Size-dependent toxicity of metal oxide particles—A comparison between nano- and micrometer size

Karlsson, Hanna; Gustafsson, Johanna; Cronholm, Pontus; Möller, Lennart

doi:10.1016/j.toxlet.2009.03.014

Cited by 828 publications

(557 citation statements)

References 40 publications

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“…18 and 19). The low cytotoxicity of NPs was confirmed by multiple repetitions of the reference experiments, which were in good agreement with the literature 33,34 .…”

Section: Nature Catalysissupporting

confidence: 89%

Magnetic field remotely controlled selective biocatalysis

et al. 2017

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M aterials that efficiently release biological molecules or therapeutic chemicals on demand using exposure to remotely controlled and safe external sources of energy, such as magnetic fields, could find applications for drug delivery 1 , biotechnology 2,3 and biosensors 4 . Because live tissue and synthetic polymers are not responsive to weak magnetic fields, the development of magnetic-field-responsive soft materials has been reported by combining magnetic nanoparticles and stimuli-responsive soft materials 5 . Magnetic nanoparticles interact with magnetic fields and transduce magnetic field energy into physical or chemical changes in the soft material. Materials that control enzymatic processes are one example of such soft materials. Enzymes are extensively used to change or degrade colloidal particles, capsules, and their assemblies to trigger release of the cargo via biocatalytic reactions 6,7 .In all eukaryotes, metabolic pathways are precisely organized and regulated. This precise control is based in part on the high selectivity of biocatalytic reactions and controlled transport of chemicals and biomacromolecules across membranes that compartmentalize cells, organelles and organs. Highly selective biocatalysis alone cannot orchestrate complex systems of biochemical reactions without the supporting role of signal-triggered synthesis, release, secretion, conversion and degrading processes that take place in different compartments in cells and organs. Despite being highly selective, enzymes cannot provide 100% selectivity. In particular, enzymes could interact with a number of substrates of a similar chemical structure (for example, proteases are highly promiscuous catalysts), be degraded by other enzymes or even by self-digestion upon secretion into a complex biological environment, or undergo undesired aggregation, crystallization or nonspecific adsorption, which would strongly damage the efficiency of the biocatalytic process. However, the overall high specificity of biocatalytic processes is strengthened by localizing the enzymatic reactions within a specific environment and spatial compartments.Inspired by this hierarchical design in live systems, diverse stimuli-responsive functional materials have been reported, involving various architectures that respond to changes in magnetic fields [8][9][10] . However, it remains challenging to create a reactive system that preserves enzyme molecules from destructive environments and undesired interactions while being able to initiate the designated reaction when needed. Different approaches have been developed to preserve enzymes for storage and delivery before activating them on demand in a magnetic field at the targeted location. A number of studies aimed at controlling the kinetics of biocatalytic reactions in model systems [11][12][13][14][15] have explored magnetic-field-triggered changes of the local concentration and mobility of enzymes. However, it is difficult to apply many of such approaches to live tissue because of limitations associated with degradat...

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“…18 and 19). The low cytotoxicity of NPs was confirmed by multiple repetitions of the reference experiments, which were in good agreement with the literature 33,34 .…”

Section: Nature Catalysissupporting

confidence: 89%

Magnetic field remotely controlled selective biocatalysis

et al. 2017

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“…(M. V. Park, et al, 2011) have compared the cytotoxicity, inflammation, genotoxicity and developmental toxicity induced by different-sized silver ENMs (20, 80 and 113nm) and stated that nano-silver particles with the smallest size have exhibited higher toxicity than the larger ones in the assays studied. All such findings suggest that the size of particles is one of the possible factors which may contribute to the toxicity of chemicals; however, in some cases no relationship has been observed between the toxicity of particles and their sizes (Karlsson, et al, 2009;Lin, et al, 2009). …”

Section: Particle Size and Size Distributionmentioning

confidence: 99%

“…There is now a growing body of literature on the potential undesirable effects caused by the exposure to different types of ENMs (Horie & Fujita, 2011;Jeng & Swanson, 2006;Karlsson, Gustafsson, Cronholm, & Möller, 2009;Magrez, et al, 2006). Although the awareness of the potential adverse effects of ENMs is increasing, there are still numerous unanswered questions which complicate the appropriate evaluation of toxicity at the nano-scale dimension.…”

