Evidence map›Paper›PMID 37790733›Full record

ArticleBiofilm2023

Multiscale spatial segregation analysis in digital images of biofilms.

Iztok Dogsa, Ines Mandic-Mulec

Abstract read
In one paragraph

Article in Biofilm, 2023. 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. Review
  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

2 authors.

Iztok DogsaChair of Microbiology, Department of Microbiology, Biotechnical Faculty, University of Ljubljana, Večna pot 111, 1000, Ljubljana, EU, Slovenia.
Ines Mandic-MulecChair of Microbiology, Department of Microbiology, Biotechnical Faculty, University of Ljubljana, Večna pot 111, 1000, Ljubljana, EU, Slovenia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Quantifying the degree of spatial segregation of two bacterial strains in mixed biofilms is an important topic in microbiology. Spatial segregation is dependent on spatial scale as two strains may appear to be well mixed if observed from a distance, but a closer look can reveal strong separation. Typically, this information is encoded in a digital image that represents the binary system, e.g., a microscopy image of a two species biofilm. To decode spatial segregation information, we have developed quantitative measures for evaluating the degree of the spatial scale-dependent segregation of two bacterial strains in a digital image. The constructed algorithm is based on the new segregation measures and overcomes drawbacks of existing approaches for biofilm segregation analysis. The new approach is implemented in a freely available software and was successfully applied to biofilms of two strains and bacterial suspensions for detection of the different spatial scale-dependent segregation levels.

Identifiers

PMID37790733
PMCPMC10542597

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.