Metabolomics Identifies multiple candidate biomarkers to diagnose and stage human African trypanosomiasis

Treatment for human African trypanosomiasis is dependent on the species of trypanosome causing the disease and the stage of the disease (stage 1 defined by parasites being present in blood and lymphatics whilst for stage 2, parasites are found beyond the blood-brain barrier in the cerebrospinal fluid (CSF)). Currently, staging relies upon detecting the very low number of parasites or elevated white blood cell numbers in CSF. Improved staging is desirable, as is the elimination of the need for lumbar puncture. Here we use metabolomics to probe samples of CSF, plasma and urine from 40 Angolan patients infected with Trypanosoma brucei gambiense, at different disease stages. Urine samples provided no robust markers indicative of infection or stage of infection due to inherent variability in urine concentrations. Biomarkers in CSF were able to distinguish patients at stage 1 or advanced stage 2 with absolute specificity. Eleven metabolites clearly distinguished the stage in most patients and two of these (neopterin and 5-hydroxytryptophan) showed 100% specificity and sensitivity between our stage 1 and advanced stage 2 samples. Neopterin is an inflammatory biomarker previously shown in CSF of stage 2 but not stage 1 patients. 5-hydroxytryptophan is an important metabolite in the serotonin synthetic pathway, the key pathway in determining somnolence, thus offering a possible link to the eponymous symptoms of “sleeping sickness”. Plasma also yielded several biomarkers clearly indicative of the presence (87% sensitivity and 95% specificity) and stage of disease (92% sensitivity and 81% specificity). A logistic regression model including these metabolites showed clear separation of patients being either at stage 1 or advanced stage 2 or indeed diseased (both stages) versus control

Novel African trypanocidal agents: membrane rigidifying peptides

The bloodstream developmental forms of pathogenic African trypanosomes are uniquely susceptible to killing by small hydrophobic peptides. Trypanocidal activity is conferred by peptide hydrophobicity and charge distribution and results from increased rigidity of the plasma membrane. Structural analysis of lipid-associated peptide suggests a mechanism of phospholipid clamping in which an internal hydrophobic bulge anchors the peptide in the membrane and positively charged moieties at the termini coordinate phosphates of the polar lipid headgroups. This mechanism reveals a necessary phenotype in bloodstream form African trypanosomes, high membrane fluidity, and we suggest that targeting the plasma membrane lipid bilayer as a whole may be a novel strategy for the development of new pharmaceutical agents. Additionally, the peptides we have described may be valuable tools for probing the biosynthetic machinery responsible for the unique composition and characteristics of African trypanosome plasma membranes

Which visual questions are difficult to answer? Analysis with Entropy of Answer Distributions

We propose a novel approach to identify the difficulty of visual questions for Visual Question Answering (VQA) without direct supervision or annotations to the difficulty. Prior works have considered the diversity of ground-truth answers of human annotators. In contrast, we analyze the difficulty of visual questions based on the behavior of multiple different VQA models. We propose to cluster the entropy values of the predicted answer distributions obtained by three different models: a baseline method that takes as input images and questions, and two variants that take as input images only and questions only. We use a simple k-means to cluster the visual questions of the VQA v2 validation set. Then we use state-of-the-art methods to determine the accuracy and the entropy of the answer distributions for each cluster. A benefit of the proposed method is that no annotation of the difficulty is required, because the accuracy of each cluster reflects the difficulty of visual questions that belong to it. Our approach can identify clusters of difficult visual questions that are not answered correctly by state-of-the-art methods. Detailed analysis on the VQA v2 dataset reveals that 1) all methods show poor performances on the most difficult cluster (about 10% accuracy), 2) as the cluster difficulty increases, the answers predicted by the different methods begin to differ, and 3) the values of cluster entropy are highly correlated with the cluster accuracy. We show that our approach has the advantage of being able to assess the difficulty of visual questions without ground-truth (i.e. the test set of VQA v2) by assigning them to one of the clusters. We expect that this can stimulate the development of novel directions of research and new algorithms. Clustering results are available online at https://github.com/tttamaki/vqd .Comment: accepted by IEEE access available at https://doi.org/10.1109/ACCESS.2020.3022063 as "An Entropy Clustering Approach for Assessing Visual Question Difficulty