Mechanistic insights into the effect of nanoparticles on zebrafish hatch
Bibliographic record
Abstract
Kimberly Jessica Ong*a, Xinxin Zhaob, Maria E. Thistlea, Tyson J. MacCormackac, Rhett J. Clarkd, Guibin Mad, Yadienka Martinez-Rubie, Benoit Simarde, Joachim Say Chye Loob, Jonathan G.C. Veinotdf & Greg G. Gossafa Department of Biological Sciences, University of Alberta, Edmonton, AB, Canadab School of Materials Science and Engineering, Nanyang Technological University, Singaporec Department of Chemistry and Biochemistry, Mount Allison University, Sackville, NB, Canadad Department of Chemistry, University of Alberta, Edmonton, AB, Canadae Molecular and Nanomaterials Architecture Group, Security and Disruptive Technologies, National Research Council of Canada, Ottawa, ON, Canadaf National Institute for Nanotechnology, National Research Council of Canada, Edmonton, AB, CanadaCorrespondence: Kimberly Jessica Ong, Department of Biological Sciences, University of Alberta, CW405 Biological Sciences Building, Edmonton, AB, T6G 2E9, Canada. +780 297 0355, 780 492 1276. kjong@ualberta.ca AbstractAquatic organisms are susceptible to waterborne nanoparticles (NP) and there is only limited understanding of the mechanisms by which these emerging contaminants may affect biological processes. This study used silicon (nSi), cadmium selenide (nCdSe), silver (nAg) and zinc NPs (nZnO) as well as single-walled carbon nanotubes (SWCNT) to assess NP effects on zebrafish (Danio rerio) hatch. Exposure of 10 mg/L nAg and nCdSe delayed zebrafish hatch and 100 mg/L of nCdSe as well as 10 and 100 mg/L of uncoated nZnO completely inhibited hatch and the embryos died within the chorion. Both the morphology and the movement of the embryos were not affected, and it was determined that the main mechanism of hatch inhibition by NPs is likely through the interaction of NPs with the zebrafish hatching enzyme. Furthermore, it was concluded that the observed effects arose from the NPs themselves and not their dissolved metal components.
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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".