On the cytokine/chemokine network during Plasmodium vivax malaria: new insights to understand the disease.

BACKGROUND: The 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. METHODS: Samples 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-ß, 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-ß. 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. CONCLUSIONS: The 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.

Risks of Adverse Outcomes for Hospitalized COVID-19 Patients during the Four Waves in Brazil According to SARS-CoV-2 Variants, Age Group, and Vaccine Status.

Brazil was hit with four consecutive waves of COVID-19 until 2022 due to the ancestral SARS-CoV-2 (B.1 lineage), followed by the emergence of variants/subvariants. Relative risks of adverse outcomes for COVID-19 patients hospitalized during the four waves were evaluated. Data were extracted from the largest Brazilian database (SIVEP-Gripe), and COVID-19 patients who were hospitalized during the peak of each of the four waves (15-week intervals) were included in this study. The outcomes of in-hospital death, invasive (IMV) and non-invasive (NIV) ventilation requirements, and intensive care unit (ICU) admission were analyzed to estimate the relative risks. A higher risk of in-hospital death was found during the second wave for all age groups, but a significant reduction was observed in the risk of death for the elderly during the third and fourth waves compared to patients in the first wave. There was an increased risk of IMV requirement and ICU admissions during the second wave for patients aged 18-59 years old compared to the first wave. Relative risk analysis showed that booster-vaccinated individuals have lower risks of in-hospital death and IMV requirement in all age groups compared to unvaccinated/partially vaccinated patients, demonstrating the relevance of full/booster vaccination in reducing adverse outcomes for patients who were hospitalized during the variant prevalence.

Profile and Outcomes of Hospitalized COVID-19 Patients during the Prevalence of the Omicron Variant According to the Brazilian Regions: A Retrospective Cohort Study from 2022.

We investigated the clinical-epidemiological profile and outcomes of COVID-19 patients hospitalized in 2022, during the Omicron variant/subvariant prevalence, in different Brazilian regions to identify the most vulnerable subgroups requiring special attention. Data from COVID-19 patients were extracted from the national Information System for Epidemiological Surveillance of Influenza (SIVEP-Gripe database), and analyses stratified by region and age group were conducted. The constructed dataset encompassed clinical-epidemiological information, intensive care unit admission, invasive and non-invasive ventilation requirements, vaccination status, and evolution (cure or death). It was observed that there were significant differences in the vaccination rates between regions, in the occurrence of unfavorable outcomes, and in the pattern of comorbidities in young patients. The north region had higher rates of unvaccinated patients and a lower percentage of those vaccinated with three doses in all age groups compared to other regions. The northeast region had the highest rates of patients admitted to the ICU for all age groups, while the north and northeast were the most affected by IMV requirements and in-hospital death in all age groups. This study showed that extended vaccination coverage, especially booster doses, can protect different population segments from developing severe disease since lower vaccination coverage was observed in regions with higher fatality rates.

An overview of Brazilian working age adults vulnerability to COVID-19.

Brazil is a country of continental dimensions, where many smaller countries would fit. In addition to demographic, socioeconomic, and cultural differences, hospital infrastructure and healthcare varies across all 27 federative units. Therefore, the evolution of COVID-19 pandemic did not manifest itself in a homogeneous and predictable trend across the nation. In late 2020 and early 2021, new waves of the COVID-19 outbreak have caused an unprecedented sanitary collapse in Brazil. Unlike the first COVID-19 wave, in subsequent waves, preliminary evidence has pointed to an increase in the daily reported cases among younger people being hospitalized, overloading the healthcare system. In this comprehensive retrospective cohort study, confirmed cases of hospitalization, ICU admission, IMV requirement and in-hospital death from Brazilian COVID-19 patients throughout 2020 until the beginning of 2021 were analyzed through a spatio-temporal study for patients aged 20-59 years. All Brazilian federative units had their data disaggregated in six periods of ten epidemiological weeks each. We found that there is a wide variation in the waves dynamic due to SARS-CoV-2 infection, both in the first and in subsequent outbreaks in different federative units over the analyzed periods. As a result, atypical waves can be seen in the Brazil data as a whole. The analysis showed that Brazil is experiencing a numerical explosion of hospitalizations and deaths for patients aged 20-59 years, especially in the state of São Paulo, with a similar proportion of hospitalizations for this age group but higher proportion of deaths compared to the first wave.

Predicting the Disease Outcome in COVID-19 Positive Patients Through Machine Learning: A Retrospective Cohort Study With Brazilian Data.

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 by June 2020. 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 8, 443 patients concerning closed cases due to cure or death. Our experimental results show the disease outcome can be predicted with a Receiver Operating Characteristic 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.

On the analysis of mortality risk factors for hospitalized COVID-19 patients: A data-driven study using the major Brazilian database.

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.