Synergic toxic effects of food contaminant mixtures in human cells
Bibliographic record
Abstract
Humans are exposed to multiple exogenous substances, notably through food consumption. Many of these compounds are suspected to impact human health, and their combination could exacerbate their harmful effects. We previously observed in human cells that, among the six most prevalent food contaminant complex mixtures identified in the French diet, synergistic interactions between component appeared in two mixtures compared with the response with the chemicals alone. In the present study, we demonstrated in human cells that these properties are driven only by two heavy metals in each mixture: tellurium (Te) with cadmium (Cd) and Cd with inorganic arsenic (As), respectively. It appeared that the predicted effects for these binary mixtures using the mathematical model of Chou and Talalay confirmed synergism between these heavy metals. Based on different cell biology experiments (cytotoxicity, genotoxicity, mutagenesis and DNA repair inhibition experiments), a detailed mechanistic analysis of these two mixtures suggests that concomitant induction of oxidative DNA damage and decrease of their repair capacity contribute to the synergistic toxic effect of these chemical mixtures. Overall, these results may have broad implications for the fields of environmental toxicology and chemical mixture risk assessment.
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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 | 0.000 |
| 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 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".