Comparison of cytotoxicity and expression of metal regulatory genes in zebrafish (Danio rerio) liver cells exposed to cadmium sulfate, zinc sulfate and quantum dots
Bibliographic record
Abstract
Recent advances in the ability to manufacture and manipulate materials at the nanometer scale have led to increased production and use of many types of nanoparticles. Quantum dots (QDs) are small, fluorescent nanoparticles composed of a core of semiconductor material (e.g. cadmium selenide, zinc sulfide) and shells or dopants of other elements. Particle core composition, size, shell, and surface chemistry have all been found to influence toxicity in cells. The aim of this study was to compare the toxicities of ionic cadmium (Cd) and zinc (Zn) and Cd- and Zn-containing QDs in zebrafish liver cells (ZFL). As expected, Cd(2+) was more toxic than Zn(2+), and the general trend of IC50-24 h values of QDs was determined to be CdTe < CdSe/ZnS or InP/ZnS, suggesting that ZnS-shelled CdSe/ZnS QDs were more cytocompatible than bare core CdTe crystals. Smaller QDs showed greater toxicity than larger QDs. Isolated mRNA from these exposures was used to measure the expression of metal response genes including metallothionein (MT), metal response element-binding transcription factor (MTF-1), divalent metal transporter (DMT-1), zrt and irt like protein (ZIP-1) and the zinc transporter, ZnT-1. CdTe exposure induced expression of these genes in a dose dependent manner similar to that of CdSO4 exposure. However, CdSe/ZnS and InP/ZnS altered gene expression of metal homeostasis genes in a manner different from that of the corresponding Cd or Zn salts. This implies that ZnS shells reduce QD toxicity attributed to the release of Cd(2+), but do not eliminate toxic effects caused by the nanoparticles themselves.
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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".