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J Clin Microbiol. 2014 Feb;52(2):572-7. doi: 10.1128/JCM.02543-13. Epub 2013 Dec 11.

Yeast identification algorithm based on use of the Vitek MS system selectively supplemented with ribosomal DNA sequencing: proposal of a reference assay for invasive fungal surveillance programs in China.

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Department of Clinical Laboratory, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.


Sequence analysis of the internal transcribed spacer (ITS) region was employed as the gold standard method for yeast identification in the China Hospital Invasive Fungal Surveillance Net (CHIF-NET). It has subsequently been found that matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) is potentially a more practical approach for this purpose. In the present study, the performance of the Vitek MS v2.0 system for the identification of yeast isolates collected from patients with invasive fungal infections in the 2011 CHIF-NET was evaluated. A total of 1,243 isolates representing 31 yeast species were analyzed, and the identification results by the Vitek MS v2.0 system were compared to those obtained by ITS sequence analysis. By the Vitek MS v2.0 system, 96.7% (n = 1,202) of the isolates were correctly assigned to the species level and 0.2% (n = 2) of the isolates were identified to the genus level, while 2.4% (n = 30) and 0.7% (n = 9) of the isolates were unidentified and misidentified, respectively. After retesting of the unidentified and misidentified strains, 97.3% (n = 1,209) of the isolates were correctly identified to the species level. Based on these results, a testing algorithm that combines the use of the Vitek MS system with selected supplementary ribosomal DNA (rDNA) sequencing was developed and validated for yeast identification purposes. By employing this algorithm, 99.7% (1,240/1,243) of the study isolates were accurately identified with the exception of two isolates of Candida fermentati and one isolate of Cryptococcus gattii. In conclusion, the proposed identification algorithm could be practically implemented in strategic programs of fungal infection surveillance.

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