Human interleukin 13 (IL-13) is a cytokine that has a profound effect on primary immune cells by inducing immunoglobulin production, proliferation of B cells, and the differentiation of cells of the monocytic lineage. IL-13 can inhibit the production of inflammatory cytokines by both macrophages and monocytes. Previously, IL-13 expression has been reported only in cells of the T-cell lineage and the mast cell line HMC-1. We now report the presence of IL-13 mRNA and protein in human alveolar macrophages (AMs) analyzed by the reverse transcription-polymerase chain reaction (RT-PCR) and enzyme-linked immunoabsorbent assay (ELISA), respectively, and IL-13 protein in bronchoalveolar lavage fluid (BALF) of subjects with pulmonary fibrosis. We have investigated 13 patients from 49 to 75 yr of age with forms of pulmonary fibrosis, and eight healthy volunteers from 24 to 61 yr of age. Their AMs were obtained by bronchoalveolar lavage (BAL) and purified by adherence. The proportion of BAL purified AMs expressing IL-13 mRNA was increased in those subjects with fibrotic lung disease, in comparison with those from control subjects (11 of 13 versus 2 of 8, P < 0.01). IL-13 protein was detectable in the BALF of 8 of 13 patients with pulmonary fibrosis, but in none of the control subjects. AMs of four subjects with systemic sclerosis were cultured and IL-13 protein was increased in the culture supernatants when compared to the control subjects, although this did not reach significance. These findings show that IL-13 mRNA is not only a product of T cells, but is also expressed in both normal AMs and those from subjects with pulmonary fibrosis, and that at least some of the IL-13 mRNA is translated into protein and secreted in subjects with pulmonary fibrosis. We hypothesize that IL-13 may be expressed by normal human AMs as part of the homeostatic control process but its production may be increased in the presence of inflammatory lung disease.

BackgroundCharacterization 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.MethodsIn 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.ResultsOf 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.ConclusionConsidering 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.Electronic supplementary materialThe online version of this article (10.1186/s12879-018-3056-2) contains supplementary material, which is available to authorized users.

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.