Metalloestrogenic Effects of Quantum Dots
Bibliographic record
Abstract
AIM: To investigate the metalloestrogenic effects of cadmium telluride quantum dots (QDs) in both human breast cancer cells and in vivo in mice. MATERIALS & METHODS: Human breast cancer cells (MCF-7 cells) were utilized to study QDs, cadmium and 17β-estradiol induced estrogen-related genomic and nongenomic signaling. Female prepubescent and ovariectomized adult mice were treated with CdTe QDs to assess whether QD-induced estrogenicity would lead to uterine changes. RESULTS & DISCUSSION: Our findings demonstrate that in vitro cadmium-containing QDs induce cellular proliferation, estrogen receptor α activation, and biphasic phosphorylation of AKT and ERK1/2, comparable with 17β-estradiol. Green QDs elicited a more robust estrogenic response than orange QDs. Addition of the selective estrogen receptor antagonist, ICI 182780, completely abolished all QD-induced estrogenic effects, suggesting that QD-induced estrogenic signaling is mediated via the estrogen receptor. In vivo, chronic treatment of mice with QDs led to a two- to three-fold increase in uterine weight, comparable or greater than 17β-estradiol. CONCLUSION: These findings suggest that certain cadmium-containing nanocrystals are endocrine disruptors, whose effects can exceed those induced by ionic cadmium or 17β-estradiol.
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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".