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Karisa J, Ominde K, Tuwei M, Bartilol B, Ondieki Z, Musani H, Wanjiku C, Mwikali K, Babu L, Rono M, Eminov M, Mbogo C, Bejon P, Mwangangi J, Laroche M, Maia M. Utility of MALDI-TOF MS for determination of species identity and blood meal sources of primary malaria vectors on the Kenyan coast. Wellcome Open Res 2024; 8:151. [PMID: 38957296 PMCID: PMC11217722 DOI: 10.12688/wellcomeopenres.18982.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/21/2024] [Indexed: 07/04/2024] Open
Abstract
Background Protein analysis using matrix-assisted laser desorption/ionisation time-of-flight mass-spectrometry (MALDI-TOF MS) represents a promising tool for entomological surveillance. In this study we tested the discriminative power of this tool for measuring species and blood meal source of main Afrotropical malaria vectors on the Kenyan coast. Methods Mosquito collections were conducted along the coastal region of Kenya. MALDI-TOF MS spectra were obtained from each individual mosquito's cephalothorax as well as the abdomens of blood-engorged mosquitoes. The same mosquitoes were also processed using gold standard tests: polymerase chain reaction (PCR) for species identification and enzyme linked immunosorbent assay (ELISA) for blood meal source identification. Results Of the 2,332 mosquitoes subjected to MALDI-TOF MS, 85% (1,971/2,332) were considered for database creation and validation. There was an overall accuracy of 97.5% in the identification of members of the An. gambiae ( An. gambiae, 100%; An. arabiensis, 91.9%; An. merus, 97.5%; and An. quadriannulatus, 90.2%) and An. funestus ( An. funestus, 94.2%; An. rivulorum, 99.4%; and An. leesoni, 94.1%) complexes. Furthermore, MALDI-TOF MS also provided accurate (94.5% accuracy) identification of blood host sources across all mosquito species. Conclusions This study provides further evidence of the discriminative power of MALDI-TOF MS to identify sibling species and blood meal source of Afrotropical malaria vectors, further supporting its utility in entomological surveillance. The low cost per sample (<0.2USD) and high throughput nature of the method represents a cost-effective alternative to molecular methods and could enable programs to increase the number of samples analysed and therefore improve the data generated from surveillance activities.
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Affiliation(s)
- Jonathan Karisa
- Kenya Medical Research Institute, Wellcome Trust Research Programme, Kilifi, Kenya, 230-80108, Kenya
- The Open University, Milton Keynes, United Kingdom, Walton Hall, Kents Hill, Milton Keynes MK7 6AA, UK
- Pwani University, Kilifi, Kenya, 195-80108, Kenya
| | - Kelly Ominde
- Kenya Medical Research Institute, Wellcome Trust Research Programme, Kilifi, Kenya, 230-80108, Kenya
| | - Mercy Tuwei
- Kenya Medical Research Institute, Wellcome Trust Research Programme, Kilifi, Kenya, 230-80108, Kenya
- Pwani University, Kilifi, Kenya, 195-80108, Kenya
| | - Brian Bartilol
- Kenya Medical Research Institute, Wellcome Trust Research Programme, Kilifi, Kenya, 230-80108, Kenya
| | - Zedekiah Ondieki
- Kenya Medical Research Institute, Wellcome Trust Research Programme, Kilifi, Kenya, 230-80108, Kenya
| | - Harun Musani
- Kenya Medical Research Institute, Wellcome Trust Research Programme, Kilifi, Kenya, 230-80108, Kenya
| | - Caroline Wanjiku
- Kenya Medical Research Institute, Wellcome Trust Research Programme, Kilifi, Kenya, 230-80108, Kenya
| | - Kioko Mwikali
- Kenya Medical Research Institute, Wellcome Trust Research Programme, Kilifi, Kenya, 230-80108, Kenya
| | - Lawrence Babu
- Kenya Medical Research Institute, Wellcome Trust Research Programme, Kilifi, Kenya, 230-80108, Kenya
| | - Martin Rono
- Kenya Medical Research Institute, Wellcome Trust Research Programme, Kilifi, Kenya, 230-80108, Kenya
- Pwani University, Kilifi, Kenya, 195-80108, Kenya
| | | | - Charles Mbogo
- Kenya Medical Research Institute, Wellcome Trust Research Programme, Kilifi, Kenya, 230-80108, Kenya
| | - Philip Bejon
- Kenya Medical Research Institute, Wellcome Trust Research Programme, Kilifi, Kenya, 230-80108, Kenya
- University of Oxford, Centre for Global Health and Tropical Medicine, Oxford, UK, Oxford, UK
| | - Joseph Mwangangi
- Kenya Medical Research Institute, Wellcome Trust Research Programme, Kilifi, Kenya, 230-80108, Kenya
| | - Maureen Laroche
- The University of Texas Medical Branch -, Galveston National Laboratory 301 University Blvd, Texas, Galveston TX 77555-1019, USA
| | - Marta Maia
- Kenya Medical Research Institute, Wellcome Trust Research Programme, Kilifi, Kenya, 230-80108, Kenya
- University of Oxford, Centre for Global Health and Tropical Medicine, Oxford, UK, Oxford, UK
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Mohammad N, Naudion P, Dia AK, Boëlle PY, Konaté A, Konaté L, Niang EHA, Piarroux R, Tannier X, Nabet C. Predicting the age of field Anopheles mosquitoes using mass spectrometry and deep learning. SCIENCE ADVANCES 2024; 10:eadj6990. [PMID: 38728404 PMCID: PMC11086620 DOI: 10.1126/sciadv.adj6990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 04/08/2024] [Indexed: 05/12/2024]
Abstract
Mosquito-borne diseases like malaria are rising globally, and improved mosquito vector surveillance is needed. Survival of Anopheles mosquitoes is key for epidemiological monitoring of malaria transmission and evaluation of vector control strategies targeting mosquito longevity, as the risk of pathogen transmission increases with mosquito age. However, the available tools to estimate field mosquito age are often approximate and time-consuming. Here, we show a rapid method that combines matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry with deep learning for mosquito age prediction. Using 2763 mass spectra from the head, legs, and thorax of 251 field-collected Anopheles arabiensis mosquitoes, we developed deep learning models that achieved a best mean absolute error of 1.74 days. We also demonstrate consistent performance at two ecological sites in Senegal, supported by age-related protein changes. Our approach is promising for malaria control and the field of vector biology, benefiting other disease vectors like Aedes mosquitoes.
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Affiliation(s)
- Noshine Mohammad
- Sorbonne Université, Inserm, Institut Pierre-Louis d’Epidémiologie et de Santé Publique, IPLESP, AP-HP, Groupe Hospitalier Pitié-Salpêtrière, Service de Parasitologie-Mycologie, 75013 Paris, France
| | - Pauline Naudion
- Sorbonne Université, Inserm, Institut Pierre-Louis d’Epidémiologie et de Santé Publique, IPLESP, AP-HP, Groupe Hospitalier Pitié-Salpêtrière, Service de Parasitologie-Mycologie, 75013 Paris, France
| | - Abdoulaye Kane Dia
- Laboratoire d'Ecologie Vectorielle et Parasitaire (LEVP), Faculté des Sciences et Techniques, Université Cheikh Anta Diop de Dakar, BP 5005 Dakar, Senegal
| | - Pierre-Yves Boëlle
- Sorbonne Université, Inserm, Institut Pierre Louis d'Épidémiologie et de Santé Publique, IPLESP, 75012 Paris, France
| | - Abdoulaye Konaté
- Laboratoire d'Ecologie Vectorielle et Parasitaire (LEVP), Faculté des Sciences et Techniques, Université Cheikh Anta Diop de Dakar, BP 5005 Dakar, Senegal
| | - Lassana Konaté
- Laboratoire d'Ecologie Vectorielle et Parasitaire (LEVP), Faculté des Sciences et Techniques, Université Cheikh Anta Diop de Dakar, BP 5005 Dakar, Senegal
| | - El Hadji Amadou Niang
- Laboratoire d'Ecologie Vectorielle et Parasitaire (LEVP), Faculté des Sciences et Techniques, Université Cheikh Anta Diop de Dakar, BP 5005 Dakar, Senegal
| | - Renaud Piarroux
- Sorbonne Université, Inserm, Institut Pierre-Louis d’Epidémiologie et de Santé Publique, IPLESP, AP-HP, Groupe Hospitalier Pitié-Salpêtrière, Service de Parasitologie-Mycologie, 75013 Paris, France
| | - Xavier Tannier
- Sorbonne Université, Inserm, Université Sorbonne Paris Nord, Laboratoire d’Informatique Médicale et d’Ingénierie des Connaissances pour la e-Santé, LIMICS, 75006 Paris, France
| | - Cécile Nabet
- Sorbonne Université, Inserm, Institut Pierre-Louis d’Epidémiologie et de Santé Publique, IPLESP, AP-HP, Groupe Hospitalier Pitié-Salpêtrière, Service de Parasitologie-Mycologie, 75013 Paris, France
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Sikulu-Lord MT, Edstein MD, Goh B, Lord AR, Travis JA, Dowell FE, Birrell GW, Chavchich M. Rapid and non-invasive detection of malaria parasites using near-infrared spectroscopy and machine learning. PLoS One 2024; 19:e0289232. [PMID: 38527002 PMCID: PMC10962802 DOI: 10.1371/journal.pone.0289232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 12/26/2023] [Indexed: 03/27/2024] Open
Abstract
BACKGROUND Novel and highly sensitive point-of-care malaria diagnostic and surveillance tools that are rapid and affordable are urgently needed to support malaria control and elimination. METHODS We demonstrated the potential of near-infrared spectroscopy (NIRS) technique to detect malaria parasites both, in vitro, using dilutions of infected red blood cells obtained from Plasmodium falciparum cultures and in vivo, in mice infected with P. berghei using blood spotted on slides and non-invasively, by simply scanning various body areas (e.g., feet, groin and ears). The spectra were analysed using machine learning to develop predictive models for infection. FINDINGS Using NIRS spectra of in vitro cultures and machine learning algorithms, we successfully detected low densities (<10-7 parasites/μL) of P. falciparum parasites with a sensitivity of 96% (n = 1041), a specificity of 93% (n = 130) and an accuracy of 96% (n = 1171) and differentiated ring, trophozoite and schizont stages with an accuracy of 98% (n = 820). Furthermore, when the feet of mice infected with P. berghei with parasitaemia ≥3% were scanned non-invasively, the sensitivity and specificity of NIRS were 94% (n = 66) and 86% (n = 342), respectively. INTERPRETATION These data highlights the potential of NIRS technique as rapid, non-invasive and affordable tool for surveillance of malaria cases. Further work to determine the potential of NIRS to detect malaria in symptomatic and asymptomatic malaria cases in the field is recommended including its capacity to guide current malaria elimination strategies.
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Affiliation(s)
- Maggy T. Sikulu-Lord
- School of the Environment, Faculty of Science, The University of Queensland, Brisbane, Queensland, Australia
| | - Michael D. Edstein
- Department of Drug Evaluation, Australian Defence Force Malaria and Infectious Disease Institute, Brisbane, Queensland, Australia
| | - Brendon Goh
- School of the Environment, Faculty of Science, The University of Queensland, Brisbane, Queensland, Australia
| | - Anton R. Lord
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Jye A. Travis
- Department of Drug Evaluation, Australian Defence Force Malaria and Infectious Disease Institute, Brisbane, Queensland, Australia
| | - Floyd E. Dowell
- Center for Grain and Animal Health Research, USDA Agricultural Research Service, Manhattan, Kansas, United States of America
| | - Geoffrey W. Birrell
- Department of Drug Evaluation, Australian Defence Force Malaria and Infectious Disease Institute, Brisbane, Queensland, Australia
| | - Marina Chavchich
- Department of Drug Evaluation, Australian Defence Force Malaria and Infectious Disease Institute, Brisbane, Queensland, Australia
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Mwanga EP, Siria DJ, Mshani IH, Mwinyi SH, Abbasi S, Jimenez MG, Wynne K, Baldini F, Babayan SA, Okumu FO. Rapid classification of epidemiologically relevant age categories of the malaria vector, Anopheles funestus. Parasit Vectors 2024; 17:143. [PMID: 38500231 PMCID: PMC10949582 DOI: 10.1186/s13071-024-06209-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 02/21/2024] [Indexed: 03/20/2024] Open
Abstract
BACKGROUND Accurately determining the age and survival probabilities of adult mosquitoes is crucial for understanding parasite transmission, evaluating the effectiveness of control interventions and assessing disease risk in communities. This study was aimed at demonstrating the rapid identification of epidemiologically relevant age categories of Anopheles funestus, a major Afro-tropical malaria vector, through the innovative combination of infrared spectroscopy and machine learning, instead of the cumbersome practice of dissecting mosquito ovaries to estimate age based on parity status. METHODS Anopheles funestus larvae were collected in rural south-eastern Tanzania and reared in an insectary. Emerging adult females were sorted by age (1-16 days old) and preserved using silica gel. Polymerase chain reaction (PCR) confirmation was conducted using DNA extracted from mosquito legs to verify the presence of An. funestus and to eliminate undesired mosquitoes. Mid-infrared spectra were obtained by scanning the heads and thoraces of the mosquitoes using an attenuated total reflection-Fourier transform infrared (ATR-FT-IR) spectrometer. The spectra (N = 2084) were divided into two epidemiologically relevant age groups: 1-9 days (young, non-infectious) and 10-16 days (old, potentially infectious). The dimensionality of the spectra was reduced using principal component analysis, and then a set of machine learning and multi-layer perceptron (MLP) models were trained using the spectra to predict the mosquito age categories. RESULTS The best-performing model, XGBoost, achieved overall accuracy of 87%, with classification accuracy of 89% for young and 84% for old An. funestus. When the most important spectral features influencing the model performance were selected to train a new model, the overall accuracy increased slightly to 89%. The MLP model, utilizing the significant spectral features, achieved higher classification accuracy of 95% and 94% for the young and old An. funestus, respectively. After dimensionality reduction, the MLP achieved 93% accuracy for both age categories. CONCLUSIONS This study shows how machine learning can quickly classify epidemiologically relevant age groups of An. funestus based on their mid-infrared spectra. Having been previously applied to An. gambiae, An. arabiensis and An. coluzzii, this demonstration on An. funestus underscores the potential of this low-cost, reagent-free technique for widespread use on all the major Afro-tropical malaria vectors. Future research should demonstrate how such machine-derived age classifications in field-collected mosquitoes correlate with malaria in human populations.
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Affiliation(s)
- Emmanuel P Mwanga
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, P.O. Box 53, Morogoro, Tanzania.
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK.
| | - Doreen J Siria
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, P.O. Box 53, Morogoro, Tanzania
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Issa H Mshani
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, P.O. Box 53, Morogoro, Tanzania
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Sophia H Mwinyi
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, P.O. Box 53, Morogoro, Tanzania
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Said Abbasi
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, P.O. Box 53, Morogoro, Tanzania
| | - Mario Gonzalez Jimenez
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Klaas Wynne
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Francesco Baldini
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Simon A Babayan
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Fredros O Okumu
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, P.O. Box 53, Morogoro, Tanzania
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
- School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- School of Life Science and Bioengineering, The Nelson Mandela African Institution of Science and Technology, P. O. Box 447, Arusha, Tanzania
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Ferreira TN, Santos LMB, Valladares V, Flanley CM, McDowell MA, Garcia GA, Mello-Silva CC, Maciel-de-Freitas R, Genta FA. Age, sex, and mating status discrimination in the sand fly Lutzomyia longipalpis using near infra-red spectroscopy (NIRS). Parasit Vectors 2024; 17:19. [PMID: 38217054 PMCID: PMC10787389 DOI: 10.1186/s13071-023-06097-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 12/13/2023] [Indexed: 01/14/2024] Open
Abstract
BACKGROUND Understanding aspects related to the physiology and capacity of vectors is essential for effectively controlling vector-borne diseases. The sand fly Lutzomyia longipalpis has great importance in medical entomology for disseminating Leishmania parasites, the causative agent of Leishmaniasis, one of the main neglected diseases listed by the World Health Organization (WHO). In this respect, it is necessary to evaluate the transmission potential of this species and the success of vector control interventions. Near-infrared spectroscopy (NIRS) has been used to estimate the age of mosquitoes in different conditions (laboratory, semi-field, and conservation), taxonomic analysis, and infection detection. However, no studies are using NIRS for sand flies. METHODS In this study, we developed analytic models to estimate the age of L. longipalpis adults under laboratory conditions, identify their copulation state, and evaluate their gonotrophic cycle and diet. RESULTS Sand flies were classified with an accuracy of 58-82% in 3 age groups and 82-92% when separating them into young (<8 days) or old (>8 days) insects. The classification between mated and non-mated sandflies was 98-100% accurate, while the percentage of hits of females that had already passed the first gonotrophic cycle was only 59%. CONCLUSIONS We consider the age and copula estimation results very promising, as they provide essential aspects of vector capacity assessment, which can be obtained quickly and at a lower cost with NIRS.
