ORCID Profile
0000-0002-9625-5176
Current Organisation
University of Glasgow
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Publisher: Springer Science and Business Media LLC
Date: 21-01-2015
Publisher: Springer Science and Business Media LLC
Date: 20-08-2008
Publisher: Springer Science and Business Media LLC
Date: 07-10-2019
DOI: 10.1186/S12936-019-2982-9
Abstract: Epidemiological surveys of malaria currently rely on microscopy, polymerase chain reaction assays (PCR) or rapid diagnostic test kits for Plasmodium infections (RDTs). This study investigated whether mid-infrared (MIR) spectroscopy coupled with supervised machine learning could constitute an alternative method for rapid malaria screening, directly from dried human blood spots. Filter papers containing dried blood spots (DBS) were obtained from a cross-sectional malaria survey in 12 wards in southeastern Tanzania in 2018/19. The DBS were scanned using attenuated total reflection-Fourier Transform Infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra in the range 4000 cm −1 to 500 cm −1 . The spectra were cleaned to compensate for atmospheric water vapour and CO 2 interference bands and used to train different classification algorithms to distinguish between malaria-positive and malaria-negative DBS papers based on PCR test results as reference. The analysis considered 296 in iduals, including 123 PCR-confirmed malaria positives and 173 negatives. Model training was done using 80% of the dataset, after which the best-fitting model was optimized by bootstrapping of 80/20 train/test-stratified splits. The trained models were evaluated by predicting Plasmodium falciparum positivity in the 20% validation set of DBS. Logistic regression was the best-performing model. Considering PCR as reference, the models attained overall accuracies of 92% for predicting P. falciparum infections (specificity = 91.7% sensitivity = 92.8%) and 85% for predicting mixed infections of P. falciparum and Plasmodium ovale (specificity = 85%, sensitivity = 85%) in the field-collected specimen. These results demonstrate that mid-infrared spectroscopy coupled with supervised machine learning (MIR-ML) could be used to screen for malaria parasites in human DBS. The approach could have potential for rapid and high-throughput screening of Plasmodium in both non-clinical settings (e.g., field surveys) and clinical settings (diagnosis to aid case management). However, before the approach can be used, we need additional field validation in other study sites with different parasite populations, and in-depth evaluation of the biological basis of the MIR signals. Improving the classification algorithms, and model training on larger datasets could also improve specificity and sensitivity. The MIR-ML spectroscopy system is physically robust, low-cost, and requires minimum maintenance.
Publisher: The Royal Society
Date: 07-03-2013
Abstract: Many malaria vector mosquitoes in Africa have an extreme preference for feeding on humans. This specialization allows them to sustain much higher levels of transmission than elsewhere, but there is little understanding of the evolutionary forces that drive this behaviour. In Tanzania, we used a semi-field system to test whether the well-documented preferences of the vectors, Anopheles arabiensis and Anopheles gambiae sensu stricto (s.s.) for cattle and humans, respectively, are predicted by the fitness they obtain from host-seeking on these species relative to other available hosts. Mosquito fitness was contrasted, when humans were fully exposed and when they were protected by a typical bednet. The fitness of both vectors varied between host species. The predicted relationship between host preference and fitness was confirmed in An. arabiensis , but not in An. gambiae s.s., whose fitness was similar on humans and other mammals. Use of typical, imperfect bednets generated only minor reductions in An. gambiae s.s. feeding success and fitness on humans, but was predicted to generate a significant reduction in the lifetime reproductive success of An. arabiensis on humans relative to cows. This supports the hypothesis that such human-protective measures could additionally benefit malaria control by increasing selection for zoophily in vectors.
Publisher: The Royal Society
Date: 09-03-2011
Abstract: Understanding the endogenous factors that drive the population dynamics of malaria mosquitoes will facilitate more accurate predictions about vector control effectiveness and our ability to destabilize the growth of either low- or high-density insect populations. We assessed whether variation in phenotypic traits predict the dynamics of Anopheles gambiae sensu lato mosquitoes, the most important vectors of human malaria. Anopheles gambiae dynamics were monitored over a six-month period of seasonal growth and decline. The population exhibited density-dependent feedback, with the carrying capacity being modified by rainfall (97% w AIC c support). The in idual phenotypic expression of the maternal ( p = 0.0001) and current ( p = 0.040) body size positively influenced population growth. Our field-based evidence uniquely demonstrates that in idual fitness can have population-level impacts and, furthermore, can mitigate the impact of exogenous drivers (e.g. rainfall) in species whose reproduction depends upon it. Once frontline interventions have suppressed mosquito densities, attempts to eliminate malaria with supplementary vector control tools may be attenuated by increased population growth and in idual fitness.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Heather Ferguson.