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Validation of the ligase detection reaction fluorescent microsphere assay for the detection of Plasmodium falciparum resistance mediating polymorphisms in Uganda

Overview of attention for article published in Malaria Journal, March 2014
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Title
Validation of the ligase detection reaction fluorescent microsphere assay for the detection of Plasmodium falciparum resistance mediating polymorphisms in Uganda
Published in
Malaria Journal, March 2014
DOI 10.1186/1475-2875-13-95
Pubmed ID
Authors

Sheila Nankoberanyi, George W Mbogo, Norbert P LeClair, Melissa D Conrad, Patrick Tumwebaze, Stephen Tukwasibwe, Moses R Kamya, Jordan Tappero, Samuel L Nsobya, Philip J Rosenthal

Abstract

Malaria remains a major public health problem, and its control has been hampered by drug resistance. For a number of drugs, Plasmodium falciparum single nucleotide polymorphisms (SNPs) are associated with altered drug sensitivity and can be used as markers of drug resistance. Several techniques have been studied to assess resistance markers. The most widely used methodology is restriction fragment length polymorphism (RFLP) analysis. The ligase detection reaction fluorescent microsphere (LDR-FM) assay was recently shown to provide high throughput assessment of P. falciparum SNPs associated with drug resistance. The aim of this study was to validate the reliability and accuracy of the LDR-FM assay in a field setting.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 3%
Burkina Faso 1 2%
Unknown 63 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 21%
Student > Master 10 15%
Researcher 9 14%
Student > Bachelor 5 8%
Student > Doctoral Student 4 6%
Other 14 21%
Unknown 10 15%
Readers by discipline Count As %
Medicine and Dentistry 13 20%
Agricultural and Biological Sciences 13 20%
Biochemistry, Genetics and Molecular Biology 7 11%
Computer Science 4 6%
Social Sciences 4 6%
Other 14 21%
Unknown 11 17%