Maggy T. Sikulu-Lord

This study is a comparative assessment of a more affordable handheld spectrometer (NIRvascan) with the traditional Labspec 4i spectrometer for predicting the chronological age and blood feeding history of female Aedes aegypti mosquitoes reared in the lab. Three separate cohorts of laboratory-reared Ae. aegypti mosquitoes were reared and collected at three age groups (1-, 10- and 17-days old). A model developed using Artificial Neural Networks (ANN) with spectra collected by the Labspec 4i NIR spectrometer predicted the age of Ae. aegypti, classifying them into two groups (< or ≥ 10 days) with a predictive accuracy of 94% (N = 366) whereas an ANN model developed using spectra collected by the NIRvascan spectrometer predicted the age of Ae. aegypti mosquitoes, classifying them into the same age group with a predictive accuracy of 90% (N = 290). ANN models developed for predicting the blood feeding history of mosquitoes were 82.8% (N = 308) and 71.4% accurate (N = 300) when Labspec 4i and NIRvascan were used, respectively. This is the first study to demonstrate that a handheld NIR instrument operated by a smart phone could potentially be used for predicting entomological parameters of mosquitoes.

This study presents a technique for categorizing Aedes aegypti mosquitoes infected with the Zika virus under laboratory conditions. Our approach involves the utilization of the near-infrared spectroscopy technique and machine learning algorithms. The model developed utilizes the absorption of light from 350 to 1000 nm. It integrates Linear Discriminant Analysis (LDA) of the signal’s windowed version to exploit non-linearities, along with Support Vector Machine (SVM) for classification purposes. Our proposed methodology can identify the presence of the Zika virus in intact mosquitoes with a balanced accuracy of 96% (row C2HT, average of columns TPR (%) and SPC (%)) when heads/thoraces of mosquitoes are scanned at 4, 7, and 10 days post virus infection. The model was 97.1% (10 DPI, row C2AB, column ACC (%)) accurate for mosquitoes that were used to test it, i.e., mosquitoes scanned 10-days post-infection and mosquitoes whose abdomens were scanned. Notable benefits include its cost-effectiveness and the capability for real-time predictions. This work also demonstrates the role played by different spectral wavelengths in predicting an infection in mosquitoes.

This study is a comparative assessment of a more affordable handheld spectrometer (NIRvascan) with the traditional Labspec 4i spectrometer for predicting the chronological age and blood feeding history of female Aedes aegypti mosquitoes reared in the lab. Three separate cohorts of laboratory-reared Ae. aegypti mosquitoes were collected at 3 age groups (1, 10 and 17 days old). Models developed using spectra collected with Labspec 4i spectrometer predicted the age of Ae. aegypti into two groups (< or ≥ 10 days) with a predictive accuracy of 94% (N=366) whereas NIRvascan predicted the age of Ae. aegypti mosquitoes into the same age group with a predictive accuracy of 90% (N=290). Models developed for predicting blood feeding history of mosquitoes were 82.8% (N=308) and 71.4% accurate (N=300) when Labspec 4i and NIRvascan were used, respectively. This is the first study to demonstrate that a handheld NIR instrument operated by a smart phone can be used for predicting mosquito traits related to the vectorial capacity. Our data indicates the feasibility of using both spectrometers for in-field application for analysis of mosquito samples to gain insights into population age structure.

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 (−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.

Background: Trichuris trichiura (whipworm) is one of the most prevalent soil transmitted helminths (STH) affecting 604–795 million people worldwide. Diagnostic tools that are affordable and rapid are required for detecting STH. Here, we assessed the performance of the near-infrared spectroscopy (NIRS) technique coupled with machine learning algorithms to detect Trichuris muris in faecal, blood, serum samples and non-invasively through the skin of mice. Methodology: We orally infected 10 mice with 30 T. muris eggs (low dose group), 10 mice with 200 eggs (high dose group) and 10 mice were used as the control group. Using the NIRS technique, we scanned faecal, serum, whole blood samples and mice non-invasively through their skin over a period of 6 weeks post infection. Using artificial neural networks (ANN) and spectra of faecal, serum, blood and non-invasive scans from one experiment, we developed 4 algorithms to differentiate infected from uninfected mice. These models were validated on mice from a second independent experiment. Principal findings: NIRS and ANN differentiated mice into the three groups as early as 2 weeks post infection regardless of the sample used. These results correlated with those from concomitant serological and parasitological investigations. Significance: To our knowledge, this is the first study to demonstrate the potential of NIRS as a diagnostic tool for human STH infections. The technique could be further developed for large scale surveillance of soil transmitted helminths in human populations.

