Toxicological Effects of Inorganic Nanoparticle Mixtures in Freshwater Mussels
Bibliographic record
Abstract
The toxicological effects of nanoparticles mixtures in aquatic organisms are poorly understood. The purpose of this study was to examine the tissue metal loadings and sublethal effects of silver (nAg), cerium oxide (nCeO), copper oxide (nCuO) and zinc oxide (nZnO) nanoparticles individually at 50 µg/L and in two mixtures to freshwater mussels Dreissena bugensis. The mixtures consisted of 12.5 µg/L of each nanoparticle (Mix50) and 50 µg/L of each nanoparticles (Mix200). After a 96-h exposure period, mussels were analyzed for morphological changes, air time survival, bioaccumulation, inflammation (cyclooxygenase or COX activity), lipid peroxidation (LPO), DNA strand breaks, labile Zn, acetylcholinesterase (AChE) and protein–ubiquitin levels. The data revealed that mussels accumulated the nanoparticles with nCeO and nAg were the least and most bioavailable, respectively. Increased tissue metal loadings were observed for nCeO and nCuO in mixtures, while no mixture effects were observed for nAg and nZnO. The weight loss during air emersion was lower in mussels exposed to nCuO alone but not by the mixture. On the one hand, labile Zn levels was increased with nZnO but returned to control values with the Mix50 and Mix200, suggesting antagonism. On the other hand, DNA strand breaks were reduced for both mixtures compared to controls or to the nanoparticles individually, suggesting potentiation of effects. The same was found for protein–ubiquitin levels, which were decreased by nCeO and nCuO alone but not when in mixtures, which increased their levels. In conclusion, the data revealed that the behavior and effects of nanoparticles were influenced by other nanoparticles where antagonist and potentiation interactions were identified.
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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".