Assessment of the toxicity of mixtures of copper, 9,10-phenanthrenequinone, and phenanthrene to<i>Daphnia magna</i>: Evidence for a reactive oxygen mechanism
Bibliographic record
Abstract
Polycyclic aromatic hydrocarbons and their derivatives are ubiquitous environmental contaminants. They are commonly present in complex mixtures with other contaminants, such as metals. The toxicities of phenanthrene (PHE) and 9,10-phenanthrenequinone (PHQ) with or without Cu were determined using Daphnia magna. Copper was the most toxic among the three chemicals tested, followed by PHQ and then PHE, with 48-h median effective concentrations (EC50s) of 0.96, 1.72, and 5.33 microM, respectively. Copper at 0.31 microM, or approximately the 5% effective concentration, decreased the EC50 of PHQ from 1.72 to 0.28 microM. Likewise, PHQ at 1.2 microM, or approximately the 10% effective concentration, significantly lowered the EC50 of Cu from 0.96 to 0.30 microM. This synergistic effect was not observed, however, in mixtures of Cu and PHE based on the response addition model. Assimilation of Cu wasfound to be similar with or without PHQ at increasing external concentrations of Cu, indicating that the increased toxicity of their mixtures is physiologically based. The ability of Cu plus PHQ to generate reactive oxygen species (ROS) was measured as well. Copper alone caused elevated ROS levels at a low concentration (0.63 microM). With PHQ present, however, this elevation in ROS occurred at an even lower Cu level (0.31 microM). Possible attenuation effects of ascorbic acid (vitamin C) on toxicity and ROS production induced by Cu, PHQ, and their mixtures were then examined. Ascorbic acid protected against Cu and Cu-plus-PHQ mixture-mediated toxicity but did not affect PHQ toxicity. Ascorbic acid also lowered ROS levels in the presence of Cu and Cu plus PHQ. We conclude that there exist potential toxic interactions between metals and modified PAHs and that these interactions can involve ROS formation.
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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 | 0.000 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".