Comparative tissue distribution and depuration characteristics of copper nanoparticles and soluble copper in rainbow trout (<i>Oncorhynchus mykiss</i>)
Bibliographic record
Abstract
Abstract The continuously growing number of products containing nanoparticles (NPs) makes their presence in the environment inevitable, and given the well-known toxicity of dissolved metals, concerns regarding the toxicity of metal-based NPs have been raised. Whether metal-based NPs present similar or different toxicological effects compared with metal salts is an emerging field. In the present study, rainbow trout were intraperitoneally injected with CuSO4 or copper NPs (CuNPs) to investigate tissue distribution and depuration characteristics. Fish injected with Cu showed an initial accumulation of Cu in the liver, kidney, gills, intestine, and carcass. The Cu concentration in the liver of CuNP-injected fish increased over time. It appears as though CuNPs accumulated in the liver at a greater rate than they were excreted. In livers of fish injected with CuSO4, the Cu concentration appeared to increase and reach an equilibrium, suggesting that copper was accumulated and excreted at the same rate. The possibility that CuNPs can accumulate at a higher rate than it is excreted in the liver warrants further investigation. The present study demonstrates the possibility of dietary uptake of CuNPs because elevated Cu concentrations were observed in carcass, gills, kidney, and intestine of fish gavaged with CuNPs. In conclusion, the results suggest that dietary CuNPs are taken up by the gut and preferentially accumulate in the liver. Environ Toxicol Chem 2019;38:80–89. © 2018 SETAC Abstract It appears as though copper nanoparticles accumulated in the liver at a greater rate than they were excreted. In livers of fish injected with CuSO4, the Cu concentration appeared to increase and reach an equilibrium, suggesting that Cu was accumulated and excreted at the same rate. CuNP = copper nanoparticle.
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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".