Recent Zika outbreaks in South America, accompanied by unexpectedly severe clinical complications have brought much interest in fast and reliable screening methods for ZIKV (Zika virus) identification. Reverse-transcriptase polymerase chain reaction (RT-PCR) is currently the method of choice to detect ZIKV in biological samples. This approach, nonetheless, demands a considerable amount of time and resources such as kits and reagents that, in endemic areas, may result in a substantial financial burden over affected individuals and health services veering away from RT-PCR analysis. This study presents a powerful combination of high-resolution mass spectrometry and a machine-learning prediction model for data analysis to assess the existence of ZIKV infection across a series of patients that bear similar symptomatic conditions, but not necessarily are infected with the disease. By using mass spectrometric data that are inputted with the developed decision-making algorithm, we were able to provide a set of features that work as a “fingerprint” for this specific pathophysiological condition, even after the acute phase of infection. Since both mass spectrometry and machine learning approaches are well-established and have largely utilized tools within their respective fields, this combination of methods emerges as a distinct alternative for clinical applications, providing a diagnostic screening—faster and more accurate—with improved cost-effectiveness when compared to existing technologies.
The increase of Zika virus (ZIKV) infections in Brazil in the last two years leaves a prophylactic measures on alert for this new and emerging pathogen. Concerning of our positive experience, we developed a new prototype using Neisseria meningitidis outer membrane vesicles (OMV) on ZIKV cell growth in a fusion of OMV in the envelope of virus particles. The fusion of nanoparticles resulting from outer membrane vesicles of N. meningitidis with infected C6/36 cells line were analyzed by Nano tracking analysis (NTA), zeta potential, differential light scattering (DLS), scan and scanning transmission eletronic microscopy (SEM and STEM) and high resolution mass spectometry (HRMS) for nanostructure characterization. Also, the vaccination effects were viewed by immune response in mice protocols immunization (ELISA and inflammatory chemokines) confirmed by Zika virus soroneutralization test. The results of immunizations in mice showed that antibody production had a titer greater than 1:160 as compared to unvaccinated mice. The immune response of the adjuvant and non-adjuvant formulation activated the cellular immune response TH1 and TH2. In addition, the serum neutralization was able to prevent infection of virus particles in the glial tumor cell model (M059J). This research shows efficient strategies without recombinant technology or DNA vaccines.
Zika virus (ZIKV) has emerged as one of the most medically relevant viral infections of the past decades; the devastating effects of this virus over the developing brain are a major matter of concern during pregnancy. Although the connection with congenital malformations are well documented, the mechanisms by which ZIKV reach the central nervous system (CNS) and the causes of impaired cortical growth in affected fetuses need to be better addressed. We performed a non-invasive, metabolomics-based screening of saliva from infants with congenital Zika syndrome (CZS), born from mothers that were infected with ZIKV during pregnancy. We were able to identify three biomarkers that suggest that this population suffered from an important inflammatory process; with the detection of mediators associated with glial activation, we propose that microcephaly is a product of immune response to the virus, as well as excitotoxicity mechanisms, which remain ongoing even after birth.
Background: Zika virus (ZIKV) has recently become a major matter of concern since its association with microcephaly cases in newborns was determined. From then on, ZIKV has been untiringly studied, and several scientific data have shown the virus' tropism to neural cells. Previous studies have proposed that ZIKV induced glioblastoma cells death, suggesting that ZIKV and ZIKV-associated molecules might induce intracellular biochemical alterations, and thereby be alternatives for neural cancer management. In this sense, the present contribution presents a prospective approach for glioblastomas management: producing a ZIKV-based therapy that stimulates glioblastoma control. We developed an attenuated ZIKV prototype based on bacterial outer membrane vesicles (OMV). The attenuated ZIKV was applied on glioblastoma cells, where cytopathic and cytostatic effects were evaluated. Glioblastoma cells, with and without treatment, were submitted to mass spectrometry analysis. Results: Microscopic analysis showed cytopathic effects induced by ZIKV prototype on glioblastoma cells after 24 and 48 h post-treatment. DNA fragmentation assay and TNF-alpha expression were indicative that ZVp induced cell damage and death. The metabolomics investigation elected 5 different biomarkers that might be associated with cell cytopathic effects, highlighting intracellular biochemical modifications induced by the attenuated ZIKV. The remarkable evidence of cell death in glioblastoma stimulated further studies that rendered a preliminary screening with other tumor cell lineages, though anti-proliferative activity test. Among the 11 tumors evaluated, prostate and ovarian tumors were the most affected by ZIKV prototype, and other 6 cell lines also presented cytostatic effects. Conclusions: Results ultimately demonstrated that this prototype not only emerges as a potential alternative for glioblastoma management, but may also be an important tool against other important tumors.
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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