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Affiliation(s)
- Tainá Neves Ferreira
- Laboratório de Bioquímica e Fisiologia de Insetos, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil
| | - Lilha M B Santos
- Laboratório de Transmissores de Hematozoários, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil
| | - Vanessa Valladares
- Malacology Laboratory, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil
| | - Catherine M Flanley
- Department of Biological Sciences, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA
| | - Mary Ann McDowell
- Department of Biological Sciences, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA
| | - Gabriela A Garcia
- Laboratório de Transmissores de Hematozoários, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil
| | | | - Rafael Maciel-de-Freitas
- Laboratório de Transmissores de Hematozoários, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil
- Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular, Rio de Janeiro, Brazil
| | - Fernando Ariel Genta
- Laboratório de Bioquímica e Fisiologia de Insetos, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil.
- Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular, Rio de Janeiro, Brazil.
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Mshani IH, Siria DJ, Mwanga EP, Sow BB, Sanou R, Opiyo M, Sikulu-Lord MT, Ferguson HM, Diabate A, Wynne K, González-Jiménez M, Baldini F, Babayan SA, Okumu F. Key considerations, target product profiles, and research gaps in the application of infrared spectroscopy and artificial intelligence for malaria surveillance and diagnosis. Malar J 2023; 22:346. [PMID: 37950315 PMCID: PMC10638832 DOI: 10.1186/s12936-023-04780-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Accepted: 11/01/2023] [Indexed: 11/12/2023] Open
Abstract
Studies on the applications of infrared (IR) spectroscopy and machine learning (ML) in public health have increased greatly in recent years. These technologies show enormous potential for measuring key parameters of malaria, a disease that still causes about 250 million cases and 620,000 deaths, annually. Multiple studies have demonstrated that the combination of IR spectroscopy and machine learning (ML) can yield accurate predictions of epidemiologically relevant parameters of malaria in both laboratory and field surveys. Proven applications now include determining the age, species, and blood-feeding histories of mosquito vectors as well as detecting malaria parasite infections in both humans and mosquitoes. As the World Health Organization encourages malaria-endemic countries to improve their surveillance-response strategies, it is crucial to consider whether IR and ML techniques are likely to meet the relevant feasibility and cost-effectiveness requirements-and how best they can be deployed. This paper reviews current applications of IR spectroscopy and ML approaches for investigating malaria indicators in both field surveys and laboratory settings, and identifies key research gaps relevant to these applications. Additionally, the article suggests initial target product profiles (TPPs) that should be considered when developing or testing these technologies for use in low-income settings.
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Affiliation(s)
- Issa H Mshani
- Ifakara Health Institute, Environmental Health, and Ecological Sciences Department, Morogoro, United Republic of Tanzania.
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK.
| | - Doreen J Siria
- Ifakara Health Institute, Environmental Health, and Ecological Sciences Department, Morogoro, United Republic of Tanzania
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK
| | - Emmanuel P Mwanga
- Ifakara Health Institute, Environmental Health, and Ecological Sciences Department, Morogoro, United Republic of Tanzania
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK
| | - Bazoumana Bd Sow
- Department of Medical Biology and Public Health, Institut de Recherche en Sciences de la Santé (IRSS), Bobo-Dioulasso, Burkina Faso
| | - Roger Sanou
- Department of Medical Biology and Public Health, Institut de Recherche en Sciences de la Santé (IRSS), Bobo-Dioulasso, Burkina Faso
| | - Mercy Opiyo
- Centro de Investigação em Saúde de Manhiça (CISM), Maputo, Mozambique
- Malaria Elimination Initiative (MEI), Institute for Global Health Sciences, University of California, San Francisco, USA
| | - Maggy T Sikulu-Lord
- Faculty of Science, School of the Environment, The University of Queensland, Brisbane, QLD, Australia
| | - Heather M Ferguson
- Ifakara Health Institute, Environmental Health, and Ecological Sciences Department, Morogoro, United Republic of Tanzania
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK
| | - Abdoulaye Diabate
- Department of Medical Biology and Public Health, Institut de Recherche en Sciences de la Santé (IRSS), Bobo-Dioulasso, Burkina Faso
| | - Klaas Wynne
- School of Chemistry, The University of Glasgow, Glasgow, G12 8QQ, UK
| | - Mario González-Jiménez
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK
- School of Chemistry, The University of Glasgow, Glasgow, G12 8QQ, UK
| | - Francesco Baldini
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK
| | - Simon A Babayan
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK.
| | - Fredros Okumu
- Ifakara Health Institute, Environmental Health, and Ecological Sciences Department, Morogoro, United Republic of Tanzania.
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK.
- School of Life Sciences and Biotechnology, Nelson Mandela African Institution of Science and Technology, Arusha, United Republic of Tanzania.
- School of Public Health, The University of the Witwatersrand, Park Town, South Africa.
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Pazmiño-Betancourth M, Ochoa-Gutiérrez V, Ferguson HM, González-Jiménez M, Wynne K, Baldini F, Childs D. Evaluation of diffuse reflectance spectroscopy for predicting age, species, and cuticular resistance of Anopheles gambiae s.l under laboratory conditions. Sci Rep 2023; 13:18499. [PMID: 37898634 PMCID: PMC10613238 DOI: 10.1038/s41598-023-45696-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 10/23/2023] [Indexed: 10/30/2023] Open
Abstract
Mid-infrared spectroscopy (MIRS) combined with machine learning analysis has shown potential for quick and efficient identification of mosquito species and age groups. However, current technology to collect spectra is destructive to the sample and does not allow targeting specific tissues of the mosquito, limiting the identification of other important biological traits such as insecticide resistance. Here, we assessed the use of a non-destructive approach of MIRS for vector surveillance, micro diffuse reflectance spectroscopy (µDRIFT) using mosquito legs to identify species, age and cuticular insecticide resistance within the Anopheles gambiae s.l. complex. These mosquitoes are the major vectors of malaria in Africa and the focus on surveillance in malaria control programs. Legs required significantly less scanning time and showed more spectral consistence compared to other mosquito tissues. Machine learning models were able to identify An. gambiae and An. coluzzii with an accuracy of 0.73, two ages groups (3 and 10 days old) with 0.77 accuracy and we obtained accuracy of 0.75 when identifying cuticular insecticide resistance. Our results highlight the potential of different mosquito tissues and µDRIFT as tools for biological trait identification on mosquitoes that transmit malaria. These results can guide new ways of identifying mosquito traits which can help the creation of innovative surveillance programs by adapting new technology into mosquito surveillance and control tools.
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Affiliation(s)
- Mauro Pazmiño-Betancourth
- School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK.
- School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK.
| | - Victor Ochoa-Gutiérrez
- School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK
- School of Physics and Astronomy, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Heather M Ferguson
- School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | | | - Klaas Wynne
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Francesco Baldini
- School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - David Childs
- School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK
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Santos V, Costa-Vera C, Rivera-Parra P, Burneo S, Molina J, Encalada D, Salvador J, Brydegaard M. Dual-Band Infrared Scheimpflug Lidar Reveals Insect Activity in a Tropical Cloud Forest. APPLIED SPECTROSCOPY 2023:37028231169302. [PMID: 37072925 DOI: 10.1177/00037028231169302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
We describe an entomological dual-band 808 and 980 nm lidar system which has been implemented in a tropical cloud forest (Ecuador). The system was successfully tested at a sample rate of 5 kHz in a cloud forest during challenging foggy conditions (extinction coefficients up to 20 km-1). At times, the backscattered signal could be retrieved from a distance of 2.929 km. We present insect and bat observations up to 200 m during a single night with an emphasis on fog aspects, potentials, and benefits of such dual-band systems. We demonstrate that the modulation contrast between insects and fog is high in the frequency domain compared to intensity in the time domain, thus allowing for better identification and quantification in misty forests. Oscillatory lidar extinction effects are shown in this work for the first time, caused by the combination of dense fog and large moths partially obstructing the beam. We demonstrate here an interesting case of a moth where left- and right-wing movements induced oscillations in both intensity and pixel spread. In addition, we were able to identify the dorsal and ventral sides of the wings by estimating the corresponding melanization with the dual-band lidar. We demonstrate that the wing beat trajectories in the dual-band parameter space are complementary rather than covarying or redundant, thus a dual-band entomological lidar approach to biodiversity studies is feasible in situ and endows species specificity differentiation. Future improvements are discussed. The introduction of these methodologies opens the door to a wealth of possible experiments to monitor, understand, and safeguard the biological resources of one of the most biodiverse countries on Earth.
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Affiliation(s)
- Victor Santos
- Departmento de Física, Escuela Politécnica Nacional, Quito
| | | | | | | | - Juan Molina
- Departmento de Física, Escuela Politécnica Nacional, Quito
| | - Diana Encalada
- Departmento de Economía, Universidad Técnica Particular de Loja, San Cayetano Alto, Loja, Ecuador
| | | | - Mikkel Brydegaard
- Department of Physics, Lund University, Lund, Sweden
- Norsk Elektro Optikk AS, Oslo, Norway
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9
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Karisa J, Ominde K, Tuwei M, Bartilol B, Ondieki Z, Musani H, Wanjiku C, Mwikali K, Babu L, Rono M, Eminov M, Mbogo C, Bejon P, Mwangangi J, Laroche M, Maia M. Utility of MALDI-TOF MS for determination of species identity and blood meal sources of primary malaria vectors on the Kenyan coast. Wellcome Open Res 2023. [DOI: 10.12688/wellcomeopenres.18982.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023] Open
Abstract
Background: Protein analysis using matrix-assisted laser desorption/ionization time-of-flight mass-spectrometry (MALDI-TOF MS) represents a promising tool for entomological surveillance. In this study we tested the discriminative power of this tool for measuring species and blood meal source of main Afrotropical malaria vectors on the Kenyan coast. Methods: Mosquito collections were conducted along the coastal region of Kenya. MALDI-TOF MS spectra were obtained from each individual mosquito’s cephalothorax as well as the abdomens of blood-engorged mosquitoes. The same mosquitoes were also processed using gold standard tests: polymerase chain reaction (PCR) for species identification and enzyme linked immunosorbent assay (ELISA) for blood meal source identification. Results: Of the 2,332 mosquitoes subjected to MALDI-TOF MS, 85% (1,971/2,332) were considered for database creation and validation. There was an overall accuracy of 97.5% in the identification of members of the An. gambiae (An. gambiae, 100%; An. arabiensis, 91.9%; An. merus, 97.5%; and An. quadriannulatus, 90.2%) and An. funestus (An. funestus, 94.2%; An. rivulorum, 99.4%; and An. leesoni, 94.1%) complexes. Furthermore, MALDI-TOF MS also provided accurate (94.5% accuracy) identification of blood host sources across all mosquito species. Conclusions: This study provides further evidence of the discriminative power of MALDI-TOF MS to identify sibling species and blood meal source of Afrotropical malaria vectors, further supporting its utility in entomological surveillance. The low cost per sample (<0.2USD) and high throughput nature of the method represents a cost-effective alternative to molecular methods and could enable programs to increase the number of samples analysed and therefore improve the data generated from surveillance activities.
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Garcia GA, Lord AR, Santos LMB, Kariyawasam TN, David MR, Couto-Lima D, Tátila-Ferreira A, Pavan MG, Sikulu-Lord MT, Maciel-de-Freitas R. Rapid and Non-Invasive Detection of Aedes aegypti Co-Infected with Zika and Dengue Viruses Using Near Infrared Spectroscopy. Viruses 2022; 15:11. [PMID: 36680052 PMCID: PMC9863061 DOI: 10.3390/v15010011] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/03/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022] Open
Abstract
The transmission of dengue (DENV) and Zika (ZIKV) has been continuously increasing worldwide. An efficient arbovirus surveillance system is critical to designing early-warning systems to increase preparedness of future outbreaks in endemic countries. The Near Infrared Spectroscopy (NIRS) is a promising high throughput technique to detect arbovirus infection in Ae. aegypti with remarkable advantages such as cost and time effectiveness, reagent-free, and non-invasive nature over existing molecular tools for similar purposes, enabling timely decision making through rapid detection of potential disease. Our aim was to determine whether NIRS can differentiate Ae. aegypti females infected with either ZIKV or DENV single infection, and those coinfected with ZIKV/DENV from uninfected ones. Using 200 Ae. aegypti females reared and infected in laboratory conditions, the training model differentiated mosquitoes into the four treatments with 100% accuracy. DENV-, ZIKV-, and ZIKV/DENV-coinfected mosquitoes that were used to validate the model could be correctly classified into their actual infection group with a predictive accuracy of 100%, 84%, and 80%, respectively. When compared with mosquitoes from the uninfected group, the three infected groups were predicted as belonging to the infected group with 100%, 97%, and 100% accuracy for DENV-infected, ZIKV-infected, and the co-infected group, respectively. Preliminary lab-based results are encouraging and indicate that NIRS should be tested in field settings to evaluate its potential role to monitor natural infection in field-caught mosquitoes.
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Affiliation(s)
- Gabriela A. Garcia
- Laboratório de Mosquitos Transmissores de Hematozoários, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Rio de Janeiro, Brazil
| | - Anton R. Lord
- School of Biological Sciences, University of Queensland, Brisbane, QLD 4072, Australia
- Spectroscopy and Data Consultants Pty Ltd., Brisbane, QLD 4035, Australia
| | - Lilha M. B. Santos
- Laboratório de Mosquitos Transmissores de Hematozoários, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Rio de Janeiro, Brazil
| | | | - Mariana R. David
- Laboratório de Mosquitos Transmissores de Hematozoários, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Rio de Janeiro, Brazil
| | - Dinair Couto-Lima
- Laboratório de Mosquitos Transmissores de Hematozoários, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Rio de Janeiro, Brazil
| | - Aline Tátila-Ferreira
- Laboratório de Mosquitos Transmissores de Hematozoários, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Rio de Janeiro, Brazil
| | - Márcio G. Pavan
- Laboratório de Mosquitos Transmissores de Hematozoários, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Rio de Janeiro, Brazil
| | - Maggy T. Sikulu-Lord
- School of Biological Sciences, University of Queensland, Brisbane, QLD 4072, Australia
| | - Rafael Maciel-de-Freitas
- Laboratório de Mosquitos Transmissores de Hematozoários, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Rio de Janeiro, Brazil
- Department of Arbovirology, Bernhard Nocht Institute of Tropical Medicine, 20359 Hamburg, Germany
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11
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Somé BM, Da DF, McCabe R, Djègbè NDC, Paré LIG, Wermé K, Mouline K, Lefèvre T, Ouédraogo AG, Churcher TS, Dabiré RK. Adapting field-mosquito collection techniques in a perspective of near-infrared spectroscopy implementation. Parasit Vectors 2022; 15:338. [PMID: 36163071 PMCID: PMC9513905 DOI: 10.1186/s13071-022-05458-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 08/27/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Near-infrared spectroscopy (NIRS) has the potential to be a useful tool for assessing key entomological parameters of malaria-transmitting mosquitoes, including age, infectious status and species identity. However, before NIRS can be reliably used in the field at scale, methods for killing mosquitoes and conserving samples prior to NIRS scanning need to be further optimized. Historically, mosquitoes used in studies have been killed with chloroform, although this approach is not without health hazards and should not be used in human dwellings. For the application of NIRS scanning it is also unclear which mosquito preservation method to use. The aim of the study reported here was to investigate the use of pyrethrum spray, a commercially available insecticide spray in Burkina Faso, for killing mosquitoes METHODS: Laboratory-reared Anopheles gambiae and Anopheles coluzzii were killed using either a pyrethrum insecticide spray routinely used in studies involving indoor mosquito collections (Kaltox Paalga®; Saphyto, Bobo-Dioulasso, Burkina Faso) or chloroform ("gold standard"). Preservative methods were also investigated to determine their impact on NIRS accuracy in predicting the species of laboratory-reared Anopheles and wild-caught mosquito species. After analysis of fresh samples, mosquitoes were stored in 80% ethanol or in silica gel for 2 weeks and re-analyzed by NIRS. In addition, experimentally infected An. coluzzii and wild-caught An. gambiae sensu lato (s.l.) were scanned as fresh samples to determine whether they contained sporozoites, then stored in the preservatives mentioned above for 2 weeks before being re-analyzed. RESULTS The difference in the accuracy of NIRS to differentiate between laboratory-reared An. gambiae mosquitoes and An. coluzzii mosquitoes killed with either insecticide (90%) or chloroform (92%) was not substantial. NIRS had an accuracy of 90% in determining mosquito species for mosquitoes killed with chloroform and preserved in ethanol or silica gel. The accuracy was the same when the pyrethrum spray was used to kill mosquitoes followed by preservation in silica gel, but was lower when ethanol was used as a preservative (80%). Regarding infection status, NIRS was able to differentiate between infected and uninfected mosquitoes, with a slightly lower accuracy for both laboratory and wild-caught mosquitoes preserved in silica gel or ethanol. CONCLUSIONS The results show that NIRS can be used to classify An. gambiae s.l. species killed by pyrethrum spray with no loss of accuracy. This insecticide may have practical advantages over chloroform for the killing of mosquitoes in NIRS analysis.
