Mitochondrial Toxicity of Cadmium Telluride Quantum Dot Nanoparticles in Mammalian Hepatocytes
Bibliographic record
Abstract
There are an increasing number of studies indicating that mitochondria are relevant targets in nanomaterial-induced toxicity. However, the underlying mechanisms by which nanoparticles (NPs) interact with these organelles and affect their functions are unknown. The aim of this study was to investigate the effects of cadmium telluride quantum dot (CdTe-QD) NPs on mitochondria in human hepatocellular carcinoma HepG2 cells. CdTe-QD treatment resulted in the enlargement of mitochondria as examined with transmission electron microscopy and confocal microscopy. CdTe-QDs appeared to associate with the isolated mitochondria as detected by their inherent fluorescence. Further analyses revealed that CdTe-QD caused disruption of mitochondrial membrane potential, increased intracellular calcium levels, impaired cellular respiration, and decreased adenosine triphosphate synthesis. The effects of CdTe-QDs on mitochondrial oxidative phosphorylation were evidenced by changes in levels and activities of the enzymes of the electron transport chain. Elevation of peroxisome proliferator-activated receptor-γ coactivator levels after CdTe-QD treatment suggested the effects of CdTe-QDs on mitochondrial biogenesis. Our results also showed that the effects of CdTe-QDs were similar or greater to those of cadmium chloride at equivalent concentrations of cadmium, suggesting that the toxic effects of CdTe-QDs were not solely due to cadmium released from the NPs. Overall, the study demonstrated that CdTe-QDs induced multifarious toxicity by causing changes in mitochondrial morphology and structure, as well as impairing their function and stimulating their biogenesis.
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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) | 0.000 | 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 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".