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Cited by 332

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Making Waves: New Developments in Toxicology With the Zebrafish

Open access
Horzmann, Katharine A, Freeman, Jennifer L·2018·Toxicological Sciences

Abstract

The laboratory zebrafish (Danio rerio) is now an accepted model in toxicologic research. The zebrafish model fills a niche between in vitro models and mammalian biomedical models. The developmental characteristics of the small fish are strategically being used by scientists to study topics ranging from high-throughput toxicity screens to toxicity in multi- and transgenerational studies. High-throughput technology has increased the utility of zebrafish embryonic toxicity assays in screening of chemicals and drugs for toxicity or effect. Additionally, advances in behavioral characterization and experimental methodology allow for observation of recognizable phenotypic changes after xenobiotic exposure. Future directions in zebrafish research are predicted to take advantage of CRISPR-Cas9 genome editing methods in creating models of disease and interrogating mechanisms of action with fluorescent reporters or tagged proteins. Zebrafish can also model developmental origins of health and disease and multi- and transgenerational toxicity. The zebrafish has many advantages as a toxicologic model and new methodologies and areas of study continue to expand the usefulness and application of the zebrafish.

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Crossref · 10.1093/toxsci/kfy044

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Metadata

Authors
Horzmann, K. A., & Freeman, J. L.
Venue
Toxicological Sciences
Published
2018-02-19
Volume
163
Issue
1
Pages
5-12
ISSN
  • 1096-0929 (ISSN-L)
  • 1096-6080
OpenAlex
W2789319500
Cited by
332
References
4
FWCI
9.25
Type
journal article
Language
en
Open access
green

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.