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Affiliation(s)
- Bernard Mouonniba Somé
- Institut de Recherche en Sciences de La Santé, Direction Régionale, 399 avenue de la liberté, 01 BP 545, Bobo-Dioulasso 01, Burkina Faso
- Université Nazi Boni, Bobo-Dioulasso, Burkina Faso
| | - Dari F. Da
- Institut de Recherche en Sciences de La Santé, Direction Régionale, 399 avenue de la liberté, 01 BP 545, Bobo-Dioulasso 01, Burkina Faso
| | - Ruth McCabe
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1PG UK
- Department of Statistics, University of Oxford, 24-29 St Giles, Oxford, OX1 3LB UK
- NIHR Health Research Protection Unit in Emerging and Zoonotic Infections, University of Liverpool, The Ronald Ross Building, 8 West Derby Street, Liverpool, L69 7BE UK
| | - Nicaise Denis C. Djègbè
- Institut de Recherche en Sciences de La Santé, Direction Régionale, 399 avenue de la liberté, 01 BP 545, Bobo-Dioulasso 01, Burkina Faso
- Université Nazi Boni, Bobo-Dioulasso, Burkina Faso
| | - Lawata Inès Géraldine Paré
- Institut de Recherche en Sciences de La Santé, Direction Régionale, 399 avenue de la liberté, 01 BP 545, Bobo-Dioulasso 01, Burkina Faso
- Université Nazi Boni, Bobo-Dioulasso, Burkina Faso
| | - Kadidia Wermé
- Institut de Recherche en Sciences de La Santé, Direction Régionale, 399 avenue de la liberté, 01 BP 545, Bobo-Dioulasso 01, Burkina Faso
| | - Karine Mouline
- Maladies Infectieuses et Vecteurs: Ecologie, Génétique, Evolution et Contrôle (MIVEGEC), IRD, CNRS, Montpellier University, Montpellier, France
| | - Thierry Lefèvre
- Maladies Infectieuses et Vecteurs: Ecologie, Génétique, Evolution et Contrôle (MIVEGEC), IRD, CNRS, Montpellier University, Montpellier, France
| | | | - Thomas S. Churcher
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1PG UK
| | - Roch Kounbobr Dabiré
- Institut de Recherche en Sciences de La Santé, Direction Régionale, 399 avenue de la liberté, 01 BP 545, Bobo-Dioulasso 01, Burkina Faso
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12
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Morgan J, Salcedo-Sora JE, Wagner I, Beynon RJ, Triana-Chavez O, Strode C. Rapid Evaporative Ionization Mass Spectrometry (REIMS): a Potential and Rapid Tool for the Identification of Insecticide Resistance in Mosquito Larvae. JOURNAL OF INSECT SCIENCE (ONLINE) 2022; 22:5. [PMID: 36082679 PMCID: PMC9459442 DOI: 10.1093/jisesa/ieac052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Indexed: 06/15/2023]
Abstract
Insecticide resistance is a significant challenge facing the successful control of mosquito vectors globally. Bioassays are currently the only method for phenotyping resistance. They require large numbers of mosquitoes for testing, the availability of a susceptible comparator strain, and often insectary facilities. This study aimed to trial the novel use of rapid evaporative ionization mass spectrometry (REIMS) for the identification of insecticide resistance in mosquitoes. No sample preparation is required for REIMS and analysis can be rapidly conducted within hours. Temephos resistant Aedes aegypti (Linnaeus) larvae from Cúcuta, Colombia and temephos susceptible larvae from two origins (Bello, Colombia, and the lab reference strain New Orleans) were analyzed using REIMS. We tested the ability of REIMS to differentiate three relevant variants: population source, lab versus field origin, and response to insecticide. The classification of these data was undertaken using linear discriminant analysis (LDA) and random forest. Classification models built using REIMS data were able to differentiate between Ae. aegypti larvae from different populations with 82% (±0.01) accuracy, between mosquitoes of field and lab origin with 89% (±0.01) accuracy and between susceptible and resistant larvae with 85% (±0.01) accuracy. LDA classifiers had higher efficiency than random forest with this data set. The high accuracy observed here identifies REIMS as a potential new tool for rapid identification of resistance in mosquitoes. We argue that REIMS and similar modern phenotyping alternatives should complement existing insecticide resistance management tools.
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Affiliation(s)
- Jasmine Morgan
- Department of Biology, Edge Hill University, Ormskirk, Lancashire, L39 4QP, UK
| | | | - Iris Wagner
- Centre for Proteome Research, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK
| | - Robert J Beynon
- Centre for Proteome Research, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK
| | - Omar Triana-Chavez
- Instituto de Biología, Facultad de Ciencias Exactas y Naturales (FCEN), University of Antioquia, Medellín, Colombia
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13
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Silva HKTDA, Barbosa TM, Santos MCD, Silva LG, de Lima LAS, Morais CLM, Bicudo TC, Gama RA, de Lima KMG. Near infrared spectroscopy (NIRS) coupled with chemometric methods to identify and estimate taxonomic relationships of flies with forensic potential (Diptera: Calliphoridae and Sarcophagidae). Acta Trop 2022; 235:106672. [PMID: 36041495 DOI: 10.1016/j.actatropica.2022.106672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 08/09/2022] [Accepted: 08/26/2022] [Indexed: 11/01/2022]
Abstract
Infrared spectroscopy has been gaining prominence in entomology, such as for solving taxonomic problems, sexing adult specimens, determining the age of immature specimens, detecting drugs of abuse in fly larvae, and can be an important technique in Forensic Entomology. In order to help identify the species of Calliphoridae and Sarcophagidae families, the present study aimed to evaluate the use of near infrared spectroscopy (NIRS) coupled with chemometric methods for separating fly specimens into taxonomic categories and understanding the taxonomic relationship between them. Spectra collected from nine species of flies were subjected to unsupervised principal component analysis (PCA) and hierarchical cluster analysis (HCA), in which we sought to visualize the relationship between the samples (segregation of genera and families) with subsequent identification. In PCA, the best model was achieved using five principal components (PCs), which explained 99.16% of total variance of the original data set. The first principal component (PC1) and the fourth principal component (PC4) provided the best segregation, the latter being more important in the segregation of the species Chrysomya albiceps, Lucilia eximia, and Ravinia belforti from the others. In the HCA dendrogram, there was a clear separation between the specimens by family (Calliphoridae and Sarcophagidae) and genera (Chrysomya, Lucilia, Oxysarcodexia, Peckia and Ravinia). This study shows that NIRS is efficient to identify flies' taxonomic properties, such as family and genera, providing quick evidence for the tested species identity.
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Affiliation(s)
- Hellyda K T de Andrade Silva
- Laboratório de Química Biológica e Quimiometria, Instituto de Química, Universidade Federal do Rio Grande do Norte, Natal, RN, Brasil
| | - Taciano M Barbosa
- Laboratório de Insetos e Vetores, Departamento de Microbiologia e Parasitologia, Universidade Federal do Rio Grande do Norte, Natal, RN, Brasil
| | - Marfran C D Santos
- Laboratório de Química Biológica e Quimiometria, Instituto de Química, Universidade Federal do Rio Grande do Norte, Natal, RN, Brasil.; Instituto Federal de Educação, Ciência e Tecnologia do Sertão Pernambucano - Campus Floresta, Floresta 56400-000, Brasil
| | - Lidiane G Silva
- Laboratório de Química Biológica e Quimiometria, Instituto de Química, Universidade Federal do Rio Grande do Norte, Natal, RN, Brasil
| | - Leomir A S de Lima
- Laboratório de Química Biológica e Quimiometria, Instituto de Química, Universidade Federal do Rio Grande do Norte, Natal, RN, Brasil
| | - Camilo L M Morais
- Laboratório de Química Biológica e Quimiometria, Instituto de Química, Universidade Federal do Rio Grande do Norte, Natal, RN, Brasil
| | - Tatiana C Bicudo
- Escola de Ciências e Tecnologia, Centro de Tecnologia, Universidade Federal do Rio Grande do Norte, Natal, RN, Brasil
| | - Renata A Gama
- Laboratório de Insetos e Vetores, Departamento de Microbiologia e Parasitologia, Universidade Federal do Rio Grande do Norte, Natal, RN, Brasil
| | - Kássio M G de Lima
- Laboratório de Química Biológica e Quimiometria, Instituto de Química, Universidade Federal do Rio Grande do Norte, Natal, RN, Brasil..
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14
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Mgaya JN, Siria DJ, Makala FE, Mgando JP, Vianney JM, Mwanga EP, Okumu FO. Effects of sample preservation methods and duration of storage on the performance of mid-infrared spectroscopy for predicting the age of malaria vectors. Parasit Vectors 2022; 15:281. [PMID: 35933384 PMCID: PMC9356448 DOI: 10.1186/s13071-022-05396-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 07/09/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Monitoring the biological attributes of mosquitoes is critical for understanding pathogen transmission and estimating the impacts of vector control interventions on the survival of vector species. Infrared spectroscopy and machine learning techniques are increasingly being tested for this purpose and have been proven to accurately predict the age, species, blood-meal sources, and pathogen infections in Anopheles and Aedes mosquitoes. However, as these techniques are still in early-stage implementation, there are no standardized procedures for handling samples prior to the infrared scanning. This study investigated the effects of different preservation methods and storage duration on the performance of mid-infrared spectroscopy for age-grading females of the malaria vector, Anopheles arabiensis. METHODS Laboratory-reared An. arabiensis (N = 3681) were collected at 5 and 17 days post-emergence, killed with ethanol, and then preserved using silica desiccant at 5 °C, freezing at - 20 °C, or absolute ethanol at room temperature. For each preservation method, the mosquitoes were divided into three groups, stored for 1, 4, or 8 weeks, and then scanned using a mid-infrared spectrometer. Supervised machine learning classifiers were trained with the infrared spectra, and the support vector machine (SVM) emerged as the best model for predicting the mosquito ages. RESULTS The model trained using silica-preserved mosquitoes achieved 95% accuracy when predicting the ages of other silica-preserved mosquitoes, but declined to 72% and 66% when age-classifying mosquitoes preserved using ethanol and freezing, respectively. Prediction accuracies of models trained on samples preserved in ethanol and freezing also reduced when these models were applied to samples preserved by other methods. Similarly, models trained on 1-week stored samples had declining accuracies of 97%, 83%, and 72% when predicting the ages of mosquitoes stored for 1, 4, or 8 weeks respectively. CONCLUSIONS When using mid-infrared spectroscopy and supervised machine learning to age-grade mosquitoes, the highest accuracies are achieved when the training and test samples are preserved in the same way and stored for similar durations. However, when the test and training samples were handled differently, the classification accuracies declined significantly. Protocols for infrared-based entomological studies should therefore emphasize standardized sample-handling procedures and possibly additional statistical procedures such as transfer learning for greater accuracy.
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Affiliation(s)
- Jacqueline N Mgaya
- Environmental Health and Ecological Science Department, Ifakara Health Institute, P.O. Box 53, Ifakara, Tanzania.
- School of Life Science and Bioengineering, The Nelson Mandela African Institute of Science and Technology, P.O. Box 447, Arusha, Tanzania.
| | - Doreen J Siria
- Environmental Health and Ecological Science Department, Ifakara Health Institute, P.O. Box 53, Ifakara, Tanzania
| | - Faraja E Makala
- Environmental Health and Ecological Science Department, Ifakara Health Institute, P.O. Box 53, Ifakara, Tanzania
| | - Joseph P Mgando
- Environmental Health and Ecological Science Department, Ifakara Health Institute, P.O. Box 53, Ifakara, Tanzania
| | - John-Mary Vianney
- School of Life Science and Bioengineering, The Nelson Mandela African Institute of Science and Technology, P.O. Box 447, Arusha, Tanzania
| | - Emmanuel P Mwanga
- Environmental Health and Ecological Science Department, Ifakara Health Institute, P.O. Box 53, Ifakara, Tanzania.
- Institute of Biodiversity, Animal Health, and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK.
| | - Fredros O Okumu
- Environmental Health and Ecological Science Department, Ifakara Health Institute, P.O. Box 53, Ifakara, Tanzania.
- School of Life Science and Bioengineering, The Nelson Mandela African Institute of Science and Technology, P.O. Box 447, Arusha, Tanzania.
- School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
- Institute of Biodiversity, Animal Health, and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK.
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15
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Infrared spectroscopy (NIRS and ATR-FTIR) together with multivariate classification for non-destructive differentiation between female mosquitoes of Aedes aegypti recently infected with dengue vs. uninfected females. Acta Trop 2022; 235:106633. [PMID: 35932844 DOI: 10.1016/j.actatropica.2022.106633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 07/12/2022] [Accepted: 08/03/2022] [Indexed: 11/23/2022]
Abstract
One of the most important steps in preventing arboviruses is entomological surveillance. The main entomological surveillance action is to detect vector foci in the shortest possible stages. In this work, near and medium infrared spectra collected from female Aedes aegypti mosquitoes recently infected and not infected with dengue were used in order to build chemometric models capable of differentiating the spectra of each class. For this, computational algorithms such as Successive Projection Algorithm (SPA) and Genetic Algorithm (GA) were used together with Linear Discriminant Analysis (LDA). The constructed models were evaluated with sensitivity and specificity calculations. It was observed that models based on near infrared (NIR) spectra have better classification results when compared to mid infrared (MIR) spectra, as well as models based on GA present better results when compared to those based on SPA. Thus, NIR-GA-LDA obtained the best results, reaching 100.00 % for sensitivity and specificity. NIR spectroscopy is 18 times faster and 116 times cheaper than RT-qPCR. The findings reported in this study may have important applications in the field of entomological surveillance, prevention and control of dengue vectors. In the future, mosquito traps equipped with portable NIR instruments capable of detecting infected mosquitoes may be used, in order to enable an action plan to prevent future outbreaks of the disease.
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Gray L, Asay BC, Hephaestus B, McCabe R, Pugh G, Markle ED, Churcher TS, Foy BD. Back to the Future: Quantifying Wing Wear as a Method to Measure Mosquito Age. Am J Trop Med Hyg 2022; 107:tpmd211173. [PMID: 35895347 PMCID: PMC9490652 DOI: 10.4269/ajtmh.21-1173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 04/15/2022] [Indexed: 11/07/2022] Open
Abstract
Vector biologists have long sought the ability to accurately quantify the age of wild mosquito populations, a metric used to measure vector control efficiency. This has proven difficult due to the difficulties of working in the field and the biological complexities of wild mosquitoes. Ideal age grading techniques must overcome both challenges while also providing epidemiologically relevant age measurements. Given these requirements, the Detinova parity technique, which estimates age from the mosquito ovary and tracheole skein morphology, has been most often used for mosquito age grading despite significant limitations, including being based solely on the physiology of ovarian development. Here, we have developed a modernized version of the original mosquito aging method that evaluated wing wear, expanding it to estimate mosquito chronological age from wing scale loss. We conducted laboratory experiments using adult Anopheles gambiae held in insectary cages or mesocosms, the latter of which also featured ivermectin bloodmeal treatments to change the population age structure. Mosquitoes were age graded by parity assessments and both human- and computational-based wing evaluations. Although the Detinova technique was not able to detect differences in age population structure between treated and control mesocosms, significant differences were apparent using the wing scale technique. Analysis of wing images using averaged left- and right-wing pixel intensity scores predicted mosquito age at high accuracy (overall test accuracy: 83.4%, average training accuracy: 89.7%). This suggests that this technique could be an accurate and practical tool for mosquito age grading though further evaluation in wild mosquito populations is required.
