Evidence map›Paper›PMID 23855986›Full record

ArticleBMC microbiology2013

Accurate identification of fastidious Gram-negative rods: integration of both conventional phenotypic methods and 16S rRNA gene analysis.

Maria G de Melo Oliveira, Susanne Abels, Reinhard Zbinden, Guido V Bloemberg, Andrea Zbinden

Abstract readEvaluation Study
In one paragraph

Article in BMC microbiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Human Oral-AssociatedMicroorganisms · 2026
    Review
  2. Review
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. JMM case reports · 2016
    Article
  10. Article
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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

5 authors.

Maria G de Melo OliveiraInstitute of Medical Microbiology, University of Zurich, 8006 Zurich, Switzerland.
Susanne Abels
Reinhard Zbinden
Guido V Bloemberg
Andrea Zbinden

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAccurate identification of fastidious Gram-negative rods (GNR) by conventional phenotypic characteristics is a challenge for diagnostic microbiology. The aim of this study was to evaluate the use of molecular methods, e.g., 16S rRNA gene sequence analysis for identification of fastidious GNR in the clinical microbiology laboratory.

resultsA total of 158 clinical isolates covering 20 genera and 50 species isolated from 1993 to 2010 were analyzed by comparing biochemical and 16S rRNA gene sequence analysis based identification. 16S rRNA gene homology analysis identified 148/158 (94%) of the isolates to species level, 9/158 (5%) to genus and 1/158 (1%) to family level. Compared to 16S rRNA gene sequencing as reference method, phenotypic identification correctly identified 64/158 (40%) isolates to species level, mainly Aggregatibacter aphrophilus, Cardiobacterium hominis, Eikenella corrodens, Pasteurella multocida, and 21/158 (13%) isolates correctly to genus level, notably Capnocytophaga sp.; 73/158 (47%) of the isolates were not identified or misidentified.

conclusionsWe herein propose an efficient strategy for accurate identification of fastidious GNR in the clinical microbiology laboratory by integrating both conventional phenotypic methods and 16S rRNA gene sequence analysis. We conclude that 16S rRNA gene sequencing is an effective means for identification of fastidious GNR, which are not readily identified by conventional phenotypic methods.

Indexed as

Bacteriological TechniquesGram-Negative BacteriaHumansMolecular Diagnostic TechniquesMolecular Sequence DataRNA, Ribosomal, 16SSensitivity and SpecificitySequence Analysis, DNARNA, Ribosomal, 16S

Identifiers

PMID23855986
PMCPMC3717280

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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.