Evidence map›Paper›PMID 27156626›Full record

ArticleTuberculosis (Edinburgh, Scotland)2016

Serial image analysis of Mycobacterium tuberculosis colony growth reveals a persistent subpopulation in sputum during treatment of pulmonary TB.

David A Barr, Mercy Kamdolozi, Yo Nishihara, Victor Ndhlovu, Margaret Khonga, Geraint R Davies, Derek J Sloan

Abstract read
In one paragraph

Article in Tuberculosis (Edinburgh, Scotland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Trial
  2. Article
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  5. Article
  6. Clinically encountered growth phenotypes of tuberculosis-causing bacilli and theirFrontiers in cellular and infection microbiology · 2022
    Review
  7. Review
  8. Article
  9. Molecular reprogramming and phenotype switching inProceedings of the National Academy of Sciences of the United States of America · 2021
    Article
  10. Review
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Review
  17. Metabolic Perspectives on Persistence.Microbiology spectrum · 2017
    Review
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

7 authors.

David A BarrWellcome Trust Liverpool Glasgow Centre for Global Health Research, University of Liverpool, 70 Pembroke Place, Liverpool, L69 3GF, UK; Brownlee Centre for Infectious Disease, Gartnavel General Hospital, 1053 Great Western Road, Glasgow, G12 0YN, UK. Electronic address: David.Barr@liverpool.ac.uk.
Mercy KamdoloziDepartment of Microbiology, College of Medicine, University of Malawi, Mahatma Gandhi Road, Blantyre 3, Malawi. Electronic address: mkamdolozi@medcol.mw.
Yo NishiharaQueen Elizabeth Central Hospital, P.O. Box 95, Blantyre 3, Malawi; Malawi Liverpool Wellcome Trust Clinical Research Programme, College of Medicine, University of Malawi, Mahatma Gandhi Road, Blantyre 3, Malawi. Electronic address: dr.yo@me.com.
Victor NdhlovuDepartment of Microbiology, College of Medicine, University of Malawi, Mahatma Gandhi Road, Blantyre 3, Malawi. Electronic address: vndhlovu@medcol.mw.
Margaret KhongaDepartment of Microbiology, College of Medicine, University of Malawi, Mahatma Gandhi Road, Blantyre 3, Malawi. Electronic address: mkhonga@medcol.mw.
Geraint R DaviesDepartment of Microbiology, College of Medicine, University of Malawi, Mahatma Gandhi Road, Blantyre 3, Malawi; Institute of Infection and Global Health, The Ronald Ross Building, University of Liverpool, 8 West Derby Street, Liverpool, L69 7BE, UK; Malawi Liverpool Wellcome Trust Clinical Research Programme, College of Medicine, University of Malawi, Mahatma Gandhi Road, Blantyre 3, Malawi. Electronic address: gerrydavies@doctors.org.uk.
Derek J SloanLiverpool Heart and Chest Hospital, Thomas Drive, Liverpool, L14 3PE, UK; Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK. Electronic address: Derek.Sloan@lstmed.ac.uk.

Funding

Wellcome Trust
6 · The paper itself

Abstract

Faster elimination of drug tolerant 'persister' bacteria may shorten treatment of tuberculosis (TB) but no method exists to quantify persisters in clinical samples. We used automated image analysis to assess whether studying growth characteristics of individual Mycobacterium tuberculosis colonies from sputum on solid media during early TB treatment facilitates 'persister' phenotyping. As Time to Detection (TTD) in liquid culture inversely correlates with total bacterial load we also evaluated the relationship between individual colony growth parameters and TTD. Sputum from TB patients in Malawi was prepared for solid and liquid culture after 0, 2 and 4 weeks of treatment. Serial photography of agar plates was used to measure time to appearance (lag time) and radial growth rate for each colony. Mixed-effects modelling was used to analyse changing growth characteristics from serial samples. 20 patients had colony measurements recorded at ≥1 time-point. Overall lag time increased by 6.5 days between baseline and two weeks (p = 0.0001). Total colony count/ml showed typical biphasic elimination, but long lag time colonies (>20days) had slower, monophasic decline. TTD was associated with minimum lag time (time to appearance of first colony1). Slower elimination of long lag time colonies suggests that these may represent a persister subpopulation of bacilli.

Indexed as

Bacterial LoadImage Interpretation, Computer-AssistedMicroscopyAntitubercular AgentsDrug Resistance, BacterialHumansMalawiMycobacterium tuberculosisPhenotypePredictive Value of TestsSputumTime FactorsTreatment OutcomeTuberculosis, PulmonaryAntitubercular AgentsBiomarkersDrug tolerancePersistersPharmacodynamics

Identifiers

PMID27156626
PMCPMC4869592

What OpenQuestion holds

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Read underepoch 390

Registered trials

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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.