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BackgroundAccording to the World Health Organization, achieving targets for control of leprosy by 2030 will require disease elimination and interruption of transmission at the national or regional level. India and Brazil have reported the highest leprosy burden in the last few decades, revealing the need for strategies and tools to help health professionals correctly manage and control the disease. ObjectiveThe main objective of this study was to develop a cross-platform app for leprosy screening based on artificial intelligence (AI) with the goal of increasing accessibility of an accurate method of classifying leprosy treatment for health professionals, especially for communities further away from major diagnostic centers. Toward this end, we analyzed the quality of leprosy data in Brazil on the National Notifiable Diseases Information System (SINAN). MethodsLeprosy data were extracted from the SINAN database, carefully cleaned, and used to build AI decision models based on the random forest algorithm to predict operational classification in paucibacillary or multibacillary leprosy. We used Python programming language to extract and clean the data, and R programming language to train and test the AI model via cross-validation. To allow broad access, we deployed the final random forest classification model in a web app via shinyApp using data available from the Brazilian Institute of Geography and Statistics and the Department of Informatics of the Unified Health System. ResultsWe mapped the dispersion of leprosy incidence in Brazil from 2014 to 2018, and found a particularly high number of cases in central Brazil in 2014 that further increased in 2018 in the state of Mato Grosso. For some municipalities, up to 80% of cases showed some data discrepancy. Of a total of 21,047 discrepancies detected, the most common was “operational classification does not match the clinical form.” After data processing, we identified a total of 77,628 cases with missing data. The sensitivity and specificity of the AI model applied for the operational classification of leprosy was 93.97% and 87.09%, respectively. ConclusionsThe proposed app was able to recognize patterns in leprosy cases registered in the SINAN database and to classify new patients with paucibacillary or multibacillary leprosy, thereby reducing the probability of incorrect assignment by health centers. The collection and notification of data on leprosy in Brazil seem to lack specific validation to increase the quality of the data for implementations via AI. The AI models implemented in this work had satisfactory accuracy across Brazilian states and could be a complementary diagnosis tool, especially in remote areas with few specialist physicians.

Introduction: Leprosy, a chronic infectious disease, is closely linked to the host immune response. According to the WHO, leprosy patients (L) and household contacts (HHC) are classified into subgroups: paucibacillary (PB) and multibacillary (MB), witch reflect the degree of infection in patients and the level of exposure of their contacts. The main goal of this study was to: i) establish a comprehensive overview of soluble mediator signatures of PBMCs upon in vitro antigen-specific stimuli and ii) identify whether the chemokine (CH) and cytokine (CY) signatures were associated with distinct clinical manifestations in (L) and immune response profiles in (HHC). Methods: Long-term PBMC cultures were carried out and supernatants collected for 12 CH and CY analisys by Cytometric Beads Array. Results and discussion: The CH and CY analysis, using continuous variable modeling, demonstrated that PBMCs from both L and HHC exhibited high levels of TNF upon M. leprae-stimuli. While lower production of IFN-γ were observed for L, low levels of CXCL8 was found for HHC. Soluble mediator signatures, analyzed using categorical variables, revealed that while high levels of TNF were observed for L, high levels of IFN-γ appeared as a hallmark of HHC. Overall, these analyses demonstrated that CXCL8, IFN-γ, and TNF were key markers differentiating L from HHC and endemic control (EC), especially considering the categorical analysis of the soluble mediator signatures. Data further demonstrated that higher levels of IFN-γ and lower levels CXCL8 was features associated with HHC(MB), whereas high levels of TNF were observed in both L subgroups. Moreover, data from integrative networks, based on correlation amongst soluble mediators, revealed that in M. leprae-stimuli, the number of correlations was lower in HHC(MB) compared to HHC(PB), but higher in L(MB) compared to L(PB). It was noted that the number of correlations decreased in the following order: EC > L > HHC. Our findings contribute to additional immunological features associated with L and HHC, witch can be useful complementary diagnostic/prognostic tools for classification of L and HHC, providing insights to enrich the research agenda about the hypothesis that HHC should be closely monitored as they may present a subclinical infection. [ABSTRACT FROM AUTHOR]

