Towards defining new nano-descriptors: extracting morphological features from transmission electron microscopy images

Bigdeli, Arafeh; Hormozi-Nezhad, M. Reza; Jalali‐Heravi, Mehdi; Abedini, Mohammad Reza; Sharif-Bakhtiar, Farzad

doi:10.1039/c4ra10375k

Cited by 18 publications

(15 citation statements)

References 27 publications

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“…However, everywhere in our surroundings we increasingly make (purposefully or by accident, as in 3D printing) nanoscale shape ensembles, that possess complex features on length-scales comparable to the particle itself [29][30][31][32][33][34][35] . Also, advancing synthetic methods now potentially offer "unlimited" freedom to controllably make a new universe of such complex geometrical ensembles that cannot be described, recognized or characterized with only a few length parameters [36][37][38][39][40][41][42] . We currently find ourselves unable to even name and share information, let alone carry out many systematic investigations of them.…”

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confidence: 99%

Classification and biological identity of complex nano shapes

et al. 2020

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Everywhere in our surroundings we increasingly come in contact with nanostructures that have distinctive complex shape features on a scale comparable to the particle itself. Such shape ensembles can be made by modern nano-synthetic methods and many industrial processes. With the ever growing universe of nanoscale shapes, names such as "nanoflowers" and "nanostars" no longer precisely describe or characterise the distinct nature of the particles. Here we capture and digitise particle shape information on the relevant size scale and create a condensed representation in which the essential shape features can be captured, recognized and correlated. We find the natural emergence of intrinsic shape groups as well-defined ensemble distributions and show how these may be analyzed and interpreted to reveal novel aspects of our nanoscale shape environment. We show how these ideas may be applied to the interaction between the nanoscale-shape and the living universe and provide a conceptual framework for the study of nanoscale shape biological recognition and identity.

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confidence: 99%

Classification and biological identity of complex nano shapes

et al. 2020

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“…However, the development of nano-QSARs has been affected by a lack of data and knowledge of NM mechanisms of toxicity that make development and validation of computational models very challenging (Fourches et al 2010(Fourches et al , 2011Puzyn et al 2010). Indeed, as a move in that direction, in one study surface morphological parameters were successfully extracted from TEM images of NPs through the use of digital image processing methods for subsequent application in QSAR models (Bigdeli et al 2014). As a way forward, there have been proposals to convert images from scanning electron microscopy, transmission electron microscopy (TEM), and atomic force microscopy into matrices in which the numerical values correspond to individual pixels of the original pictures (Puzyn et al 2009).…”

Section: Applicability Of Qsar Models To Predict the Bioaccumulation mentioning

confidence: 99%

“…As a way forward, there have been proposals to convert images from scanning electron microscopy, transmission electron microscopy (TEM), and atomic force microscopy into matrices in which the numerical values correspond to individual pixels of the original pictures (Puzyn et al 2009). Indeed, as a move in that direction, in one study surface morphological parameters were successfully extracted from TEM images of NPs through the use of digital image processing methods for subsequent application in QSAR models (Bigdeli et al 2014).…”

Section: Applicability Of Qsar Models To Predict the Bioaccumulation mentioning

confidence: 99%

An assessment of applicability of existing approaches to predicting the bioaccumulation of conventional substances in nanomaterials

Utembe

Wepener

Yu³

et al. 2018

Enviro Toxic and Chemistry

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The experimental determination of bioaccumulation is challenging, and a number of approaches have been developed for its prediction. It is important to assess the applicability of these predictive approaches to nanomaterials (NMs), which have been shown to bioaccumulate. The octanol/water partition coefficient (K ) may not be applicable to some NMs that are not found in either the octanol or water phases but rather are found at the interface. Thus the K values obtained for certain NMs are shown not to correlate well with the experimentally determined bioaccumulation. Implementation of quantitative structure-activity relationships (QSARs) for NMs is also challenging because the bioaccumulation of NMs depends on nano-specific properties such as shape, size, and surface area. Thus there is a need to develop new QSAR models based on these new nanodescriptors; current efforts appear to focus on digital processing of NM images as well as the conversion of surface chemistry parameters into adsorption indices. Water solubility can be used as a screening tool for the exclusion of NMs with short half-lives. Adaptation of fugacity/aquivalence models, which include physicochemical properties, may give some insights into the bioaccumulation potential of NMs, especially with the addition of a biota component. The use of kinetic models, including physiologically based pharmacokinetic models, appears to be the most suitable approach for predicting bioaccumulation of NMs. Furthermore, because bioaccumulation of NMs depends on a number of biotic and abiotic factors, it is important to take these factors into account when one is modeling bioaccumulation and interpreting bioaccumulation results. Environ Toxicol Chem 2018;37:2972-2988. © 2018 SETAC.

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“…[14] Predictive models based on experimentally measured or theoretically calculated descriptors that encode NMs structural characteristics, can be built to predict a property or activity of interest. [3,15,16] However, only a few such models and tools have been proposed to date in the nanoinformatics field for the prediction of properties or activities of NMs. [8,[16][17][18][19][20][21] One promising approach is to develop computational tools that extract additional information from existing experimental datasets, i.e., to enrich the experimental datasets with computationally determined descriptors, thus maximizing the utility of the experimental datasets.…”

Section: Introductionmentioning

confidence: 99%

Zeta‐Potential Read‐Across Model Utilizing Nanodescriptors Extracted via the NanoXtract Image Analysis Tool Available on the Enalos Nanoinformatics Cloud Platform

Varsou

Afantitis

Tsoumanis

et al. 2020

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Zeta potential is one of the most critical properties of nanomaterials (NMs) which provides an estimation of the surface charge, and therefore electrostatic stability in medium and, in practical terms, influences the NM's tendency to form agglomerates and to interact with cellular membranes. This paper describes a robust and accurate read‐across model to predict NM zeta potential utilizing as the input data a set of image descriptors derived from transmission electron microscopy (TEM) images of the NMs. The image descriptors are calculated using NanoXtract (https://enaloscloud.novamechanics.com/EnalosWebApps/NanoXtract/), a unique online tool that generates 18 image descriptors from the TEM images, which can then be explored by modeling to identify those most predictive of NM behavior and biological effects. NM TEM images are used to develop a model for prediction of zeta potential based on grouping of the NMs according to their nearest neighbors. The model provides interesting insights regarding the most important similarity features between NMs—in addition to core composition the main elongation emerged, which links to key drivers of NM toxicity such as aspect ratio. Both the NanoXtract image analysis tool and the validated model for zeta potential (https://enaloscloud.novamechanics.com/EnalosWebApps/ZetaPotential/) are freely available online through the Enalos Nanoinformatics platform.

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Towards defining new nano-descriptors: extracting morphological features from transmission electron microscopy images

Cited by 18 publications

References 27 publications

Classification and biological identity of complex nano shapes

Classification and biological identity of complex nano shapes

An assessment of applicability of existing approaches to predicting the bioaccumulation of conventional substances in nanomaterials

Zeta‐Potential Read‐Across Model Utilizing Nanodescriptors Extracted via the NanoXtract Image Analysis Tool Available on the Enalos Nanoinformatics Cloud Platform

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