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Affiliation(s)
- Lyndsey Gray
- Center for Vector-Borne Infectious Diseases, Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado
| | | | | | - Ruth McCabe
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College, London, United Kingdom
| | - Greg Pugh
- Center for Vector-Borne Infectious Diseases, Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado
| | - Erin D. Markle
- Center for Vector-Borne Infectious Diseases, Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado
| | - Thomas S. Churcher
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College, London, United Kingdom
| | - Brian D. Foy
- Center for Vector-Borne Infectious Diseases, Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado
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Autofluorescent Biomolecules in Diptera: From Structure to Metabolism and Behavior. Molecules 2022; 27:molecules27144458. [PMID: 35889334 PMCID: PMC9318335 DOI: 10.3390/molecules27144458] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 07/08/2022] [Accepted: 07/08/2022] [Indexed: 02/04/2023] Open
Abstract
Light-based phenomena in insects have long attracted researchers’ attention. Surface color distribution patterns are commonly used for taxonomical purposes, while optically-active structures from Coleoptera cuticle or Lepidoptera wings have inspired technological applications, such as biosensors and energy accumulation devices. In Diptera, besides optically-based phenomena, biomolecules able to fluoresce can act as markers of bio-metabolic, structural and behavioral features. Resilin or chitinous compounds, with their respective blue or green-to-red autofluorescence (AF), are commonly related to biomechanical and structural properties, helpful to clarify the mechanisms underlying substrate adhesion of ectoparasites’ leg appendages, or the antennal abilities in tuning sound detection. Metarhodopsin, a red fluorescing photoproduct of rhodopsin, allows to investigate visual mechanisms, whereas NAD(P)H and flavins, commonly relatable to energy metabolism, favor the investigation of sperm vitality. Lipofuscins are AF biomarkers of aging, as well as pteridines, which, similarly to kynurenines, are also exploited in metabolic investigations. Beside the knowledge available in Drosophila melanogaster, a widely used model to study also human disorder and disease mechanisms, here we review optically-based studies in other dipteran species, including mosquitoes and fruit flies, discussing future perspectives for targeted studies with various practical applications, including pest and vector control.
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Joy T, Chen M, Arnbrister J, Williamson D, Li S, Nair S, Brophy M, Garcia VM, Walker K, Ernst K, Gouge DH, Carrière Y, Riehle MA. Assessing Near-Infrared Spectroscopy (NIRS) for Evaluation of Aedes aegypti Population Age Structure. INSECTS 2022; 13:insects13040360. [PMID: 35447802 PMCID: PMC9029691 DOI: 10.3390/insects13040360] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 04/03/2022] [Accepted: 04/05/2022] [Indexed: 02/04/2023]
Abstract
Given that older Aedes aegypti (L.) mosquitoes typically pose the greatest risk of pathogen transmission, the capacity to age grade wild Ae. aegypti mosquito populations would be a valuable tool in monitoring the potential risk of arboviral transmission. Here, we compared the effectiveness of near-infrared spectroscopy (NIRS) to age grade field-collected Ae. aegypti with two alternative techniques—parity analysis and transcript abundance of the age-associated gene SCP1. Using lab-reared mosquitoes of known ages from three distinct populations maintained as adults under laboratory or semi-field conditions, we developed and validated four NIRS models for predicting the age of field-collected Ae. aegypti. To assess the accuracy of these models, female Ae. aegypti mosquitoes were collected from Maricopa County, AZ, during the 2017 and 2018 monsoon season, and a subset were age graded using the three different age-grading techniques. For both years, each of the four NIRS models consistently graded parous mosquitoes as significantly older than nulliparous mosquitoes. Furthermore, a significant positive linear association occurred between SCP1 and NIRS age predictions in seven of the eight year/model combinations, although considerable variation in the predicted age of individual mosquitoes was observed. Our results suggest that although the NIRS models were not adequate in determining the age of individual field-collected mosquitoes, they have the potential to quickly and cost effectively track changes in the age structure of Ae. aegypti populations across locations and over time.
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Affiliation(s)
- Teresa Joy
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Minhao Chen
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Joshua Arnbrister
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Daniel Williamson
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Shujuan Li
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Shakunthala Nair
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Maureen Brophy
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Valerie Madera Garcia
- Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ 85724, USA; (V.M.G.); (K.E.)
| | - Kathleen Walker
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Kacey Ernst
- Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ 85724, USA; (V.M.G.); (K.E.)
| | - Dawn H. Gouge
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Yves Carrière
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Michael A. Riehle
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
- Correspondence: ; Tel.: +1-520-626-8500
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Tátila-Ferreira A, Garcia GA, Dos Santos LMB, Pavan MG, de C Moreira CJ, Victoriano JC, da Silva-Junior R, Dos Santos-Mallet JR, Verly T, Britto C, Sikulu-Lord MT, Maciel-de-Freitas R. Near infrared spectroscopy accurately detects Trypanosoma cruzi non-destructively in midguts, rectum and excreta samples of Triatoma infestans. Sci Rep 2021; 11:23884. [PMID: 34903840 PMCID: PMC8668913 DOI: 10.1038/s41598-021-03465-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 11/29/2021] [Indexed: 11/09/2022] Open
Abstract
Chagas disease is a neglected tropical disease caused by Trypanosoma cruzi parasite with an estimated 70 million people at risk. Traditionally, parasite presence in triatomine vectors is detected through optical microscopy which can be low in sensitivity or molecular techniques which can be costly in endemic countries. The aim of this study was to evaluate the ability of a reagent-free technique, the Near Infrared Spectroscopy (NIRS) for rapid and non-invasive detection of T. cruzi in Triatoma infestans body parts and in wet/dry excreta samples of the insect. NIRS was 100% accurate for predicting the presence of T. cruzi infection Dm28c strain (TcI) in either the midgut or the rectum and models developed from either body part could predict infection in the other part. Models developed to predict infection in excreta samples were 100% accurate for predicting infection in both wet and dry samples. However, models developed using dry excreta could not predict infection in wet samples and vice versa. This is the first study to report on the potential application of NIRS for rapid and non-invasive detection of T. cruzi infection in T. infestans in the laboratory. Future work should demonstrate the capacity of NIRS to detect T. cruzi in triatomines originating from the field.
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Affiliation(s)
- Aline Tátila-Ferreira
- Laboratório de Transmissores de Hematozoários, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Gabriela A Garcia
- Laboratório de Transmissores de Hematozoários, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Lilha M B Dos Santos
- Laboratório de Transmissores de Hematozoários, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Márcio G Pavan
- Laboratório de Transmissores de Hematozoários, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Carlos José de C Moreira
- Laboratório de Doenças Parasitárias, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Juliana C Victoriano
- Laboratório Interdisciplinar de Vigilância Entomológica de Diptera E Hemiptera, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Renato da Silva-Junior
- Laboratório Interdisciplinar de Vigilância Entomológica de Diptera E Hemiptera, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
- Universidade Iguaçu - UNIG, Rio de Janeiro, Brazil
| | - Jacenir R Dos Santos-Mallet
- Laboratório Interdisciplinar de Vigilância Entomológica de Diptera E Hemiptera, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
- Universidade Iguaçu - UNIG, Rio de Janeiro, Brazil
| | - Thaiane Verly
- Laboratório de Biologia Molecular e Doenças Endêmicas, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Constança Britto
- Laboratório de Biologia Molecular e Doenças Endêmicas, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Maggy T Sikulu-Lord
- The School of Public Health, The University of Queensland, Herston, QLD, 4006, Australia
| | - Rafael Maciel-de-Freitas
- Laboratório de Transmissores de Hematozoários, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.
- Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
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20
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Lucas ER, Darby AC, Torr SJ, Donnelly MJ. A gene expression panel for estimating age in males and females of the sleeping sickness vector Glossina morsitans. PLoS Negl Trop Dis 2021; 15:e0009797. [PMID: 34555037 PMCID: PMC8491940 DOI: 10.1371/journal.pntd.0009797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 10/05/2021] [Accepted: 09/08/2021] [Indexed: 12/02/2022] Open
Abstract
Many vector-borne diseases are controlled by methods that kill the insect vectors responsible for disease transmission. Recording the age structure of vector populations provides information on mortality rates and vectorial capacity, and should form part of the detailed monitoring that occurs in the wake of control programmes, yet tools for obtaining estimates of individual age remain limited. We investigate the potential of using markers of gene expression to predict age in tsetse flies, which are the vectors of deadly and economically damaging African trypanosomiases. We use RNAseq to identify candidate expression markers, and test these markers using qPCR in laboratory-reared Glossina morsitans morsitans of known age. Measuring the expression of six genes was sufficient to obtain a prediction of age with root mean squared error of less than 8 days, while just two genes were sufficient to classify flies into age categories of ≤15 and >15 days old. Further testing of these markers in field-caught samples and in other species will determine the accuracy of these markers in the field.
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Affiliation(s)
- Eric R. Lucas
- Liverpool School of Tropical Medicine, Liverpool, United Kingdom
| | - Alistair C. Darby
- Institute of Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Stephen J. Torr
- Liverpool School of Tropical Medicine, Liverpool, United Kingdom
| | - Martin J. Donnelly
- Liverpool School of Tropical Medicine, Liverpool, United Kingdom
- Wellcome Sanger Institute, Cambridge, United Kingdom
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21
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Wei C, Wang J, Ji J. Forensic Classification of Pigments by Attenuated Total Reflectance – Fourier Transform Infrared Spectroscopy and Chemometrics. ANAL LETT 2021. [DOI: 10.1080/00032719.2020.1801712] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Chenjie Wei
- School of investigation, People’s Public Security University of China, Beijing, China
| | - Jifen Wang
- School of investigation, People’s Public Security University of China, Beijing, China
| | - Jiahua Ji
- School of investigation, People’s Public Security University of China, Beijing, China
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22
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Detection of Plasmodium falciparum in laboratory-reared and naturally infected wild mosquitoes using near-infrared spectroscopy. Sci Rep 2021; 11:10289. [PMID: 33986416 PMCID: PMC8119679 DOI: 10.1038/s41598-021-89715-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 04/21/2021] [Indexed: 11/18/2022] Open
Abstract
There is an urgent need for high throughput, affordable methods of detecting pathogens inside insect vectors to facilitate surveillance. Near-infrared spectroscopy (NIRS) has shown promise to detect arbovirus and malaria in the laboratory but has not been evaluated in field conditions. Here we investigate the ability of NIRS to identify Plasmodium falciparum in Anopheles coluzzii mosquitoes. NIRS models trained on laboratory-reared mosquitoes infected with wild malaria parasites can detect the parasite in comparable mosquitoes with moderate accuracy though fails to detect oocysts or sporozoites in naturally infected field caught mosquitoes. Models trained on field mosquitoes were unable to predict the infection status of other field mosquitoes. Restricting analyses to mosquitoes of uninfectious and highly-infectious status did improve predictions suggesting sensitivity and specificity may be better in mosquitoes with higher numbers of parasites. Detection of infection appears restricted to homogenous groups of mosquitoes diminishing NIRS utility for detecting malaria within mosquitoes.
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23
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Goh B, Ching K, Soares Magalhães RJ, Ciocchetta S, Edstein MD, Maciel-de-Freitas R, Sikulu-Lord MT. The application of spectroscopy techniques for diagnosis of malaria parasites and arboviruses and surveillance of mosquito vectors: A systematic review and critical appraisal of evidence. PLoS Negl Trop Dis 2021; 15:e0009218. [PMID: 33886567 PMCID: PMC8061870 DOI: 10.1371/journal.pntd.0009218] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
CONCLUSIONS/SIGNIFICANCE The potential of RS as a surveillance tool for malaria and arbovirus vectors and MIRS for the diagnosis and surveillance of arboviruses is yet to be assessed. NIRS capacity as a surveillance tool for malaria and arbovirus vectors should be validated under field conditions, and its potential as a diagnostic tool for malaria and arboviruses needs to be evaluated. It is recommended that all 3 techniques evaluated simultaneously using multiple machine learning techniques in multiple epidemiological settings to determine the most accurate technique for each application. Prior to their field application, a standardised protocol for spectra collection and data analysis should be developed. This will harmonise their application in multiple field settings allowing easy and faster integration into existing disease control platforms. Ultimately, development of rapid and cost-effective point-of-care diagnostic tools for malaria and arboviruses based on spectroscopy techniques may help combat current and future outbreaks of these infectious diseases.
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Affiliation(s)
- Brendon Goh
- School of Public Health, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Koek Ching
- School of Public Health, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Ricardo J Soares Magalhães
- UQ Spatial Epidemiology Laboratory, School of Veterinary Science, The University of Queensland, Brisbane, Australia
- Children's Health Research Centre, Children's Health and Environment Program, The University of Queensland, Brisbane, Australia
| | - Silvia Ciocchetta
- School of Public Health, Faculty of Medicine, The University of Queensland, Brisbane, Australia
- UQ Spatial Epidemiology Laboratory, School of Veterinary Science, The University of Queensland, Brisbane, Australia
| | - Michael D Edstein
- Australian Defence Force, Malaria and Infectious Disease Institute, Brisbane, Australia
| | | | - Maggy T Sikulu-Lord
- School of Public Health, Faculty of Medicine, The University of Queensland, Brisbane, Australia
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24
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Time Flies-Age Grading of Adult Flies for the Estimation of the Post-Mortem Interval. Diagnostics (Basel) 2021; 11:diagnostics11020152. [PMID: 33494172 PMCID: PMC7909779 DOI: 10.3390/diagnostics11020152] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 01/18/2021] [Accepted: 01/19/2021] [Indexed: 11/16/2022] Open
Abstract
The estimation of the minimum time since death is one of the main applications of forensic entomology. This can be done by calculating the age of the immature stage of necrophagous flies developing on the corpse, which is confined to approximately 2–4 weeks, depending on temperature and species of the first colonizing wave of flies. Adding the age of the adult flies developed on the dead body could extend this time frame up to several weeks when the body is in a building or closed premise. However, the techniques for accurately estimating the age of adult flies are still in their beginning stages or not sufficiently validated. Here we review the current state of the art of analysing the aging of flies by evaluating the ovarian development, the amount of pteridine in the eyes, the degree of wing damage, the modification of their cuticular hydrocarbon patterns, and the increasing number of growth layers in the cuticula. New approaches, including the use of age specific molecular profiles based on the levels of gene and protein expression and the application of near infrared spectroscopy, are introduced, and the forensic relevance of these methods is discussed.
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25
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Santos LMB, Mutsaers M, Garcia GA, David MR, Pavan MG, Petersen MT, Corrêa-Antônio J, Couto-Lima D, Maes L, Dowell F, Lord A, Sikulu-Lord M, Maciel-de-Freitas R. High throughput estimates of Wolbachia, Zika and chikungunya infection in Aedes aegypti by near-infrared spectroscopy to improve arbovirus surveillance. Commun Biol 2021; 4:67. [PMID: 33452445 PMCID: PMC7810739 DOI: 10.1038/s42003-020-01601-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 12/15/2020] [Indexed: 12/20/2022] Open
Abstract
Deployment of Wolbachia to mitigate dengue (DENV), Zika (ZIKV) and chikungunya (CHIKV) transmission is ongoing in 12 countries. One way to assess the efficacy of Wolbachia releases is to determine invasion rates within the wild population of Aedes aegypti following their release. Herein we evaluated the accuracy, sensitivity and specificity of the Near Infrared Spectroscopy (NIRS) in estimating the time post death, ZIKV-, CHIKV-, and Wolbachia-infection in trapped dead female Ae. aegypti mosquitoes over a period of 7 days. Regardless of the infection type, time post-death of mosquitoes was accurately predicted into four categories (fresh, 1 day old, 2–4 days old and 5–7 days old). Overall accuracies of 93.2, 97 and 90.3% were observed when NIRS was used to detect ZIKV, CHIKV and Wolbachia in dead Ae. aegypti female mosquitoes indicating NIRS could be potentially applied as a rapid and cost-effective arbovirus surveillance tool. However, field data is required to demonstrate the full capacity of NIRS for detecting these infections under field conditions. Santos et al. demonstrate that the Near Infrared Spectroscopy (NIRS) can accurately estimate the death time of trapped female Aedes aegypti and vector infection with Zika virus, Chikungunya virus, or Wolbachia in a 7-day trapping period. This study suggests that NIRS may provide an accurate and inexpensive tool that improves arbovirus surveillance systems.
