Toxicological effects of functionalized single-walled carbon nanotubes (SWCNTs) on embryonic zebrafish (Danio rerio)
Bibliographic record
Abstract
Since their discovery in 1993, single-walled carbon nanotubes (SWCNTs) have played an integral role in nanotechnology and have been extensively studied due to their unique structural, electrical and mechanical properties. Chemical functionalization and coating of SWCNTs can improve their solubility in water and organic solvents to allow use in solution-based techniques and extend range of applications. We tested 1) aqueous lignin-wrapped 10-20nm and 2) carboxy-functionalized 10-20 nm SWCNTs, manufactured by the National Research Council - Steacie Institute for Molecular Sciences (NRC-SIMS), to determine the effect of different degrees of solubility. To determine the toxicological effect, we exposed zebrafish (Danio rerio) embryos over a 72-hour period to a range (1, 10, 50, 100 and 200mg/L) of functionalized and un-functionalized SWCNTs as a paired control following Organization for Economic Co-Operation and Development (OECD) guidelines. Toxicological endpoints such as lethality, hatch inhibition and changes in gene expression were measured. While preliminary results show no significant difference in survival and hatch compared to control, effects may occur at the molecular level. Our goal was to determine the toxicological effects of different degrees of functionalized SWCNTs at whole animal and molecular levels.
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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".