The impact of zinc oxide nanoparticles in freshwater mussels exposed to municipal effluents
Bibliographic record
Abstract
Zinc oxide nanoparticles (nano-ZnO) are used in the production of transparent sunscreens and cosmetics, which are released into the environment through municipal effluents. The purpose of this study was to examine the toxicity of nano-ZnO to freshwater mussels (Elliptio complanata) in the presence of municipal effluents. Mussels were exposed for 21 days at 15 o C to 1 and 10 µg/L nanoZnO, and ZnCl2 in the presence of a physico-chemically treated municipal effluent (3 and 10 % v/v). After the exposure period and a 24 h depuration step, mussels were analyzed for free Zn in gills, metallothioneins (MT), oxidative stress (production of malondialdehyde (MDA) during lipid peroxidation), gonad alkali-labile phosphate (ALP) levels and genotoxicity. Gill MT levels were increased at 10 µg/L nano-ZnO and ZnCl2 and in the presence of the municipal effluent. MT levels were positively correlated with free Zn in gills and negatively correlated with MDA levels, indicating its involvement in the prevention of oxidative stress. However, MDA levels were significantly related to DNA damage in gills, indicating that MT induction did not prevent oxidative-mediated damage in cells. Gonad ALP levels were increased by exposure to ZnCl2 and to the highest concentration of municipal effluent. DNA strand breaks were increased in mussels treated to nano-ZnO indepentely of municipal effluent. Multivariate discriminant function analysis revealed that control mussels differed from mussels exposed to the municipal effluent and from those exposed to nano-ZnO or ZnCl2 alone. When the municipal effluent was added, changes in MDA, MT and labile Zn were produced and formed another cluster, suggesting a change in the toxicity of the municipal effluent in the presence of nano-ZnO.
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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".