Nanosafety-data-reusability-34-datasets

Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment (2017)

• Authors: Hans J. P. Marvin, Yamine Bouzembrak, Esmée M. Janssen, Meike van der Zande, Finbarr Murphy, Barry Sheehan, Martin Mullins, Hans Bouwmeester
• Journal: Nanotoxicology
• Article Type: journal-article
• Dataset Type: Toxicological Datasets
• Subject: Toxicology,Biomedical Engineering
• Date: 2017-1-2
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• URL: https://www.tandfonline.com/doi/pdf/10.1080/17435390.2016.1278481
• DOI: 10.1080/17435390.2016.1278481

Original Study Abstract

In this study, a Bayesian Network (BN) was developed for the prediction of the hazard potential and biological effects with the focus on metal- and metal-oxide nanomaterials to support human health risk assessment. The developed BN captures the (inter) relationships between the exposure route, the nanomaterials physicochemical properties and the ultimate biological effects in a holistic manner and was based on international expert consultation and the scientific literature (e.g., in vitro/in vivo data). The BN was validated with independent data extracted from published studies and the accuracy of the prediction of the nanomaterials hazard potential was 72% and for the biological effect 71%, respectively. The application of the BN is shown with scenario studies for TiO2, SiO2, Ag, CeO2, ZnO nanomaterials. It is demonstrated that the BN may be used by different stakeholders at several stages in the risk assessment to predict certain properties of a nanomaterials of which little information is available or to prioritize nanomaterials for further screening.

Data Sample

Shape	Nanoparticle	Dissolution	Surface area	Surface charge	Surface coatings	Surface reactivity	Aggregation	Particle size	Administration route	Study type	Cytotoxicity	Neurological effects	Pulmonary effects	Fibrosis	RCNS effects	Immunological effects	Genotoxicity	Inflammation	NM Hazard
Amorph	TiO2		51 - 101.25				High	10 to 50	Inhalation	In vivo			Low					Low	Medium
Amorph	TiO2		51 - 101.25				High	10 to 50	Inhalation	In vivo			Low					Medium	High
Amorph	TiO2		51 - 101.25				High	10 to 50	Inhalation	In vivo			Low					Low	Medium
Amorph	TiO2		51 - 101.25				High	10 to 50	Inhalation	In vivo			Low					Low	Medium
Amorph	TiO2		51 - 101.25				High	10 to 50	Inhalation	In vivo			Low					Medium	High

Data Summary

Variable	Count (unique values)
Shape	460
Nanoparticle	468
Dissolution	238
Surface area	224
Surface charge	192
Surface coatings	208
Surface reactivity	153
Aggregation	258
Particle size	468
Administration route	138
Study type	468
Cytotoxicity	313
Neurological effects	26
Pulmonary effects	71
Fibrosis	40
RCNS effects	46
Immunological effects	4
Genotoxicity	22
Inflammation	121
NM Hazard	468

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