In vitro immunotoxicology of quantum dots and comparison with dissolved cadmium and tellurium
Bibliographic record
Abstract
The increasing use of products derived from nanotechnology has raised concerns about their potential toxicity, especially at the immunocompetence level in organisms. This study compared the immunotoxicity of cadmium sulfate/cadmium telluride (CdS/Cd-Te) mixture quantum dots (QDs) and their dissolved components, cadmium chloride (CdCl2 )/sodium telluride (NaTeO3 ) salts, and a CdCl2 /NaTeO3 mixture on four animal models commonly used in risk assessment studies: one bivalve (Mytilus edulis), one fish (Oncorhynchus mykiss), and two mammals (mice and humans). Our results of viability and phagocytosis biomarkers revealed that QDs were more toxic than dissolved metals for blue mussels. For other species, dissolved metals (Cd, Te, and Cd-Te mixture) were more toxic than the nanoparticles (NPs). The most sensitive species toward QDs, according to innate immune cells, was humans (inhibitory concentration [IC50 ] = 217 μg/mL). However, for adaptative immunity, lymphoblastic transformation in mice was decreased for small QD concentrations (EC50 = 4 μg/mL), and was more sensitive than other model species tested. Discriminant function analysis revealed that blue mussel hemocytes were able to discriminate the toxicity of QDs, Cd, Te, and Cd-Te mixture (Partial Wilk's λ = 0.021 and p < 0.0001). For rainbow trout and human cells, the immunotoxic effects of QDs were similar to those obtained with the dissolved fraction of Cd and Te mixture. For mice, the toxicity of QDs markedly differed from those observed with Cd, Te, and dissolved Cd-Te mixture. The results also suggest that aquatic species responded more differently than vertebrates to these compounds. The results lead to the recommendation that mussels and mice were most able to discriminate the effects of Cd-based NPs from the effects of dissolved Cd and Te at the immunocompetence level.
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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".