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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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Gao Z, Harrington LC, Zhu W, Barrientos LM, Alfonso-Parra C, Avila FW, Clark JM, He L. Accurate age-grading of field-aged mosquitoes reared under ambient conditions using surface-enhanced Raman spectroscopy and artificial neural networks. JOURNAL OF MEDICAL ENTOMOLOGY 2023; 60:917-923. [PMID: 37364175 DOI: 10.1093/jme/tjad067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 04/27/2023] [Accepted: 06/08/2023] [Indexed: 06/28/2023]
Abstract
Age-grading mosquitoes are significant because only older mosquitoes are competent to transmit pathogens to humans. However, we lack effective tools to do so, especially at the critical point where mosquitoes become a risk to humans. In this study, we demonstrated the capability of using surface-enhanced Raman spectroscopy and artificial neural networks to accurately age-grade field-aged low-generation (F2) female Aedes aegypti mosquitoes held under ambient conditions (error was 1.9 chronological days, in the range 0-22 days). When degree days were used for model calibration, the accuracy was further improved to 20.8 degree days (approximately equal to 1.4 chronological days), which indicates the impact of temperature fluctuation on prediction accuracy. This performance is a significant advancement over binary classification. The great accuracy of this method outperforms traditional age-grading methods and will facilitate effective epidemiological studies, risk assessment, vector intervention monitoring, and evaluation.
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Affiliation(s)
- Zili Gao
- Department of Food Science, University of Massachusetts, Amherst, MA 01003, USA
- Raman, IR and XRF Core Facility, University of Massachusetts, Amherst, MA 01003, USA
| | - Laura C Harrington
- Department of Entomology, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, USA
| | - Wei Zhu
- Department of Mathematics and Statistics, University of Massachusetts, Amherst, MA 01003, USA
| | - Luisa M Barrientos
- Max Planck Tandem Group in Mosquito Reproductive Biology, Universidad de Antioquia, Medellin, Colombia
| | - Catalina Alfonso-Parra
- Max Planck Tandem Group in Mosquito Reproductive Biology, Universidad de Antioquia, Medellin, Colombia
- Instituto Colombiano de Medicina Tropical, Universidad CES, Sabaneta, Colombia
| | - Frank W Avila
- Max Planck Tandem Group in Mosquito Reproductive Biology, Universidad de Antioquia, Medellin, Colombia
| | - John M Clark
- Department of Veterinary and Animal Sciences, University of Massachusetts, Amherst, MA 01003, USA
| | - Lili He
- Department of Food Science, University of Massachusetts, Amherst, MA 01003, USA
- Raman, IR and XRF Core Facility, University of Massachusetts, Amherst, MA 01003, USA
- Department of Chemistry, University of Massachusetts, Amherst, MA 01003, USA
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Mwanga EP, Siria DJ, Mitton J, Mshani IH, González-Jiménez M, Selvaraj P, Wynne K, Baldini F, Okumu FO, Babayan SA. Using transfer learning and dimensionality reduction techniques to improve generalisability of machine-learning predictions of mosquito ages from mid-infrared spectra. BMC Bioinformatics 2023; 24:11. [PMID: 36624386 PMCID: PMC9830685 DOI: 10.1186/s12859-022-05128-5] [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] [Received: 07/26/2022] [Accepted: 12/26/2022] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Old mosquitoes are more likely to transmit malaria than young ones. Therefore, accurate prediction of mosquito population age can drastically improve the evaluation of mosquito-targeted interventions. However, standard methods for age-grading mosquitoes are laborious and costly. We have shown that Mid-infrared spectroscopy (MIRS) can be used to detect age-specific patterns in mosquito cuticles and thus can be used to train age-grading machine learning models. However, these models tend to transfer poorly across populations. Here, we investigate whether applying dimensionality reduction and transfer learning to MIRS data can improve the transferability of MIRS-based predictions for mosquito ages. METHODS We reared adults of the malaria vector Anopheles arabiensis in two insectaries. The heads and thoraces of female mosquitoes were scanned using an attenuated total reflection-Fourier transform infrared spectrometer, which were grouped into two different age classes. The dimensionality of the spectra data was reduced using unsupervised principal component analysis or t-distributed stochastic neighbour embedding, and then used to train deep learning and standard machine learning classifiers. Transfer learning was also evaluated to improve transferability of the models when predicting mosquito age classes from new populations. RESULTS Model accuracies for predicting the age of mosquitoes from the same population as the training samples reached 99% for deep learning and 92% for standard machine learning. However, these models did not generalise to a different population, achieving only 46% and 48% accuracy for deep learning and standard machine learning, respectively. Dimensionality reduction did not improve model generalizability but reduced computational time. Transfer learning by updating pre-trained models with 2% of mosquitoes from the alternate population improved performance to ~ 98% accuracy for predicting mosquito age classes in the alternative population. CONCLUSION Combining dimensionality reduction and transfer learning can reduce computational costs and improve the transferability of both deep learning and standard machine learning models for predicting the age of mosquitoes. Future studies should investigate the optimal quantities and diversity of training data necessary for transfer learning and the implications for broader generalisability to unseen datasets.
