Heavy Metal Induced Cell Necrosis: Involves Apoptosis Death Signals Initiated by Mitochondrial Injury
Bibliographic record
Abstract
Introduction: Severe industrial diseases result from the hepatic accumulation of mercury, cadmium or chromium in humans and on the other hand cadmium and dichromate and mercuric salts may induce lung or kidney cancer. Acute or chronic CdCl2, HgCl2 or dichromate administration induces hepatic and nephrotoxicity in rodents. Oxidative stress is often cited as a possible cause of metal induced cell death but the death signaling pathways involved have not yet been well investigated. Method and Materials: To search for death signaling mechanisms we used accelerated cytotoxicity mechanism screening techniques (ACMS) on isolated rat hepatocytes as our cellular model. Results: Adding the CdC12, HgC12 or K2Cr2O7 to isolated hepatocytes caused a rapid increase in reactive oxygen species (ROS) formation and a decline in mitochondrial membrane potential. Then lipid per-oxidation and celllysis ensued. Cytotoxicity was prevented by ROS scavengers and various inhibitors of the mitochondrial permeability transition (MPT) e.g. cyclosporin A, carnitine or trifluoperazine. Antioxidants prevented hepatocyte lysis induced by CdC12 , K2Cr207 but not HgC12. Conclusion: Hepatocyte lysis was also prevented by various apoptosis inhibitors e.g. cycloheximide, dactinomycin and a tetrapeptide caspase 3 inhibitor which suggested that metal induced hepatocyte lysis involves apoptotic death signals initiated by MPT and ROS.
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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) | 0.003 | 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".