Evidence map›Paper›PMID 31823372›Full record

ArticleJournal of microscopy2020

Application of automated electron microscopy imaging and machine learning to characterise and quantify nanoparticle dispersion in aqueous media.

M Ilett, J Wills, P Rees, S Sharma, S Micklethwaite, A Brown, R Brydson, N Hondow

Open access · hybridAbstract read
In one paragraph

Article in Journal of microscopy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.5field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 35 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 2 countries.

M IlettSchool of Chemical and Process Engineering, University of Leeds, Leeds, U.K.
J WillsDepartment of Veterinary Medicine, University of Cambridge, Cambridge, U.K.
P ReesCentre for Nanohealth, Swansea University, College of Engineering, Swansea, U.K.
S SharmaSchool of Chemical and Process Engineering, University of Leeds, Leeds, U.K.
S MicklethwaiteSchool of Chemical and Process Engineering, University of Leeds, Leeds, U.K.
A BrownSchool of Chemical and Process Engineering, University of Leeds, Leeds, U.K.
R BrydsonSchool of Chemical and Process Engineering, University of Leeds, Leeds, U.K.
N HondowSchool of Chemical and Process Engineering, University of Leeds, Leeds, U.K.
University of Leeds · GBGower College Swansea · GBUniversity of Cambridge · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

For many nanoparticle applications it is important to understand dispersion in liquids. For nanomedicinal and nanotoxicological research this is complicated by the often complex nature of the biological dispersant and ultimately this leads to severe limitations in the analysis of the nanoparticle dispersion by light scattering techniques. Here we present an alternative analysis and associated workflow which utilises electron microscopy. The need to collect large, statistically relevant datasets by imaging vacuum dried, plunge frozen aliquots of suspension was accomplished by developing an automated STEM imaging protocol implemented in an SEM fitted with a transmission detector. Automated analysis of images of agglomerates was achieved by machine learning using two free open-source software tools: CellProfiler and ilastik. The specific results and overall workflow described enable accurate nanoparticle agglomerate analysis of particles suspended in aqueous media containing other potential confounding components such as salts, vitamins and proteins. LAY DESCRIPTION: In order to further advance studies in both nanomedicine and nanotoxicology, we need to continue to understand the dispersion of nanoparticles in biological fluids. These biological environments often contain a number of components such as salts, vitamins and proteins which can lead to difficulties when using traditional techniques to monitor dispersion. Here we present an alternative analysis which utilises electron microscopy. In order to use this approach statistically relevant large image datasets were collected from appropriately prepared samples of nanoparticle suspensions by implementing an automated imaging protocol. Automated analysis of these images was achieved through machine learning using two readily accessible freeware; CellProfiler and ilastik. The workflow presented enables accurate nanoparticle dispersion analysis of particles suspended in more complex biological media.

Indexed as

Machine LearningNanoparticlesAutomationBiophysical PhenomenaCulture MediaDynamic Light ScatteringFerric CompoundsImage Processing, Computer-AssistedMicroscopy, ElectronSoftwareWaterWorkflowCulture MediaFerric Compoundsferric oxideWaterAgglomerationautomated imagingmachine learningnanoparticles

Identifiers

PMID31823372
PMCPMC7496512
OpenAlexW2996458602

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.