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Vector Competence and the Susceptibility of Anopheles pullus and Anopheles belenrae to Plasmodium vivax-Infected Blood From Thai Patients.

Ratawan UbaleeHeung-Chul KimSiriporn PhasomkusolsilJaruwan TawongRatree TakhampunyaAmnart KayhaSuparat ChairuksaWaranya BuadokVichit PhunkitcharBetty K Poole-SmithSilas A DavidsonWon-Ja LeeTerry A Klein
Published in: Journal of medical entomology (2022)
There are eight Anopheles spp. present in the Republic of Korea (ROK), including five members of the Anopheles Hyrcanus Group that cannot be identified using current morphological methods. The vector competence of only Anopheles sinensis s.s., An. lesteri, and An. kleini have been investigated. As the geographical distribution of Anopheles spp. varies in the ROK, determining the relative vector competence of the Anopheles spp. provides a basis for delineating malaria risks to Korean populations and U.S. military/civilian populations deployed to the ROK. Anopheles belenrae and An. pullus, collected from a malaria high-risk area in the ROK, were evaluated for vector competence of P. vivax. A total of 1,000 each of An. dirus (Thai strain), and Korean strains of An. pullus and An. belenrae were fed on P. vivax infected blood collected from Thai patients via artificial membrane feeding. The overall oocyst infection rates for An. dirus, An. pullus, and An. belenrae dissected on days 8-9 postfeed were 64.1, 12.0, and 11.6%, respectively. The overall sporozoite infection rates for those species dissected on days 14-15 postfeed were 84.5, 3.4, and 5.1% respectively. The salivary gland sporozoite indices for positive females with +4 (>1,000 sporozoites) were observed in An. dirus (72.8%), but not observed for either An. pullus or An. belenrae. Most sporozoite-positive An. pullus (83.3%) and An. belenrae (71.4%) females were observed with only +1 (1-10 sporozoites) salivary glands. These data indicate that both An. belenrae and An. pullus are very poor vectors of P. vivax.
Keyphrases
  • plasmodium falciparum
  • aedes aegypti
  • end stage renal disease
  • newly diagnosed
  • chronic kidney disease
  • peritoneal dialysis
  • escherichia coli
  • machine learning
  • electronic health record