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Affiliation(s)
- Emmanuel P. Mwanga
- grid.414543.30000 0000 9144 642XEnvironmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania ,grid.8756.c0000 0001 2193 314XSchool of Biodiversity, One Health, and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ UK
| | - Doreen J. Siria
- grid.414543.30000 0000 9144 642XEnvironmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania
| | - Joshua Mitton
- grid.8756.c0000 0001 2193 314XSchool of Biodiversity, One Health, and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ UK ,grid.8756.c0000 0001 2193 314XSchool of Computing Science, University of Glasgow, Glasgow, G12 8QQ UK
| | - Issa H. Mshani
- grid.414543.30000 0000 9144 642XEnvironmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania ,grid.8756.c0000 0001 2193 314XSchool of Biodiversity, One Health, and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ UK
| | - Mario González-Jiménez
- grid.8756.c0000 0001 2193 314XSchool of Chemistry, University of Glasgow, Glasgow, G12 8QQ UK
| | | | - Klaas Wynne
- grid.8756.c0000 0001 2193 314XSchool of Chemistry, University of Glasgow, Glasgow, G12 8QQ UK
| | - Francesco Baldini
- grid.8756.c0000 0001 2193 314XSchool of Biodiversity, One Health, and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ UK
| | - Fredros O. Okumu
- grid.414543.30000 0000 9144 642XEnvironmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania ,grid.8756.c0000 0001 2193 314XSchool of Biodiversity, One Health, and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ UK ,grid.11951.3d0000 0004 1937 1135School of Public Health, University of Witwatersrand, Johannesburg, South Africa
| | - Simon A. Babayan
- grid.8756.c0000 0001 2193 314XSchool of Biodiversity, One Health, and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ UK
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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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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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Valladares V, Pasquini C, Thiengo SC, Mello-Silva CC. Feasibility of near-infrared spectroscopy for species identification and parasitological diagnosis of freshwater snails of the genus Biomphalaria (Planorbidae). PLoS One 2021; 16:e0259832. [PMID: 34762684 PMCID: PMC8584770 DOI: 10.1371/journal.pone.0259832] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 10/27/2021] [Indexed: 11/19/2022] Open
Abstract
Near Infrared Spectroscopy (NIRS) has been applied in epidemiological surveillance studies of insect vectors of parasitic diseases, such as the Dengue's mosquitoes. However, regarding mollusks, vectors of important worldwide helminth diseases such as schistosomiasis, fascioliasis and angiostrongyliasis, NIRS studies are rare. This work proposes to establish and standardize the procedure of data collection and analysis using NIRS applied to medical malacology, i.e., to mollusk vectors identifications. Biomphalaria shells and live snails were analyzed regarding several operational aspects, such as: moisture, shell side and position of the live animal for acquisition of NIR spectra. Representative spectra of Biomphalaria shells and live snails were collected using an average of 50 scans per sample and resolution of 16 cm-1. For shells, the sample should first be dried for a minimum of 15 days at an average temperature of 26±1°C, and then placed directly in the equipment measurement window with its left side facing the light beam. Live animals should be dried with absorbent paper; placed into a glass jar, and analyzed similarly to the shells. Once standardized, the technique was applied aiming at two objectives: identification of Biomphalaria using only the shells and parasitological diagnosis for Schistosoma mansoni infection. The discrimination of the three Biomphalaria species intermediate hosts of S. mansoni only by shell has technical limit due to the scarcity of organic material. Nevertheless, it was possible to differentiate B. straminea from B. tenagophila and B. glabrata with 96% accuracy. As for the parasitological diagnosis, it was possible to differentiate infected mollusks shedding S. mansoni cercariae from the non-infected ones with 82, 5% accuracy. In conclusion, the Near Infrared Spectroscopy (NIR's) technique has proven to be an innovative and sound tool to detect infection by S. mansoni in the different species of Biomphalaria intermediate hosts.