Arbovirus and malaria infections affect more than half of the world’s population causing major financial and physical burden. Current diagnostic tools such as microscopy, molecular and serological techniques are technically demanding, costly, or time consuming. Near-infrared spectroscopy has recently been demonstrated as a potential diagnostic tool for malaria and arbovirus and as a screening tool for disease vectors. However, pathogen specific infrared peaks that allow detection of these infections are yet to be described. In this study, we identified unique NIRS peaks from existing laboratory strains of four major arboviruses including Barmah Forest virus (BFV), Dengue virus (DENV), Ross River virus (RRV), Sindbis virus (SINV) and Plasmodium falciparum . Secondly, to determine the diagnostic ability of these peaks, we developed machine learning algorithms using Artificial Neural network (ANN) to differentiate arboviruses from media in which they are grown. Signature peaks for BFV were identified within the visible region at 410, 430, 562 and 588nm and the NIR region at, 946, 958, 1130, 1154 and 1780 nm. DENV related peaks were seen at 410nm within the visible region and 1130 nm within the NIR region. Signature peaks for RRV were observed within the visible region at 410 and 430 nm and within the NIR region at 1130 and 1780 nm, while SINV had a prominent peak at 410 nm within the visible region. Peaks at 514, 528, 547, 561, 582, and 595nm and peaks at 1388, 1432, 1681, 1700, 1721, 1882, 1905, 2245, 2278, 2300 nm were unique for P. falciparum . NIRS predictive sensitivity defined as the ability to predict an arbovirus as an infection was 90% (n = 20) for BFV, 100% (n =10) for RRV and 97.5% (n= 40) for DENV, while infection specificity defined as the ability to predict media as not-infected was 100% (n= 10). Our findings indicate that spectral signatures obtained by NIRS are potential biomarkers for diagnosis arboviruses and malaria. Author summary: More than half of the world is at risk of contracting either malaria or arboviral infections. In resource limited settings, timely detection of these infections and the ability to screen thousands of people in a day using affordable tools is key to preventing their spread and unprecedented outbreaks. This emphasizes the need to develop portable, rapid, and easy to use diagnostic and surveillance tools. The near-infrared spectroscopy technique has recently been shown to be a potential tool for the diagnosis and surveillance of both malaria and arboviral infections. However, signature spectral biomarkers for these infections remain to be described. This study has identified several spectral biomarkers for DEN, RRV, BVF, SIN arboviruses and P. falciparum parasites . These biomarkers will assist the future assessment of this technique as a diagnostic and or surveillance tool for these infections in the field.

The interest of the entomological community in the application of the Near Infrared (NIR) and Mid Infrared (MIR) spectroscopy techniques for characterisation of insect species has seen a dramatic increase over the last decade. In this chapter, we discuss the application of the Near infrared spectroscopy technique for characterising insects including mosquitoes, triatomine bugs, Culicoides and houseflies under varying environmental and experimental conditions. We focus our discussion on the recent progress made in the application of the NIRS technique to predict the age, species and infection status of mosquito vectors of malaria and arbovirus infections relative to traditional tools such as dissections and molecular techniques and how the tool could be applied in vector surveillance programs to determine disease hotspots and direct current and future interventions. Lastly, we briefly discuss the application of MIR spectroscopy technique for age grading, species identification, blood meal identification and as a potential diagnostic tool for malaria parasites.

The transmission of dengue (DENV) and Zika (ZIKV) has been continuously increasing worldwide. An efficient arbovirus surveillance system is critical to designing early-warning systems to increase preparedness of future outbreaks in endemic countries. The Near Infrared Spectroscopy (NIRS) is a promising high throughput technique to detect arbovirus infection in Ae. aegypti with remarkable advantages such as cost and time effectiveness, reagent-free, and non-invasive nature over existing molecular tools for similar purposes, enabling timely decision making through rapid detection of potential disease. Our aim was to determine whether NIRS can differentiate Ae. aegypti females infected with either ZIKV or DENV single infection, and those coinfected with ZIKV/DENV from uninfected ones. Using 200 Ae. aegypti females reared and infected in laboratory conditions, the training model differentiated mosquitoes into the four treatments with 100% accuracy. DENV-, ZIKV-, and ZIKV/DENV-coinfected mosquitoes that were used to validate the model could be correctly classified into their actual infection group with a predictive accuracy of 100%, 84%, and 80%, respectively. When compared with mosquitoes from the uninfected group, the three infected groups were predicted as belonging to the infected group with 100%, 97%, and 100% accuracy for DENV-infected, ZIKV-infected, and the co-infected group, respectively. Preliminary lab-based results are encouraging and indicate that NIRS should be tested in field settings to evaluate its potential role to monitor natural infection in field-caught mosquitoes.

Assessing the health and wellbeing of animals in their natural habitats is a critical yet difficult aspect of species management and conservation. Non- and minimally invasive techniques are increasingly being sought by people working with animals so they can easily monitor the health and wellbeing of the animals without causing them distress. One noninvasive technique that has been in this space is infrared (IR) spectroscopy, which measures the interaction of IR radiation with matter. The ability of IR spectroscopy, especially near-infrared (NIR) and mid infrared (MIR) spectroscopy, to evaluate and monitor animal nutrition, as well as to discriminate physiological attributes of animals (e.g., stress, reproductive status), has been demonstrated by several studies with livestock and wildlife species. However, the use of IR spectroscopy techniques is laden with several challenges, including sample preparation and stability, reliability, and transferability of prediction models. This paper reviews research on the analysis of fecal samples using IR spectroscopy as a proxy in animal health and wellbeing studies. The main challenges of using IR spectroscopy as a portable technology for in-field analysis are also discussed while highlighting key aspects to be considered for in-field applications of NIR and MIR spectroscopy in animal health and wellbeing studies.