Chagas disease is a debilitating and often fatal pathology resulting from infection by the protozoan parasite Trypanosoma cruzi. In its recommendations, the World Health Organization states that the diagnosis of T. cruzi infection is usually based on the detection of antibodies against T. cruzi antigens and performed with two methodologically different assays. An inconclusive result can be resolved with a third “confirmatory” assay. The objective of this article is to evaluate the effectiveness of the Chagas Western Blot IgG assay (LDBio Diagnostics, Lyon, France) as a confirmatory serologic test. The Chagas Western Blot IgG assay was performed with native antigens derived from a T. cruzi strain of the TcVI genotype. Retrospective sera were provided by two parasitology laboratories (France and Argentina). The sensitivity, specificity, positive predictive value and negative predictive value of the Chagas blot were all 100% in our sera collection. The Chagas blot is an easy and qualitative method for the diagnosis of Chagas disease, with results in less than 2 h. This immunoblot has potential as a supplemental test for the confirmation of the presence of antibodies against T. cruzi in serum specimens. Nonetheless, the very good initial results presented here will need to be confirmed in larger studies.

Identifying fungal clones propagated during outbreaks in hospital settings is a problem that increasingly confronts biologists. Current tools based on DNA sequencing or microsatellite analysis require specific manipulations that are difficult to implement in the context of routine diagnosis. Using deep learning to classify the mass spectra obtained during the routine identification of fungi by MALDI-TOF mass spectrometry could be of interest to differentiate isolates belonging to epidemic clones from others. As part of the management of a nosocomial outbreak due to Candida parapsilosis in two Parisian hospitals, we studied the impact of the preparation of the spectra on the performance of a deep neural network. Our purpose was to differentiate 39 otherwise fluconazole-resistant isolates belonging to a clonal subset from 56 other isolates, most of which were fluconazole-susceptible, collected during the same period and not belonging to the clonal subset. Our study carried out on spectra obtained on four different machines from isolates cultured for 24 or 48 h on three different culture media showed that each of these parameters had a significant impact on the performance of the classifier. In particular, using different culture times between learning and testing steps could lead to a collapse in the accuracy of the predictions. On the other hand, including spectra obtained after 24 and 48 h of growth during the learning step restored the good results. Finally, we showed that the deleterious effect of the device variability used for learning and testing could be largely improved by including a spectra alignment step during preprocessing before submitting them to the neural network. Taken together, these experiments show the great potential of deep learning models to identify spectra of specific clones, providing that crucial parameters are controlled during both culture and preparation steps before submitting spectra to a classifier.