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Analysis of the Clonality of Candida tropicalis Strains from a General Hospital in Beijing Using Multilocus Sequence Typing

Overview of attention for article published in PLOS ONE, November 2012
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Title
Analysis of the Clonality of Candida tropicalis Strains from a General Hospital in Beijing Using Multilocus Sequence Typing
Published in
PLOS ONE, November 2012
DOI 10.1371/journal.pone.0047767
Pubmed ID
Authors

Yuan Wu, Haijian Zhou, Jing Wang, Lianqing Li, Wenge Li, Zhigang Cui, Xia Chen, Ruiqi Cen, Jinxing Lu, Ying Cheng

Abstract

Multilocus sequence typing (MLST) based on six loci was used to analyze the relationship of 58 Candida tropicalis isolates from individual patients in a general hospital in Beijing, China. A total of 52 diploid sequence types (DSTs) were generated by the MLST, all of which were new to the central database. Unweighted Pair Group Method with Arithmetic Mean (UPGMA) dendrograms were constructed, which showed that the 58 isolates were distributed robustly and 6 main groups were clustered regardless of the specimen source and medical department. The minimum spanning tree (MST) of the 58 isolates (52 DSTs) and all 401 isolates (268 DSTs) in the C. tropicalis central database (http://pubmlst.org/ctropicalis/) indicated that the isolates in this study clustered in three relative pure clonal complexes, and 2 clustered with isolates from Taiwan, Belgium, Brazil, and the US. This study presents the first MLST analysis of C. tropicalis isolates from Mainland China, which may be useful for further studies on the similarity, genetic relationship, and molecular epidemiology of C. tropicalis strains worldwide.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 6%
Unknown 16 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 35%
Student > Master 3 18%
Researcher 2 12%
Student > Doctoral Student 1 6%
Lecturer 1 6%
Other 1 6%
Unknown 3 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 24%
Biochemistry, Genetics and Molecular Biology 3 18%
Immunology and Microbiology 3 18%
Medicine and Dentistry 2 12%
Unknown 5 29%