Background: Brazil has the second highest case count of Hansen's disease (leprosy, HD), but factors contributing to transmission in highly endemic areas of the country remain unclear. Recent studies have shown associations of helminth infection and leprosy, supporting a biological plausibility for increased leprosy transmission in areas with helminths. However, spatial analyses of the overlap of these infections are limited. Therefore, we aimed to spatially analyze these two diseases in a co-endemic area of Minas Gerais, Brazil, in order to identify potential epidemiologic associations. Methods: An ecological study using public health surveillance records and census data was conducted to investigate whether the occurrence of HD -and specifically multibacillary (MB) disease- was associated with the presence of schistosomiasis in a community of 41 municipalities in eastern Minas Gerais, Brazil from 2011 to 2015. Multivariate logistic regression and spatial cluster analyses using geographic information systems (GIS) were performed. Results: The average annual incidence of HD in the study area was 35.3 per 100,000 while Schistosoma mansoni average annual incidence was 26 per 100,000. Both HD and schistosomiasis were spatially distributed showing significant clustering across the study area. Schistosomiasis was present in 10.4% of the tracts with HD and thirteen high-high clusters of local bivariate autocorrelation for HD and schistosomiasis cases were identified. A multivariate non-spatial analysis found that census tracts with MB disease were more likely to have schistosomiasis when adjusted for population density, household density, and household income (aOR = 1.7, 95% CI 1.0, 2.7). This remained significant when accounting for spatial correlation (aOR = 1.1, 95% CI (1.0, 1.2)). Conclusion: We found clustering of both HD and schistosomiasis in this area with some statistically significant overlap of multibacillary HD with S. mansoni infection. Not only did we provide an effective approach to study the epidemiology of two endemic neglected tropical diseases with geographic spatial analyses, we highlight the need for further clinical and translational studies to study the potential epidemiologic associations uncovered. Author summary: As cases of Hansen's disease decrease globally, areas with high endemicity still struggle to control infections. In Brazil, which has the second highest burden of disease globally, new measures are necessary to reach elimination. Not only are geographic information systems (GIS) important tools to describe the heterogeneity of infectious diseases on a finer scale, it can also be used to study the overlap of two different endemic infections. Due to several studies showing potential associations between helminth infection and Hansen's disease, this study explores a co-endemic helminth infection (Schistosoma mansoni) as a potential factor in the continued transmission of leprosy. Our objective was to spatially analyze these two diseases and identify potential epidemiologic associations in 41 municipalities in the state of Minas Gerais, Brazil. Our study found the risk of MB disease was over 1.5 time greater in census tracts with reported schistosomiasis than in tracts without, adjusted for socioeconomic and environmental factors. We also identified several clusters of schistosomiasis and MB disease. While these findings can provide immediate data for control programs to utilize, it also calls for further studies on the interplay of co-infections in the manifestations of HD that may shed light on ongoing transmission in highly endemic areas. [ABSTRACT FROM AUTHOR]

Schistosomiasis, a highly prevalent parasitic disease, affects more than 200 million people worldwide. Current diagnostics based on parasite egg detection in stool detect infection only at a late stage, and current antibody-based tests cannot distinguish past from current infection. Here, we developed and used a multiplexed antibody profiling platform to obtain a comprehensive repertoire of antihelminth humoral profiles including isotype, subclass, Fc receptor (FcR) binding, and glycosylation profiles of antigen-specific antibodies. Using Essential Regression (ER) and SLIDE, interpretable machine learning methods, we identified latent factors (context-specific groups) that move beyond biomarkers and provide insights into the pathophysiology of different stages of schistosome infection. By comparing profiles of infected and healthy individuals, we identified modules with unique humoral signatures of active disease, including hallmark signatures of parasitic infection such as elevated immunoglobulin G4 (IgG4). However, we also captured previously uncharacterized humoral responses including elevated FcR binding and specific antibody glycoforms in patients with active infection, helping distinguish them from those without active infection but with equivalent antibody titers. This signature was validated in an independent cohort. Our approach also uncovered two distinct endotypes, nonpatent infection and prior infection, in those who were not actively infected. Higher amounts of IgG1 and FcR1/FcR3A binding were also found to be likely protective of the transition from nonpatent to active infection. Overall, we unveiled markers for antibody-based diagnostics and latent factors underlying the pathogenesis of schistosome infection. Our results suggest that selective antigen targeting could be useful in early detection, thus controlling infection severity. Editor's summary: Schistosomiasis is a parasitic flatworm infection, and diagnosis is based on egg detection in stool. Current tests cannot distinguish between active and past infections. Saha and Chakraborty et al. developed a multiplexed antibody approach that used interpretable machine learning to distinguish different stages of disease. Comparing healthy and infected individuals across two human cohorts from Brazil and Kenya revealed previously uncharacterized signatures of active disease, including increased Fc receptor binding and specific antibody glycoforms. These signatures helped to separate individuals with active infection from those with past infection as well as to establish two distinct endotypes, those with low intensity or early infection and those with cleared prior infections, in those without active infection. These data suggest that profiling of humoral immune responses could be used to diagnose cases of schistosomiasis with higher precision. —Brandon Berry [ABSTRACT FROM AUTHOR]

