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Statements

Subject Item
n2:RIV%2F61989592%3A15110%2F09%3A00009795%21RIV10-MZ0-15110___
rdf:type
skos:Concept n13:Vysledek
dcterms:description
Background. Rapid, 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. Results. A 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 id Background. Rapid, 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. Results. A 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 id
dcterms:title
Performance of optimized McRAPD in identification of 9 yeast species frequently isolated from patient samples: potential for automation Performance of optimized McRAPD in identification of 9 yeast species frequently isolated from patient samples: potential for automation
skos:prefLabel
Performance of optimized McRAPD in identification of 9 yeast species frequently isolated from patient samples: potential for automation Performance of optimized McRAPD in identification of 9 yeast species frequently isolated from patient samples: potential for automation
skos:notation
RIV/61989592:15110/09:00009795!RIV10-MZ0-15110___
n5:aktivita
n14:P
n5:aktivity
P(NR8365)
n5:cisloPeriodika
234
n5:dodaniDat
n9:2010
n5:domaciTvurceVysledku
n7:1312553 n7:9599134 n7:8021066 n7:5610850 n7:4123263
n5:druhVysledku
n11:J
n5:duvernostUdaju
n16:S
n5:entitaPredkladatele
n17:predkladatel
n5:idSjednocenehoVysledku
333061
n5:idVysledku
RIV/61989592:15110/09:00009795
n5:jazykVysledku
n12:eng
n5:klicovaSlova
pathogenic yeasts; identification; McRAPD; melting analysis
n5:klicoveSlovo
n6:McRAPD n6:identification n6:pathogenic%20yeasts n6:melting%20analysis
n5:kodStatuVydavatele
GB - Spojené království Velké Británie a Severního Irska
n5:kontrolniKodProRIV
[DF4336B5810E]
n5:nazevZdroje
BMC Microbiology
n5:obor
n18:EE
n5:pocetDomacichTvurcuVysledku
5
n5:pocetTvurcuVysledku
6
n5:projekt
n15:NR8365
n5:rokUplatneniVysledku
n9:2009
n5:svazekPeriodika
9
n5:tvurceVysledku
Trtková, Jitka Pavlíček, P. Koukalová, Dagmar Rusková, Lenka Hamal, Petr Raclavský, Vladislav
s:issn
1471-2180
s:numberOfPages
21
n10:organizacniJednotka
15110