O abandono do tratamento da tuberculose é um dos principais obstáculos para o controle da doença no Brasil e no mundo. Em 2018, o estado do Rio de Janeiro registrou 15,5% de abandono do tratamento nos casos novos, superior ao recomendado. Investigar os fatores associados ao abandono do tratamento segundo características sociodemográficas e clínico-epidemiológicas dos casos novos de tuberculose no estado do Rio de Janeiro, Brasil. Foram utilizados dados do Sistema de Informação de Agravos de Notificação de 2015 a 2019. A associação das variáveis explicativas com o abandono do tratamento foi explorada por meio de modelos de regressão logística binária univariada e multivariada. Dos 14.831 indivíduos com tuberculose incluídos no estudo, 1.852 abandonaram o tratamento, o que representa 12,5% da amostra. Homens jovens, com baixa escolaridade, não brancos, fumantes e usuários de drogas ilícitas apresentaram predisposição ao abandono, com a população de rua apresentando maior chance de abandono do tratamento para tuberculose. O estudo mostra a importância de acompanhar o perfil dos pacientes com tuberculose para desenvolver estratégias que aumentem a cooperação e adesão do paciente.

COVID-19 has shown a broad clinical spectrum, ranging from asymptomatic to mild, moderate, and severe infections. Many symptoms have already been identified as typical of COVID-19, but few studies show how they can be useful in identifying clusters of patients with different severity of illness. This interpretation may help to recognize the different profiles of symptoms of COVID-19 expressed in a population at certain time. The aim of this study was to identify symptom-based clusters of hospitalized patients with severe acute respiratory illness by SARS-CoV-2 in Brazil. The clusters were evaluated based on sociodemographic characteristics, admission to the Intensive Care Unit (ICU), use of respiratory support, and outcome. The Multiple Correspondence Analysis (MCA)-based cluster analysis was applied to symptoms presented before admission. Pearson’s chi-square test was used to compare the proportions of symptoms between the clusters and to examine differences in the calculated rates for the following variables - sex, age group, race, Brazilian region, use of respiratory support, admission to the ICU and outcome. Three COVID-19 clusters with distinct symptom profiles were identified by MCA-based cluster analysis. Cluster 1 had the mildest severity profile, with the lowest frequencies for most symptoms investigated. Cluster 2 had a severe respiratory profile, with the highest frequencies of patients with dyspnea, respiratory discomfort and O2 saturation< 95%. Cluster 2 was also the most prevalent in all Brazilian regions and had the highest percentages of patients who used invasive respiratory support (27.4%) (p-value<0.001), were admitted to the ICU (42.6%) (p -value<0.001) and died (39.0%) (p-value<0.001). Cluster 3 had a prominent profile of gastrointestinal symptoms. The study identified three distinct COVID-19 clusters based on the symptoms presented by patients with severe acute respiratory illness by SARS-CoV-2, but without distinction in their prevalence in the Brazilian regions.

Os objetivos deste trabalho consistiram em desenvolver e aplicar uma metodologia a fim de identificar estudantesuniversitários com maior risco de evasão durante a pandemia de covid-19 e propor ações minimizando este risco. Aproposta apresentada utilizou a amostragem por Bola de Neve e as redes de contato para entender como atuar paraaumentar a motivação acadêmica. Uma metodologia de pesquisa-ação foi aplicada à comunidade de estudantes deciências exatas e tecnologia do CCET/UNIRIO e, com os dados coletados, identificou-se os alunos que poderiam estarem maior risco de evasão. Verificou-se que 50% dos estudantes respondentes apontaram que as alterações na vidapessoal durante a pandemia da covid-19 poderiam impedir a continuação no curso, sendo este o principal risco deevasão identificado. Ainda, 41% dos respondentes indicaram a falta de motivação acadêmica como razão parainterromper o curso. A partir destas e de outras informações, foi possível avaliar o quanto estes estudantes estavam conectados e engajados com a continuidade da sua vida acadêmica e identificar ações que poderiam contribuir paramanter o seu vínculo com a universidade. Por fim, foram realizados eventos de acolhimento e diálogo comparticipação de docentes, técnicos e discentes, visando diminuir as chances de evasão dos alunos identificados. Esteseventos sinalizaram a expectativa dos discentes por mais diálogo em sala de aula, o que subsidiou a continuidade doprojeto.

The social isolation enforced as a result of the new coronavirus (COVID-19) pandemic may impact families’ lifestyle and eating habits. The present study aimed to assess the behaviour and dietary patterns of Brazilian children and adolescents during the social isolation imposed by the COVID-19 pandemic.The present study was conducted using an online, anonymous cross-sectional survey with 589 children and 720 adolescents from Brazil during a nationwide social isolation policy. The Mann-Whitney U-test or the Kruskal-Wallis with the Dunn post-hoc method and a radar chart were used to compare the weekly consumption of each food by age group and isolation status. p < 0.05 was considered statistically significant. Analyses were conducted using R statistical software, version 4.0.2 (R Foundation for Statistical Computing). We found that isolated families showed breakfast eating habits and the consumption of raw salad, vegetables, beans and soft drinks. Lower-class isolated families and those from the Northeast region consumed fruits, juices, vegetables and beans less frequently. Compared to children, adolescents were less isolated (p = 0.016), less active (p < 0.001), exposed to longer screen time (p < 0.001), showed an inadequate sleeping pattern (p = 0.002) and were from lower-class families (p < 0.001). Social isolation affected the eating habits of children and adolescents. Non-isolated families presented a lower consumption of healthy food, especially those among the lower class, from Northeast Brazil, as well as adolescents.

