Evidence mapPaperPMID 34746836Full record

ArticleCurrent research in physiology2021

Semi-automated analysis of supraclavicular thermal images increases speed of brown adipose tissue analysis without increasing variation in results.

James M Law, David E Morris, Lindsay J Robinson, Michael E Symonds, Helen Budge

Abstract read
In one paragraph

Article in Current research in physiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
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

5 authors.

James M LawEarly Life Research Unit, Division of Child Health, Obstetrics & Gynaecology, University of Nottingham, United Kingdom.
David E MorrisBioengineering Research Group, Faculty of Engineering, University of Nottingham, United Kingdom.
Lindsay J RobinsonEarly Life Research Unit, Division of Child Health, Obstetrics & Gynaecology, University of Nottingham, United Kingdom.
Michael E SymondsEarly Life Research Unit, Division of Child Health, Obstetrics & Gynaecology, University of Nottingham, United Kingdom.
Helen BudgeEarly Life Research Unit, Division of Child Health, Obstetrics & Gynaecology, University of Nottingham, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Interest in brown adipose tissue remains high a decade after it was determined to be present outside of the neonatal period. In vivo imaging, however, has remained a challenge due to the lack of a imaging modality suitable for large healthy-volunteer studies, post-prandial investigations and vulnerable groups, such as children. Infrared thermography is increasingly accepted as a valid, non-invasive and flexible alternative but there is a wide approach to analysis between different groups. Defining the region of interest with anatomical borders rather than using a simple polygon may have advantages in terms of consistency but makes image analysis slower, limiting some applications. Our novel semi-automated method, using a custom-built graphical user interface, allows an 86% improvement in speed of image analysis (54.9 (38.3-71.4) seconds/image) without increases in variation between analysers or with repeated analysis. The improved efficiency demonstrated makes feasible larger studies, longer imaging periods or increased image acquisition frequency, providing an opportunity to study novel features of brown adipose tissue function.

Indexed as

automationBAT, Brown adipose tissuebrown adipose tissueGUI, Graphical user interfacehumaninfrared thermographymatlabmethod comparisonM, ManualROI, Region of interestSA, Semi-automatedUCP, Uncoupling protein

Identifiers

PMID34746836
PMCPMC8562194

What Socratic holds

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LicenceCC BY-NC-ND
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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 Socratic graph.