Evidence map›Paper›PMID 27149069›Full record

ArticlePloS one2016

A Novel Computerized Cell Count Algorithm for Biofilm Analysis.

Mareike Klinger-Strobel, Herbert Suesse, Dagmar Fischer, Mathias W Pletz, Oliwia Makarewicz

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
3.0field-weighted citation impact, top 9% 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

15 citing papers in PubMed, 30 citations in OpenAlex.

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  10. Clinically Approved Drugs Inhibit theFrontiers in microbiology · 2019
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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 4 institutions in 1 country.

Mareike Klinger-StrobelCenter for Infectious Diseases and Infection Control, Jena University Hospital, Erlanger Allee 101, 07747, Jena, Germany.
Herbert SuesseComputer Vision Group, Friedrich-Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany.
Dagmar FischerInstitute of Pharmacy, Friedrich-Schiller University Jena, Otto-Schott-Strasse 41, 07745, Jena, Germany.
Mathias W PletzCenter for Infectious Diseases and Infection Control, Jena University Hospital, Erlanger Allee 101, 07747, Jena, Germany.
Oliwia MakarewiczCenter for Infectious Diseases and Infection Control, Jena University Hospital, Erlanger Allee 101, 07747, Jena, Germany.
Jena University Hospital · DEFriedrich Schiller University Jena · DEInfectoGnostics Research Campus Jena · DESchott (Germany) · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biofilms are the preferred sessile and matrix-embedded life form of most microorganisms on surfaces. In the medical field, biofilms are a frequent cause of treatment failure because they protect the bacteria from antibiotics and immune cells. Antibiotics are selected according to the minimal inhibitory concentration (MIC) based on the planktonic form of bacteria. Determination of the minimal biofilm eradicating concentration (MBEC), which can be up to 1,000-fold greater than the MIC, is not currently conducted as routine diagnostic testing, primarily because of the methodical hurdles of available biofilm assessing protocols that are time- and cost-consuming. Comparative analysis of biofilms is also limited as most quantitative methods such as crystal violet staining are indirect and highly imprecise. In this paper, we present a novel algorithm for assessing biofilm resistance to antibiotics that overcomes several of the limitations of alternative methods. This algorithm aims for a computer-based analysis of confocal microscope 3D images of biofilms after live/dead stains providing various biofilm parameters such as numbers of viable and dead cells and their vertical distributions within the biofilm, or biofilm thickness. The performance of this algorithm was evaluated using computer-simulated 2D and 3D images of coccal and rodent cells varying different parameters such as cell density, shading or cell size. Finally, genuine biofilms that were untreated or treated with nitroxoline or colistin were analyzed and the results were compared with quantitative microbiological standard methods. This novel algorithm allows a direct, fast and reproducible analysis of biofilms after live/dead staining. It performed well in biofilms of moderate cell densities in a 2D set-up however the 3D analysis remains still imperfect and difficult to evaluate. Nevertheless, this is a first try to develop an easy but conclusive tool that eventually might be implemented into routine diagnostics to determine the MBEC and to improve outcomes of patients with biofilm-associated infections.

Indexed as

AlgorithmsAnti-Bacterial AgentsBacteriaBiofilmsCell CountColistinComputer SimulationMicrobial Sensitivity TestsPlanktonAnti-Bacterial AgentsColistin

Identifiers

PMID27149069
PMCPMC4858220
OpenAlexW2345735071

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.