Assessment of the toxicity of mixtures of nickel or cadmium with 9,10-phenanthrenequinone to<i>Daphnia magna</i>: Impact of a reactive oxygen-mediated mechanism with different redox-active metals
Bibliographic record
Abstract
Recently, we showed that reactive oxygen species (ROS) formation was involved in the toxicity of the redox-active metal Cu and mixtures of Cu plus a photomodified polycyclic aromatic hydrocarbon (PAH), phenanthrenequinone (PHQ), to Daphnia magna. It is unknown, however, if similar results can be observed for metals with lower or no redox activity and their mixtures with PHQ. In the present study using D. magna, the toxicity of Ni, a weakly redox-active metal, and of Cd, a non-redox active metal, was examined with or without PHQ. The abilities of Ni, Cd, PHQ, and binary mixtures of metal plus PHQ to generate ROS were measured using a 2',7'-dichlorofluorescein fluorescence assay. The results were compared with the results of Cu and mixtures of Cu plus PHQ from a recent study by our group. The order of metal toxicity to D. magna was found to be Cd > or = Cu > Ni. As with Cu/PHQ mixtures, synergistic toxicity was observed for mixtures of Ni and PHQ, whereas additive toxicity was observed for mixtures of Cd and PHQ. Alone, PHQ had no impact on ROS levels in D. magna. Nickel alone caused elevated ROS, which was further enhanced in the presence of PHQ. Neither Cd nor Cd/PHQ mixtures increased ROS production. Attenuation of toxicity and ROS production was observed in response to treatment with low concentrations of L-ascorbic acid. These results indicate potential toxic interactions between metals and modified PAHs. With redox-active metals, such as Cu and Ni, and modified PAHs, such as PHQ, these interactions can involve ROS formation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".