Brazil is, at the time of writing, the global epicenter of COVID-19, but information on risk factors for hospitalization and mortality in the country is still limited. Demographic and clinical data of COVID-19 patients until June 11th, 2020 were retrieved from the State Health Secretariat of Espírito Santo, Brazil. Potential risk factors for COVID-19 hospitalization and death were analyzed by univariate and multivariable logistic regression models. A total of 10,713 COVID-19 patients were included in this study; 81.0% were younger than 60 years, 55.2% were female, 89.2% were not hospitalized, 32.9% had at least one comorbidity, and 7.7% died. The most common symptoms on admission were cough (67.7%) and fever (62.6%); 7.1% of the patients were asymptomatic. Cardiovascular diseases (23.7%) and diabetes (10.3%) were the two most common chronic diseases. Multivariate logistic regression analysis identified an association of all explanatory variables, except for cough and diarrhea, with hospitalization. Older age (odds ratio [OR] = 3.95, P < 0.001) and shortness of breath (OR = 3.55, P < 0.001) were associated with increase of odds to COVID-19 death in hospitalized patients. Our study provided evidence that older age, male gender, Asian, indigenous or unknown race, comorbidities (smoking, kidney disease, obesity, pulmonary disease, diabetes, and cardiovascular disease), as well as fever and shortness of breath increased the risk of hospitalization. For death outcome in hospitalized patients, only older age and shortness of breath increased the risk.

Evaluating next-generation sequencing (NGS) data requires an extensive knowledge of bioinformatics and programming commands, which could limit the studies in this area. We propose a user-friendly system to analyse raw NGS data from HIV-1 patient samples to identify amino acid variants and the virus susceptibility to antiretrovirals. SIRA-HIV was developed as an R Shiny web application. The software Segminator II was applied to analyse viral data. Four genotypic interpretation systems were implemented in R language to classify the HIV susceptibility - the French National Agency for AIDS Research (ANRS), the Stanford HIV Drug Resistance Database (HIVdb), the Rega Institute (Rega) and the Brazilian Network for HIV-1 Genotyping (Brazilian Algorithm). SIRA-HIV was structured in two analysis components. The Drug Resistance Positions module shows the resistance positions, their frequencies, and the coverage. In the Genotypic Resistance Interpretation Algorithms module, the rule-based systems are available to interpret HIV-1 drug resistance genotyping results. SIRA-HIV exhibited comparable results to Deep Gen HIV, HyDRA, and PASeq. As advantage, the proposed application shows susceptibility levels from the most widely used rule-based systems and works locally, allowing analysis not to rely on the internet. SIRA-HIV could be a promising system to aid in HIV-1 patient data analysis.

Uma das maiores dificuldades que o professor encara no processo educativo é ter a verdadeira ciência de que forma está se processando a aprendizagem do aluno. Ter acesso a um feedback imediato do aluno, com o objetivo de melhorar seu desempenho, é crucial no processo de ensino-aprendizagem. O presente trabalho relata a experiência da utilização da ferramenta Plickers como tecnologia avaliativa do curso de Bioestatística. No final do semestre, um questionário foi distribuído aos estudantes com o objetivo de investigar suas opiniões sobre a eficácia do uso da ferramenta na avaliação formativa. O Plickers é um sistema de respostas inovador e de baixo custo. Cada aluno recebe um cartão com um código estilo QR, contendo em cada lado uma letra A, B, C ou D. Após a apresentação de um pergunta de múltipla escolha ou verdadeiro/falso, cada aluno segura o cartão com a resposta indicada no topo. O professor faz a leitura das respostas instantaneamente por meio de um smartphone com câmera e o aplicativo Plickers instalado. Apesar das dificuldades encontradas com o uso de tecnologias digitais, Plickers se mostrou adequado como ferramenta de avaliação formativa. Foi possível observar um maior envolvimento dos alunos, levando à criação de um ambiente de aprendizado eficaz. Além disso, a ferramenta ajudou a fornecer um aprendizado individualizado, tornando as aulas mais interessantes, divertidas e informativas. Foi uma experiência considerada de sucesso e que se encontra em ação no período atual.

Resistance to antiretrovirals (ARVs) is a major problem faced by HIV-infected individuals. Different rule-based algorithms were developed to infer HIV-1 susceptibility to antiretrovirals from genotypic data. However, there is discordance between them, resulting in difficulties for clinical decisions about which treatment to use. Here, we developed ensemble classifiers integrating three interpretation algorithms - Agence Nationale de Recherche sur le SIDA (ANRS), Rega, and the genotypic resistance interpretation system from Stanford HIV Drug Resistance Database (HIVdb). Three approaches were applied to develop a classifier with a single resistance profile - stacked generalization, a simple plurality vote scheme and the selection of the interpretation system with the best performance. The strategies were compared with the Friedman’s test and the performance of the classifiers was evaluated using the F-measure, sensitivity and specificity values. We found that the three strategies had similar performances for the selected antiretrovirals. For some cases, the stacking technique with naive Bayes as the learning algorithm showed a statistically superior F-measure. This study demonstrates that ensemble classifiers can be an alternative tool for clinical decision-making since they provide a single resistance profile from the most commonly used resistance interpretation systems.