BackgroundParacoccidioidomycosis (PCM) is a neglected systemic mycosis caused by a dimorphic fungus of the Paracoccidioides genus. The standard diagnosis is based on isolation of the fungi in culture, and by microscopic visualization of characteristic multiple budding yeast cells in biological samples. However, in some situations, access to the site of injury prevents the collection of biological material. A variety of immuno-serological techniques has proven useful for allowing inferring diagnosis with a certain degree of certainty, thus optimizing time. The aim of this study was to standardize and validate the Dot-ELISA (DE) assay, comparing it with the serological standard, double immunodiffusion (DI).MethodsIn order to standardize the DE assay, 143 serum samples were used. Out of those, 23 were from apparently healthy patients, 77 were from patients with confirmed PCM and 43 were from patients with other lung infections (tuberculosis, aspergillosis and histoplasmosis). To validate the DE technique, 300 serum samples from patients with PCM clinical suspicion (probable and possible cases) were employed, and these results were compared with those of DI.ResultsThe DE assay showed sensitivity of 91%, specificity of 95.4%, positive predictive value of 96%, negative predictive value of 98.2%, accuracy of 93%, and great precision (k = 0.93). In addition, the nitrocellulose membranes have proved to be viable for using at least 90 days after P. brasiliensis B-339 antigen sensitization.ConclusionDot-ELISA method was found to be an extremely promising tool as serologic screening technique, because of its high sensitivity. Furthermore, Dot-ELISA shows the prospect of being transferred to laboratories of mycoserology including those with fewer resources or even to be used directly in the field. It has an excellent shelf life – membranes coated with antigen can be used for testing without changes in the pattern of reactivity among laboratories – and presents reliable values of sensitivity, specificity, predictive values, accuracy and a high correlation with the serological standard methodology. Based on the present findings, it possible to state that this technique constitutes a remarkable option to be used in routine diagnosis for public health centers.
Negative results in serological routine screening of patients with microbiologically proven Paracoccidioidomycosis (PCM) are occasionally reported. Failure in detecting anti- antibodies has been ascribed to factors either related to serological techniques or to the status of the host immune reactivity. Recently, this issue has been renewed by the recognition that the genera comprises two species, and , which have distinct antigenic profiles and, therefore, may elicit different host antibody responses. We describe a patient with the acute form PCM due to with negative results on two reference centers' routine screening for antibodies, but positive results with antigens. The present case report suggests that antibodies elicited during infection recognize antigenic fractions shared by both species, highlighting the difficulties in distinguishing the two infections by means of the currently available routine serological assays.
Cell adhesion on materials surface is critical because this phenomenon occurs before other events, as cell spreading, cell migration and cell differentiation. It is commonly accepted that the adhesion of cells on solid substrate is influenced by several substratum surface properties, such as wettability, surface charge, roughness and topography. Plasma technique is a convenient method for modifying surface properties of materials without affecting physical properties. In this study, poly(lactide-co-glycolide), PLGA, membranes were modified by oxygen and nitrogen plasma to improve polymer hydrophilicity and verify their effect on Vero cells culture. The PLGA membranes, which were characterized by SEM and contact angle, showed increased surface rugosity and narrower contact angles. Cell adhesion, cytotoxicity assay, SEM and cytochemistry analysis showed that plasma treatment was beneficial to cell growth by improving cell-polymer interaction.
Brazil and many other Latin American countries are areas of endemicity for different neglected diseases, and the fungal infection paracoccidioidomycosis (PCM) is one of them. Among the clinical manifestations, pneumopathy associated with skin and mucosal lesions is the most frequent. PCM definitive diagnosis depends on yeast microscopic visualization and immunological tests, but both present ambiguous results and difficulty in differentiating PCM from other fungal infections. This research has employed metabolomics analysis through high-resolution mass spectrometry to identify PCM biomarkers in serum samples in order to improve diagnosis for this debilitating disease. To upgrade the biomarker selection, machine learning approaches, using Random Forest classifiers, were combined with metabolomics data analysis. The proposed combination of these two analytical methods resulted in the identification of a set of 19 PCM biomarkers that show accuracy of 97.1%, specificity of 100%, and sensitivity of 94.1%. The obtained results are promising and present great potential to improve PCM definitive diagnosis and adequate pharmacological treatment, reducing the incidence of PCM sequelae and resulting in a better quality of life. IMPORTANCE Paracoccidioidomycosis (PCM) is a fungal infection typically found in Latin American countries, especially in Brazil. The identification of this disease is based on techniques that may fail sometimes. Intending to improve PCM detection in patient samples, this study used the combination of two of the newest technologies, artificial intelligence and metabolomics. This combination allowed PCM detection, independently of disease form, through identification of a set of molecules present in patients’ blood. The great difference in this research was the ability to detect disease with better confidence than the routine methods employed today. Another important point is that among the molecules, it was possible to identify some indicators of contamination and other infection that might worsen patients’ condition. Thus, the present work shows a great potential to improve PCM diagnosis and even disease management, considering the possibility to identify concomitant harmful factors.
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