Over the last decades, microRNAs (miRNAs) have emerged as important molecules associated with the regulation of gene expression in humans and other organisms, expanding the strategies available to diagnose and handle several diseases. This paper presents a systematic review of literature of miRNAs related to cancer development and explores the main techniques used to quantify these molecules and their limitations as screening strategy. The bibliographic research was conducted using the online databases, PubMed, Google Scholar, Web of Science, and Science Direct searching the terms “microRNA detection”, “miRNA detection”, “miRNA and prostate cancer”, “miRNA and cervical cancer”, “miRNA and cervix cancer”, “miRNA and breast cancer”, and “miRNA and early cancer diagnosis”. Along the systematic review over 26,000 published papers were reported, and 252 papers were returned after applying the inclusion and exclusion criteria, which were considered during this review. The aim of this study is to identify potential miRNAs related to cancer development that may be useful for early cancer diagnosis, notably in the breast, prostate, and cervical cancers. In addition, we suggest a preliminary top 20 miRNA panel according to their relevance during the respective cancer development. Considering the progressive number of new cancer cases every year worldwide, the development of new diagnostic tools is critical to refine the accuracy of screening tests, improving the life expectancy and allowing a better prognosis for the affected patients.

Innate immune cells play a critical role during the onset of HIV infection and remain active until the final events that characterize AIDS. The viral impact on innate immune cell response may be a result of direct infection or indirect modulation, and each cell type responds in a specific manner to HIV. During HIV infection, the immune system works in a dynamic way, where innate and adaptive cells contribute with each other stimulating their function and modulating phenotypes and consequently infection resolution. Understanding the alterations in the cell populations induced by the virus is pivotal and can help to combat HIV at the time of infection and above all, to prevent the establishment of viral reservoirs. In this review, we will describe the frequency and the subtypes of infected cells such as of monocytes, DCs, neutrophils, eosinophils, mast cells/basophils, NK cells, NKT cells and γδ T cells, and we discuss the possibility of cell-targeting strategies. Our aim is to consolidate the existing knowledge of the interaction between HIV and cells that constitute the innate immune response.

Monocytes are key cells in the immune dysregulation observed during human immunodeficiency virus (HIV) infection. The events that take place specifically in monocytes may contribute to the systemic immune dysfunction characterized by excessive immune activation in infected individuals, which directly correlates with pathogenesis and progression of the disease. Here, we investigated the immune dysfunction in monocytes from untreated and treated HIV + patients and associated these findings with epigenetic changes. Monocytes from HIV patients showed dysfunctional ability of phagocytosis and killing, and exhibited dysregulated cytokines and reactive oxygen species production after M. tuberculosis challenge in vitro. In addition, we showed that the expression of enzymes responsible for epigenetic changes was altered during HIV infection and was more prominent in patients that had high levels of soluble CD163 (sCD163), a newly identified plasmatic HIV progression biomarker. Among the enzymes, histone acetyltransferase 1 (HAT1) was the best epigenetic biomarker correlated with HIV - sCD163 high patients. In conclusion, we confirmed that HIV impairs effector functions of monocytes and these alterations are associated with epigenetic changes that once identified could be used as targets in therapies aiming the reduction of the systemic activation state found in HIV patients.

Although much research has been done related to biomarker discovery for tuberculosis infection, a set of biomarkers that can discriminate between active and latent TB diseases remains elusive. In the current study we correlate clinical aspects of TB disease with changes in the immune response as determined by biomarkers detected in plasma. Our study measured 18 molecules in human plasma in 17 patients with active disease (APTB), 14 individuals with latent tuberculosis infection (LTBI) and 16 uninfected controls (CTRL). We found that active tuberculosis patients have increased plasma levels of IL-6, IP-10, TNF-α, sCD163 and sCD14. Statistical analysis of these biomarkers indicated that simultaneous measurement of sCD14 and IL-6 was able to diagnose active tuberculosis infection with 83% accuracy. We also demonstrated that TNF-α and sCD163 were correlated with tuberculosis severity. We showed that the simultaneous detection of both plasma sCD14 and IL-6 is a promising diagnostic approach to identify APTB, and further, measurement of TNF-α and sCD163 can identify the most severe cases of tuberculosis.

Introduction Syphilis is a sexually transmitted disease (STD) caused by Treponema pallidum subspecies pallidum. In 2016, it was declared an epidemic in Brazil due to its high morbidity and mortality rates, mainly in cases of maternal syphilis (MS) and congenital syphilis (CS) with unfavorable outcomes. This paper aimed to mathematically describe the relationship between MS and CS cases reported in Brazil over the interval from 2010 to 2020, considering the likelihood of diagnosis and effective and timely maternal treatment during prenatal care, thus supporting the decision-making and coordination of syphilis response efforts. Methods The model used in this paper was based on stochastic Petri net (SPN) theory. Three different regressions, including linear, polynomial, and logistic regression, were used to obtain the weights of an SPN model. To validate the model, we ran 100 independent simulations for each probability of an untreated MS case leading to CS case (PUMLC) and performed a statistical t-test to reinforce the results reported herein. Results According to our analysis, the model for predicting congenital syphilis cases consistently achieved an average accuracy of 93% or more for all tested probabilities of an untreated MS case leading to CS case. Conclusions The SPN approach proved to be suitable for explaining the Notifiable Diseases Information System (SINAN) dataset using the range of 75–95% for the probability of an untreated MS case leading to a CS case (PUMLC). In addition, the model’s predictive power can help plan actions to fight against the disease.

Our therapy protocols were not effective in restoring the functional alterations induced by HIV, especially those found on macrophages. These findings indicate that we still need to develop new approaches and improve the current therapy protocols, focusing on the reestablishment of cellular functions and prevention/treatment of opportunistic infections.

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