Evidence map›Paper›PMID 26035063›Full record

ArticleScientific reports2015

A comparative analysis of high-throughput platforms for validation of a circulating microRNA signature in diabetic retinopathy.

Ryan J Farr, Andrzej S Januszewski, Mugdha V Joglekar, Helena Liang, Annie K McAulley, Alex W Hewitt, Helen E Thomas, Tom Loudovaris, Thomas W H Kay, Alicia Jenkins and 1 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 1 pooled it
3.2field-weighted citation impact, top 8% 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

38 citing papers in PubMed, 1 synthesis or guideline pooled it, 77 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

11 authors at 3 institutions in 1 country.

Ryan J FarrDiabetes and Islet biology Group, NHMRC Clinical Trials Centre, Faculty of Medicine, The University of Sydney, Level 6, Medical Foundation Building, 92-94 Parramatta Road, Camperdown, NSW 2050, Australia.
Andrzej S JanuszewskiBiomarkers Laboratory, NHMRC Clinical Trials Centre, Faculty of Medicine, The University of Sydney, Level 6, Medical Foundation Building, 92-94 Parramatta Road, Camperdown, NSW 2050, Australia.
Mugdha V JoglekarDiabetes and Islet biology Group, NHMRC Clinical Trials Centre, Faculty of Medicine, The University of Sydney, Level 6, Medical Foundation Building, 92-94 Parramatta Road, Camperdown, NSW 2050, Australia.
Helena LiangClinical Genetics Unit, Center for Eye Research Australia (CERA), The University of Melbourne, Royal Victorian Eye and Ear Hospital, Peter Howson Wing, Level 1, 32 Gisborne Street, Melbourne, VIC 3002, Australia.
Annie K McAulleyClinical Genetics Unit, Center for Eye Research Australia (CERA), The University of Melbourne, Royal Victorian Eye and Ear Hospital, Peter Howson Wing, Level 1, 32 Gisborne Street, Melbourne, VIC 3002, Australia.
Alex W HewittClinical Genetics Unit, Center for Eye Research Australia (CERA), The University of Melbourne, Royal Victorian Eye and Ear Hospital, Peter Howson Wing, Level 1, 32 Gisborne Street, Melbourne, VIC 3002, Australia.
Helen E ThomasImmunology and Diabetes Unit, St. Vincent's Institute of Medical Research, 9 Princes St, Fitzroy, VIC 3065, Australia.
Tom LoudovarisImmunology and Diabetes Unit, St. Vincent's Institute of Medical Research, 9 Princes St, Fitzroy, VIC 3065, Australia.
Thomas W H KayImmunology and Diabetes Unit, St. Vincent's Institute of Medical Research, 9 Princes St, Fitzroy, VIC 3065, Australia.
Alicia JenkinsBiomarkers Laboratory, NHMRC Clinical Trials Centre, Faculty of Medicine, The University of Sydney, Level 6, Medical Foundation Building, 92-94 Parramatta Road, Camperdown, NSW 2050, Australia.
Anandwardhan A HardikarDiabetes and Islet biology Group, NHMRC Clinical Trials Centre, Faculty of Medicine, The University of Sydney, Level 6, Medical Foundation Building, 92-94 Parramatta Road, Camperdown, NSW 2050, Australia.
University of Sydney · AUSt Vincents Institute of Medical Research · AUUniversity of Melbourne · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNAs are now increasingly recognized as biomarkers of disease progression. Several quantitative real-time PCR (qPCR) platforms have been developed to determine the relative levels of microRNAs in biological fluids. We systematically compared the detection of cellular and circulating microRNA using a standard 96-well platform, a high-content microfluidics platform and two ultra-high content platforms. We used extensive analytical tools to compute inter- and intra-run variability and concordance measured using fidelity scoring, coefficient of variation and cluster analysis. We carried out unprejudiced next generation sequencing to identify a microRNA signature for Diabetic Retinopathy (DR) and systematically assessed the validation of this signature on clinical samples using each of the above four qPCR platforms. The results indicate that sensitivity to measure low copy number microRNAs is inversely related to qPCR reaction volume and that the choice of platform for microRNA biomarker validation should be made based on the abundance of miRNAs of interest.

Indexed as

Gene Expression ProfilingHigh-Throughput Nucleotide SequencingBiomarkersCluster AnalysisDiabetic RetinopathyFluorescein AngiographyHumansMicroRNAsBiomarkersMicroRNAs

Identifiers

PMID26035063
PMCPMC4649912
OpenAlexW630853671

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.