Evidence map›Paper›PMID 36748522›Full record

ArticleMicrobial genomics2022

Novel application of metagenomics for the strain-level detection of bacterial contaminants within non-sterile industrial products - a retrospective, real-time analysis.

Edward Cunningham-Oakes, Tom Pointon, Barry Murphy, Stuart Campbell-Lee, Thomas R Connor, Eshwar Mahenthiralingam

Abstract read
In one paragraph

Article in Microbial genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

6 authors.

Edward Cunningham-OakesMicrobiomes, Microbes and Informatics Group, Organisms and Environment Division, School of Biosciences, Cardiff University, CF10 3AX, UK.
Tom PointonUnilever Research and Development, Port Sunlight, Bebbington, CH63 3JW, UK.
Barry MurphyUnilever Research and Development, Port Sunlight, Bebbington, CH63 3JW, UK.
Stuart Campbell-LeeUnilever Research and Development, Port Sunlight, Bebbington, CH63 3JW, UK.
Thomas R ConnorMicrobiomes, Microbes and Informatics Group, Organisms and Environment Division, School of Biosciences, Cardiff University, CF10 3AX, UK.
Eshwar MahenthiralingamMicrobiomes, Microbes and Informatics Group, Organisms and Environment Division, School of Biosciences, Cardiff University, CF10 3AX, UK.

Funding

Biotechnology and Biological Sciences Research Council BB/M009122/1Medical Research Council MR/L015080/1
6 · The paper itself

Abstract

The home and personal care (HPC) industry generally relies on initial cultivation and subsequent biochemical testing for the identification of microorganisms in contaminated products. This process is slow (several days for growth), labour intensive, and misses organisms which fail to revive from the harsh environment of preserved consumer products. Since manufacturing within the HPC industry is high-throughput, the process of identification of microbial contamination could benefit from the multiple cultivation-independent methodologies that have developed for the detection and analysis of microbes. We describe a novel workflow starting with automated DNA extraction directly from a HPC product, and subsequently applying metagenomic methodologies for species and strain-level identification of bacteria. The workflow was validated by application to a historic microbial contamination of a general-purpose cleaner (GPC). A single strain of

Indexed as

BacteriaMetagenomeMetagenomicsRetrospective StudiesWorkflowbioinformaticsmetagenome binningmetagenomicsmicrobial contaminationstrain identification

Identifiers

PMID36748522
PMCPMC9836090

What OpenQuestion holds

Textmetadata
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.