Abstract Background During the pre-patent stage of infection, juvenile Schistosoma blood flukes co-opt signals from the adaptive immune system to facilitate parasite development, but the types of responses that are induced at this early stage of infection, and the parasite antigens they target, have not been characterized. Results Through analysis of experimental pre-patent infections, we show that the S. mansoni cysteine protease SmCB1 is rapidly targeted by an antigen-specific IgE response. The induction of this response is independent of schistosome eggs as infection with male or female worms alone also induced SmCB1-specific IgE. We also show that the SmCB1-specific IgE response is dependent on cognate CD4+ T cell help and IL-4, suggesting that pre-patent Th2 responses provide T cell help for the SmCB1-specific IgE response. Finally, exposed human subjects also produced IgE against SmCB1. Conclusions Our data demonstrate that, like eggs, schistosome worms also induce functional type 2 responses and that a parasite cysteine protease is an inducer of type 2 responses during the early stages of schistosome infection.

Background High-resolution metabolomics (HRM) is an innovative tool to study challenging infectious diseases like leprosy, where the pathogen cannot be grown with standard methods. Here, we use HRM to better understand associations between disease manifestations, nutrition, and host metabolism. Methods From 2018 to 2019, adults with leprosy and controls were recruited in Minas Gerais, Brazil. Plasma metabolites were detected using an established HRM workflow and characterized by accurate mass, mass to charge ratio m/z and retention time. The mummichog informatics package compared metabolic pathways between cases and controls and between multibacillary (MB) and paucibacillary (PB) leprosy. Additionally, select individual metabolites were quantified and compared. Results Thirty-nine cases (62% MB and 38% PB) and 25 controls were enrolled. We found differences (P <.05) in several metabolic pathways, including fatty acid metabolism, carnitine shuttle, retinol, vitamin D3, and C-21 steroid metabolism, between cases and controls with lower retinol and associated metabolites in cases. Between MB and PB, leukotrienes, prostaglandins, tryptophan, and cortisol were all found to be lower in MB (P <.05). Discussion Metabolites associated with several nutrient-related metabolic pathways appeared differentially regulated in leprosy, especially MB versus PB. This pilot study demonstrates the metabolic interdependency of these pathways, which may play a role in the pathophysiology of disease. [ABSTRACT FROM AUTHOR]

Abstract Background During the pre-patent stage of infection, juvenile Schistosoma blood flukes co-opt signals from the adaptive immune system to facilitate parasite development, but the types of responses that are induced at this early stage of infection, and the parasite antigens they target, have not been characterized. Results Through analysis of experimental pre-patent infections, we show that the S. mansoni cysteine protease SmCB1 is rapidly targeted by an antigen-specific IgE response. The induction of this response is independent of schistosome eggs as infection with male or female worms alone also induced SmCB1-specific IgE. We also show that the SmCB1-specific IgE response is dependent on cognate CD4+ T cell help and IL-4, suggesting that pre-patent Th2 responses provide T cell help for the SmCB1-specific IgE response. Finally, exposed human subjects also produced IgE against SmCB1. Conclusions Our data demonstrate that, like eggs, schistosome worms also induce functional type 2 responses and that a parasite cysteine protease is an inducer of type 2 responses during the early stages of schistosome infection.

Background: Characterization of the Mycobacterium leprae genome has made possible the development of Polymerase Chain Reaction (PCR) systems that can amplify different genomic regions. Increased reliability and technical efficiency of quantitative PCR (qPCR) makes it a promising tool for early diagnosis of leprosy. Index cases that are multibacillary spread the bacillus silently, even before they are clinically diagnosed. Early detection and treatment could prevent transmission in endemic areas.Methods: In this study, the qPCR technique is used to detect DNA of M. leprae in samples of slit skin smears (SSS) of the ear lobe and blood of leprosy patients and their asymptomatic household contacts residing in Governador Valadares, MG, Brazil, a hyperendemic area for leprosy. A total of 164 subjects participated in the study: 43 index cases, 113 household contacts, and, as negative controls, 8 individuals who reported no contact with patients nor history of leprosy in the family. The qPCR was performed to amplify 16S rRNA fragments and was specifically designed for M. leprae.Results: Of asymptomatic household contacts, 23.89% showed bacillary DNA by qPCR in samples of SSS and blood. Also, 48.84% of patients diagnosed with leprosy were positive for qPCR while the bacillary load was positive in only 30.23% of patients. It is important to note that most patients were already receiving treatment when the collection of biological material for qPCR was performed. The level of bacillary DNA from household contacts was similar to the DNA levels detected in the group of paucibacillary patients.Conclusion: Considering that household contacts comprise a recognizable group of individuals with a high risk of disease, as they live in close proximity to a source of infection, qPCR can be used to estimate the risk of progress towards leprosy among household contacts and as a routine screening method for a chemoprophylactic protocol. [ABSTRACT FROM AUTHOR]