Meta-analysis of cellular toxicity for cadmium-containing quantum dots
Abstract
Understanding the relationships between the physicochemical properties of engineered nanomaterials and their toxicity is critical for environmental and health risk analysis. However, this task is confounded by material diversity, heterogeneity of published data and limited sampling within individual studies. Here, we present an approach for analysing and extracting pertinent knowledge from published studies focusing on the cellular toxicity of cadmium-containing semiconductor quantum dots. From 307 publications, we obtain 1,741 cell viability-related data samples, each with 24 qualitative and quantitative attributes describing the material properties and experimental conditions. Using random forest regression models to analyse the data, we show that toxicity is closely correlated with quantum dot surface properties (including shell, ligand and surface modifications), diameter, assay type and exposure time. Our approach of integrating quantitative and categorical data provides a roadmap for interrogating the wide-ranging toxicity data in the literature and suggests that meta-analysis can help develop methods for predicting the toxicity of engineered nanomaterials.
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Several literature studies have identified causal relationships between the biological outcomes and important ENMs properties.
“Several literature studies have identified causal relationships between the biological outcomes and important ENMs properties.239”
From QSAR without borders (2020) · cites this paper as [239]
Toxicity prediction models trained on these attribute combinations can sometimes identify causal relationships, which can be effectively achieved with the Bayesian Network approach.
“Toxicity prediction models trained on these attribute combinations can sometimes identify causal relationships,239 which can be effectively achieved with the Bayesian Network (BN, also called a Bayesian Belief network, BBN) approach.249”
From QSAR without borders (2020) · cites this paper as [239]
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“A limited number of studies dealt with dendrimers such as Poly-(amido amine) (Jones, Ghandehari, and Facelli 2015), polymers (chitosan/streptokinase, Poly(N-isopropylacrylamide)) (Bygd, Forsmark, and Bratlie 2015; Baharifar and Amani 2016) and quantum dots (Oh et al. 2016; Bilal et al. 2019).”
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“As can be seen in Figure 1, the traditional QDs containing the core of CdX (CdTe, CdSe or CdS) are considered to be the best QDs for bioimaging (Oh et al., 2016; Xu et al., 2019).”
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“Oh et al. (2016) established cellular toxicity models to predict the toxicity of 17 quantum dots.”
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- Authors
- Oh, E., Liu, R., Nel, A., Gemill, K. B., Bilal, M., Cohen, Y., & Medintz, I. L.
- Affiliations
- United States Naval Research Laboratory (US)
- University of California, Los Angeles (US)
- Corresponding author
- Liu, Rong
- Cohen, Yoram
- Medintz, Igor L.
- Venue
- Nature Nanotechnology
- Published
- 2016-02-29
- Volume
- 11
- Issue
- 5
- Pages
- 479-486
- ISSN
- 1748-3387 (ISSN-L)
- 1748-3395
- OpenAlex
- W2292437912
- Cited by
- 502
- References
- 0
- Citations by year
- 2026: 24 · 2025: 36 · 2024: 39 · 2023: 40 · 2022: 62 · 2021: 53 · 2020: 78 · 2019: 44 · 2018: 56 · 2017: 48 · 2016: 22
- FWCI
- 2.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Nanotechnology (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Science Foundation
- Defense Threat Reduction Agency
- U.S. Naval Research Laboratory
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Quantum Dots Synthesis And Properties
- Physical Sciences › Materials Science › Materials Chemistry › Nanoparticles: synthesis and applications
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Heavy Metal Exposure and Toxicity
- Keywords
- cadmium quantum dots, semiconductor quantum dots, cytotoxicity, engineered nanomaterials, nanoparticle toxicity, cell viability, surface modification, ligands, shells, particle diameter, toxicity prediction, physicochemical properties, random forest regression
- MeSH terms
- Animals, Cadmium, Cell Survival, Humans, Models, Theoretical, Inhibitory Concentration 50, Quantum Dots, Workflow
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