Evidence map›Paper›PMID 17384212›Full record

ArticleThe Journal of molecular diagnostics : JMD2007

Single nucleotide polymorphism profiling assay to confirm the identity of human tissues.

Ronald Huijsmans, Jan Damen, Hans van der Linden, Mirjam Hermans

Open access · bronzeAbstract read
In one paragraph

Article in The Journal of molecular diagnostics : JMD, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.3field-weighted citation impact, top 42% 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

8 citing papers in PubMed, 14 citations in OpenAlex.

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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

4 authors at 1 institution in 1 country.

Ronald HuijsmansMultidisciplinary Laboratory of Molecular Diagnostics, Jeroen Bosch Hospital, 's-Hertogenbosch, The Netherlands.
Jan Damen
Hans van der Linden
Mirjam Hermans
Jeroen Bosch Ziekenhuis · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To identify issues of sample mix-ups, various molecular techniques are currently used. These techniques, however, are time consuming and require experience and/or DNA sequencing equipment or have a relatively high risk of errors because of contamination. Therefore, a quick and straightforward single nucleotide polymorphism (SNP) profiling assay was developed to link human tissues to a source. SNPs are common sequence variations in the human genome, and each individual has a unique combination of these nucleotide variations. Using potentially mislabeled paraffin-embedded tissues, DNA was extracted and SNP profiles were determined by real-time polymerase chain reaction analysis of the purified DNA using a selection of 10 commercially available SNP amplification assays. These profiles were compared with profiles of the supposed owners. All issues (34 in total) of potential sample mix-ups during the last 3 years were adequately solved, with six cases described here. The SNP profiling assay provides a quick (within 24 hours), easy, and reliable way to link human samples to a source, without polymerase chain reaction postprocessing. The chance for two randomly chosen individuals to have an identical profile is 1 in 18,000. Solving potential sample mix-ups will secure downstream evaluations and critical decisions concerning the patients involved.

Indexed as

AllelesBreast NeoplasmsFemaleFluorescent DyesGene Expression ProfilingGene FrequencyGenetic TestingHumansLoss of HeterozygosityParaffin EmbeddingPolymorphism, Single NucleotideReproducibility of ResultsSpecimen HandlingFluorescent Dyes

Identifiers

PMID17384212
PMCPMC1867440
OpenAlexW2017945650

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.