Section: Introductionmentioning

confidence: 99%

(Q)SAR modelling of nanomaterial toxicity: A critical review

et al. 2015

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There is an increasing recognition that nanomaterials pose a risk to human health, and that the novel engineered nanomaterials (ENMs) in the nanotechnology industry and their increasing industrial usage poses the most immediate problem for hazard assessment, as many of them remain untested. The large number of materials and their variants (different sizes and coatings for instance) that require testing and ethical pressure towards non-animal testing means that expensive animal bioassay is precluded, and the use of (quantitative) structure activity relationships ((Q)SAR) models as an alternative source of hazard information should be explored.(Q)SAR modelling can be applied to fill the critical knowledge gaps by making the best use of existing data, prioritize physicochemical parameters driving toxicity, and provide practical solutions to the risk assessment problems caused by the diversity of ENMs. This paper covers the core components required for successful application of (Q)SAR technologies to ENMs toxicity prediction, and summarizes the published nano-(Q)SAR studies and outlines the challenges ahead for nano-(Q)SAR modelling. It provides a critical review of (1) the present status of the availability of ENMs characterization/toxicity data, (2) the characterization of nanostructures that meets the need of (Q)SAR analysis, (3) the summary of published nano-(Q)SAR studies and their limitations, (4) the in silico tools for (Q)SAR screening of nanotoxicity and (5) the prospective directions for the development of nano-(Q)SAR models.

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“…The specification of diameter in this regulation reflects an understanding that fine particles (diameter <2.5 µm) are transported deep into the lung and have been correlated with adverse health effects (U.S. EPA, 2004). Some studies report that ultrafine particles (diameter <0.1 µm) of certain composition are more toxic than larger fine particles of the same composition (Karlsson et al 2009;Oberdörster 2001). Atmospheric ultrafine particles dominate the particle number size distribution but contribute little to the particle mass or volume size distributions.…”

Section: Introductionmentioning

confidence: 99%

Simulating Particle Size Distributions over California and Impact on Lung Deposition Fraction

Kelly

Avise

Cai

et al. 2011

Aerosol Science and Technology

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Reliable simulations of particle mass size distributions by regional photochemical air quality models are needed in regulatory applications because the U.S. EPA's National Ambient Air Quality Standards specify limits on the mass concentration of particles in a specific size range (i.e., aerodynamic diameter <2.5 µm). Considering the associations between adverse health effects and exposure to ultrafine particles, air quality models may need to accurately simulate particle number size distributions in addition to mass size distributions in future applications. In this study, predictions of particle number and mass size distributions by the Community Multiscale Air Quality model with the standard and an updated emission size distribution are evaluated using wintertime observations in California. Differences in modeled lung deposition fraction for simulated and observed particle number size distributions are also evaluated. Simulated mass size distributions are generally broader and shifted to larger diameters than observations, and observed differences in inorganic and carbon (elemental and organic) distributions are not captured by the model. These model limitations can be reasonably accounted for in regulatory modeling applications. Simulated number size distributions are considerably less accurate than mass size distributions and are difficult to represent in air quality models due to large sub-grid-scale concentration gradients. However, modeled number size distributions are This article has been reviewed by the staff of the California Air Resources Board and has been approved for publication. Approval does not signify that the contents necessarily reflect the views and policies of the California Air Resources Board, nor does mention of trade names or commercial products constitute endorsement or recommendation for use.Address correspondence to James T. Kelly, Planning and Technical Support Division, Air Resources Board, California Environmental Protection Agency, 1001 I Street, Sacramento, CA 95812, USA. E-mail: jkelly@ucdavis.edu responsive to updates of the emission size distribution, and reasonable simulation of background number size distributions might be possible with an improved treatment of emission size distributions. Modeled lung deposition fractions for simulated number size distributions peak in the same lung region as those for number size distributions observed in the background. However, differences in modeled and observed total number concentrations generally suggest large differences in the total number of deposited particles. Future model development on simulating particle mass size distributions should focus on improving predictions of the mass fraction of particles <2.5 µm. Model development for particle number size distributions should focus on reducing differences in modeled lung deposition for modeled and observed distributions.

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Size-dependent toxicity of metal oxide particles—A comparison between nano- and micrometer size

Cited by 828 publications

References 40 publications

Magnetic field remotely controlled selective biocatalysis

Magnetic field remotely controlled selective biocatalysis

(Q)SAR modelling of nanomaterial toxicity: A critical review

Simulating Particle Size Distributions over California and Impact on Lung Deposition Fraction

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