Mercury Exposure Increases Circulating Net Matrix Metalloproteinase (MMP)‐2 and MMP‐9 Activities
Bibliographic record
Abstract
Mercury (Hg) exposure causes health problems that may result from increased oxidative stress and matrix metalloproteinase (MMP) levels. We investigated whether there is an association between the circulating levels of MMP-2, MMP-9, their endogenous inhibitors (the tissue inhibitors of metalloproteinases; TIMPs) and the circulating Hg levels in 159 subjects environmentally exposed to Hg. Blood and plasma Hg were determined by inductively coupled plasma-mass spectrometry (ICP-MS). MMP and TIMP concentrations were measured in plasma samples by gelatin zymography and ELISA respectively. Thiobarbituric acid-reactive species (TBARS) were measured in plasma to assess oxidative stress. Selenium (Se) levels were determined by ICP-MS because it is an antioxidant. The relations between bioindicators of Hg and the metalloproteinases levels were examined using multivariate regression models. While we found no relation between blood or plasma Hg and MMP-9, plasma Hg levels were negatively associated with TIMP-1 and TIMP-2 levels, and thereby with increasing MMP-9/TIMP-1 and MMP-2/TIMP-2 ratios, thus indicating a positive association between plasma Hg and circulating net MMP-9 and MMP-2 activities. These findings provide a new insight into the possible biological mechanisms of Hg toxicity, particularly in cardiovascular diseases.
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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) | 0.002 | 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 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".