Plasma levels of eight different mediators and their potential as biomarkers of various clinical malaria conditions in African children.

BACKGROUND: Plasmodium falciparum infection can lead to several clinical manifestations ranging from asymptomatic infections (AM) and uncomplicated malaria (UM) to potentially fatal severe malaria (SM), including cerebral malaria (CM). Factors implicated in the progression towards severe disease are not fully understood. METHODS: In the present study, an enzyme-linked immunosorbent assay (ELISA) method was used to investigate the plasma content of several biomarkers of the immune response, namely Neopterin, sCD163, suPAR, Pentraxin 3 (PTX3), sCD14, Fractalkine (CX3CL1), sTREM-1 and MIG (CXCL9), in patients with distinct clinical manifestations of malaria. The goal of this study was to determine the relative involvement of these inflammatory mediators in the pathogenesis of malaria and test their relevance as biomarkers of disease severity. RESULTS: ROC curve analysis show that children with AM were characterized by high levels of Fractalkine and sCD163 whereas children with UM were distinguishable by the presence of PTX3 in their plasma. Furthermore, principal component analysis indicated that the combination of Fractalkine, MIG, and Neopterin was the best predictor of AM condition, while suPAR, PTX3 and sTREM-1 combination was the best indicator of UM when compared to AM. The association of Neopterin, suPAR and Fractalkine was strongly predictive of SM or CM compared to UM. CONCLUSIONS: The results indicate that the simultaneous evaluation of these bioactive molecules as quantifiable blood parameters may be helpful to get a better insight into the clinical syndromes in children with malaria.

Learning the propagation properties of rectangular metal plates for Lamb wave-based mapping.

The inspection of sizeable plate-based metal structures such as storage tanks or marine vessel hulls is a significant stake in the industry, which necessitates reliable and time-efficient solutions. Although Lamb waves have been identified as a promising solution for long-range non-destructive testing, and despite the substantial progress made in autonomous navigation and environment sensing, a Lamb-wave-based robotic system for extensive structure monitoring is still lacking. Following previous work on ultrasonic Simultaneous Localization and Mapping (SLAM), we introduce a method to achieve plate geometry inference without prior knowledge of the material propagation properties, which may be lacking during a practical inspection task in challenging and outdoor environments. Our approach combines focalization to adjust the propagation model parameters and beamforming to infer the plate boundaries location by relying directly on acoustic measurements acquired along the mobile unit trajectory. For each candidate model, the focusing ability of the corresponding beamformer is assessed over high-pass filtered beamforming maps to further improve the robustness of the plate geometry estimates. We then recover the optimal space-domain beamformer through a simulated annealing optimization process. We evaluate our method on three sets of experimental data acquired in different conditions and show that accurate plate geometry inference can be achieved without any prior propagation model. Finally, the results show that the optimal beamformer outperforms the beamformer resulting from the predetermined propagation model in non-nominal acquisition conditions.