Combined exposure to lead, inorganic mercury and methylmercury shows deviation from additivity for cardiovascular toxicity in rats
Bibliographic record
Abstract
Environmental exposure to metal mixtures in the human population is common. Mixture risk assessments are often challenging because of a lack of suitable data on the relevant mixture. A growing number of studies show an association between lead or mercury exposure and cardiovascular effects. We investigated the cardiovascular effects of single metal exposure or co-exposure to methylmercury [MeHg(I)], inorganic mercury [Hg(II)] and lead [Pb(II)]. Male Wistar rats received four different metal mixtures for 28 days through the drinking water. The ratios of the metals were based on reference and environmental exposure values. Blood and pulse pressure, cardiac output and electrical activity of the heart were selected as end-points. While exposure to only MeHg(I) increased the systolic blood pressure and decreased cardiac output, the effects were reversed with combined exposures (antagonism). In contrast to these effects, combined exposures negatively affected the electrical activity of the heart (synergism). Thus, it appears that estimates of blood total Hg levels need to be paired with estimates of what species of mercury dominate exposure as well as whether lead co-exposure is present to link total blood Hg levels to cardiovascular effects. Based on current human exposure data and our results, there may be an increased risk of cardiac events as a result of combined exposures to Hg(II), MeHg(I) and Pb(II). This increased risk needs to be clarified by analyzing lead and Hg exposure data in relation to cardiac electrical activity in epidemiological studies.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".