Background Early detection of Mycobacterium leprae is a key strategy for disrupting the transmission chain of leprosy and preventing the potential onset of physical disabilities. Clinical diagnosis is essential, but some of the presented symptoms may go unnoticed, even by specialists. In areas of greater endemicity, serological and molecular tests have been performed and analyzed separately for the follow-up of household contacts, who are at high risk of developing the disease. The accuracy of these tests is still debated, and it is necessary to make them more reliable, especially for the identification of cases of leprosy between contacts. We proposed an integrated analysis of molecular and serological methods using artificial intelligence by the random forest (RF) algorithm to better diagnose and predict new cases of leprosy. Methods The study was developed in Governador Valadares, Brazil, a hyperendemic region for leprosy. A longitudinal study was performed, including new cases diagnosed in 2011 and their respective household contacts, who were followed in 2011, 2012, and 2016. All contacts were diligently evaluated by clinicians from Reference Center for Endemic Diseases (CREDEN-PES) before being classified as asymptomatic. Samples of slit skin smears (SSS) from the earlobe of the patients and household contacts were collected for quantitative polymerase chain reaction (qPCR) of 16S rRNA, and peripheral blood samples were collected for ELISA assays to detect LID-1 and ND-O-LID. Results The statistical analysis of the tests revealed sensitivity for anti-LID-1 (63.2%), anti-ND-O-LID (57.9%), qPCR SSS (36.8%), and smear microscopy (30.2%). However, the use of RF allowed for an expressive increase in sensitivity in the diagnosis of multibacillary leprosy (90.5%) and especially paucibacillary leprosy (70.6%). It is important to report that the specificity was 92.5%. Conclusion The proposed model using RF allows for the diagnosis of leprosy with high sensitivity and specificity and the early identification of new cases among household contacts.
Background According 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. Objective The 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). Methods Leprosy 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. Results We 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. Conclusions The 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.
IntroductionThe aim of the present study was to investigate the association between the single nucleotide polymorphism (SNP) rs1927914 A/G in TLR4 gene and the immunological profile of household contacts (HHC) of leprosy patients. Leprosy classification is usually complex and requires the assessment of several clinical and laboratorial features.MethodsHerein, we have applied distinct models of descriptive analysis to explore qualitative/quantitative changes in chemokine and cytokine production in HHC further categorized according to operational classification [HHC(PB) and HHC(MB)] and according to TLR4SNP.Results and discussionOur results showed that M. leprae stimuli induced an outstanding production of chemokines (CXCL8;CCL2; CXCL9; CXCL10) by HHC(PB), while increase levels of pro-inflammatory cytokines (IL-6; TNF; IFN-γ; IL-17) were observed for HHC(MB). Moreover, the analysis of chemokine and cytokine signatures demonstrated that A allele was associated with a prominent soluble mediator secretion (CXCL8; CXCL9; IL-6; TNF; IFN-γ). Data analysis according to TLR4 SNP genotypes further demonstrated that AA and AG were associated with a more prominent secretion of soluble mediators as compared to GG, supporting the clustering of AA and AG genotypes into dominant genetic model. CXCL8, IL-6, TNF and IL-17 displayed distinct profiles in HHC(PB) vs HHC(MB) or AA+AG vs GG genotype. In general, chemokine/cytokine networks analysis showed an overall profile of AA+GA-selective (CXCL9–CXCL10) and GG-selective (CXCL10–IL-6) axis regardless of the operational classification. However, mirrored inverted CCL2–IL-10 axis and a (IFN-γ–IL-2)-selective axis were identified in HHC(MB). CXCL8 presented outstanding performance to classify AA+AG from GG genotypes and HHC(PB) from HHC(MB). TNF and IL-17 presented elevated accuracy to classify AA+AG from GG genotypes and HHC(PB) (low levels) from HHC(MB) (high levels), respectively. Our results highlighted that both factors: i) differential exposure to M. leprae and ii) TLR4 rs1927914 genetic background impact the immune response of HHC. Our main results reinforce the relevance of integrated studies of immunological and genetic biomarkers that may have implications to improve the classification and monitoring of HHC in future studies.
Background Immunological biomarkers have often been used as a complementary approach to support clinical diagnosis in several infectious diseases. The lack of commercially available laboratory tests for conclusive early diagnosis of leprosy has motivated the search for novel methods for accurate diagnosis. In the present study, we describe an integrated analysis of a cytokine release assay using a machine learning approach to create a decision tree algorithm. This algorithm was used to classify leprosy clinical forms and monitor household contacts. Methods A model of Mycobacterium leprae (M. leprae) antigen-specific in vitro assay with subsequent cytokine measurements by ELISA was employed to measure the levels of TNF, IFN-γ, IL-4, and IL-10 in culture supernatants of peripheral blood mononuclear cells from leprosy patients, healthy controls as well as household contacts. Receiver Operating Characteristic (ROC) curve analysis was carried out to define each cytokine's global accuracy and performance indices to identify clinical subgroups. Results Data demonstrated that TNF [Control Culture (CC): AUC=0.72; antigen-stimulated culture (Ml): AUC=0.80] and IL-10 (CC: AUC=0.77; Ml: AUC=0.71) were the most accurate biomarkers to classify subgroups of household contacts and leprosy patients, respectively. Decision tree classifier algorithms were for TNF analysis categorized subgroups of household contacts according to the operational classification with moderate accuracy (CC:79%, 48/61, and Ml:84%, 51/61). Additionally, IL-10 analysis categorized leprosy patients' subgroups with moderate accuracy (CC:73%, 22/30 and Ml:70%, 21/30). Conclusions Together, our findings demonstrated that a cytokine-release assay is a promising method to complement clinical diagnosis, ultimately contributing to effective control of the disease.
