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Detection of Plasmodium berghei infected Anopheles stephensi using near-infrared spectroscopy

BACKGROUND: The proportion of mosquitoes infected with malaria is an important entomological metric used to assess the intensity of transmission and the impact of vector control interventions. Currently, the prevalence of mosquitoes with salivary gland sporozoites is estimated by dissecting mosquito...

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Autores principales: Esperança, Pedro M., Blagborough, Andrew M., Da, Dari F., Dowell, Floyd E., Churcher, Thomas S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6027764/
https://www.ncbi.nlm.nih.gov/pubmed/29954424
http://dx.doi.org/10.1186/s13071-018-2960-z
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author Esperança, Pedro M.
Blagborough, Andrew M.
Da, Dari F.
Dowell, Floyd E.
Churcher, Thomas S.
author_facet Esperança, Pedro M.
Blagborough, Andrew M.
Da, Dari F.
Dowell, Floyd E.
Churcher, Thomas S.
author_sort Esperança, Pedro M.
collection PubMed
description BACKGROUND: The proportion of mosquitoes infected with malaria is an important entomological metric used to assess the intensity of transmission and the impact of vector control interventions. Currently, the prevalence of mosquitoes with salivary gland sporozoites is estimated by dissecting mosquitoes under a microscope or using molecular methods. These techniques are laborious, subjective, and require either expensive equipment or training. This study evaluates the potential of near-infrared spectroscopy (NIRS) to identify laboratory reared mosquitoes infected with rodent malaria. METHODS: Anopheles stephensi mosquitoes were reared in the laboratory and fed on Plasmodium berghei infected blood. After 12 and 21 days post-feeding mosquitoes were killed, scanned and analysed using NIRS and immediately dissected by microscopy to determine the number of oocysts on the midgut wall or sporozoites in the salivary glands. A predictive classification model was used to determine parasite prevalence and intensity status from spectra. RESULTS: The predictive model correctly classifies infectious and uninfectious mosquitoes with an overall accuracy of 72%. The false negative and false positive rates were 30 and 26%, respectively. While NIRS was able to differentiate between uninfectious and highly infectious mosquitoes, differentiating between mid-range infectious groups was less accurate. Multiple scans of the same specimen, with repositioning the mosquito between scans, is shown to improve accuracy. On a smaller dataset NIRS was unable to predict whether mosquitoes harboured oocysts. CONCLUSIONS: To our knowledge, we provide the first evidence that NIRS can differentiate between infectious and uninfectious mosquitoes. Currently, distinguishing between different intensities of infection is challenging. The classification model provides a flexible framework and allows for different error rates to be optimised, enabling the sensitivity and specificity of the technique to be varied according to requirements.
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spelling pubmed-60277642018-07-09 Detection of Plasmodium berghei infected Anopheles stephensi using near-infrared spectroscopy Esperança, Pedro M. Blagborough, Andrew M. Da, Dari F. Dowell, Floyd E. Churcher, Thomas S. Parasit Vectors Research BACKGROUND: The proportion of mosquitoes infected with malaria is an important entomological metric used to assess the intensity of transmission and the impact of vector control interventions. Currently, the prevalence of mosquitoes with salivary gland sporozoites is estimated by dissecting mosquitoes under a microscope or using molecular methods. These techniques are laborious, subjective, and require either expensive equipment or training. This study evaluates the potential of near-infrared spectroscopy (NIRS) to identify laboratory reared mosquitoes infected with rodent malaria. METHODS: Anopheles stephensi mosquitoes were reared in the laboratory and fed on Plasmodium berghei infected blood. After 12 and 21 days post-feeding mosquitoes were killed, scanned and analysed using NIRS and immediately dissected by microscopy to determine the number of oocysts on the midgut wall or sporozoites in the salivary glands. A predictive classification model was used to determine parasite prevalence and intensity status from spectra. RESULTS: The predictive model correctly classifies infectious and uninfectious mosquitoes with an overall accuracy of 72%. The false negative and false positive rates were 30 and 26%, respectively. While NIRS was able to differentiate between uninfectious and highly infectious mosquitoes, differentiating between mid-range infectious groups was less accurate. Multiple scans of the same specimen, with repositioning the mosquito between scans, is shown to improve accuracy. On a smaller dataset NIRS was unable to predict whether mosquitoes harboured oocysts. CONCLUSIONS: To our knowledge, we provide the first evidence that NIRS can differentiate between infectious and uninfectious mosquitoes. Currently, distinguishing between different intensities of infection is challenging. The classification model provides a flexible framework and allows for different error rates to be optimised, enabling the sensitivity and specificity of the technique to be varied according to requirements. BioMed Central 2018-06-28 /pmc/articles/PMC6027764/ /pubmed/29954424 http://dx.doi.org/10.1186/s13071-018-2960-z Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Esperança, Pedro M.
Blagborough, Andrew M.
Da, Dari F.
Dowell, Floyd E.
Churcher, Thomas S.
Detection of Plasmodium berghei infected Anopheles stephensi using near-infrared spectroscopy
title Detection of Plasmodium berghei infected Anopheles stephensi using near-infrared spectroscopy
title_full Detection of Plasmodium berghei infected Anopheles stephensi using near-infrared spectroscopy
title_fullStr Detection of Plasmodium berghei infected Anopheles stephensi using near-infrared spectroscopy
title_full_unstemmed Detection of Plasmodium berghei infected Anopheles stephensi using near-infrared spectroscopy
title_short Detection of Plasmodium berghei infected Anopheles stephensi using near-infrared spectroscopy
title_sort detection of plasmodium berghei infected anopheles stephensi using near-infrared spectroscopy
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6027764/
https://www.ncbi.nlm.nih.gov/pubmed/29954424
http://dx.doi.org/10.1186/s13071-018-2960-z
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