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Affiliation(s)
- Lilha M B Santos
- Laboratório de Transmissores de Hematozoários, IOC, Fundação Oswaldo Cruz, Rio de Janeiro, RJ, 21040-360, Brazil
| | - Mathijs Mutsaers
- Laboratório de Transmissores de Hematozoários, IOC, Fundação Oswaldo Cruz, Rio de Janeiro, RJ, 21040-360, Brazil.,Laboratory for Microbiology, Parasitology and Hygiene (LMPH), University of Antwerp, 2000, Antwerp, Belgium
| | - Gabriela A Garcia
- Laboratório de Transmissores de Hematozoários, IOC, Fundação Oswaldo Cruz, Rio de Janeiro, RJ, 21040-360, Brazil
| | - Mariana R David
- Laboratório de Transmissores de Hematozoários, IOC, Fundação Oswaldo Cruz, Rio de Janeiro, RJ, 21040-360, Brazil
| | - Márcio G Pavan
- Laboratório de Transmissores de Hematozoários, IOC, Fundação Oswaldo Cruz, Rio de Janeiro, RJ, 21040-360, Brazil.,Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular (INCT-EM), Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, 21941-902, Brazil
| | - Martha T Petersen
- Laboratório de Transmissores de Hematozoários, IOC, Fundação Oswaldo Cruz, Rio de Janeiro, RJ, 21040-360, Brazil
| | - Jessica Corrêa-Antônio
- Laboratório de Transmissores de Hematozoários, IOC, Fundação Oswaldo Cruz, Rio de Janeiro, RJ, 21040-360, Brazil
| | - Dinair Couto-Lima
- Laboratório de Transmissores de Hematozoários, IOC, Fundação Oswaldo Cruz, Rio de Janeiro, RJ, 21040-360, Brazil
| | - Louis Maes
- Laboratory for Microbiology, Parasitology and Hygiene (LMPH), University of Antwerp, 2000, Antwerp, Belgium
| | - Floyd Dowell
- USDA-ARS, Center for Grain and Animal Health Research, Manhattan, KS, 66502, USA
| | - Anton Lord
- The School of Public Health, The University of Queensland, Herston, QLD, 4006, Australia.,QIMR Berghofer Medical Research Institute, Herston, QLD, 4006, Australia
| | - Maggy Sikulu-Lord
- The School of Public Health, The University of Queensland, Herston, QLD, 4006, Australia
| | - Rafael Maciel-de-Freitas
- Laboratório de Transmissores de Hematozoários, IOC, Fundação Oswaldo Cruz, Rio de Janeiro, RJ, 21040-360, Brazil. .,Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular (INCT-EM), Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, 21941-902, Brazil.
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26
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Farlow R, Russell TL, Burkot TR. Nextgen Vector Surveillance Tools: sensitive, specific, cost-effective and epidemiologically relevant. Malar J 2020; 19:432. [PMID: 33239015 PMCID: PMC7687713 DOI: 10.1186/s12936-020-03494-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 11/11/2020] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND Vector surveillance provides critical data for decision-making to ensure that malaria control programmes remain effective and responsive to any threats to a successful control and elimination programme. The quality and quantity of data collected is dependent on the sampling tools and laboratory techniques used which may lack the sensitivity required to collect relevant data for decision-making. Here, 40 vector control experts were interviewed to assess the benefits and limitations of the current vector surveillance tools and techniques. In addition, experts shared ideas on "blue sky" indicators which encompassed ideas for novel methods to monitor presently used indicators, or to measure novel vector behaviours not presently measured. Algorithms for deploying surveillance tools and priorities for understanding vector behaviours are also needed for collecting and interpreting vector data. RESULTS The available tools for sampling and analysing vectors are often hampered by high labour and resource requirements (human and supplies) coupled with high outlay and operating costs and variable tool performance across species and geographic regions. The next generation of surveillance tools needs to address the limitations of present tools by being more sensitive, specific and less costly to deploy to enable the collection and use of epidemiologically relevant vector data to facilitate more proactive vector control guidance. Ideas and attributes for Target Product Profiles (TPPs) generated from this analysis provide targets for research and funding to develop next generation tools. CONCLUSIONS More efficient surveillance tools and a more complete understanding of vector behaviours and populations will provide a basis for more cost effective and successful malaria control. Understanding the vectors' behaviours will allow interventions to be deployed that target vulnerabilities in vector behaviours and thus enable more effective control. Through defining the strengths and weaknesses of the current vector surveillance methods, a foundation and initial framework was provided to define the TPPs for the next generation of vector surveillance methods. The draft TTPs presented here aim to ensure that the next generation tools and technologies are not encumbered by the limitations of present surveillance methods and can be readily deployed in low resource settings.
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Affiliation(s)
| | - Tanya L Russell
- Australian Institute of Tropical Health and Medicine, James Cook University, Cairns, Australia
| | - Thomas R Burkot
- Australian Institute of Tropical Health and Medicine, James Cook University, Cairns, Australia.
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27
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Near-Infrared Spectroscopy Evaluations for the Differentiation of Carbapenem-Resistant from Susceptible Enterobacteriaceae Strains. Diagnostics (Basel) 2020; 10:diagnostics10100736. [PMID: 32977503 PMCID: PMC7598181 DOI: 10.3390/diagnostics10100736] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 09/11/2020] [Accepted: 09/21/2020] [Indexed: 11/24/2022] Open
Abstract
Antimicrobial Resistance (AMR) caused by Carbapenem-Resistant Enterobacteriaceae (CRE) is a global threat. Accurate identification of these bacterial species with associated AMR is critical for their management. While highly accurate methods to detect CRE are available, they are costly, timely and require expert skills, making their application infeasible in low-resource settings. Here, we investigated the potential of Near-Infrared Spectroscopy (NIRS) for a range of applications: (i) the detection and differentiation of isolates of two pathogenic Enterobacteriaceae species, Klebsiella pneumoniae and Escherichia coli, and (ii) the differentiation of carbapenem resistant and susceptible K. pneumoniae. NIRS has successfully differentiated between K. pneumoniae and E. coli isolates with a predictive accuracy of 89.04% (95% CI; 88.7–89.4%). K. pneumoniae isolates harbouring carbapenem-resistance determinants were differentiated from susceptible K. pneumoniae strains with an accuracy of 85% (95% CI; 84.2–86.1%). To our knowledge, this is the largest proof of concept demonstration for the utility and feasibility of NIRS to rapidly differentiate between K. pneumoniae and E. coli as well as carbapenem-resistant K. pneumoniae from susceptible strains.
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28
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An autoencoder and artificial neural network-based method to estimate parity status of wild mosquitoes from near-infrared spectra. PLoS One 2020; 15:e0234557. [PMID: 32555660 PMCID: PMC7302571 DOI: 10.1371/journal.pone.0234557] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Accepted: 05/27/2020] [Indexed: 11/19/2022] Open
Abstract
After mating, female mosquitoes need animal blood to develop their eggs. In the process of acquiring blood, they may acquire pathogens, which may cause different diseases in humans such as malaria, zika, dengue, and chikungunya. Therefore, knowing the parity status of mosquitoes is useful in control and evaluation of infectious diseases transmitted by mosquitoes, where parous mosquitoes are assumed to be potentially infectious. Ovary dissections, which are currently used to determine the parity status of mosquitoes, are very tedious and limited to few experts. An alternative to ovary dissections is near-infrared spectroscopy (NIRS), which can estimate the age in days and the infectious state of laboratory and semi-field reared mosquitoes with accuracies between 80 and 99%. No study has tested the accuracy of NIRS for estimating the parity status of wild mosquitoes. In this study, we train an artificial neural network (ANN) models on NIR spectra to estimate the parity status of wild mosquitoes. We use four different datasets: An. arabiensis collected from Minepa, Tanzania (Minepa-ARA); An. gambiae s.s collected from Muleba, Tanzania (Muleba-GA); An. gambiae s.s collected from Burkina Faso (Burkina-GA); and An.gambiae s.s from Muleba and Burkina Faso combined (Muleba-Burkina-GA). We train ANN models on datasets with spectra preprocessed according to previous protocols. We then use autoencoders to reduce the spectra feature dimensions from 1851 to 10 and re-train the ANN models. Before the autoencoder was applied, ANN models estimated parity status of mosquitoes in Minepa-ARA, Muleba-GA, Burkina-GA and Muleba-Burkina-GA with out-of-sample accuracies of 81.9±2.8 (N = 274), 68.7±4.8 (N = 43), 80.3±2.0 (N = 48), and 75.7±2.5 (N = 91), respectively. With the autoencoder, ANN models tested on out-of-sample data achieved 97.1±2.2% (N = 274), 89.8 ± 1.7% (N = 43), 93.3±1.2% (N = 48), and 92.7±1.8% (N = 91) accuracies for Minepa-ARA, Muleba-GA, Burkina-GA, and Muleba-Burkina-GA, respectively. These results show that a combination of an autoencoder and an ANN trained on NIR spectra to estimate the parity status of wild mosquitoes yields models that can be used as an alternative tool to estimate parity status of wild mosquitoes, especially since NIRS is a high-throughput, reagent-free, and simple-to-use technique compared to ovary dissections.
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Ong OTW, Kho EA, Esperança PM, Freebairn C, Dowell FE, Devine GJ, Churcher TS. Ability of near-infrared spectroscopy and chemometrics to predict the age of mosquitoes reared under different conditions. Parasit Vectors 2020; 13:160. [PMID: 32228670 PMCID: PMC7106667 DOI: 10.1186/s13071-020-04031-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 03/24/2020] [Indexed: 01/04/2023] Open
Abstract
Background Practical, field-ready age-grading tools for mosquito vectors of disease are urgently needed because of the impact that daily survival has on vectorial capacity. Previous studies have shown that near-infrared spectroscopy (NIRS), in combination with chemometrics and predictive modeling, can forecast the age of laboratory-reared mosquitoes with moderate to high accuracy. It remains unclear whether the technique has utility for identifying shifts in the age structure of wild-caught mosquitoes. Here we investigate whether models derived from the laboratory strain of mosquitoes can be used to predict the age of mosquitoes grown from pupae collected in the field. Methods NIRS data from adult female Aedes albopictus mosquitoes reared in the laboratory (2, 5, 8, 12 and 15 days-old) were analysed against spectra from mosquitoes emerging from wild-caught pupae (1, 7 and 14 days-old). Different partial least squares (PLS) regression methods trained on spectra from laboratory mosquitoes were evaluated on their ability to predict the age of mosquitoes from more natural environments. Results Models trained on spectra from laboratory-reared material were able to predict the age of other laboratory-reared mosquitoes with moderate accuracy and successfully differentiated all day 2 and 15 mosquitoes. Models derived with laboratory mosquitoes could not differentiate between field-derived age groups, with age predictions relatively indistinguishable for day 1–14. Pre-processing of spectral data and improving the PLS regression framework to avoid overfitting can increase accuracy, but predictions of mosquitoes reared in different environments remained poor. Principal components analysis confirms substantial spectral variations between laboratory and field-derived mosquitoes despite both originating from the same island population. Conclusions Models trained on laboratory mosquitoes were able to predict ages of laboratory mosquitoes with good sensitivity and specificity though they were unable to predict age of field-derived mosquitoes. This study suggests that laboratory-reared mosquitoes do not capture enough environmental variation to accurately predict the age of the same species reared under different conditions. Further research is needed to explore alternative pre-processing methods and machine learning techniques, and to understand factors that affect absorbance in mosquitoes before field application using NIRS.![]()
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Affiliation(s)
- Oselyne T W Ong
- Mosquito Control Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
| | - Elise A Kho
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, Queensland, Australia
| | - Pedro M Esperança
- Department of Infectious Disease Epidemiology, MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Chris Freebairn
- Private Contracting Entomologist, Hammond Island, Queensland, Australia
| | - Floyd E Dowell
- USDA, Centre for Grain and Animal Health Research, 1515 College Avenue, Manhattan, KS, 66502, USA
| | - Gregor J Devine
- Mosquito Control Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Thomas S Churcher
- Department of Infectious Disease Epidemiology, MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
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Mosquito Age Grading and Vector-Control Programmes. Trends Parasitol 2019; 36:39-51. [PMID: 31836285 DOI: 10.1016/j.pt.2019.10.011] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Revised: 10/24/2019] [Accepted: 10/24/2019] [Indexed: 12/16/2022]
Abstract
An ability to characterize the age of mosquito populations could provide cost-effective and compelling entomological evidence for the potential epidemiological impacts of vector control. The average age of a mosquito population is the most important determinant of vectorial capacity and the likelihood of disease transmission. Yet, despite decades of research, defining the age of a wild-caught mosquito remains a challenging, impractical, and unreliable process. Emerging chemometric and existing transcriptional approaches may overcome many of the limitations of current morphological techniques, but their utility in terms of field-based monitoring programmes remains largely untested. Herein, we review the potential advantages and disadvantages of new and existing age-grading tools in an operational context.
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Mwanga EP, Minja EG, Mrimi E, Jiménez MG, Swai JK, Abbasi S, Ngowo HS, Siria DJ, Mapua S, Stica C, Maia MF, Olotu A, Sikulu-Lord MT, Baldini F, Ferguson HM, Wynne K, Selvaraj P, Babayan SA, Okumu FO. Detection of malaria parasites in dried human blood spots using mid-infrared spectroscopy and logistic regression analysis. Malar J 2019; 18:341. [PMID: 31590669 PMCID: PMC6781347 DOI: 10.1186/s12936-019-2982-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 09/28/2019] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Epidemiological surveys of malaria currently rely on microscopy, polymerase chain reaction assays (PCR) or rapid diagnostic test kits for Plasmodium infections (RDTs). This study investigated whether mid-infrared (MIR) spectroscopy coupled with supervised machine learning could constitute an alternative method for rapid malaria screening, directly from dried human blood spots. METHODS Filter papers containing dried blood spots (DBS) were obtained from a cross-sectional malaria survey in 12 wards in southeastern Tanzania in 2018/19. The DBS were scanned using attenuated total reflection-Fourier Transform Infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra in the range 4000 cm-1 to 500 cm-1. The spectra were cleaned to compensate for atmospheric water vapour and CO2 interference bands and used to train different classification algorithms to distinguish between malaria-positive and malaria-negative DBS papers based on PCR test results as reference. The analysis considered 296 individuals, including 123 PCR-confirmed malaria positives and 173 negatives. Model training was done using 80% of the dataset, after which the best-fitting model was optimized by bootstrapping of 80/20 train/test-stratified splits. The trained models were evaluated by predicting Plasmodium falciparum positivity in the 20% validation set of DBS. RESULTS Logistic regression was the best-performing model. Considering PCR as reference, the models attained overall accuracies of 92% for predicting P. falciparum infections (specificity = 91.7%; sensitivity = 92.8%) and 85% for predicting mixed infections of P. falciparum and Plasmodium ovale (specificity = 85%, sensitivity = 85%) in the field-collected specimen. CONCLUSION These results demonstrate that mid-infrared spectroscopy coupled with supervised machine learning (MIR-ML) could be used to screen for malaria parasites in human DBS. The approach could have potential for rapid and high-throughput screening of Plasmodium in both non-clinical settings (e.g., field surveys) and clinical settings (diagnosis to aid case management). However, before the approach can be used, we need additional field validation in other study sites with different parasite populations, and in-depth evaluation of the biological basis of the MIR signals. Improving the classification algorithms, and model training on larger datasets could also improve specificity and sensitivity. The MIR-ML spectroscopy system is physically robust, low-cost, and requires minimum maintenance.
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Affiliation(s)
- Emmanuel P Mwanga
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania.
| | - Elihaika G Minja
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania
| | - Emmanuel Mrimi
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania
| | | | - Johnson K Swai
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania
| | - Said Abbasi
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania
| | - Halfan S Ngowo
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Doreen J Siria
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania
| | - Salum Mapua
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania
- School of Life Sciences, University of Keele, Keele, Staffordshire, ST5 5BG, UK
| | - Caleb Stica
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania
| | - Marta F Maia
- KEMRI Wellcome Trust Research Programme, P.O. Box 230, Kilifi, 80108, Kenya
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Old Road Campus Roosevelt Drive, Oxford, OX3 7FZ, UK
| | - Ally Olotu
- KEMRI Wellcome Trust Research Programme, P.O. Box 230, Kilifi, 80108, Kenya
- Interventions and Clinical Trials Department, Ifakara Health Institute, Bagamoyo, Tanzania
| | - Maggy T Sikulu-Lord
- School of Public Health, University of Queensland, Saint Lucia, Australia
- Department of Mathematics, Statistics and Computer Science, Marquette University, Wisconsin, USA
| | - Francesco Baldini
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Heather M Ferguson
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Klaas Wynne
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | | | - Simon A Babayan
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Fredros O Okumu
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania.
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK.
- School of Public Health, University of Witwatersrand, Johannesburg, South Africa.
