↓ Skip to main content

Detection of Plasmodium falciparum infected Anopheles gambiae using near-infrared spectroscopy

Overview of attention for article published in Malaria Journal, March 2019
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

twitter
25 tweeters

Citations

dimensions_citation
27 Dimensions

Readers on

mendeley
78 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Detection of Plasmodium falciparum infected Anopheles gambiae using near-infrared spectroscopy
Published in
Malaria Journal, March 2019
DOI 10.1186/s12936-019-2719-9
Pubmed ID
Authors

Marta F. Maia, Melissa Kapulu, Michelle Muthui, Martin G. Wagah, Heather M. Ferguson, Floyd E. Dowell, Francesco Baldini, Lisa Ranford-Cartwright

Abstract

Large-scale surveillance of mosquito populations is crucial to assess the intensity of vector-borne disease transmission and the impact of control interventions. However, there is a lack of accurate, cost-effective and high-throughput tools for mass-screening of vectors. A total of 750 Anopheles gambiae (Keele strain) mosquitoes were fed Plasmodium falciparum NF54 gametocytes through standard membrane feeding assay (SMFA) and afterwards maintained in insectary conditions to allow for oocyst (8 days) and sporozoite development (14 days). Thereupon, each mosquito was scanned using near infra-red spectroscopy (NIRS) and processed by quantitative polymerase chain reaction (qPCR) to determine the presence of infection and infection load. The spectra collected were randomly assigned to either a training dataset, used to develop calibrations for predicting oocyst- or sporozoite-infection through partial least square regressions (PLS); or to a test dataset, used for validating the calibration's prediction accuracy. NIRS detected oocyst- and sporozoite-stage P. falciparum infections with 88% and 95% accuracy, respectively. This study demonstrates proof-of-concept that NIRS is capable of rapidly identifying laboratory strains of human malaria infection in African mosquito vectors. Accurate, low-cost, reagent-free screening of mosquito populations enabled by NIRS could revolutionize surveillance and elimination strategies for the most important human malaria parasite in its primary African vector species. Further research is needed to evaluate how the method performs in the field following adjustments in the training datasets to include data from wild-caught infected and uninfected mosquitoes.

Twitter Demographics

The data shown below were collected from the profiles of 25 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 78 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 18%
Researcher 13 17%
Student > Master 11 14%
Student > Bachelor 4 5%
Lecturer 4 5%
Other 8 10%
Unknown 24 31%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 18%
Biochemistry, Genetics and Molecular Biology 13 17%
Environmental Science 4 5%
Medicine and Dentistry 4 5%
Computer Science 4 5%
Other 12 15%
Unknown 27 35%

Attention Score in Context

This research output has an Altmetric Attention Score of 15. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 May 2019.
All research outputs
#2,082,022
of 22,790,780 outputs
Outputs from Malaria Journal
#434
of 5,561 outputs
Outputs of similar age
#49,827
of 350,786 outputs
Outputs of similar age from Malaria Journal
#11
of 127 outputs
Altmetric has tracked 22,790,780 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,561 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.8. This one has done particularly well, scoring higher than 92% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 350,786 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 127 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.