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A molecular barcode and web-based data analysis tool to identify imported Plasmodium vivax malaria. | LitMetric

AI Article Synopsis

  • The study addresses the limitations of using patient travel history to differentiate between imported and locally acquired malaria cases, particularly due to the dormant liver stages of the Plasmodium vivax parasite.* -
  • By using machine learning and analyzing a dataset of 799 P. vivax genomes, researchers developed 33-SNP, 50-SNP, and 55-SNP barcodes that effectively predict the country of origin of malaria infections.* -
  • An online tool, vivaxGEN-geo, was created to facilitate data analysis, and the methods used can be adapted for other applications in malaria control efforts.*

Article Abstract

Traditionally, patient travel history has been used to distinguish imported from autochthonous malaria cases, but the dormant liver stages of Plasmodium vivax confound this approach. Molecular tools offer an alternative method to identify, and map imported cases. Using machine learning approaches incorporating hierarchical fixation index and decision tree analyses applied to 799 P. vivax genomes from 21 countries, we identified 33-SNP, 50-SNP and 55-SNP barcodes (GEO33, GEO50 and GEO55), with high capacity to predict the infection's country of origin. The Matthews correlation coefficient (MCC) for an existing, commonly applied 38-SNP barcode (BR38) exceeded 0.80 in 62% countries. The GEO panels outperformed BR38, with median MCCs > 0.80 in 90% countries at GEO33, and 95% at GEO50 and GEO55. An online, open-access, likelihood-based classifier framework was established to support data analysis (vivaxGEN-geo). The SNP selection and classifier methods can be readily amended for other use cases to support malaria control programs.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9789135PMC
http://dx.doi.org/10.1038/s42003-022-04352-2DOI Listing

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