Evidence map›Paper›PMID 18275600›Full record

ArticleBMC microbiology2008

Optimized molecular resolution of cross-contamination alerts in clinical mycobacteriology laboratories.

Ana Martín, Marta Herranz, Miguel Martínez Lirola, Rosa Fernández Fernández, INDAL-TB group, Emilio Bouza, Darío García de Viedma

Abstract read
In one paragraph

Article in BMC microbiology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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

7 authors.

Ana MartínServicio de Microbiología Clínica y Enfermedades Infecciosas, Hospital Gregorio Marañón, Universidad Complutense, Madrid, CIBER-Enfermedades Respiratorias CIBERES, Spain. amartsan@yahoo.es
Marta Herranz
Miguel Martínez Lirola
Rosa Fernández Fernández
INDAL-TB group
Emilio Bouza
Darío García de Viedma

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe phenomenon of misdiagnosing tuberculosis (TB) by laboratory cross-contamination when culturing Mycobacterium tuberculosis (MTB) has been widely reported and it has an obvious clinical, therapeutic and social impact. The final confirmation of a cross-contamination event requires the molecular identification of the same MTB strain cultured from both the potential source of the contamination and from the false-positive candidate. The molecular tool usually applied in this context is IS6110-RFLP which takes a long time to provide an answer, usually longer than is acceptable for microbiologists and clinicians to make decisions. Our purpose in this study is to evaluate a novel PCR-based method, MIRU-VNTR as an alternative to assure a rapid and optimized analysis of cross-contamination alerts.

resultsMIRU-VNTR was prospectively compared with IS6110-RFLP for clarifying 19 alerts of false positivity from other laboratories. MIRU-VNTR highly correlated with IS6110-RFLP, reduced the response time by 27 days and clarified six alerts unresolved by RFLP. Additionally, MIRU-VNTR revealed complex situations such as contamination events involving polyclonal isolates and a false-positive case due to the simultaneous cross-contamination from two independent sources.

conclusionUnlike standard RFLP-based genotyping, MIRU-VNTR i) could help reduce the impact of a false positive diagnosis of TB, ii) increased the number of events that could be solved and iii) revealed the complexity of some cross-contamination events that could not be dissected by IS6110-RFLP.

Indexed as

Interspersed Repetitive SequencesMinisatellite RepeatsDiagnostic ErrorsFalse Positive ReactionsHumansMycobacterium tuberculosisPolymerase Chain ReactionPolymorphism, Restriction Fragment LengthProspective StudiesSpecimen HandlingTuberculosis

Identifiers

PMID18275600
PMCPMC2291055

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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.