Background A decade ago, the advancements in the microbiome data sequencing techniques initiated the development of research of the microbiome and its relationship with the host organism. The development of sophisticated bioinformatics and data science tools for the analysis of large amounts of data followed. Since then, the analyzed gut microbiome data, where microbiome is defined as a network of microorganisms inhabiting the human intestinal system, has been associated with several conditions such as irritable bowel syndrome - IBS, colorectal cancer, diabetes, obesity, and metabolic syndrome, and lately in the study of Parkinson’s and Alzheimer’s diseases as well. This paper aims to provide an understanding of differences between microbial data of individuals who have been diagnosed with multiple sclerosis and those who were not by exploiting data science techniques on publicly available data. Methods This study examines the relationship between multiple sclerosis (MS), an autoimmune central nervous system disease, and gut microbial community composition, using the samples acquired by 16s rRNA sequencing technique. We have used three different sets of MS samples sequenced during three independent studies (Jangi et al, Nat Commun 7:1–11, 2016), (Miyake et al, PLoS ONE 10:0137429, 2015), (McDonald et al, Msystems 3:00031–18, 2018) and this approach strengthens our results. Analyzed sequences were from healthy control and MS groups of sequences. The extracted set of statistically significant bacteria from the (Jangi et al, Nat Commun 7:1–11, 2016) dataset samples and their statistically significant predictive functions were used to develop a Random Forest classifier. In total, 8 models based on two criteria: bacteria abundance (at six taxonomic levels) and predictive functions (at two levels), were constructed and evaluated. These include using taxa abundances at different taxonomy levels as well as predictive function analysis at different hierarchical levels of KEGG pathways. Results The highest accuracy of the classification model was obtained at the genus level of taxonomy (76.82 % ) and the third hierarchical level of KEGG pathways (70.95 % ). The second dataset’s 18 MS samples (Miyake et al, PLoS ONE 10:0137429, 2015) and 18 self-reported healthy samples from the (McDonald et al, Msystems 3:00031–18, 2018) dataset were used to validate the developed classification model. The significance of this step is to show that the model is not overtrained for a specific dataset but can also be used on other independent datasets. Again, the highest classification model accuracy for both validating datasets combined was obtained at the genus level of taxonomy (70.98 % ) and third hierarchical level of KEGG pathways (67.24 % ). The accuracy of the independent set remained very relevant. Conclusions Our results demonstrate that the developed classification model provides a good tool that can be used to suggest the presence or absence of MS condition by collecting and analyzing gut microbiome samples. The accuracy of the model can be further increased by using sequencing methods that allow higher taxa resolution (i.e. shotgun metagenomic sequencing).

Purpose: Ventilator associated pneumonia (VAP) is one of the most common hospital-acquired infection in patients hospitalized in intensive care unit (ICU). Aim of this study was to evaluate predictive values of quantitative and qualitative culture of broncholaveolar lavage (BAL) in the diagnosis of VAP comparing with clinical pulmonary infection score (CPIS), and to determine positive and negative predictive values of the tests in patients on mechanical ventilation. Methodology: 209 samples were prospectively taken from the patients hospitalized in ICU on mechanical ventilation; along with the clinical pulmonary infection score (CPIS). After sampling, quantitative and qualitative culture of BAL was done. As the cut off value of quantitative culture 104 CFU/mL was taken, according CDC recommendations. Results: In our study, sensitivity of the quantitative culture of BAL was 91%, specificity 70%, positive predictive value 80% and negative predictive value was 85%. Sensitivity of the qualitative culture of BAL was 93%, specificity 36%, positive predictive value 70,2% and negative predictive value was 76%. Conclusion: Quantitative culture of BAL has better predictive values in VAP diagnosis in patients on mechanical ventilation, helping in the discrimination between colonization and the infection. Qualitative culture of BAL has higher sensitivity, but lower specificity than quantitative culture.

 O processo de avaliação do Ensino Superior no Brasil é representado pelo Sistema Nacional de Avaliação do Ensino Superior (SINAES) que deve apresentar informações que possam informar a sociedade sobre as características desta modalidade de ensino bem como para orientação das políticas públicas de Educação e adequação dos cursos e instituições do Ensino Superior para oferecer condições satisfatórias de qualidade de ensino e estrutura física e pedagógica. Entre os tópicos avaliados pelo SINAES estão o desempenho dos estudantes ao final do curso (Exame Nacional de Desempenho dos Estudantes – ENADE), a análise do projeto político pedagógico do curso e a participação docente neste processo de ensino, pesquisa e extensão. Nesse sentido, o objetivo do presente trabalho será questionar brevemente a relação existente entre a avaliação do ENADE e a construção do PPP dos cursos e a prática docente no processo de formação do estudante. A partir da análise em relação às práticas docentes apresentadas na lei de diretrizes e bases 9394/96 torna-se possível inferir que o objetivo das Universidades e as ações docentes previstas legalmente não constituem objetos de análises definidos no instrumento de avaliação do SINAES. Desta forma, é importante considerarmos que a avaliação existente não permite uma orientação do sistema educacional brasileiro do Ensino Superior, já que suas informações não fornecem elementos suficientes para orientar esta modalidade de ensino.

Remote photoplethysmography proved useful as a non-contact method for estimating physiological data from different parts of the body. Recent studies have mainly focused on the face region for extraction of blood pulsation signal. In this paper, we have demonstrated the feasibility of non-contact remote photoplethysmography in the monitoring of free flap tissue. An experimental study was conducted, where video recordings of free flaps are obtained during breast reconstruction surgery for 8 patients. The hemoglobin absorption rate of the free flap is closely related to signal-to-noise ratio (SNR) values extracted from free flap pulsation signal during surgery. Obtained results show significantly lower SNR values when the free flap is disconnected from the blood supply compared to the SNR values when the flap is intact or after successful blood supply establishment. This method shows potential as a convenient, non-invasive and reliable tool in post-operative microvascular free flap monitoring.