Evidence map›Paper›PMID 27141321›Full record

ArticleJournal of pathology informatics2016

Quantitative analysis of myocardial tissue with digital autofluorescence microscopy.

Thomas Jensen, Henrik Holten-Rossing, Ida M H Svendsen, Christina Jacobsen, Ben Vainer

Open access · goldAbstract read
In one paragraph

Article in Journal of pathology informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.5field-weighted citation impact, top 29% 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

5 citing papers in PubMed, 12 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

5 authors at 2 institutions in 1 country.

Thomas JensenDepartment of Pathology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.
Henrik Holten-RossingDepartment of Pathology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.
Ida M H SvendsenDepartment of Forensic Pathology, University of Copenhagen, Copenhagen, Denmark.
Christina JacobsenDepartment of Forensic Pathology, University of Copenhagen, Copenhagen, Denmark.
Ben VainerDepartment of Pathology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.
University of Copenhagen · DKRigshospitalet · DK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe opportunity offered by whole slide scanners of automated histological analysis implies an ever increasing importance of digital pathology. To go beyond the importance of conventional pathology, however, digital pathology may need a basic histological starting point similar to that of hematoxylin and eosin staining in conventional pathology. This study presents an automated fluorescence-based microscopy approach providing highly detailed morphological data from unstained microsections. This data may provide a basic histological starting point from which further digital analysis including staining may benefit.

methodsThis study explores the inherent tissue fluorescence, also known as autofluorescence, as a mean to quantitate cardiac tissue components in histological microsections. Data acquisition using a commercially available whole slide scanner and an image-based quantitation algorithm are presented.

resultsIt is shown that the autofluorescence intensity of unstained microsections at two different wavelengths is a suitable starting point for automated digital analysis of myocytes, fibrous tissue, lipofuscin, and the extracellular compartment. The output of the method is absolute quantitation along with accurate outlines of above-mentioned components. The digital quantitations are verified by comparison to point grid quantitations performed on the microsections after Van Gieson staining.

conclusionThe presented method is amply described as a prestain multicomponent quantitation and outlining tool for histological sections of cardiac tissue. The main perspective is the opportunity for combination with digital analysis of stained microsections, for which the method may provide an accurate digital framework.

Indexed as

Autofluorescencedigital pathologyhistomorphometry

Identifiers

PMID27141321
PMCPMC4837794
OpenAlexW2333988358

What OpenQuestion holds

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LicenceCC BY-NC-SA
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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 OpenQuestion graph.