Adapting field-mosquito collection techniques in a perspective of near-infrared spectroscopy implementation

Some, Bernard; Da, Dari F.; McCabe, Ruth; Djègbè, Nicaise Denis Codjo; Paré, Lawata Inès Géraldine; Wermé, Kadidia; Mouline, Karine; Lefèvre, Thierry; Ouédraogo, Anicet Georges; Churcher, Thomas S; Dabiré, Roch K.

doi:10.1186/s13071-022-05458-6

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…Here, we provide clues on the reliable Anopheles species identification using WIPs and the DL process. We identified some species with 100% accuracy, even those belonging to the Gambiae complex of species 34 , 62 . This precision is higher than those provided by MIR or NIR technology.…”

Section: Discussionmentioning

confidence: 98%

Deep learning and wing interferential patterns identify Anopheles species and discriminate amongst Gambiae complex species

Cannet,

Simon-Chane,

Akhoundi

et al. 2023

Sci Rep

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We present a new and innovative identification method based on deep learning of the wing interferential patterns carried by mosquitoes of the Anopheles genus to classify and assign 20 Anopheles species, including 13 malaria vectors. We provide additional evidence that this approach can identify Anopheles spp. with an accuracy of up to 100% for ten out of 20 species. Although, this accuracy was moderate (> 65%) or weak (50%) for three and seven species. The accuracy of the process to discriminate cryptic or sibling species is also assessed on three species belonging to the Gambiae complex. Strikingly, An. gambiae, An. arabiensis and An. coluzzii, morphologically indistinguishable species belonging to the Gambiae complex, were distinguished with 100%, 100%, and 88% accuracy respectively. Therefore, this tool would help entomological surveys of malaria vectors and vector control implementation. In the future, we anticipate our method can be applied to other arthropod vector-borne diseases.

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Section: Discussionmentioning

confidence: 98%

Deep learning and wing interferential patterns identify Anopheles species and discriminate amongst Gambiae complex species

Cannet,

Simon-Chane,

Akhoundi

et al. 2023

Sci Rep

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Exploring near-infrared spectroscopy ability to predict the age and species of An. gambiae mosquitoes from different environmental conditions in Burkina Faso

Djègbè,

Da,

Somé

et al. 2024

Preprint

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Background Near infrared spectroscopy (NIRS) showed ability to predict some important entomological parameters in laboratory-reared and wild mosquitoes with moderate to high accuracy. Before validating the technique as a routine tool, it is necessary to assess NIRS accuracy on these variables under different environmental conditions similar to natural setting, as temperature and humidity could impact the mosquito cuticle and interfere with the machine prediction. This study aims to investigate the influence of environmental conditions on NIRS accuracy to determine age and species of An. gambiae s.l. Methods Environmental conditions of three important seasonal periods in Burkina Faso covering the onset, the peak and the end of the rainy season were mimicked in the laboratory using incubators. Emerged An. gambiae and An. coluzzii from laboratory colony were reared in each environmental condition and analysed by NIRS to predict mosquito species. Wild An. gambiae s.l. were caught during the 3 different periods described above and analysed by NIRS to compare the two results. Furthermore, first generation of Anopheles was used to assess NIRS ability to determine mosquito age in each environmental condition. Results NIRS discriminated between laboratory-reared Anopheles with 83% of accuracy independently of any environmental condition. Similar trend was found in wild-caught Anopheles. NIRS prediction accuracies varied slightly in laboratory Anopheles (77% − 85%) and more strongly in their field counterparts (67% − 84%). In both cases, prediction models developed from the season of interest were a little more accurate than models trained with insectary conditions or from a different period of the year, indicating temperature and humidity can impact NIRS accuracy. Models derived from laboratory-mosquitoes reared under fluctuating environmental conditions predicted field-derived mosquito species with a low accuracy (59%). Models trained on smaller datasets and varying conditions were reliably classified age into two categories (< 9 days or ≥ 9 days, 79% − 84% accuracy). Conclusion NIRS was able to predict An. gambiae s.l. species and classified age into two categories under different environmental conditions with modest accuracy. Models trained using wild mosquitoes from one season could predict species in wild mosquitoes from a different season, though with slightly lower accuracy.

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Illuminating Malaria: Spectroscopy’s Vital Role in Diagnosis and Research

Wood,

Adegoke,

Chakkumpulakkal Puthan Veettil

et al. 2024

Spectroscopy Journal

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Spectroscopic techniques have emerged as crucial tools in the field of malaria research, offering immense potential for improved diagnosis and enhanced understanding of the disease. This review article pays tribute to the pioneering contributions of Professor Henry Mantsch in the realm of clinical biospectroscopy, by comprehensively exploring the diverse applications of spectroscopic methods in malaria research. From the identification of reliable biomarkers to the development of innovative diagnostic approaches, spectroscopic techniques spanning the ultraviolet to far-infrared regions have played a pivotal role in advancing our knowledge of malaria. This review will highlight the multifaceted ways in which spectroscopy has contributed to the field, with a particular emphasis on its impact on diagnostic advancements and drug research. By leveraging the minimally invasive and highly accurate nature of spectroscopic techniques, researchers have made significant strides in improving the detection and monitoring of malaria parasites. These advancements hold the promise of enhancing patient outcomes and aiding in the global efforts towards the eradication of this devastating disease.

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Adapting field-mosquito collection techniques in a perspective of near-infrared spectroscopy implementation

Cited by 3 publications

References 23 publications

Deep learning and wing interferential patterns identify Anopheles species and discriminate amongst Gambiae complex species

Deep learning and wing interferential patterns identify Anopheles species and discriminate amongst Gambiae complex species

Exploring near-infrared spectroscopy ability to predict the age and species of An. gambiae mosquitoes from different environmental conditions in Burkina Faso

Illuminating Malaria: Spectroscopy’s Vital Role in Diagnosis and Research

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