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González Jiménez M, Babayan SA, Khazaeli P, Doyle M, Walton F, Reedy E, Glew T, Viana M, Ranford-Cartwright L, Niang A, Siria DJ, Okumu FO, Diabaté A, Ferguson HM, Baldini F, Wynne K. Prediction of mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning. Wellcome Open Res 2019; 4:76. [PMID: 31544155 PMCID: PMC6753605 DOI: 10.12688/wellcomeopenres.15201.3] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/10/2019] [Indexed: 11/20/2022] Open
Abstract
Despite the global efforts made in the fight against malaria, the disease is resurging. One of the main causes is the resistance that Anopheles mosquitoes, vectors of the disease, have developed to insecticides. Anopheles must survive for at least 10 days to possibly transmit malaria. Therefore, to evaluate and improve malaria vector control interventions, it is imperative to monitor and accurately estimate the age distribution of mosquito populations as well as their population sizes. Here, we demonstrate a machine-learning based approach that uses mid-infrared spectra of mosquitoes to characterise simultaneously both age and species identity of females of the African malaria vector species Anopheles gambiae and An. arabiensis, using laboratory colonies. Mid-infrared spectroscopy-based prediction of mosquito age structures was statistically indistinguishable from true modelled distributions. The accuracy of classifying mosquitoes by species was 82.6%. The method has a negligible cost per mosquito, does not require highly trained personnel, is rapid, and so can be easily applied in both laboratory and field settings. Our results indicate this method is a promising alternative to current mosquito species and age-grading approaches, with further improvements to accuracy and expansion for use with wild mosquito vectors possible through collection of larger mid-infrared spectroscopy data sets.
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Affiliation(s)
| | - Simon A. Babayan
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Pegah Khazaeli
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Margaret Doyle
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Finlay Walton
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Elliott Reedy
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Thomas Glew
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Mafalda Viana
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Lisa Ranford-Cartwright
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Abdoulaye Niang
- Department of Medical Biology and Public Health, Institut de Recherche en Science de la Santé (IRSS), Bobo-Dioulasso, Burkina Faso
| | - Doreen J. Siria
- Environmental Health & Ecological Sciences Department, Ifakara Health Institute, Off Mlabani Passage, PO Box 53, Ifakara, Tanzania
| | - Fredros O. Okumu
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
- Environmental Health & Ecological Sciences Department, Ifakara Health Institute, Off Mlabani Passage, PO Box 53, Ifakara, Tanzania
| | - Abdoulaye Diabaté
- Department of Medical Biology and Public Health, Institut de Recherche en Science de la Santé (IRSS), Bobo-Dioulasso, Burkina Faso
| | - Heather M. Ferguson
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Francesco Baldini
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Klaas Wynne
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
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Age grading An. gambiae and An. arabiensis using near infrared spectra and artificial neural networks. PLoS One 2019; 14:e0209451. [PMID: 31412028 PMCID: PMC6693756 DOI: 10.1371/journal.pone.0209451] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2018] [Accepted: 07/29/2019] [Indexed: 01/28/2023] Open
Abstract
Background Near infrared spectroscopy (NIRS) is currently complementing techniques to age-grade mosquitoes. NIRS classifies lab-reared and semi-field raised mosquitoes into < or ≥ 7 days old with an average accuracy of 80%, achieved by training a regression model using partial least squares (PLS) and interpreted as a binary classifier. Methods and findings We explore whether using an artificial neural network (ANN) analysis instead of PLS regression improves the current accuracy of NIRS models for age-grading malaria transmitting mosquitoes. We also explore if directly training a binary classifier instead of training a regression model and interpreting it as a binary classifier improves the accuracy. A total of 786 and 870 NIR spectra collected from laboratory reared An. gambiae and An. arabiensis, respectively, were used and pre-processed according to previously published protocols. The ANN regression model scored root mean squared error (RMSE) of 1.6 ± 0.2 for An. gambiae and 2.8 ± 0.2 for An. arabiensis; whereas the PLS regression model scored RMSE of 3.7 ± 0.2 for An. gambiae, and 4.5 ± 0.1 for An. arabiensis. When we interpreted regression models as binary classifiers, the accuracy of the ANN regression model was 93.7 ± 1.0% for An. gambiae, and 90.2 ± 1.7% for An. arabiensis; while PLS regression model scored the accuracy of 83.9 ± 2.3% for An. gambiae, and 80.3 ± 2.1% for An. arabiensis. We also find that a directly trained binary classifier yields higher age estimation accuracy than a regression model interpreted as a binary classifier. A directly trained ANN binary classifier scored an accuracy of 99.4 ± 1.0 for An. gambiae and 99.0 ± 0.6% for An. arabiensis; while a directly trained PLS binary classifier scored 93.6 ± 1.2% for An. gambiae and 88.7 ± 1.1% for An. arabiensis. We further tested the reproducibility of these results on different independent mosquito datasets. ANNs scored higher estimation accuracies than when the same age models are trained using PLS. Regardless of the model architecture, directly trained binary classifiers scored higher accuracies on classifying age of mosquitoes than regression models translated as binary classifiers. Conclusion We recommend training models to estimate age of An. arabiensis and An. gambiae using ANN model architectures (especially for datasets with at least 70 mosquitoes per age group) and direct training of binary classifier instead of training a regression model and interpreting it as a binary classifier.
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González Jiménez M, Babayan SA, Khazaeli P, Doyle M, Walton F, Reedy E, Glew T, Viana M, Ranford-Cartwright L, Niang A, Siria DJ, Okumu FO, Diabaté A, Ferguson HM, Baldini F, Wynne K. Prediction of mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning. Wellcome Open Res 2019; 4:76. [PMID: 31544155 PMCID: PMC6753605 DOI: 10.12688/wellcomeopenres.15201.2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/31/2019] [Indexed: 01/14/2023] Open
Abstract
Despite the global efforts made in the fight against malaria, the disease is resurging. One of the main causes is the resistance that Anopheles mosquitoes, vectors of the disease, have developed to insecticides. Anopheles must survive for at least 10 days to possibly transmit malaria. Therefore, to evaluate and improve malaria vector control interventions, it is imperative to monitor and accurately estimate the age distribution of mosquito populations as well as their population sizes. Here, we demonstrate a machine-learning based approach that uses mid-infrared spectra of mosquitoes to characterise simultaneously both age and species identity of females of the African malaria vector species Anopheles gambiae and An. arabiensis, using laboratory colonies. Mid-infrared spectroscopy-based prediction of mosquito age structures was statistically indistinguishable from true modelled distributions. The accuracy of classifying mosquitoes by species was 82.6%. The method has a negligible cost per mosquito, does not require highly trained personnel, is rapid, and so can be easily applied in both laboratory and field settings. Our results indicate this method is a promising alternative to current mosquito species and age-grading approaches, with further improvements to accuracy and expansion for use with wild mosquito vectors possible through collection of larger mid-infrared spectroscopy data sets.
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Affiliation(s)
| | - Simon A. Babayan
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Pegah Khazaeli
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Margaret Doyle
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Finlay Walton
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Elliott Reedy
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Thomas Glew
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Mafalda Viana
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Lisa Ranford-Cartwright
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Abdoulaye Niang
- Department of Medical Biology and Public Health, Institut de Recherche en Science de la Santé (IRSS), Bobo-Dioulasso, Burkina Faso
| | - Doreen J. Siria
- Environmental Health & Ecological Sciences Department, Ifakara Health Institute, Off Mlabani Passage, PO Box 53, Ifakara, Tanzania
| | - Fredros O. Okumu
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
- Environmental Health & Ecological Sciences Department, Ifakara Health Institute, Off Mlabani Passage, PO Box 53, Ifakara, Tanzania
| | - Abdoulaye Diabaté
- Department of Medical Biology and Public Health, Institut de Recherche en Science de la Santé (IRSS), Bobo-Dioulasso, Burkina Faso
| | - Heather M. Ferguson
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Francesco Baldini
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Klaas Wynne
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
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Mwanga EP, Mapua SA, Siria DJ, Ngowo HS, Nangacha F, Mgando J, Baldini F, González Jiménez M, Ferguson HM, Wynne K, Selvaraj P, Babayan SA, Okumu FO. Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis. Malar J 2019; 18:187. [PMID: 31146762 PMCID: PMC6543689 DOI: 10.1186/s12936-019-2822-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Accepted: 05/25/2019] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND The propensity of different Anopheles mosquitoes to bite humans instead of other vertebrates influences their capacity to transmit pathogens to humans. Unfortunately, determining proportions of mosquitoes that have fed on humans, i.e. Human Blood Index (HBI), currently requires expensive and time-consuming laboratory procedures involving enzyme-linked immunosorbent assays (ELISA) or polymerase chain reactions (PCR). Here, mid-infrared (MIR) spectroscopy and supervised machine learning are used to accurately distinguish between vertebrate blood meals in guts of malaria mosquitoes, without any molecular techniques. METHODS Laboratory-reared Anopheles arabiensis females were fed on humans, chickens, goats or bovines, then held for 6 to 8 h, after which they were killed and preserved in silica. The sample size was 2000 mosquitoes (500 per host species). Five individuals of each host species were enrolled to ensure genotype variability, and 100 mosquitoes fed on each. Dried mosquito abdomens were individually scanned using attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra (4000 cm-1 to 400 cm-1). The spectral data were cleaned to compensate atmospheric water and CO2 interference bands using Bruker-OPUS software, then transferred to Python™ for supervised machine-learning to predict host species. Seven classification algorithms were trained using 90% of the spectra through several combinations of 75-25% data splits. The best performing model was used to predict identities of the remaining 10% validation spectra, which had not been used for model training or testing. RESULTS The logistic regression (LR) model achieved the highest accuracy, correctly predicting true vertebrate blood meal sources with overall accuracy of 98.4%. The model correctly identified 96% goat blood meals, 97% of bovine blood meals, 100% of chicken blood meals and 100% of human blood meals. Three percent of bovine blood meals were misclassified as goat, and 2% of goat blood meals misclassified as human. CONCLUSION Mid-infrared spectroscopy coupled with supervised machine learning can accurately identify multiple vertebrate blood meals in malaria vectors, thus potentially enabling rapid assessment of mosquito blood-feeding histories and vectorial capacities. The technique is cost-effective, fast, simple, and requires no reagents other than desiccants. However, scaling it up will require field validation of the findings and boosting relevant technical capacity in affected countries.
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Affiliation(s)
- Emmanuel P Mwanga
- Environmental Health and Ecological Science Thematic Group, Ifakara Health Institute, Morogoro, Tanzania.
| | - Salum A Mapua
- Environmental Health and Ecological Science Thematic Group, Ifakara Health Institute, Morogoro, Tanzania
| | - Doreen J Siria
- Environmental Health and Ecological Science Thematic Group, Ifakara Health Institute, Morogoro, Tanzania
| | - Halfan S Ngowo
- Environmental Health and Ecological Science Thematic Group, Ifakara Health Institute, Morogoro, Tanzania
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Francis Nangacha
- Environmental Health and Ecological Science Thematic Group, Ifakara Health Institute, Morogoro, Tanzania
| | - Joseph Mgando
- Environmental Health and Ecological Science Thematic Group, Ifakara Health Institute, Morogoro, Tanzania
| | - Francesco Baldini
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | | | - Heather M Ferguson
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Klaas Wynne
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | | | - Simon A Babayan
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Fredros O Okumu
- Environmental Health and Ecological Science Thematic Group, Ifakara Health Institute, Morogoro, Tanzania
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
- School of Public Health, University of Witwatersrand, Johannesburg, South Africa
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González Jiménez M, Babayan SA, Khazaeli P, Doyle M, Walton F, Reedy E, Glew T, Viana M, Ranford-Cartwright L, Niang A, Siria DJ, Okumu FO, Diabaté A, Ferguson HM, Baldini F, Wynne K. Prediction of mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning. Wellcome Open Res 2019; 4:76. [PMID: 31544155 PMCID: PMC6753605 DOI: 10.12688/wellcomeopenres.15201.1] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/24/2019] [Indexed: 01/17/2023] Open
Abstract
Despite the global efforts made in the fight against malaria, the disease is resurging. One of the main causes is the resistance that Anopheles mosquitoes, vectors of the disease, have developed to insecticides. Anopheles must survive for at least 10 days to possibly transmit malaria. Therefore, to evaluate and improve malaria vector control interventions, it is imperative to monitor and accurately estimate the age distribution of mosquito populations as well as their population sizes. Here, we demonstrate a machine-learning based approach that uses mid-infrared spectra of mosquitoes to characterise simultaneously both age and species identity of females of the African malaria vector species Anopheles gambiae and An. arabiensis. mid-infrared spectroscopy-based prediction of mosquito age structures was statistically indistinguishable from true modelled distributions. The accuracy of classifying mosquitoes by species was 82.6%. The method has a negligible cost per mosquito, does not require highly trained personnel, is rapid, and so can be easily applied in both laboratory and field settings. Our results indicate this method is a promising alternative to current mosquito species and age-grading approaches, with further improvements to accuracy and expansion for use with other mosquito vectors possible through collection of larger mid-infrared spectroscopy data sets.
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Affiliation(s)
| | - Simon A. Babayan
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Pegah Khazaeli
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Margaret Doyle
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Finlay Walton
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Elliott Reedy
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Thomas Glew
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Mafalda Viana
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Lisa Ranford-Cartwright
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Abdoulaye Niang
- Department of Medical Biology and Public Health, Institut de Recherche en Science de la Santé (IRSS), Bobo-Dioulasso, Burkina Faso
| | - Doreen J. Siria
- Environmental Health & Ecological Sciences Department, Ifakara Health Institute, Off Mlabani Passage, PO Box 53, Ifakara, Tanzania
| | - Fredros O. Okumu
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
- Environmental Health & Ecological Sciences Department, Ifakara Health Institute, Off Mlabani Passage, PO Box 53, Ifakara, Tanzania
| | - Abdoulaye Diabaté
- Department of Medical Biology and Public Health, Institut de Recherche en Science de la Santé (IRSS), Bobo-Dioulasso, Burkina Faso
| | - Heather M. Ferguson
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Francesco Baldini
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Klaas Wynne
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
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Maia MF, Kapulu M, Muthui M, Wagah MG, Ferguson HM, Dowell FE, Baldini F, Ranford-Cartwright L. Detection of Plasmodium falciparum infected Anopheles gambiae using near-infrared spectroscopy. Malar J 2019; 18:85. [PMID: 30890179 PMCID: PMC6423776 DOI: 10.1186/s12936-019-2719-9] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Accepted: 03/11/2019] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND 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. METHODS 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. RESULTS 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. CONCLUSIONS 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.
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Affiliation(s)
- Marta F Maia
- Swiss Tropical and Public Health Institute, Socinstrasse 57, 4020, Basel, Switzerland.
- University of Basel, Petersplatz 1, 4001, Basel, Switzerland.
- KEMRI Wellcome Trust Research Programme, P.O. Box 230, Kilifi, 80108, Kenya.
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Old Road Campus Roosevelt Drive, Oxford, OX3 7FZ, UK.
| | - Melissa Kapulu
- KEMRI Wellcome Trust Research Programme, P.O. Box 230, Kilifi, 80108, Kenya
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Old Road Campus Roosevelt Drive, Oxford, OX3 7FZ, UK
| | - Michelle Muthui
- KEMRI Wellcome Trust Research Programme, P.O. Box 230, Kilifi, 80108, Kenya
| | - Martin G Wagah
- KEMRI Wellcome Trust Research Programme, P.O. Box 230, Kilifi, 80108, Kenya
- Department of Public Health, School of Human and Health Sciences, Pwani University, Kilifi, Kenya
| | - Heather M Ferguson
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Graham Kerr Building, Glasgow, G12 8QQ, UK
| | - Floyd E Dowell
- USDA, Agricultural Research Service, Center for Grain and Animal Health Research, 1515 College Avenue, Manhattan, KS, 66502, USA
| | - Francesco Baldini
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Graham Kerr Building, Glasgow, G12 8QQ, UK
| | - Lisa Ranford-Cartwright
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Graham Kerr Building, Glasgow, G12 8QQ, UK
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Nansen C, Strand MR. Proximal Remote Sensing to Non-destructively Detect and Diagnose Physiological Responses by Host Insect Larvae to Parasitism. Front Physiol 2018; 9:1716. [PMID: 30564138 PMCID: PMC6288355 DOI: 10.3389/fphys.2018.01716] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Accepted: 11/15/2018] [Indexed: 11/13/2022] Open
Abstract
As part of identifying and characterizing physiological responses and adaptations by insects, it is paramount to develop non-destructive techniques to monitor individual insects over time. Such techniques can be used to optimize the timing of when in-depth (i.e., destructive sampling of insect tissue) physiological or molecular analyses should be deployed. In this article, we present evidence that hyperspectral proximal remote sensing can be used effectively in studies of host responses to parasitism. We present time series body reflectance data acquired from individual soybean loopers (Chrysodeixis includens) without parasitism (control) or parasitized by one of two species of parasitic wasps with markedly different life histories: Microplitis demolitor, a solitary larval koinobiont endoparasitoid and Copidosoma floridanum, a polyembryonic (gregarious) egg-larval koinobiont endoparasitoid. Despite considerable temporal variation in reflectance data 1-9 days post-parasitism, the two parasitoids caused uniquely different host body reflectance responses. Based on reflectance data acquired 3-5 days post-parasitism, all three treatments (control larvae, and those parasitized by either M. demolitor or C. floridanum) could be classified with >85 accuracy. We suggest that hyperspectral proximal imaging technologies represent an important frontier in insect physiology, as they are non-invasive and can be used to account for important time scale factors, such as: minutes of exposure or acclimation to abiotic factors, circadian rhythms, and seasonal effects. Although this study is based on data from a host-parasitoid system, results may be of broad relevance to insect physiologists. Described approaches provide a non-invasive and rapid method that can provide insights into when to destructively sample tissue for more detailed mechanistic studies of physiological responses to stressors and environmental conditions.
