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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
general
An Image Analysis Algorithm for Malaria Parasite Stage Classification and Viability Quantification
PLoS ONE, Volume 8, No. 4, Article e61812, Year 2013
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Description
With more than 40% of the world's population at risk, 200-300 million infections each year, and an estimated 1.2 million deaths annually, malaria remains one of the most important public health problems of mankind today. With the propensity of malaria parasites to rapidly develop resistance to newly developed therapies, and the recent failures of artemisinin-based drugs in Southeast Asia, there is an urgent need for new antimalarial compounds with novel mechanisms of action to be developed against multidrug resistant malaria. We present here a novel image analysis algorithm for the quantitative detection and classification of Plasmodium lifecycle stages in culture as well as discriminating between viable and dead parasites in drug-treated samples. This new algorithm reliably estimates the number of red blood cells (isolated or clustered) per fluorescence image field, and accurately identifies parasitized erythrocytes on the basis of high intensity DAPI-stained parasite nuclei spots and Mitotracker-stained mitochondrial in viable parasites. We validated the performance of the algorithm by manual counting of the infected and non-infected red blood cells in multiple image fields, and the quantitative analyses of the different parasite stages (early rings, rings, trophozoites, schizonts) at various time-point post-merozoite invasion, in tightly synchronized cultures. Additionally, the developed algorithm provided parasitological effective concentration 50 (EC50) values for both chloroquine and artemisinin, that were similar to known growth inhibitory EC50 values for these compounds as determined using conventional SYBR Green I and lactate dehydrogenase-based assays. © 2013 Moon et al.
Authors & Co-Authors
Lee, Sukjun
South Korea, Seoul
Institut Pasteur Korea
Freitas-Júnior, Lûcio Holanda De
Unknown Affiliation
Ayong, Lawrence S.
South Korea, Seoul
Institut Pasteur Korea
Hansen, Michael Adsetts Edberg
Unknown Affiliation
Statistics
Citations: 38
Authors: 4
Affiliations: 2
Identifiers
Doi:
10.1371/journal.pone.0061812
ISSN:
19326203
Research Areas
Infectious Diseases
Study Design
Cross Sectional Study
Study Approach
Quantitative