Breath biomarkers of pediatric malaria: reproducibility and response to antimalarial therapy.

BACKGROUND: Many insect-borne pathogens appear to manipulate the odors of their hosts in ways that influence vector behaviors. In our prior work, we identified characteristic changes in volatile emissions of cultured Plasmodium falciparum parasites in vitro and during natural human falciparum malaria. In the current study, we prospectively evaluate the reproducibility of these findings in an independent cohort of children in Blantyre, Malawi. METHODS: We enrolled febrile children under evaluation for malaria and collected breath from children with and without malaria, as well as healthy controls. Using gas-chromatography/mass spectrometry, we characterized breath volatiles associated with malaria. By repeated sampling of children with malaria before and after antimalarial use, we determined how breath profiles respond to treatment. In addition, we investigated the stage-specificity of biomarkers through correlation with asexual and sexual stage parasitemia. RESULTS: Our data provide robust evidence that P. falciparum infection leads to specific, reproducible changes in breath compounds. While no individual compound served as adequate classifier in isolation, selected volatiles together yielded high sensitivity for diagnosis of malaria. Overall, the results of our predictive models suggest the presence of volatile signatures that reproducibly predict malaria infection status and determine response to therapy, even in cases of low parasitemia.

Classifying continuous, real-time e-nose sensor data using a bio-inspired spiking network modelled on the insect olfactory system.

In many application domains, conventional e-noses are frequently outperformed in both speed and accuracy by their biological counterparts. Exploring potential bio-inspired improvements, we note a number of neuronal network models have demonstrated some success in classifying static datasets by abstracting the insect olfactory system. However, these designs remain largely unproven in practical settings, where sensor data is real-time, continuous, potentially noisy, lacks a precise onset signal and accurate classification requires the inclusion of temporal aspects into the feature set. This investigation therefore seeks to inform and develop the potential and suitability of biomimetic classifiers for use with typical real-world sensor data. Taking a generic classifier design inspired by the inhibition and competition in the insect antennal lobe, we apply it to identifying 20 individual chemical odours from the timeseries of responses of metal oxide sensors. We show that four out of twelve available sensors and the first 30 s (10%) of the sensors' continuous response are sufficient to deliver 92% accurate classification without access to an odour onset signal. In contrast to previous approaches, once training is complete, sensor signals can be fed continuously into the classifier without requiring discretization. We conclude that for continuous data there may be a conceptual advantage in using spiking networks, in particular where time is an essential component of computation. Classification was achieved in real time using a GPU-accelerated spiking neural network simulator developed in our group.