxic by mass than silver ions, and most of this toxicity was removed by ultracentrifuging. At the protein level, the two forms of silver had different impacts. Both increased protein thiol content, while only AgNP increased carbonyl levels. In 2DE of samples labelled for carbonyls, no feature was significantly affected by both compounds, indicating different modes of toxicity. Identified proteins showed functional overlap between the two compounds: vitellogenins (vtg) were present in most features identified, indicating their role as a general stress sensor. In addition to vtg, hemoglobin levels were increased by the AgNP exposure while 14-3-3 protein (a regulatory protein) carbonylation levels were reduced by AgNO3. Overall, this study confirms the previously observed lower acute toxicity of AgNPs, while demonstrating that the toxicity of both forms of silver follow somewhat different biologic pathways, potentially leading to different interactions with natural compounds or pollutants in the aquatic environment.
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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 | 0.003 | 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".