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Cited by 204

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Zebrafish as a Model to Evaluate Nanoparticle Toxicity

Open access
Haque, Enamul, Ward, Alister C.·2018·Nanomaterials

Abstract

Nanoparticles are increasingly being developed for in vivo use, from targeted drug delivery to diagnostics, where they have enormous potential, while they are also being used for a variety of applications that can result in environmental exposure for humans. Understanding how specific nanoparticles interact with cells and cell systems is essential to gauge their safety with respect to either clinical or environmental exposure. Zebrafish is being increasingly employed as a model to evaluate nanoparticle biocompatibility. This review describes this model and how it can be used to assess nanoparticle toxicity at multiple levels, including mortality, teratogenicity, immunotoxicity, genotoxicity, as well as alterations in reproduction, behavior and a range of other physiological readouts. This review also provides an overview of studies using this model to assess the toxicity of metal, metal oxide and carbon-based nanoparticles. It is anticipated that this information will inform research aimed at developing biocompatible nanoparticles for a range of uses.

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Crossref · 10.3390/nano8070561

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Metadata

Authors
Haque, E., & Ward, A. C.
Venue
Nanomaterials
Publisher
MDPI AG
Published
2018-07-23
Volume
8
Issue
7
Pages
561
ISSN
2079-4991 (ISSN-L)
OpenAlex
W2883779460
Cited by
204
References
3
FWCI
8.78
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Zebrafish as a Model to Evaluate Nanoparticle Toxicity. https://app.cochat.ai/papers/10.3390/nano8070561
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Haque, E., & Ward, A. C. (2018). Zebrafish as a Model to Evaluate Nanoparticle Toxicity. Nanomaterials. https://doi.org/10.3390/nano8070561

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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.