ABSTRACT
In this work we present a comparative analysis of influencer marketing evolution on Facebook and Instagram, spanning the pre and post Covid-19 pandemic onset periods. We collected and characterized a large-scale cross-platform dataset, comprised of 9.5 million sponsored posts. We analyzed the relative growth rates of the number of ads and of user engagement within different topics of interest, such as sports, retail, travel, and politics. We discuss which topics have been most impacted by the onset of the pandemic, both in terms of sponsored content supply and demand. With this work we hope to expand the understanding of influence dynamics on social networks and provide support for the development of more contextualized and effective branding strategies. © 2022 ACM.
ABSTRACT
This case-control study paired by gender and age analyzes factors associated with the death of indigenous people from COVID-19 in the state of Amapá, Brazil. Data were collected from a public secondary database produced by the Amapá State Department of Health. Cases (n=29) were deaths of indigenous people from COVID-19 and controls were cures of the disease (n=87), recorded between April 2020 and January 2021. Data from individuals with active disease were excluded. Univariate analysis followed by multiple logistic regression were performed to study the independent variables associated with death. Most cases of death were women (51.7%), without comorbidities (62.1%), residing in cities of the Metropolitan Region of Macapá (RMM) (65.5%) and in urban areas (89.7%). Median age of the death group was 72 years (interquartile range=21.5). The final multiple model showed that indigenous individuals with cardiovascular comorbidity had a 4.01 times greater chance (95% confidence interval – 95% CI=1.05-15.36) of death by COVID-19 when compared with indigenous people without comorbidities. And that indigenous people residing in the RMM had a 2.90 times greater chance (95%CI = 1.10-7.67) of death when compared with indigenous residing in the countryside.
ABSTRACT
Introduction: Covid-19 is an infectious disease with systemic involvement, which causes intense changes in the blood system, such as neutrophilia and lymphopenia, as well as changes in coagulation function and the concentration of acute phase proteins. Infected patients require laboratory follow-up to assist in clinical and therapeutic management. It is important to define efficient parameters to predict the clinical course of the disease, especially when the overall symptoms are becoming worse, in an attempt to anticipate therapeutic measures and to ensure the most appropriate assistance. Purpose: To correlate neutrophil and lymphocyte counts and their subtypes with the severity and outcome of patients with Covid-19. Materials and methods: Patients hospitalized for severe Covid-19, of both genders and without evidence of bacterial pneumonia, seen at the CHC-UFPR between April and June 2020, were included. Lymphocyte subpopulation analysis was performed by multiparametric flow cytometry (MFC) on whole blood sample using antibodies against CD45, CD3, CD4, CD8 and CD19. A BD FACSCanto™ II cytometer and Infinicyt™ 2.0 analysis software were used. ROC curve and other statistical relationships were performed with IBM SPSS™ v. 25 software. Results: Patients were divided as moderate (not intubated, n = 41) and severe (intubated, n = 35). From the median total leukocyte, neutrophil and lymphocyte counts and their subsets, we define the cutoff values with the highest correlation with hospital discharge. Patients with lymphocyte counts higher than 489/μL, CD4 counts higher than 326 and CD8 counts higher than 121 had a greater chance of evolving with a better prognosis (p < 0.001). Patients who had neutrophil-to-lymphocyte ratio (NLR) higher than 15.2 showed greater correlation with worse prognosis. Patients with lymphopenia below cutoff values are 40 to 55% more likely to be intubated and 50 to 63% to progress to death. Patients with NLR higher than 15.2 have 53.1% more chances of being intubated and 78.1% of evolving to death. Discussion: Laboratory evaluation is essential in the follow-up of patients with Covid-19. In addition to routinely used biochemical markers, cellular analysis can provide valuable information about the clinic and its progression. Lymphopenia and neutrophilia are common parameters in patients with severe disease, so NLR analysis presents itself as an objective scale for stratification of infected patients with a high correlation with possible outcomes. Associated with this, assessment of the immune profile with low levels of T-cells and especially low levels of positive CD4 cells has been associated with worse prognosis in patients with severe Covid-19. Conclusion: We conclude that analysis of the neutrophil/lymphocyte ratio routinely obtained from the complete blood count may provide relevant prognostic information for patients with Covid-19. In addition, flow cytometry analysis of CD4 and CD8 T-lymphocytes can complement the screening of patients with Covid-19 by providing information on the immune profile of the disease.
ABSTRACT
In addition to the variation in extraction methodologies, the biological activities of propolis can vary depending on the collection period, seasonality, temperature and local greenery, elements that can limit the concentration of bioactive compounds in the product (Bankova et al., 1998;Souza, Inoue, Gomes, Funari, & Orsi, 2010). In this context, the present work aimed to optimize the methodology of ethanol extract of propolis production and to evaluate the effect of seasonality on the chemical composition and biological activities of this product. 2. After the analysis of the results from the first part of the project, to standardize the extraction and drying temperature, the ethanolic solution that best stood out in the chemical and microbiological analysis found previously was adopted, and a completely randomized experimental design was used in a factorial scheme containing 2 (baths) x 3 (drying) temperatures (Figure 1). The results were expressed as antioxidant activity (AA), calculated through the DPPH solution's absorbance decline rate after 45 minutes of reaction (stable phase) compared to the reference solution (DPPH in ethanol), by the formula: % Antioxidant activity = 100 - [(Sample - White) · 100 / Control] where: