Evidence map›Paper›PMID 16891478›Full record

ArticleJournal of clinical microbiology2006

Multicenter comparison of nucleic acid extraction methods for detection of severe acute respiratory syndrome coronavirus RNA in stool specimens.

A Petrich, J Mahony, S Chong, G Broukhanski, F Gharabaghi, G Johnson, L Louie, K Luinstra, B Willey, P Akhaven and 10 more

Open access · greenAbstract readComparative StudyEvaluation StudyMulticenter Study
In one paragraph

Article in Journal of clinical microbiology, 2006. 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
2.3field-weighted citation impact, top 13% 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, 59 citations in OpenAlex.

  1. Article
  2. Mutation profile of SARS-CoV-2 genome in a sample from the first year of the pandemic in Colombia.Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases · 2022
    Article
  3. Review
  4. Article
  5. SARS-CoV, MERS-CoV and SARS-CoV-2: A Diagnostic Challenge.Measurement : journal of the International Measurement Confederation · 2021
    Article
  6. Review
  7. COVID-19: molecular diagnostics overview.Journal of molecular medicine (Berlin, Germany) · 2020
    Review
  8. Article
  9. Performance of a commercial assay for detecting and quantifying HEV RNA in faeces.Journal of clinical virology : the official publication of the Pan American Society for Clinical Virology · 2018
    Article
  10. Article
  11. Article
  12. Detection of small amounts of human adenoviruses in stools: comparison of a new immuno real-time PCR assay with classical tools.Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases · 2014
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

20 authors at 8 institutions in 2 countries.

A PetrichSt. Joseph's Healthcare, L424, Microbiology, 50 Charlton Ave. East, Hamilton, ON L8N 4A6, Canada. petricha@mcmaster.ca
J Mahony
S Chong
G Broukhanski
F Gharabaghi
G Johnson
L Louie
K Luinstra
B Willey
P Akhaven
L Chui
F Jamieson
M Louie
T Mazzulli
R Tellier
M Smieja
W Cai
M Chernesky
S E Richardson
Ontario Laboratory Working Group for the Rapid Diagnosis of Emerging Infections
St. Joseph's Hospital · CAMount Sinai Hospital · CAMinistry of Health and Long Term Care · CAGreat Ormond Street Hospital · GBHospital for Sick Children · CAMcMaster University · CASickKids Foundation · CAWildlife Habitat Canada (Canada) · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The emergence of a novel coronavirus (CoV) as the cause of severe acute respiratory syndrome (SARS) catalyzed the development of rapid diagnostic tests. Stool samples have been shown to be appropriate for diagnostic testing for SARS CoV, although it has been recognized to be a heterogeneous and difficult sample that contains amplification inhibitors. Limited information on the efficiency of extraction methods for the purification and concentration of SARS CoV RNA from stool samples is available. Our study objectives were to determine the optimal extraction method for SARS CoV RNA detection and to examine the effect of increased specimen volume for the detection of SARS CoV RNA in stool specimens. We conducted a multicenter evaluation of four automated and four manual extraction methods using dilutions of viral lysate in replicate mock stool samples, followed by quantitation of SARS CoV RNA using real-time reverse transcriptase PCR. The sensitivities of the manual methods ranged from 50% to 100%, with the Cortex Biochem Magazorb method, a magnetic bead isolation method, allowing detection of all 12 positive samples. The sensitivities of the automated methods ranged from 75% to 100%. The bioMérieux NucliSens automated extractor and miniMag extraction methods each had a sensitivity of 100%. Examination of the copy numbers detected and the generation of 10-fold dilutions of the extracted material indicated that a number of extraction methods retained inhibitory substances that prevented optimal amplification. Increasing the volume of sample input did improve detection. This information could be useful for the extraction of other RNA viruses from stool samples and demonstrates the need to evaluate extraction methods for different specimen types.

Indexed as

Molecular Diagnostic TechniquesFecesHumansReverse Transcriptase Polymerase Chain ReactionRNA, ViralSensitivity and SpecificitySevere Acute Respiratory SyndromeSevere acute respiratory syndrome-related coronavirusRNA, Viral

Identifiers

PMID16891478
PMCPMC1594626
OpenAlexW1981981071

What OpenQuestion holds

Textmetadata
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.