Cadmium telluride quantum dots cause oxidative stress leading to extrinsic and intrinsic apoptosis in hepatocellular carcinoma HepG2 cells
Bibliographic record
Abstract
The mechanisms of toxicity related to human hepatocellular carcinoma HepG2 cell exposures to cadmium telluride quantum dots (CdTe-QDs) were investigated. CdTe-QDs caused cytotoxicity in HepG2 cells in a dose- and time-dependent manner. Treated cells showed an increase in reactive oxygen species (ROS). Altered antioxidant levels were demonstrated by depletion of reduced glutathione (GSH), a decreased ratio of reduced glutathione to oxidized glutathione (GSH/GSSG) and an increased NF-E2-related Factor 2 (Nrf2) activation. Enzyme assays showed that superoxide dismutase (SOD) activity was elevated whereas catalase (CAT) and glutathione-S-transferase (GST) activities were depressed. Further analyses revealed that CdTe-QD exposure resulted in apoptosis, indicated by changes in levels of caspase-3 activity, poly ADP-ribose polymerase (PARP) cleavage and phosphatidylserine externalization. Extrinsic apoptotic pathway markers such as Fas levels and caspase-8 activity increased as a result of CdTe-QD exposure. Involvement of the intrinsic/mitochondrial apoptotic pathway was indicated by decreased levels of B-cell lymphoma 2 (Bcl2) protein and mitochondrial cytochrome c, and by increased levels of mitochondrial Bcl-2-associated X protein (Bax) and cytosolic cytochrome c. Further, mitogen-activated protein kinases (MAPKs) such as c-Jun N-terminal kinases (JNK), extracellular signal-regulated kinases (Erk1/2), and p38 were all activated. Our findings reveal that CdTe-QDs cause oxidative stress, interfere with antioxidant defenses and activate protein kinases, leading to apoptosis via both extrinsic and intrinsic pathways. Since the effects of CdTe-QDs on selected biomarkers were similar or greater compared to those of CdCl2 at equivalent concentrations of cadmium, the study suggests that the toxicity of CdTe-QDs arises from a combination of the effects of cadmium and ROS generated from the NPs.
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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".