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Affiliation(s)
- Christian Nansen
- Department of Entomology and Nematology, University of California, Davis, Davis, CA, United States
| | - Michael R. Strand
- Department of Entomology, University of Georgia, Athens, GA, United States
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Fennell J, Veys C, Dingle J, Nwezeobi J, van Brunschot S, Colvin J, Grieve B. A method for real-time classification of insect vectors of mosaic and brown streak disease in cassava plants for future implementation within a low-cost, handheld, in-field multispectral imaging sensor. PLANT METHODS 2018; 14:82. [PMID: 30250493 PMCID: PMC6148801 DOI: 10.1186/s13007-018-0350-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2017] [Accepted: 09/16/2018] [Indexed: 05/31/2023]
Abstract
BACKGROUND The paper introduces a multispectral imaging system and data-processing approach for the identification and discrimination of morphologically indistinguishable cryptic species of the destructive crop pest, the whitefly Bemisia tabaci. This investigation and the corresponding system design, was undertaken in two phases under controlled laboratory conditions. The first exploited a prototype benchtop variant of the proposed sensor system to analyse four cryptic species of whitefly reared under similar conditions. The second phase, of the methodology development, employed a commercial high-precision laboratory hyperspectral imager to recover reference data from five cryptic species of whitefly, immobilized through flash freezing, and taken from across four feeding environments. RESULTS The initial results, for the single feeding environment, showed that a correct species classification could be achieved in 85-95% of cases, utilising linear Partial Least Squares approaches. The robustness of the classification approach was then extended both in terms of the automated spatial extraction of the most pertinent insect body parts, to assist with the spectral classification model, as well as the incorporation of a non-linear Support Vector Classifier to maintain the overall classification accuracy at 88-98%, irrespective of the feeding and crop environment. CONCLUSION This study demonstrates that through an integration of both the spatial data, associated with the multispectral images being used to separate different regions of the insect, and subsequent spectral analysis of those sub-regions, that B. tabaci viral vectors can be differentiated from other cryptic species, that appear morphologically indistinguishable to a human observer, with an accuracy of up to 98%. The implications for the engineering design for an in-field, handheld, sensor system is discussed with respect to the learning gained from this initial stage of the methodology development.
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Affiliation(s)
- Joseph Fennell
- School of Physics and Astronomy, University of Manchester, Oxford Road, Manchester, M13 9PL UK
| | - Charles Veys
- School of Electrical and Electronic Engineering, University of Manchester, Oxford Road, Manchester, M13 9PL UK
| | - Jose Dingle
- School of Electrical and Electronic Engineering, University of Manchester, Oxford Road, Manchester, M13 9PL UK
| | - Joachim Nwezeobi
- Natural Resources Institute, University of Greenwich, Chatham Maritime, Kent, ME4 4TB UK
| | - Sharon van Brunschot
- Natural Resources Institute, University of Greenwich, Chatham Maritime, Kent, ME4 4TB UK
| | - John Colvin
- Natural Resources Institute, University of Greenwich, Chatham Maritime, Kent, ME4 4TB UK
| | - Bruce Grieve
- School of Electrical and Electronic Engineering, University of Manchester, Oxford Road, Manchester, M13 9PL UK
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Barbosa TM, de Lima LAS, Dos Santos MCD, Vasconcelos SD, Gama RA, Lima KMG. A novel use of infra-red spectroscopy (NIRS and ATR-FTIR) coupled with variable selection algorithms for the identification of insect species (Diptera: Sarcophagidae) of medico-legal relevance. Acta Trop 2018; 185:1-12. [PMID: 29698658 DOI: 10.1016/j.actatropica.2018.04.025] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Revised: 04/14/2018] [Accepted: 04/22/2018] [Indexed: 11/16/2022]
Abstract
Unequivocal identification of fly specimens is an essential requirement in forensic entomology. Herein, a simple, non-destructive and rapid method based on two vibrational spectroscopy techniques [Near-Infrared Spectroscopy (NIRS) and attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy] coupled with variable selection techniques such as genetic algorithm-linear discriminant analysis (GA-LDA) and successive projection algorithm-linear discriminant analysis (SPA-LDA) were applied for identifying and discriminating six species of flesh flies (Diptera: Sarcophagidae) native to Neotropical regions. This novel approach is based on the unique spectral "fingerprints" of their biochemical composition. One hundred sixty (160) NIRS and FT-IR specimens (120 male, 40 female) were acquired; different pre-processing methods such as baseline correction, derivative and Savitzky-Golay smoothing were also performed. In addition, the multivariate classification accuracy results were tested based on sensitivity, specificity, positive (or precision) and negative predictive values, Youden index, positive and negative likelihood ratios. Principal components analysis (PCA) was employed for male vs. female category using NIRS, strongly showing the separation between the classes with only three principal components and 99% explained variance. Differentiation between the genera Oxysarcodexia, Peckia and Ravinia was efficiently confirmed by both techniques. In comparison with other biological methods, this approach represents an effective choice for fast and non-destructive identification in forensic entomology.
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Affiliation(s)
- Taciano M Barbosa
- Insects of Forensic Importance Research Group, Department of Zoology, Federal University of Pernambuco, Av. Prof. Moraes Rego, Recife, PE, 50.670-420, Brazil
| | - Leomir A S de Lima
- Biological Chemistry and Chemometrics, Institute of Chemistry, Federal University of Rio Grande do Norte, Natal, RN, 59.072-970, Brazil
| | - Marfran C D Dos Santos
- Insects of Forensic Importance Research Group, Department of Zoology, Federal University of Pernambuco, Av. Prof. Moraes Rego, Recife, PE, 50.670-420, Brazil; Biological Chemistry and Chemometrics, Institute of Chemistry, Federal University of Rio Grande do Norte, Natal, RN, 59.072-970, Brazil
| | - Simão D Vasconcelos
- Insects of Forensic Importance Research Group, Department of Zoology, Federal University of Pernambuco, Av. Prof. Moraes Rego, Recife, PE, 50.670-420, Brazil
| | - Renata A Gama
- Laboratory of Insect and Vectors, Department of Microbiology and Parasitology, Federal University of Rio Grande do Norte, Natal, RN, 59.072-970, Brazil
| | - Kássio M G Lima
- Biological Chemistry and Chemometrics, Institute of Chemistry, Federal University of Rio Grande do Norte, Natal, RN, 59.072-970, Brazil.
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Gebru A, Jansson S, Ignell R, Kirkeby C, Prangsma JC, Brydegaard M. Multiband modulation spectroscopy for the determination of sex and species of mosquitoes in flight. JOURNAL OF BIOPHOTONICS 2018; 11:e201800014. [PMID: 29508537 DOI: 10.1002/jbio.201800014] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2018] [Accepted: 02/28/2018] [Indexed: 06/08/2023]
Abstract
We present a dual-wavelength polarimetric measurement method to distinguish species and sexes of disease transmitting mosquitoes in flight. By measuring co- and de-polarized backscattered light at 808 and 1550 nm, the degree of linear polarization, wingbeat frequency, reflectance, spectral ratio and glossiness of mosquitoes can be retrieved. Body and wing contributions to these signals can be separated. Whereas the optical cross section is sensitive to the aspect of observation, thus the heading direction of the insect in flight, we demonstrate that polarimetric- and spectral-band ratios are largely invariant to the aspect of observation. We show that wing glossiness, as well as wing- and body-spectral ratios are particularly efficient in distinguishing Anopheles coluzzii and Anopheles arabiensis, 2 closely related species of malaria vectors. Spectral and polarimetric ratios relate to microstructural and melanization features of the wing and body of these species. We conclude that multiband modulation spectroscopy is a useful expansion of the parameter space that can be used to improve the specificity of entomological lidars.
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Affiliation(s)
- Alem Gebru
- FaunaPhotonics APS, Copenhagen, Denmark
- Department of Physics, Lund Laser Centre, Lund University, Lund, Sweden
- Department of Biology, Centre for Animal Movement, Lund University, Lund, Sweden
| | - Samuel Jansson
- Department of Physics, Lund Laser Centre, Lund University, Lund, Sweden
- Department of Biology, Centre for Animal Movement, Lund University, Lund, Sweden
| | - Rickard Ignell
- Chemical Ecology Unit, Department of Plant Protection Biology, SLU, Alnarp, Sweden
| | - Carsten Kirkeby
- FaunaPhotonics APS, Copenhagen, Denmark
- National Veterinary Institute, Technical University of Denmark, Frederiksberg C, Denmark
| | | | - Mikkel Brydegaard
- FaunaPhotonics APS, Copenhagen, Denmark
- Department of Physics, Lund Laser Centre, Lund University, Lund, Sweden
- Department of Biology, Centre for Animal Movement, Lund University, Lund, Sweden
- Norsk Elektro Optikk AS, Skedsmokorset, Norway
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Esperança PM, Blagborough AM, Da DF, Dowell FE, Churcher TS. Detection of Plasmodium berghei infected Anopheles stephensi using near-infrared spectroscopy. Parasit Vectors 2018; 11:377. [PMID: 29954424 PMCID: PMC6027764 DOI: 10.1186/s13071-018-2960-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Accepted: 06/18/2018] [Indexed: 11/18/2022] Open
Abstract
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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Affiliation(s)
- Pedro M. Esperança
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1PG UK
| | - Andrew M. Blagborough
- Department of Life Sciences, Imperial College London, South Kensington, London, SW7 2AZ UK
| | - Dari F. Da
- Institut de Recherche en Sciences de la Santé, Direction Régionale, 399 Avenue de la liberté, Bobo Dioulasso, 01 01 BP 545 Burkina Faso
| | - Floyd E. Dowell
- USDA, Agricultural Research Service, Center for Grain and Animal Health Research, 1515 College Avenue, Manhattan, KS 66502 USA
| | - Thomas S. Churcher
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1PG UK
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Sikulu-Lord MT, Devine GJ, Hugo LE, Dowell FE. First report on the application of near-infrared spectroscopy to predict the age of Aedes albopictus Skuse. Sci Rep 2018; 8:9590. [PMID: 29941924 PMCID: PMC6018420 DOI: 10.1038/s41598-018-27998-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Accepted: 06/12/2018] [Indexed: 11/09/2022] Open
Abstract
To date, no methodology has been described for predicting the age of Aedes albopictus Skuse mosquitoes, commonly known as Asian tiger mosquitoes. In this study, we report the potential of near-infrared spectroscopy (NIRS) technique for characterizing the age of female laboratory reared Ae. albopictus. Using leave-one-out cross-validation analysis on a training set, laboratory reared mosquitoes preserved in RNAlater for up to a month were assessed at 1, 3, 7, 9, 13, 16, 20 and 25 days post emergence. Mosquitoes (N = 322) were differentiated into two age classes (< or ≥ 7 days) with 93% accuracy, into three age classes (<7, 7-13 and >13 days old) with 76% accuracy, and on a continuous age scale to within ±3 days of their actual average age. Similarly, models predicted mosquitoes (N = 146) excluded from the training model with 94% and 71% accuracy to the two and the three age groups, respectively. We show for the first time that NIRS, with an improved spectrometer and fibre configuration, can be used to predict the age of laboratory reared female Ae. albopictus. Characterization of the age of Ae. albopictus populations is crucial for determining the efficacy of vector control interventions that target their survival.
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Affiliation(s)
- Maggy T Sikulu-Lord
- Mosquito Control Laboratory, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, Queensland, 4006, Australia.
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, 306 Carmody Road, St Lucia, Queensland, 4072, Australia.
| | - Gregor J Devine
- Mosquito Control Laboratory, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, Queensland, 4006, Australia
| | - Leon E Hugo
- Mosquito Control Laboratory, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, Queensland, 4006, Australia
| | - Floyd E Dowell
- USDA, Agricultural Research Service, Center for Grain and Animal Health Research, 1515 College Avenue, Manhattan, KS, 66502, USA
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Milali MP, Sikulu-Lord MT, Kiware SS, Dowell FE, Povinelli RJ, Corliss GF. Do NIR spectra collected from laboratory-reared mosquitoes differ from those collected from wild mosquitoes? PLoS One 2018; 13:e0198245. [PMID: 29851994 PMCID: PMC5978888 DOI: 10.1371/journal.pone.0198245] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Accepted: 05/16/2018] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Near infrared spectroscopy (NIRS) is a high throughput technique that measures absorbance of specific wavelengths of light by biological samples and uses this information to classify the age of lab-reared mosquitoes as younger or older than seven days with an average accuracy greater than 80%. For NIRS to estimate ages of wild mosquitoes, a sample of wild mosquitoes with known age in days would be required to train and test the model. Mark-release-recapture is the most reliable method to produce wild-caught mosquitoes of known age in days. However, it is logistically demanding, time inefficient, subject to low recapture rates, and raises ethical issues due to the release of mosquitoes. Using labels from Detinova dissection results in a mathematical model with poor accuracy. Alternatively, a model trained on spectra from laboratory-reared mosquitoes where age in days is known can be applied to estimate the age of wild mosquitoes, but this would be appropriate only if spectra collected from laboratory-reared and wild mosquitoes are similar. METHODS AND FINDINGS We performed k-means (k = 2) cluster analysis on a mixture of spectra collected from lab-reared and wild Anopheles arabiensis to determine if there is any significant difference between these two groups. While controlling the numbers of mosquitoes included in the model at each age, we found two clusters with no significant difference in distribution of spectra collected from lab-reared and wild mosquitoes (p = 0.25). We repeated the analysis using hierarchical clustering, and similarly, no significant difference was observed (p = 0.13). CONCLUSION We find no difference between spectra collected from laboratory-reared and wild mosquitoes of the same age and species. The results strengthen and support the on-going practice of applying the model trained on spectra collected from laboratory-reared mosquitoes, especially first-generation laboratory-reared mosquitoes.
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Affiliation(s)
- Masabho P. Milali
- Department of Mathematics, Statistics and Computer Science, Marquette University, Wisconsin, United States of America
- Ifakara Health Institute, Environmental Health and Ecological Sciences Thematic Group, Ifakara, Tanzania
| | - Maggy T. Sikulu-Lord
- Queensland Alliance of Agriculture and Food Innovation, The University of Queensland, Brisbane, Australia
| | - Samson S. Kiware
- Department of Mathematics, Statistics and Computer Science, Marquette University, Wisconsin, United States of America
- Ifakara Health Institute, Environmental Health and Ecological Sciences Thematic Group, Ifakara, Tanzania
| | - Floyd E. Dowell
- USDA, Agricultural Research Service, Center for Grain and Animal Health Research, Manhattan, KS, United States of America
| | - Richard J. Povinelli
- Department of Electrical and Computer Engineering, Marquette University, Wisconsin, United States of America
| | - George F. Corliss
- Department of Electrical and Computer Engineering, Marquette University, Wisconsin, United States of America
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Fernandes JN, dos Santos LMB, Chouin-Carneiro T, Pavan MG, Garcia GA, David MR, Beier JC, Dowell FE, Maciel-de-Freitas R, Sikulu-Lord MT. Rapid, noninvasive detection of Zika virus in Aedes aegypti mosquitoes by near-infrared spectroscopy. SCIENCE ADVANCES 2018; 4:eaat0496. [PMID: 29806030 PMCID: PMC5966221 DOI: 10.1126/sciadv.aat0496] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Accepted: 04/10/2018] [Indexed: 06/08/2023]
Abstract
The accelerating global spread of arboviruses, such as Zika virus (ZIKV), highlights the need for more proactive mosquito surveillance. However, a major challenge during arbovirus outbreaks has been the lack of rapid and affordable tests for pathogen detection in mosquitoes. We show for the first time that near-infrared spectroscopy (NIRS) is a rapid, reagent-free, and cost-effective tool that can be used to noninvasively detect ZIKV in heads and thoraces of intact Aedes aegypti mosquitoes with prediction accuracies of 94.2 to 99.3% relative to quantitative reverse transcription polymerase chain reaction (RT-qPCR). NIRS involves simply shining a beam of light on a mosquito to collect a diagnostic spectrum. We estimated in this study that NIRS is 18 times faster and 110 times cheaper than RT-qPCR. We anticipate that NIRS will be expanded upon for identifying potential arbovirus hotspots to guide the spatial prioritization of vector control.
