دورية أكاديمية
Rapid classification of epidemiologically relevant age categories of the malaria vector, Anopheles funestus.
| العنوان: | Rapid classification of epidemiologically relevant age categories of the malaria vector, Anopheles funestus. |
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| المؤلفون: | Mwanga EP; Environmental Health and Ecological Sciences Department, Ifakara Health Institute, P.O. Box 53, Morogoro, Tanzania. emwanga@ihi.or.tz.; School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK. emwanga@ihi.or.tz., Siria DJ; 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., Mshani IH; 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., Mwinyi SH; 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., Abbasi S; Environmental Health and Ecological Sciences Department, Ifakara Health Institute, P.O. Box 53, Morogoro, Tanzania., Jimenez MG; 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., Wynne K; School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK., Baldini F; School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK., Babayan SA; School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK., Okumu FO; 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. |
| المصدر: | Parasites & vectors [Parasit Vectors] 2024 Mar 18; Vol. 17 (1), pp. 143. Date of Electronic Publication: 2024 Mar 18. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: BioMed Central Country of Publication: England NLM ID: 101462774 Publication Model: Electronic Cited Medium: Internet ISSN: 1756-3305 (Electronic) Linking ISSN: 17563305 NLM ISO Abbreviation: Parasit Vectors Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: London : BioMed Central |
| مواضيع طبية MeSH: | Malaria* , Anopheles*/parasitology, Animals ; Female ; Humans ; Infant ; Child, Preschool ; Child ; Infant, Newborn ; Mosquito Vectors/parasitology ; Spectroscopy, Fourier Transform Infrared ; Tanzania |
| مستخلص: | 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. (© 2024. The Author(s).) |
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| معلومات مُعتمدة: | 214643/Z/18/Z United Kingdom WT_ Wellcome Trust; United Kingdom WT_ Wellcome Trust; MR/P025501/1 United Kingdom MRC_ Medical Research Council; Grant No. OPP1099295 United States HHMI Howard Hughes Medical Institute; INV-030025 United States GATES Bill & Melinda Gates Foundation |
| فهرسة مساهمة: | Keywords: Anopheles funestus; Deep learning; Ifakara Health Institute; Machine learning; Malaria; Mid-infrared spectroscopy |
| تواريخ الأحداث: | Date Created: 20240319 Date Completed: 20240320 Latest Revision: 20240321 |
| رمز التحديث: | 20240321 |
| مُعرف محوري في PubMed: | PMC10949582 |
| DOI: | 10.1186/s13071-024-06209-5 |
| PMID: | 38500231 |
| قاعدة البيانات: | MEDLINE |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 1756-3305 |
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| DOI: | 10.1186/s13071-024-06209-5 |