Evidence map›Paper›PMID 34081710›Full record

ArticlePLoS neglected tropical diseases2021

Automated phenotyping of mosquito larvae enables high-throughput screening for novel larvicides and offers potential for smartphone-based detection of larval insecticide resistance.

Steven D Buckingham, Frederick A Partridge, Beth C Poulton, Benjamin S Miller, Rachel A McKendry, Gareth J Lycett, David B Sattelle

Open access · goldAbstract read
In one paragraph

Article in PLoS neglected tropical diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.7field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 16 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 1 country.

Steven D BuckinghamUCL Centre for Respiratory Biology, UCL Respiratory, Division of Medicine, University College London, London, United Kingdom.ORCID 0000-0001-5852-8666
Frederick A PartridgeUCL Centre for Respiratory Biology, UCL Respiratory, Division of Medicine, University College London, London, United Kingdom.ORCID 0000-0001-6236-4297
Beth C PoultonUCL Centre for Respiratory Biology, UCL Respiratory, Division of Medicine, University College London, London, United Kingdom.ORCID 0000-0002-9831-5697
Benjamin S MillerLondon Centre for Nanotechnology, Faculty of Maths & Physical Sciences, University College London, London, United Kingdom.ORCID 0000-0003-3032-018X
Rachel A McKendryLondon Centre for Nanotechnology, Faculty of Maths & Physical Sciences, University College London, London, United Kingdom.ORCID 0000-0003-2018-6829
Gareth J LycettLiverpool School of Tropical Medicine, Liverpool, United Kingdom.ORCID 0000-0002-2422-053X
David B SattelleUCL Centre for Respiratory Biology, UCL Respiratory, Division of Medicine, University College London, London, United Kingdom.ORCID 0000-0003-0705-5242
University College London · GBLiverpool School of Tropical Medicine · GBLondon Centre for Nanotechnology · GB

Funding

Biotechnology and Biological Sciences Research Council BB/F021933/1Medical Research Council MC_PC_16052Medical Research Council MR/V001264/1
6 · The paper itself

Abstract

Pyrethroid-impregnated nets have contributed significantly to halving the burden of malaria but resistance threatens their future efficacy and the pipeline of new insecticides is short. Here we report that an invertebrate automated phenotyping platform (INVAPP), combined with the algorithm Paragon, provides a robust system for measuring larval motility in Anopheles gambiae (and An. coluzzi) as well as Aedes aegypti with the capacity for high-throughput screening for new larvicides. By this means, we reliably quantified both time- and concentration-dependent actions of chemical insecticides faster than using the WHO standard larval assay. We illustrate the effectiveness of the system using an established larvicide (temephos) and demonstrate its capacity for library-scale chemical screening using the Medicines for Malaria Venture (MMV) Pathogen Box library. As a proof-of-principle, this library screen identified a compound, subsequently confirmed to be tolfenpyrad, as an effective larvicide. We have also used the INVAPP / Paragon system to compare responses in larvae derived from WHO classified deltamethrin resistant and sensitive mosquitoes. We show how this approach to monitoring larval response to insecticides can be adapted for use with a smartphone camera application and therefore has potential for further development as a simple portable field-assay with associated real-time, geo-located information to identify hotspots.

Indexed as

AutomationInsecticide ResistanceSmartphoneAedesAnimalsAnophelesCulicidaeHigh-Throughput Screening AssaysInsecticidesLarvaMosquito ControlMotor ActivityPhenotypePyrethrinsTemefosInsecticidesPyrethrinsTemefos

Identifiers

PMID34081710
PMCPMC8205174
OpenAlexW3164715889

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.