ArticleBMC microbiology2009
Performance of optimized McRAPD in identification of 9 yeast species frequently isolated from patient samples: potential for automation.
Article in BMC microbiology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed, 11 citations in OpenAlex.
- Dynamic time warping assessment of high-resolution melt curves provides a robust metric for fungal identification.PloS one · 2017Article
- Epidemic Achromobacter xylosoxidans strain among Belgian cystic fibrosis patients and review of literature.BMC microbiology · 2016Review
- Rapid identification of medically important Candida isolates using high resolution melting analysis.PloS one · 2015Article
- Usefulness of PCR-HRMA in identification of non-fermentative Gram-negative rods recovered from patients suffering from cystic fibrosis or chronic obstructive pulmonary disease.Folia microbiologica · 2014Article
- Usefulness of McRAPD for typing and importance of biofilm production in a case of nosocomial ventriculoperitoneal shunt infection caused by Candida lusitaniae.Folia microbiologica · 2011Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRapid, easy, economical and accurate species identification of yeasts isolated from clinical samples remains an important challenge for routine microbiological laboratories, because susceptibility to antifungal agents, probability to develop resistance and ability to cause disease vary in different species. To overcome the drawbacks of the currently available techniques we have recently proposed an innovative approach to yeast species identification based on RAPD genotyping and termed McRAPD (Melting curve of RAPD). Here we have evaluated its performance on a broader spectrum of clinically relevant yeast species and also examined the potential of automated and semi-automated interpretation of McRAPD data for yeast species identification.
resultsA simple fully automated algorithm based on normalized melting data identified 80% of the isolates correctly. When this algorithm was supplemented by semi-automated matching of decisive peaks in first derivative plots, 87% of the isolates were identified correctly. However, a computer-aided visual matching of derivative plots showed the best performance with average 98.3% of the accurately identified isolates, almost matching the 99.4% performance of traditional RAPD fingerprinting.
conclusionSince McRAPD technique omits gel electrophoresis and can be performed in a rapid, economical and convenient way, we believe that it can find its place in routine identification of medically important yeasts in advanced diagnostic laboratories that are able to adopt this technique. It can also serve as a broad-range high-throughput technique for epidemiological surveillance.
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