Background Schistosomiasis is a chronic disease that affects over 200 million people worldwide. A pivotal role of IL-10 is down-regulating Th1 and Th2 responses to schistosome antigens, which can favor the parasite establishment. The SmATPDases degrade ATP and ADP in AMP and adenosine, a molecule with anti-inflammatory properties. We evaluated the expression of SmATPDases 1 and 2 enzymes in S. mansoni eggs obtained from infected individuals as a possible parasite-related factor that could influence the host immune response and the clinical outcome of the disease. Methods Fecal samples were collected from 40 infected individuals to detect coding regions of the enzymes by the qPCR. The production of cytokines was measured in supernatants of PBMC cultures. The analysis was performed by the global median determination for each cytokine and set up high producers (HP) of cytokines. Results Six individuals expressed SmATPDase 1 in their fecal samples, 6 expressed SmATPDase 2, and 6 expressed both enzymes. The group who expressed only SmATPDase 1 showed a high frequency of IFN-γ, TNF, IL-4 HP, and a low frequency of IL-6 HP. The group who expressed only SmATPDase 2 showed a high frequency of IFN-γ, IL-6, and IL-4 HP and a low frequency of IL-10 HP. The group who expressed both enzymes showed a high frequency of IL-10 HP and low frequencies of IFN-γ, IL-6, IL-2, IL-4, and IL-13 HP. In the group that had SmATPDase 2 expression was observed higher indices the ratio between IFN-γ/IL-10 than individuals that showed expression both enzymes. The positive correlation between infection intensity and IL-10 levels remained only in the positive SmATPDase group. Overall, the analysis revealed that 62.5% of the cytokines presented reduced frequency in the group of individuals expressing both enzymes, the IL-10 is the only cytokine induced by the expression of both enzymes and the expression profile of SmATPDases is relevant data for grouping individuals. Conclusions The expression of both enzymes in the parasite's eggs seems to be a new undescribed factor that negatively modulates the host immune response by inducing high IL-10 production, which, in turn, can contribute to the survival of the parasite.
Introdução: A hanseníase é uma doença infectocontagiosa que se manifesta por meio de sinais e sintomas dermatoneurológicos. Apresenta alta taxa de prevalência e de detecção de casos novos em vários municípios do Brasil, com destaque para Governador Valadares, em Minas Gerais, e entorno. O estudo da hanseníase associada às helmintíases, especialmente à esquistossomose, torna-se relevante principalmente em zonas rurais, onde ocorre alta incidência dessa verminose. Além das parasitoses, a deficiência nutricional é também considerada um fator de risco para o desenvolvimento dessa doença. Sabe-se que as infecções crônicas por helmintos promovem alterações imunológicas que podem desencadear uma resposta do tipo Th2, e assim favorecer a infecção pelo M. leprae, tornando o indivíduo mais suscetível a desenvolver a forma virchowiana, considerada mais agressiva. Objetivo: investigar o papel de coinfecções parasitárias e deficiências de micronutrientes na transmissão e nas manifestações clínicas da hanseníase. Metodologia: a busca ativa de casos novos de hanseníase teve seu início no distrito rural de Limeira de Mantena (MG), e deu continuidade aos casos diagnosticados no Centro de Referência em Doenças Endêmicas e Programas Especiais (CREDEN-PES/SMS/GV). Em Limeira de Mantena, a comunidade foi convidada a participar de uma palestra sobre hanseníase, e após esclarecimentos sobre a doença, os indivíduos, de maneira voluntária, foram encaminhados para exame dermatoneurológico. Após diagnóstico clínico e coleta de material biológico para os ensaios laboratoriais, procedeu-se ao tratamento. Todos os contatos domiciliares dos pacientes foram agendados para consulta e exames. Foi aplicado questionário para obtenção de dados demográficos e socioeconômicos, e recordatório alimentar para complementar o estudo dos micronutrientes. Amostras de fezes foram solicitadas para realização do exame parasitológico e a coleta de sangue foi realizada para avaliação da produção de citocinas por células mononucleares do sangue periférico, para análise do perfil imunológico dos participantes. Resultado: resultados preliminares mostraram maior produção de IL-10 e menor produção de IFN-g pelos pacientes multibacilares (MB) comparados com indivíduos saudáveis. Exames parasitológicos de fezes apresentaram resultados positivos para S. mansoni, E. coli e. histolytica. Conclusão: as análises estatísticas dos dados obtidos estão em andamento. Vários casos de hanseníase diagnosticados como MB pela classificação operacional da OMS apresentaram infecção pelo S. mansoni.
Background and objectives: A role of IL-10 is down-regulating T cell responses to schistosome antigens. Since SmATPDases can be correlated to modulation of the immune response, we evaluated the expression of enzymes in S.mansoni eggs. Patients/Methods: Fecal samples were collected from 40 infected individuals to detect coding regions of the SmATPDases. The cytokines was measured in supernatants of PBMC. The analysis was performed by the global median determination and set up high producers (HP) of cytokines. Results: Six individuals expressed SmATPDase1, six expressed SmATPDase2, and six expressed both enzymes. The group who expressed only SmATPDase1 showed a high frequency of IFN-γ, TNF, IL-4 HP, individuals who expressed only SmATPDase2 showed a high frequency of IFN-γ, IL-6, and IL-4 HP and individuals who expressed both enzymes showed a high frequency of IL-10 HP. In the group that showed expression both enzymes was observed lower indices the ratio between IFN-γ/IL-10. The positive correlation between infection intensity and IL-10 levels remained only in the positive SmATPDase group. The IL-10 is the only cytokine induced by the expression of both enzymes. Conclusions: The expression of both enzymes seems to be a factor that modulates the host immune response by inducing high IL-10 production.
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