The Plasmodium cell cycle, wherein millions of parasites differentiate and proliferate, occurs in synchrony with the vertebrate host's circadian cycle. The underlying mechanisms are unknown. Here we addressed this question in a mouse model of Plasmodium chabaudi infection. Inflammatory gene expression and carbohydrate metabolism are both enhanced in interferon-γ (IFNγ)-primed leukocytes and liver cells from P. chabaudi-infected mice. Tumor necrosis factor α (TNFα) expression oscillates across the host circadian cycle, and increased TNFα correlates with hypoglycemia and a higher frequency of non-replicative ring forms of trophozoites. Conversely, parasites proliferate and acquire biomass during food intake by the host. Importantly, cyclic hypoglycemia is attenuated and synchronization of P. chabaudi stages is disrupted in IFNγ, TNF receptor, or diabetic mice. Hence, the daily rhythm of systemic TNFα production and host food intake set the pace for Plasmodium synchronization with the host's circadian cycle. This mechanism indicates that Plasmodium parasites take advantage of the host's feeding habits.

Background Brazil became the epicenter of the COVID-19 epidemic in a brief period of a few months after the first officially registered case. The knowledge of the epidemiological/clinical profile and the risk factors of Brazilian COVID-19 patients can assist in the decision making of physicians in the implementation of early and most appropriate measures for poor prognosis patients. However, these reports are missing. Here we present a comprehensive study that addresses this demand. Methods This data-driven study was based on the Brazilian Ministry of Health Database (SIVEP-Gripe) regarding notified cases of hospitalized COVID-19 patients during the period from February 26th to August 10th, 2020. Demographic data, clinical symptoms, comorbidities and other additional information of patients were analyzed. Results The hospitalization rate was higher for male gender (56.56%) and for older age patients of both sexes. Overall, the lethality rate was quite high (41.28%) among hospitalized patients, especially those over 60 years of age. Most prevalent symptoms were cough, dyspnoea, fever, low oxygen saturation and respiratory distress. Cardiac disease, diabetes, obesity, kidney disease, neurological disease, and pneumopathy were the most prevalent comorbidities. A high prevalence of hospitalized COVID-19 patients with cardiac disease (65.7%) and diabetes (53.55%) and with a high lethality rate of around 50% was observed. The intensive care unit (ICU) admission rate was 39.37% and of these 62.4% died. 24.4% of patients required invasive mechanical ventilation (IMV), with high mortality among them (82.98%). The main mortality risk predictors were older age and IMV requirement. In addition, socioeconomic conditions have been shown to significantly influence the disease outcome, regardless of age and comorbidities. Conclusion Our study provides a comprehensive overview of the hospitalized Brazilian COVID-19 patients profile and the mortality risk factors. The analysis also evidenced that the disease outcome is influenced by multiple factors, as unequally affects different segments of population.

No abstract

BackgroundThe clinical outcome of malaria depends on the delicate balance between pro-inflammatory and immunomodulatory cytokine responses triggered during infection. Despite the numerous reports on characterization of plasma levels of cytokines/chemokines, there is no consensus on the profile of these mediators during blood stage malaria. The identification of acute phase biomarkers might contribute to a better understanding of the disease, allowing the use of more effective therapeutic approaches to prevent the progression towards severe disease. In the present study, the plasma levels of cytokines and chemokines and their association with parasitaemia and number of previous malaria episodes were evaluated in Plasmodium vivax-infected patients during acute and convalescence phase, as well as in healthy donors.MethodsSamples of plasma were obtained from peripheral blood samples from four different groups: P. vivax-infected, P. vivax-treated, endemic control and malaria-naïve control. The cytokine (IL-6, IL-10, IL-17, IL-27, TGF-β, IFN-γ and TNF) and chemokine (MCP-1/CCL2, IP-10/CXCL10 and RANTES/CCL5) plasma levels were measured by CBA or ELISA. The network analysis was performed using Spearman correlation coefficient.Results Plasmodium vivax infection induced a pro-inflammatory response driven by IL-6 and IL-17 associated with an immunomodulatory profile mediated by IL-10 and TGF-β. In addition, a reduction was observed of IFN-γ plasma levels in P. vivax group. A lower level of IL-27 was observed in endemic control group in comparison to malaria-naïve control group. No significant results were found for IL-12p40 and TNF. It was also observed that P. vivax infection promoted higher levels of MCP-1/CCL2 and IP-10/CXCL10 and lower levels of RANTES/CCL5. The plasma level of IL-10 was elevated in patients with high parasitaemia and with more than five previous malaria episodes. Furthermore, association profile between cytokine and chemokine levels were observed by correlation network analysis indicating signature patterns associated with different parasitaemia levels.ConclusionsThe P. vivax infection triggers a balanced immune response mediated by IL-6 and MCP-1/CCL2, which is modulated by IL-10. In addition, the results indicated that IL-10 plasma levels are influenced by parasitaemia and number of previous malaria episodes.

The first officially registered case of COVID-19 in Brazil was on February 26, 2020. Since then, the situation has worsened with more than 672,000 confirmed cases and at least 36,000 reported deaths at the time of this writing. Accurate diagnosis of patients with COVID-19 is extremely important to offer adequate treatment, and avoid overloading the healthcare system. Characteristics of patients such as age, comorbidities and varied clinical symptoms can help in classifying the level of infection severity, predict the disease outcome and the need for hospitalization. Here, we present a study to predict a poor prognosis in positive COVID-19 patients and possible outcomes using machine learning. The study dataset comprises information of 13,690 patients concerning closed cases due to cure or death. Our experimental results show the disease outcome can be predicted with a ROC AUC of 0.92, Sensitivity of 0.88 and Specificity of 0.82 for the best prediction model. This is a preliminary retrospective study which can be improved with the inclusion of further data. Conclusion: Machine learning techniques fed with demographic and clinical data along with comorbidities of the patients can assist in the prognostic prediction and physician decision-making, allowing a faster response and contributing to the non-overload of healthcare systems.