Attenuation of Hg(II)-induced cellular and DNA damage in human blood cells by uric acid
Bibliographic record
Abstract
Mercury (Hg) is a widespread environmental pollutant and toxicant that induces multiple organ damage in humans and animals. Hg toxicity is mediated by the induction of oxidative stress in the target cells. We used uric acid (UA), a potent antioxidant found in biological fluids, to protect human red blood cells (RBC) and lymphocytes against Hg-mediated cell, organelle, and genotoxicity. RBCs were incubated with mercuric chloride (HgCl2), an Hg(II) compound, either alone or in the presence of UA. Incubation of RBCs with only HgCl2 increased the production of nitrogen and oxygen radical species, enhanced methemoglobin levels, heme degradation, free ferrous iron, oxidation of proteins and membrane lipids, and reduced the antioxidant capacity of cells. UA enhanced the antioxidant capacity of RBCs and restored metabolic, plasma membrane-bound, and antioxidant enzyme activities. Scanning electron microscopy showed that UA prevented HgCl2-mediated morphological changes in RBCs. HgCl2 dissipated the mitochondrial membrane potential and increased lysosomal membrane damage in lymphocytes, but UA pre-treatment attenuated these effects. Genotoxicity analysis by comet assay showed that UA protected lymphocyte DNA from HgCl2-induced damage. Importantly, UA itself did not exhibit any deleterious effects on RBCs or lymphocytes. Thus, UA protects human blood cells from Hg(II)-mediated oxidative damage, reducing the harmful effects of this extremely toxic metal. We suggest that UA has a similar protective role in plasma against heavy metal toxicity.
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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) | 0.001 | 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".