دورية أكاديمية
أعرض في EDS

Rapid assessment of the blood-feeding histories of wild-caught malaria mosquitoes using mid-infrared spectroscopy and machine learning.

التفاصيل البيبلوغرافية
العنوان: Rapid assessment of the blood-feeding histories of wild-caught malaria mosquitoes using mid-infrared spectroscopy and machine learning.
المؤلفون: Mwanga EP; Environmental Health and Ecological Sciences Department, Ifakara Health Institute, 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., Mchola IS; Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania., Makala FE; Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania., Mshani IH; Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania.; School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK., Siria DJ; Environmental Health and Ecological Sciences Department, Ifakara Health Institute, 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, 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, Morogoro, Tanzania., Seleman G; Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania., Mgaya JN; Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania., Jiménez MG; School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK., Wynne K; School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK., Sikulu-Lord MT; Faculty of Science, School of the Environment, The University of Queensland, Brisbane, QLD, Australia., Selvaraj P; Institute for Disease Modelling, Bill and Melinda Gates Foundation, Seattle, USA., Okumu FO; Environmental Health and Ecological Sciences Department, Ifakara Health Institute, 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., 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.
المصدر: Malaria journal [Malar J] 2024 Mar 26; Vol. 23 (1), pp. 86. Date of Electronic Publication: 2024 Mar 26.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: BioMed Central Country of Publication: England NLM ID: 101139802 Publication Model: Electronic Cited Medium: Internet ISSN: 1475-2875 (Electronic) Linking ISSN: 14752875 NLM ISO Abbreviation: Malar J Subsets: MEDLINE
أسماء مطبوعة: Original Publication: London : BioMed Central, [2002-
مواضيع طبية MeSH: Malaria*/epidemiology , Anopheles*, Animals ; Humans ; Female ; Mosquito Vectors ; Machine Learning ; Spectrophotometry, Infrared ; Feeding Behavior
مستخلص: Background: The degree to which Anopheles mosquitoes prefer biting humans over other vertebrate hosts, i.e. the human blood index (HBI), is a crucial parameter for assessing malaria transmission risk. However, existing techniques for identifying mosquito blood meals are demanding in terms of time and effort, involve costly reagents, and are prone to inaccuracies due to factors such as cross-reactivity with other antigens or partially digested blood meals in the mosquito gut. This study demonstrates the first field application of mid-infrared spectroscopy and machine learning (MIRS-ML), to rapidly assess the blood-feeding histories of malaria vectors, with direct comparison to PCR assays.
Methods and Results: Female Anopheles funestus mosquitoes (N = 1854) were collected from rural Tanzania and desiccated then scanned with an attenuated total reflectance Fourier-transform Infrared (ATR-FTIR) spectrometer. Blood meals were confirmed by PCR, establishing the 'ground truth' for machine learning algorithms. Logistic regression and multi-layer perceptron classifiers were employed to identify blood meal sources, achieving accuracies of 88%-90%, respectively, as well as HBI estimates aligning well with the PCR-based standard HBI.
Conclusions: This research provides evidence of MIRS-ML effectiveness in classifying blood meals in wild Anopheles funestus, as a potential complementary surveillance tool in settings where conventional molecular techniques are impractical. The cost-effectiveness, simplicity, and scalability of MIRS-ML, along with its generalizability, outweigh minor gaps in HBI estimation. Since this approach has already been demonstrated for measuring other entomological and parasitological indicators of malaria, the validation in this study broadens its range of use cases, positioning it as an integrated system for estimating pathogen transmission risk and evaluating the impact of interventions.
(© 2024. The Author(s).)
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معلومات مُعتمدة: 214643/Z/18/Z United Kingdom WT_ Wellcome Trust; INV-030025 United States GATES Bill & Melinda Gates Foundation; United Kingdom WT_ Wellcome Trust; INV-003079 United States GATES Bill & Melinda Gates Foundation; MR/P025501/1 United Kingdom MRC_ Medical Research Council
فهرسة مساهمة: Keywords: Anopheles; Human blood index machine learning; Transfer learning; VectorSphere
تواريخ الأحداث: Date Created: 20240327 Date Completed: 20240328 Latest Revision: 20240418
رمز التحديث: 20240418
مُعرف محوري في PubMed: PMC10964711
DOI: 10.1186/s12936-024-04915-0
PMID: 38532415
قاعدة البيانات: MEDLINE
الوصف
تدمد:1475-2875
DOI:10.1186/s12936-024-04915-0