2006
DOI: 10.1128/jcm.02364-05
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Multilocus Variable-Number Tandem-Repeat Analysis for Investigation of Clostridium difficile Transmission in Hospitals

Abstract: Clostridium difficile is a major cause of antibiotic-associated gastrointestinal illness. Recently, an increased incidence of hospital-acquired infections with severe outcomes has been reported in North America and Europe. Current molecular-typing methods for detection of outbreaks and nosocomial transmission are labor-intensive, subjective, or insufficiently discriminatory to differentiate between closely related strains. This report describes the development of multilocus variable-number tandem-repeat (VNTR)… Show more

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Cited by 118 publications
(133 citation statements)
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“…1) and AFLP results (not shown). Using different MLVA targets and different analytic methods, others have found MLVA to be a useful tool in tracking health care-related C. difficile infections (20).…”
Section: Discussionmentioning
confidence: 99%
“…1) and AFLP results (not shown). Using different MLVA targets and different analytic methods, others have found MLVA to be a useful tool in tracking health care-related C. difficile infections (20).…”
Section: Discussionmentioning
confidence: 99%
“…For MLVA, five regions with short tandem repeats were sequenced: A6Cd, B7Cd, C6Cd, G8Cd [17] and CDR60 [18], with a change of reverse primer for G8Cd, as described elsewhere [19]. The number of tandem repeats was counted manually after software processing (Sequencing Analysis Software, Applied Biosystems).…”
Section: Methodsmentioning
confidence: 99%
“…Multilocus variable-number tandem repeat analysis (MLVA) is a well-known typing method that has been successfully developed for many bacterial species or for serovars within a species (Liang et al, 2007;Liao et al, 2006;Lindstedt et al, 2003;Marsh et al, 2006;Noller et al, 2003). Based on the analysis of multilocus variable-number tandem repeats (VNTRs), MLVA can be an extremely powerful method for distinguishing between closely related isolates in the investigation of disease outbreaks (Liang et al, 2007;Noller et al, 2003;Torpdahl et al, 2007).…”
Section: Introductionmentioning
confidence: 99%