Evidence map›Paper›PMID 31339905›Full record

ArticlePloS one2019

Rapid metagenomics analysis of EMS vehicles for monitoring pathogen load using nanopore DNA sequencing.

Taylor Sheahan, Rhys Hakstol, Senthilkumar Kailasam, Graeme D Glaister, Andrew J Hudson, Hans-Joachim Wieden

Abstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. 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.

Taylor SheahanAlberta RNA Research and Training Institute, Department of Chemistry and Biochemistry, University of Lethbridge, Lethbridge, Alberta, Canada.
Rhys HakstolAlberta RNA Research and Training Institute, Department of Chemistry and Biochemistry, University of Lethbridge, Lethbridge, Alberta, Canada.
Senthilkumar KailasamAlberta RNA Research and Training Institute, Department of Chemistry and Biochemistry, University of Lethbridge, Lethbridge, Alberta, Canada.
Graeme D GlaisterAlberta RNA Research and Training Institute, Department of Chemistry and Biochemistry, University of Lethbridge, Lethbridge, Alberta, Canada.
Andrew J HudsonAlberta RNA Research and Training Institute, Department of Chemistry and Biochemistry, University of Lethbridge, Lethbridge, Alberta, Canada.
Hans-Joachim WiedenAlberta RNA Research and Training Institute, Department of Chemistry and Biochemistry, University of Lethbridge, Lethbridge, Alberta, Canada.ORCID 0000-0001-5181-6059

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pathogen monitoring, detection and removal are essential to public health and outbreak management. Systems are in place for monitoring the microbial load of hospitals and public health facilities with strategies to mitigate pathogen spread. However, no such strategies are in place for ambulances, which are tasked with transporting at-risk individuals in immunocompromised states. As standard culturing techniques require a laboratory setting, and are time consuming and labour intensive, our approach was designed to be portable, inexpensive and easy to use based on the MinION third-generation sequencing platform from Oxford Nanopore Technologies. We developed a transferable sampling-to-analysis pipeline to characterize the microbial community in emergency medical service vehicles. Our approach identified over sixty-eight organisms in ambulances to the genera level, with a proportion of these being connected with health-care associated infections, such as Clostridium spp. and Staphylococcus spp. We also monitored the microbiome of different locations across three ambulances over time, and examined the dynamic community of microorganisms found in emergency medical service vehicles. Observed differences identified hot spots, which may require heightened monitoring and extensive cleaning. Through metagenomics analysis it is also possible to identify how microorganisms spread between patients and colonize an ambulance over time. The sequencing results aid in the development of practices to mitigate disease spread, while also providing a useful tool for outbreak prediction through ongoing analysis of the ambulance microbiome to identify new and emerging pathogens. Overall, this pipeline allows for the tracking and monitoring of pathogenic microorganisms of epidemiological interest, including those related to health-care associated infections.

Indexed as

MicrobiotaAmbulancesBiological MonitoringClostridiumCross InfectionDNA, BacterialMetagenomicsNanopore SequencingStaphylococcusDNA, Bacterial

Identifiers

PMID31339905
PMCPMC6655686

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.