Evidence map›Paper›PMID 29715298›Full record

ArticlePloS one2018

Fast and simple tool for the quantification of biofilm-embedded cells sub-populations from fluorescent microscopic images.

Mikhail I Bogachev, Vladimir Yu Volkov, Oleg A Markelov, Elena Yu Trizna, Diana R Baydamshina, Vladislav Melnikov, Regina R Murtazina, Pavel V Zelenikhin, Irshad S Sharafutdinov, Airat R Kayumov

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
12.6field-weighted citation impact, top 1% 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

34 citing papers in PubMed, 102 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

10 authors at 3 institutions in 1 country.

Mikhail I BogachevRadio Systems Department & Biomedical Engineering Research Center, St. Petersburg Electrotechnical University, St. Petersburg, Russia.ORCID 0000-0002-0356-5651
Vladimir Yu VolkovRadio Systems Department & Biomedical Engineering Research Center, St. Petersburg Electrotechnical University, St. Petersburg, Russia.
Oleg A MarkelovRadio Systems Department & Biomedical Engineering Research Center, St. Petersburg Electrotechnical University, St. Petersburg, Russia.ORCID 0000-0002-6099-8867
Elena Yu TriznaInstitute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia.ORCID 0000-0002-9881-0559
Diana R BaydamshinaInstitute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia.ORCID 0000-0002-6935-5745
Vladislav MelnikovRadio Systems Department & Biomedical Engineering Research Center, St. Petersburg Electrotechnical University, St. Petersburg, Russia.
Regina R MurtazinaInstitute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia.
Pavel V ZelenikhinInstitute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia.ORCID 0000-0003-1971-1745
Irshad S SharafutdinovInstitute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia.
Airat R KayumovInstitute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia.ORCID 0000-0001-7195-1557
Kazan Federal University · RUSaint Petersburg State Electrotechnical University · RUSaint Petersburg State University of Aerospace and Instrumentation · RU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fluorescent staining is a common tool for both quantitative and qualitative assessment of pro- and eukaryotic cells sub-population fractions by using microscopy and flow cytometry. However, direct cell counting by flow cytometry is often limited, for example when working with cells rigidly adhered either to each other or to external surfaces like bacterial biofilms or adherent cell lines and tissue samples. An alternative approach is provided by using fluorescent microscopy and confocal laser scanning microscopy (CLSM), which enables the evaluation of fractions of cells subpopulations in a given sample. For the quantitative assessment of cell fractions in microphotographs, we suggest a simple two-step algorithm that combines single cells selection and the statistical analysis. To facilitate the first step, we suggest a simple procedure that supports finding the balance between the detection threshold and the typical size of single cells based on objective cell size distribution analysis. Based on a series of experimental measurements performed on bacterial and eukaryotic cells under various conditions, we show explicitly that the suggested approach effectively accounts for the fractions of different cell sub-populations (like the live/dead staining in our samples) in all studied cases that are in good agreement with manual cell counting on microphotographs and flow cytometry data. This algorithm is implemented as a simple software tool that includes an intuitive and user-friendly graphical interface for the initial adjustment of algorithm parameters to the microphotographs analysis as well as for the sequential analysis of homogeneous series of similar microscopic images without further user intervention. The software tool entitled BioFilmAnalyzer is freely available online at https://bitbucket.org/rogex/biofilmanalyzer/downloads/.

Indexed as

AlgorithmsBacteriaBiofilmsColonic NeoplasmsFlow CytometryHumansImage Processing, Computer-AssistedMicroscopy, FluorescenceSingle-Cell AnalysisSoftwareTumor Cells, Cultured

Identifiers

PMID29715298
PMCPMC5929543
OpenAlexW2951506323

What OpenQuestion holds

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LicenceCC BY
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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.