Evidence map›Paper›PMID 26555667›Full record

ArticlePharmaceutical research2016

Correcting the Relative Bias of Light Obscuration and Flow Imaging Particle Counters.

Dean C Ripple, Zhishang Hu

Abstract read
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In one paragraph

Article in Pharmaceutical research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
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

10 citing papers in PubMed.

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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

2 authors.

Dean C RippleBiomolecular Measurement Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA. dean.ripple@nist.gov.
Zhishang HuCenter for Computational and Systems Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeIndustry and regulatory bodies desire more accurate methods for counting and characterizing particles. Measurements of proteinaceous-particle concentrations by light obscuration and flow imaging can differ by factors of ten or more.

methodsWe propose methods to correct the diameters reported by light obscuration and flow imaging instruments. For light obscuration, diameters were rescaled based on characterization of the refractive index of typical particles and a light scattering model for the extinction efficiency factor. The light obscuration models are applicable for either homogeneous materials (e.g., silicone oil) or for chemically homogeneous, but spatially non-uniform aggregates (e.g., protein aggregates). For flow imaging, the method relied on calibration of the instrument with silica beads suspended in water-glycerol mixtures.

resultsThese methods were applied to a silicone-oil droplet suspension and four particle suspensions containing particles produced from heat stressed and agitated human serum albumin, agitated polyclonal immunoglobulin, and abraded ethylene tetrafluoroethylene polymer. All suspensions were measured by two flow imaging and one light obscuration apparatus. Prior to correction, results from the three instruments disagreed by a factor ranging from 3.1 to 48 in particle concentration over the size range from 2 to 20 μm. Bias corrections reduced the disagreement from an average factor of 14 down to an average factor of 1.5.

conclusionsThe methods presented show promise in reducing the relative bias between light obscuration and flow imaging.

Indexed as

Diagnostic ImagingFluorocarbonsHumansImmunoglobulinsLightParticle SizePolymersPolytetrafluoroethyleneSerum AlbuminSilicone OilsSuspensionsFluorocarbonsImmunoglobulinspoly(ethylene-tetrafluoroethylene)PolymersPolytetrafluoroethyleneSerum AlbuminSilicone OilsSuspensionsflow imagingflow microscopylight obscurationparticleprotein aggregate

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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.