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Cirrhotic patients with chronic hepatitis C should be monitored for the evaluation of liver function and screening of hepatocellular carcinoma even after sustained virological response (SVR). The stage of inflammatory resolution and regression of fibrosis is likely to happen, once treatment and viral clearance are achieved. However, liver examinations by elastography show that 30-40% of patients do not exhibit a reduction of liver stiffness. This work was a cohort study in cirrhotic patients whose purpose was to identify immunological factors involved in the regression of liver stiffness in chronic hepatitis C and characterize possible serum biomarkers with prognostic value. The sample universe consisted of 31 cirrhotic patients who underwent leukocyte immunophenotyping, quantification of cytokines/chemokines and metalloproteinase inhibitors in the pretreatment (M1) and in the evaluation of SVR (M2). After exclusion criteria application, 16 patients included were once more evaluated in M3 (like M1) and classified into regressors (R) or non-regressors (NR), decrease or not ≥ 25% stiffness, respectively. The results from ROC curve, machine learning (ML) and linear discriminant analysis showed that TCD4 + lymphocytes (absolute) are the most important biomarkers for the prediction of the regression (AUC = 0.90). NR patients presented levels less than R of liver stiffness since baseline, whereas NK cells were increased in NR. Therefore, it was concluded that there is a difference in the profile of circulating immune cells in R and NR, thus allowing the development of a predictive model of regression of liver stiffness after SVR. These findings should be validated in greater numbers of patients.

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The adaptability between different environments remains a challenge for Mobile Augmented Reality (MAR). If not done seamlessly, such transitions may cause discontinuities in navigation, consequently disorienting users and undermining the acceptance of this technology. The transition between environments is hard because there are currently no localization techniques that work well in any place: sensor-based applications can be harmed by obstacles that hamper sensor communication (e.g., GPS) and by infrastructure limitations (e.g., Wi-Fi), and image-based applications can be affected by lighting conditions that impair computer vision techniques. Hence, this paper presents an adaptive model to perform transitions between different types of environments for MAR applications. The model has a hybrid approach, choosing the best combination of long-range sensors, short-range sensors, and computer vision techniques to perform fluid transitions between environments that mitigate problems in location, orientation, and registration. To assess the model, we developed a MAR application and conducted a navigation test with volunteers to validate transitions between outdoor and indoor environments, followed by a short interview. The results show that the transitions were well succeeded, since the application self-adapted to the studied environments, seamlessly changing sensors when needed.

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Staphylococcus aureus causes a large variety of infections, where many of them are acquired in the hospital environment. A significant part of the population is a nasal carrier of this type of microorganism. The present study evaluated the nasal colonization by S. aureus, identifying its resistance profile in nursing students from a private educational institute of higher education. Nasal swab samples were collected and identified for S. aureus. Moreover, an antibiogram assay was performed, followed by the search for ermA and ermC genes using PCR. Sixty -two students were included and we isolated 20 positive samples (32,5%) for S. aureus. For the phenotypic profile, 30% were found to be resistant to Erythromycin and 10% to Oxacillin and Cefoxitin. For the D-test in the genotypic profile, 25% presented mecA gene (MRSA), 5% of ermA gene, 35% of ermC gene and 10% with ermC and mecA genes. These data reinforce the necessity of monitoring bacterial colonization in hospital environment, which are potentially resistant in health professionals.

Staphylococcus aureus causa uma grande variedade de infecções, muitas delas adquiridas no ambiente hospitalar. Uma parcela significativa da população é carreadora nasal desses micro-organismos. O presente trabalho avaliou a colonização nasal por S. aureus identificando seu perfil de resistência em estudantes de enfermagem de uma instituição privada de ensino superior. Foram coletadas e identificadas amostras de swab nasal para S. aureus e realizado o antibiograma e a detecção por PCR dos genes mecA, ermA e ermC. Foram incluídos 62 alunos e isoladas 20 amostras (32,3%) positivas para S. aureus, no perfil fenotípico, 30% apresentaram resistência à Eritromicina e 10% para Oxacilina, Cefoxitina e para o teste D, no perfil genotípico 25% apresentaram gene mecA (MRSA), 5% do gene ermA e 35% do gene ermC, e 10% com genes ermC e mecA. Esses dados reforçam a necessidade de monitoramento de colonização por bactérias potencialmente resistente em profissionais da saúde.

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