Evidence map›Paper›PMID 32546123›Full record

ArticleBMC microbiology2020

The microbiome of diabetic foot ulcers: a comparison of swab and tissue biopsy wound sampling techniques using 16S rRNA gene sequencing.

J Travis, M Malone, H Hu, A Baten, K Johani, F Huygens, K Vickery, K Benkendorff

Open access · goldAbstract readComparative Study
In one paragraph

Article in BMC microbiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
3.0field-weighted citation impact, top 8% 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

26 citing papers in PubMed, 37 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. Observational
  5. Article
  6. Article
  7. Article
  8. Article
  9. Skin microbiota and diabetic foot ulcers.Frontiers in microbiology · 2025
    Review
  10. Iranian journal of microbiology · 2024
    Article
  11. Article
  12. Article
  13. Article
  14. Microbiome of limb-threatening diabetic foot ulcers indicates the association of fastidious Stenotrophomonas and major amputation.Journal of microbiology, immunology, and infection = Wei mian yu gan ran za zhi · 2024
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Prevalence and Predictors ofOpen forum infectious diseases · 2022
    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

8 authors at 7 institutions in 3 countries.

J TravisSchool of Environment, Science and Engineering, Southern Cross University, Lismore, NSW, Australia.
M MaloneLimb Preservation and Wound Research Academic Unit, Western Sydney LHD, Liverpool, Sydney, NSW, 2170, Australia.
H HuSurgical Infection Research Group Faculty of Medicine and Health Sciences, Macquarie University, Sydney, Australia.
A BatenAgresearch, Grasslands Research Centre, Palmerston North, New Zealand.
K JohaniIngham Institute of Applied Medical Research, Liverpool, Sydney, NSW, 2170, Australia.
F HuygensInstitute of Health and Biomedical Innovation, Queensland University of Technology, Herston, QLD, Australia.
K VickerySurgical Infection Research Group Faculty of Medicine and Health Sciences, Macquarie University, Sydney, Australia.
K BenkendorffSchool of Environment, Science and Engineering, Southern Cross University, Lismore, NSW, Australia. Kirsten.benkendorff@scu.edu.au.ORCID 0000-0003-4052-3888
Macquarie University · AUAgResearch · NZCamden and Campbelltown Hospitals · AUCoffs Harbour City Council · AUQueensland University of Technology · AURiyadh Armed Forces Hospital · SASouthern Cross University · AU

Funding

Wound Management and Innovation Co-operative Research Centre SP41-1
6 · The paper itself

Abstract

backgroundHealth-care professionals need to collect wound samples to identify potential pathogens that contribute to wound infection. Obtaining appropriate samples from diabetic foot ulcers (DFUs) where there is a suspicion of infection is of high importance. Paired swabs and tissue biopsies were collected from DFUs and both sampling techniques were compared using 16S rRNA gene sequencing.

resultsMean bacterial abundance determined using quantitative polymerase chain reaction (qPCR) was significantly lower in tissue biopsies (p = 0.03). The mean number of reads across all samples was significantly higher in wound swabs [Formula: see text] = 32,014) compared to tissue ([Formula: see text] = 15,256, p = 0.001). Tissue biopsies exhibited greater overall diversity of bacteria relative to swabs (Shannon's H diversity p = 0.009). However, based on a presence/absence analysis of all paired samples, the frequency of occurrence of bacteria from genera of known and potential pathogens was generally higher in wound swabs than tissue biopsies. Multivariate analysis identified significantly different bacterial communities in swabs compared to tissue (p = 0.001). There was minimal correlation between paired wound swabs and tissue biopsies in the number and types of microorganisms. RELATE analysis revealed low concordance between paired DFU swab and tissue biopsy samples (Rho = 0.043, p = 0.34).

conclusionsUsing 16S rRNA gene sequencing this study identifies the potential for using less invasive swabs to recover high relative abundances of known and potential pathogen genera from DFUs when compared to the gold standard collection method of tissue biopsy.

Indexed as

BacteriaBiopsyDiabetic FootDNA, BacterialDNA, RibosomalHumansMultivariate AnalysisPhylogenyReal-Time Polymerase Chain ReactionRNA, Ribosomal, 16SSequence Analysis, DNADNA, BacterialDNA, RibosomalRNA, Ribosomal, 16S16S rRNA gene sequencingDiabetic foot ulcersSwabsTissue biopsiesWound pathogensWound sampling methods

Identifiers

PMID32546123
PMCPMC7296698
OpenAlexW3034579476

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.