Parasites & Vectors · 2018 · 38 citations · 27 references
Veterinary MedicineEntomologyInfectious DiseasesDiagnostic ParasitologyParasitologyVector-borne Disease ControlInfrared SpectroscopyMalariaMicroscopic ExaminationMosquito BiologyMosquito-borne DiseasesPathogen DetectionAnopheles Mosquito BiologyMolecular Detection Of ParasitesMolecular Parasite DetectionMosquito-borne Disease DiagnosisMosquito-borne Disease InvestigationNear-infrared Spectroscopy AssessmentParasite Prevalence StudiesParasite Prevalence Surveys
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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pROC: an open-source package for R and S+ to analyze and compare ROC curves
Xavier Robin, Natacha Turck, Alexandre Hainard et al. · BMC Bioinformatics · 2011 · 14K citations · Full text
ROCR: visualizing classifier performance in R
Tobias Sing, Oliver Sander, Niko Beerenwinkel et al. · Computer applications in the biosciences · 2005 · 3.3K citations · Full text