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Affiliation(s)
- Vanessa Valladares
- Evaluation and Promotion of Environmental Health Laboratory, Instituto Oswaldo Cruz-Fiocruz, Rio de Janeiro, RJ, Brazil
| | - Célio Pasquini
- National Institute of Advanced Analytical Sciences and Technologies (INCTAA), State University of Campinas—UNICAMP / Chemistry Institute, Campinas, SP, Brazil
| | | | - Clélia Christina Mello-Silva
- Evaluation and Promotion of Environmental Health Laboratory, Instituto Oswaldo Cruz-Fiocruz, Rio de Janeiro, RJ, Brazil
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10
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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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11
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Valladares V, Pasquini C, Thiengo SC, Fernandez MA, Mello-Silva CC. Field Application of NIR Spectroscopy for the Discrimination of the Biomphalaria Species That Are Intermediate Hosts of Schistosoma mansoni in Brazil. Front Public Health 2021; 9:636206. [PMID: 33777886 PMCID: PMC7994760 DOI: 10.3389/fpubh.2021.636206] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 02/11/2021] [Indexed: 11/13/2022] Open
Abstract
Near Infrared Spectroscopy (NIRS) is a spectroscopic technique that evaluates the vibrational energy levels of the chemical bonds of molecules within a wavelength range of 750–2,500 nm. This simple method acquires spectra that provide qualitative and quantitative data on the chemical components of the biomass of living organisms through the interaction between the electromagnetic waves and the sample. NIRS is an innovative, rapid, and non-destructive technique that can contribute to the differentiation of species based on their chemical phenotypes. Chemical profiles were obtained by NIRS from three snail species (Biomphalaria glabrata, Biomphalaria straminea, and Biomphalaria tenagophila) that are intermediate hosts of Schistosoma mansoni in Brazil. The correct identification of these species is important from an epidemiological viewpoint, given that each species has distinct biological and physiological characteristics. The present study aimed to develop a chemometric model for the interspecific and intra-specific classification of the three species, focusing on laboratory and field populations. The data were obtained from 271 live animals, including 150 snails recently collected from the field, with the remainder being raised in the laboratory. Populations were sampled at three localities in the Brazilian state of Rio de Janeiro, in the municipalities of Sumidouro (B. glabrata) and Paracambi (B. straminea), and the borough of Jacarepaguá in the Rio de Janeiro city (B. tenagophila). The chemometric analysis was run in the Unscrambler® software. The intra-specific classification of the field and laboratory populations obtained accuracy rates of 72.5% (B. tenagophila), 77.5% (B. straminea), and 85.0% (B. glabrata). The interspecific differentiation had a hit rate of 75% for the field populations and 80% for the laboratory populations. The results indicate chemical and metabolic differences between populations of the same species from the field and the laboratory. The chemical phenotype, which is closely related to the metabolic profile of the snails, varied between environments. Overall, the NIRS technique proved to be a potentially valuable tool for medical malacology, enabling the systematic discrimination of the Biomphalaria snails that are the intermediate hosts of S. mansoni in Brazil.
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Affiliation(s)
- Vanessa Valladares
- Environmental Health Monitoring and Prevention Laboratory, Instituto Oswaldo Cruz-Fiocruz, Rio de Janeiro, Brazil
| | - Célio Pasquini
- Chemistry Institute, Universidade Estadual de Campinas/UNICAMP, Campinas, Brazil
| | - Silvana C Thiengo
- Malacology Laboratory, Instituto Oswaldo Cruz-Fiocruz, Rio de Janeiro, Brazil
| | - Monica A Fernandez
- Malacology Laboratory, Instituto Oswaldo Cruz-Fiocruz, Rio de Janeiro, Brazil
| | - Clélia C Mello-Silva
- Environmental Health Monitoring and Prevention Laboratory, Instituto Oswaldo Cruz-Fiocruz, Rio de Janeiro, Brazil
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12
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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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13
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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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14
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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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15
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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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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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