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Affiliation(s)
- Jill N. Fernandes
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St. Lucia, Queensland 4072, Australia
| | - Lílha M. B. dos Santos
- Instituto Oswaldo Cruz, Laboratório de Mosquitos Transmissores de Hematozoários, Rio de Janeiro, Rio de Janeiro 21040-360, Brazil
| | - Thaís Chouin-Carneiro
- Instituto Oswaldo Cruz, Laboratório de Mosquitos Transmissores de Hematozoários, Rio de Janeiro, Rio de Janeiro 21040-360, Brazil
| | - Márcio G. Pavan
- Instituto Oswaldo Cruz, Laboratório de Mosquitos Transmissores de Hematozoários, Rio de Janeiro, Rio de Janeiro 21040-360, Brazil
| | - Gabriela A. Garcia
- Instituto Oswaldo Cruz, Laboratório de Mosquitos Transmissores de Hematozoários, Rio de Janeiro, Rio de Janeiro 21040-360, Brazil
| | - Mariana R. David
- Instituto Oswaldo Cruz, Laboratório de Mosquitos Transmissores de Hematozoários, Rio de Janeiro, Rio de Janeiro 21040-360, Brazil
| | - John C. Beier
- Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Floyd E. Dowell
- U.S. Department of Agriculture, Agricultural Research Service, Center for Grain and Animal Health Research, 1515 College Avenue, Manhattan, KS 66502, USA
| | - Rafael Maciel-de-Freitas
- Instituto Oswaldo Cruz, Laboratório de Mosquitos Transmissores de Hematozoários, Rio de Janeiro, Rio de Janeiro 21040-360, Brazil
- Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-901, Brazil
| | - Maggy T. Sikulu-Lord
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St. Lucia, Queensland 4072, Australia
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Lambert B, Sikulu-Lord MT, Mayagaya VS, Devine G, Dowell F, Churcher TS. Monitoring the Age of Mosquito Populations Using Near-Infrared Spectroscopy. Sci Rep 2018; 8:5274. [PMID: 29588452 PMCID: PMC5869673 DOI: 10.1038/s41598-018-22712-z] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2017] [Accepted: 02/28/2018] [Indexed: 01/07/2023] Open
Abstract
Mosquito control with bednets, residual sprays or fumigation remains the most effective tool for preventing vector-borne diseases such as malaria, dengue and Zika, though there are no widely used entomological methods for directly assessing its efficacy. Mosquito age is the most informative metric for evaluating interventions that kill adult mosquitoes but there is no simple or reliable way of measuring it in the field. Near-Infrared Spectroscopy (NIRS) has been shown to be a promising, high-throughput method that can estimate the age of mosquitoes. Currently the ability of NIRS to measure mosquito age is biased, and has relatively high individual mosquito measurement error, though its capacity to rigorously monitor mosquito populations in the field has never been assessed. In this study, we use machine learning methods from the chemometric literature to generate more accurate, unbiased estimates of individual mosquito age. These unbiased estimates produce precise population-level measurements, which are relatively insensitive to further increases in NIRS accuracy when feasible numbers of mosquitoes are sampled. The utility of NIRS to directly measure the impact of pyrethroid resistance on mosquito control is illustrated, showing how the technology has potential as a highly valuable tool for directly assessing the efficacy of mosquito control interventions.
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Affiliation(s)
- Ben Lambert
- 0000 0004 1936 8948grid.4991.5Department of Zoology, University of Oxford, South Parks Road, Oxford, OX1 3PS UK ,0000 0001 2113 8111grid.7445.2MRC Centre for Outbreak Analysis and Modelling, Infectious Disease Epidemiology, Imperial College London, London, W2 1PG UK
| | - Maggy T. Sikulu-Lord
- 0000 0000 9320 7537grid.1003.2Queensland Alliance of Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland Australia
| | - Vale S. Mayagaya
- 0000 0000 9144 642Xgrid.414543.3Ifakara Health Institute, Biomedical Unit, Ifakara and Dar es Salaam Branches, Ifakara and Dar es Salaam, Tanzania
| | - Greg Devine
- 0000 0001 2294 1395grid.1049.cMosquito Control Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland Australia
| | - Floyd Dowell
- 0000 0004 0404 0958grid.463419.dUSDA, Agricultural Research Service, Center for Grain and Animal Health Research, 1515 College Avenue, Manhattan, KS 66502 USA
| | - Thomas S. Churcher
- 0000 0001 2113 8111grid.7445.2MRC Centre for Outbreak Analysis and Modelling, Infectious Disease Epidemiology, Imperial College London, London, W2 1PG UK
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Krajacich BJ, Meyers JI, Alout H, Dabiré RK, Dowell FE, Foy BD. Analysis of near infrared spectra for age-grading of wild populations of Anopheles gambiae. Parasit Vectors 2017; 10:552. [PMID: 29116006 PMCID: PMC5678599 DOI: 10.1186/s13071-017-2501-1] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Accepted: 10/27/2017] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Understanding the age-structure of mosquito populations, especially malaria vectors such as Anopheles gambiae, is important for assessing the risk of infectious mosquitoes, and how vector control interventions may impact this risk. The use of near-infrared spectroscopy (NIRS) for age-grading has been demonstrated previously on laboratory and semi-field mosquitoes, but to date has not been utilized on wild-caught mosquitoes whose age is externally validated via parity status or parasite infection stage. In this study, we developed regression and classification models using NIRS on datasets of wild An. gambiae (s.l.) reared from larvae collected from the field in Burkina Faso, and two laboratory strains. We compared the accuracy of these models for predicting the ages of wild-caught mosquitoes that had been scored for their parity status as well as for positivity for Plasmodium sporozoites. RESULTS Regression models utilizing variable selection increased predictive accuracy over the more common full-spectrum partial least squares (PLS) approach for cross-validation of the datasets, validation, and independent test sets. Models produced from datasets that included the greatest range of mosquito samples (i.e. different sampling locations and times) had the highest predictive accuracy on independent testing sets, though overall accuracy on these samples was low. For classification, we found that intramodel accuracy ranged between 73.5-97.0% for grouping of mosquitoes into "early" and "late" age classes, with the highest prediction accuracy found in laboratory colonized mosquitoes. However, this accuracy was decreased on test sets, with the highest classification of an independent set of wild-caught larvae reared to set ages being 69.6%. CONCLUSIONS Variation in NIRS data, likely from dietary, genetic, and other factors limits the accuracy of this technique with wild-caught mosquitoes. Alternative algorithms may help improve prediction accuracy, but care should be taken to either maximize variety in models or minimize confounders.
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Affiliation(s)
- Benjamin J. Krajacich
- Arthropod-borne and Infectious Diseases Laboratory, Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO USA
| | - Jacob I. Meyers
- Arthropod-borne and Infectious Diseases Laboratory, Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO USA
| | - Haoues Alout
- Arthropod-borne and Infectious Diseases Laboratory, Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO USA
| | - Roch K. Dabiré
- Direction Régionale de l’Ouest (DRO), Institut de Recherche en Sciences de la Santé (IRSS), Bobo Dioulasso, Burkina Faso
| | - Floyd E. Dowell
- Stored Product Insect and Engineering Research Unit, United States Department of Agriculture/Agricultural Research Services, Center for Grain and Animal Health Research, Manhattan, KS USA
| | - Brian D. Foy
- Arthropod-borne and Infectious Diseases Laboratory, Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO USA
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Khoshmanesh A, Christensen D, Perez-Guaita D, Iturbe-Ormaetxe I, O'Neill SL, McNaughton D, Wood BR. Screening of Wolbachia Endosymbiont Infection in Aedes aegypti Mosquitoes Using Attenuated Total Reflection Mid-Infrared Spectroscopy. Anal Chem 2017; 89:5285-5293. [PMID: 28332822 DOI: 10.1021/acs.analchem.6b04827] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Dengue fever is the most common mosquito transmitted viral infection afflicting humans, estimated to generate around 390 million infections each year in over 100 countries. The introduction of the endosymbiotic bacterium Wolbachia into Aedes aegypti mosquitoes has the potential to greatly reduce the public health burden of the disease. This approach requires extensive polymerase chain reaction (PCR) testing of the Wolbachia-infection status of mosquitoes in areas where Wolbachia-A. aegypti are released. Here, we report the first example of small organism mid-infrared spectroscopy where we have applied attenuated total reflection Fourier transform infrared (ATR-FT-IR) spectroscopy and multivariate modeling methods to determine sex, age, and the presence of Wolbachia (wMel strain) in laboratory mosquitoes and sex and age in field mosquitoes. The prediction errors using partial least squares discriminant analysis (PLS-DA) discrimination models for laboratory studies on independent test sets ranged from 0 to 3% for age and sex grading and 3% to 5% for Wolbachia infection diagnosis using dry mosquito abdomens while field study results using an artificial neural network yielded a 10% error. The application of FT-IR analysis is inexpensive, easy to use, and portable and shows significant potential to replace the reliance on more expensive and laborious PCR assays.
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Affiliation(s)
- Aazam Khoshmanesh
- Centre for Biospectroscopy, School of Chemistry, Monash University , Clayton, Victoria 3800, Australia
| | - Dale Christensen
- Centre for Biospectroscopy, School of Chemistry, Monash University , Clayton, Victoria 3800, Australia
| | - David Perez-Guaita
- Centre for Biospectroscopy, School of Chemistry, Monash University , Clayton, Victoria 3800, Australia
| | - Inaki Iturbe-Ormaetxe
- Institute of Vector-Borne Disease, Monash University , Clayton, Victoria 3800, Australia
| | - Scott L O'Neill
- Institute of Vector-Borne Disease, Monash University , Clayton, Victoria 3800, Australia
| | - Don McNaughton
- Centre for Biospectroscopy, School of Chemistry, Monash University , Clayton, Victoria 3800, Australia
| | - Bayden R Wood
- Centre for Biospectroscopy, School of Chemistry, Monash University , Clayton, Victoria 3800, Australia
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Near-Infrared Spectroscopy, a Rapid Method for Predicting the Age of Male and Female Wild-Type and Wolbachia Infected Aedes aegypti. PLoS Negl Trop Dis 2016; 10:e0005040. [PMID: 27768689 PMCID: PMC5074478 DOI: 10.1371/journal.pntd.0005040] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2016] [Accepted: 09/14/2016] [Indexed: 01/01/2023] Open
Abstract
Estimating the age distribution of mosquito populations is crucial for assessing their capacity to transmit disease and for evaluating the efficacy of available vector control programs. This study reports on the capacity of the near-infrared spectroscopy (NIRS) technique to rapidly predict the ages of the principal dengue and Zika vector, Aedes aegypti. The age of wild-type males and females, and males and females infected with wMel and wMelPop strains of Wolbachia pipientis were characterized using this method. Calibrations were developed using spectra collected from their heads and thoraces using partial least squares (PLS) regression. A highly significant correlation was found between the true and predicted ages of mosquitoes. The coefficients of determination for wild-type females and males across all age groups were R2 = 0.84 and 0.78, respectively. The coefficients of determination for the age of wMel and wMelPop infected females were 0.71 and 0.80, respectively (P< 0.001 in both instances). The age of wild-type female Ae. aegypti could be identified as < or ≥ 8 days old with an accuracy of 91% (N = 501), whereas female Ae. aegypti infected with wMel and wMelPop were differentiated into the two age groups with an accuracy of 83% (N = 284) and 78% (N = 229), respectively. Our results also indicate NIRS can distinguish between young and old male wild-type, wMel and wMelPop infected Ae. aegypti with accuracies of 87% (N = 253), 83% (N = 277) and 78% (N = 234), respectively. We have demonstrated the potential of NIRS as a predictor of the age of female and male wild-type and Wolbachia infected Ae. aegypti mosquitoes under laboratory conditions. After field validation, the tool has the potential to offer a cheap and rapid alternative for surveillance of dengue and Zika vector control programs.
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50
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Govella NJ, Maliti DF, Mlwale AT, Masallu JP, Mirzai N, Johnson PCD, Ferguson HM, Killeen GF. An improved mosquito electrocuting trap that safely reproduces epidemiologically relevant metrics of mosquito human-feeding behaviours as determined by human landing catch. Malar J 2016; 15:465. [PMID: 27618941 PMCID: PMC5020444 DOI: 10.1186/s12936-016-1513-1] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2016] [Accepted: 09/02/2016] [Indexed: 12/02/2022] Open
Abstract
Background Reliable quantification of mosquito host—seeking behaviours is required to determine the efficacy of vector control methods. For malaria, the gold standard approach remains the risky human landing catch (HLC). Here compare the performance of an improved prototype of the mosquito electrocuting grid trap (MET) as a safer alternative with HLC for measuring malaria vector behaviour in Dar es Salaam, Tanzania. Methods Mosquito trapping was conducted at three sites within Dar es Salaam representing a range of urbanicity over a 7-month period (December 2012–July 2013, 168 sampling nights). At each site, sampling was conducted in a block of four houses, with two houses being allocated to HLC and the other to MET on each night of study. Sampling was conducted both indoors and outdoors (from 19:00 to 06:00 each night) at all houses, with trapping method (HLC and MET) being exchanged between pairs of houses at each site using a crossover design. Results The MET caught significantly more Anopheles gambiae sensu lato than the HLC, both indoors (RR [95 % confidence interval (CI)]) = 1.47 [1.23–1.76], P < 0.0001 and outdoors = 1.38 [1.14–1.67], P < 0.0001). The sensitivity of MET compared with HLC did not detectably change over the course of night for either An. gambiae s.l. (OR [CI]) = 1.01 [0.94–1.02], P = 0.27) or Culex spp. (OR [CI]) = 0.99 [0.99–1.0], P = 0.17) indoors and declined only slightly outdoors: An. gambiae s.l. (OR [CI]) = 0.92 [0.86–0.99], P = 0.04), and Culex spp. (OR [CI]) = 0.99 [0.98–0.99], P = 0.03). MET-based estimates of the proportions of mosquitoes caught indoors (Pi) or during sleeping hours (Pfl), as well as the proportion of human exposure to bites that would otherwise occurs indoors (πi), were statistically indistinguishable from those based on HLC for An. gambiae s.l. (P = 0.43, 0.07 and 0.48, respectively) and Culex spp. (P = 0.76, 0.24 and 0.55, respectively). Conclusions This improved MET prototype is highly sensitive tool that accurately quantifies epidemiologically-relevant metrics of mosquito biting densities, behaviours and human exposure distribution. Electronic supplementary material The online version of this article (doi:10.1186/s12936-016-1513-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Nicodem J Govella
- Environmental Health and Ecological Sciences Thematic Group, Coordination Office, Ifakara Health Institute, PO Box 78373, Kiko Avenue, Mikocheni, Dar es Salaam, United Republic of Tanzania.
| | - Deodatus F Maliti
- Environmental Health and Ecological Sciences Thematic Group, Coordination Office, Ifakara Health Institute, PO Box 78373, Kiko Avenue, Mikocheni, Dar es Salaam, United Republic of Tanzania.,College of Medical, Veterinary and Life Sciences, Boyd Orr Centre for Population and Ecosystem Health, University of Glasgow, Glasgow, UK
| | - Amos T Mlwale
- Environmental Health and Ecological Sciences Thematic Group, Coordination Office, Ifakara Health Institute, PO Box 78373, Kiko Avenue, Mikocheni, Dar es Salaam, United Republic of Tanzania
| | - John P Masallu
- Environmental Health and Ecological Sciences Thematic Group, Coordination Office, Ifakara Health Institute, PO Box 78373, Kiko Avenue, Mikocheni, Dar es Salaam, United Republic of Tanzania
| | - Nosrat Mirzai
- Bioelectronics Unit, University of Glasgow, Graham Kerr Building, Glasgow, G12 8QQ, UK
| | - Paul C D Johnson
- College of Medical, Veterinary and Life Sciences, Boyd Orr Centre for Population and Ecosystem Health, University of Glasgow, Glasgow, UK
| | - Heather M Ferguson
- College of Medical, Veterinary and Life Sciences, Boyd Orr Centre for Population and Ecosystem Health, University of Glasgow, Glasgow, UK
| | - Gerry F Killeen
- Environmental Health and Ecological Sciences Thematic Group, Coordination Office, Ifakara Health Institute, PO Box 78373, Kiko Avenue, Mikocheni, Dar es Salaam, United Republic of Tanzania.,Vector Biology Department, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK
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