Letícia Martins Raposo

Bio: Letícia Martins Raposo is an academic researcher from Universidade Federal do Estado do Rio de Janeiro. The author has contributed to research in topics: Cytology & Colposcopy. The author has an hindex of 5, co-authored 17 publications receiving 85 citations. Previous affiliations of Letícia Martins Raposo include Polytechnic University of Milan & Federal University of Rio de Janeiro.

Risk Factors for Hospitalization and Mortality due to COVID-19 in Espírito Santo State, Brazil

Abstract: 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 Espirito 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.

Eating habits of children and adolescents during the COVID-19 pandemic: The impact of social isolation.

Abstract: BACKGROUND: 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. METHODS: 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 Statisitical Computing). RESULTS: 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). CONCLUSIONS: 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.

Lopinavir Resistance Classification with Imbalanced Data Using Probabilistic Neural Networks

Abstract: Resistance to antiretroviral drugs has been a major obstacle for long-lasting treatment of HIV-infected patients. The development of models to predict drug resistance is recognized as useful for helping the decision of the best therapy for each HIV+ individual. The aim of this study was to develop classifiers for predicting resistance to the HIV protease inhibitor lopinavir using a probabilistic neural network (PNN). The data were provided by the Molecular Virology Laboratory of the Health Sciences Center, Federal University of Rio de Janeiro (CCS-UFRJ/Brazil). Using bootstrap and stepwise techniques, ten features were selected by logistic regression (LR) to be used as inputs to the network. Bootstrap and cross-validation were used to define the smoothing parameter of the PNN networks. Four balanced models were designed and evaluated using a separate test set. The accuracies of the classifiers with the test set ranged from 0.89 to 0.94, and the area under the receiver operating characteristic (ROC) curve (AUC) ranged from 0.96 to 0.97. The sensitivity ranged from 0.94 to 1.00, and the specificity was between 0.88 and 0.92. Four classifiers showed performances very close to three existing expert-based interpretation systems, the HIVdb, the Rega and the ANRS algorithms, and to a k-Nearest Neighbor.

Desempenho do exame citológico e da captura híbrida II no rastreamento de lesões intraepiteliais escamosas de alto grau em mulheres HIV+ Performance by cytology and hybrid capture II in screening for high-grade squamous intraepithelial lesions in women with HIV

Abstract: HIV-infected women are at increased risk of developing high-grade squamous intraepithelial lesions (HSIL), the precursor lesions for cervical cancer. This study estimated and compared the performance of cytology and hybrid capture II in screening for precursor lesions of cervical cancer among HIV-infected women. The study population consisted of women from the open prospective cohort at the Evandro Chagas Clinical Research Institute, Oswaldo Cruz Foundation (IPEC/Fiocruz). Colposcopy and histology were considered jointly in defining the gold standard. Cytology showed 31.8% sensitivity and 95.5% specificity, while hybrid capture II showed higher sensitivity (100%) and lower specificity (52%). The positive likelihood ratio was 7.1 for cytology and 2.1 for hybrid capture II, while the negative likelihood ratio was 0.7 for cytology and 0.0 for hybrid capture II.

Ensemble Classifiers for Predicting HIV-1 Resistance from Three Rule-Based Genotypic Resistance Interpretation Systems

Abstract: 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.

Learning from class-imbalanced data

Abstract: 527 articles related to imbalanced data and rare events are reviewed.Viewing reviewed papers from both technical and practical perspectives.Summarizing existing methods and corresponding statistics by a new taxonomy idea.Categorizing 162 application papers into 13 domains and giving introduction.Some opening questions are discussed at the end of this manuscript. Rare events, especially those that could potentially negatively impact society, often require humans decision-making responses. Detecting rare events can be viewed as a prediction task in data mining and machine learning communities. As these events are rarely observed in daily life, the prediction task suffers from a lack of balanced data. In this paper, we provide an in depth review of rare event detection from an imbalanced learning perspective. Five hundred and seventeen related papers that have been published in the past decade were collected for the study. The initial statistics suggested that rare events detection and imbalanced learning are concerned across a wide range of research areas from management science to engineering. We reviewed all collected papers from both a technical and a practical point of view. Modeling methods discussed include techniques such as data preprocessing, classification algorithms and model evaluation. For applications, we first provide a comprehensive taxonomy of the existing application domains of imbalanced learning, and then we detail the applications for each category. Finally, some suggestions from the reviewed papers are incorporated with our experiences and judgments to offer further research directions for the imbalanced learning and rare event detection fields.

Global pandemics interconnected - obesity, impaired metabolic health and COVID-19.

Abstract: Obesity and impaired metabolic health are established risk factors for the non-communicable diseases (NCDs) type 2 diabetes mellitus, cardiovascular disease, neurodegenerative diseases, cancer and nonalcoholic fatty liver disease, otherwise known as metabolic associated fatty liver disease (MAFLD). With the worldwide spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), obesity and impaired metabolic health also emerged as important determinants of severe coronavirus disease 2019 (COVID-19). Furthermore, novel findings indicate that specifically visceral obesity and characteristics of impaired metabolic health such as hyperglycaemia, hypertension and subclinical inflammation are associated with a high risk of severe COVID-19. In this Review, we highlight how obesity and impaired metabolic health increase complications and mortality in COVID-19. We also summarize the consequences of SARS-CoV-2 infection for organ function and risk of NCDs. In addition, we discuss data indicating that the COVID-19 pandemic could have serious consequences for the obesity epidemic. As obesity and impaired metabolic health are both accelerators and consequences of severe COVID-19, and might adversely influence the efficacy of COVID-19 vaccines, we propose strategies for the prevention and treatment of obesity and impaired metabolic health on a clinical and population level, particularly while the COVID-19 pandemic is present.

Mortality-related risk factors of COVID-19: a systematic review and meta-analysis of 42 studies and 423,117 patients.

Abstract: BACKGROUND: Mortality rates of coronavirus disease-2019 (COVID-19) continue to rise across the world. The impact of several risk factors on coronavirus mortality has been previously reported in several meta-analyses limited by small sample sizes. In this systematic review, we aimed to summarize available findings on the association between comorbidities, complications, smoking status, obesity, gender, age and D-dimer, and risk of mortality from COVID-19 using a large dataset from a number of studies. METHOD: Electronic databases including Google Scholar, Cochrane Library, Web of Sciences (WOS), EMBASE, Medline/PubMed, COVID-19 Research Database, and Scopus, were systematically searched till 31 August 2020. We included all human studies regardless of language, publication date or region. Forty-two studies with a total of 423,117 patients met the inclusion criteria. To pool the estimate, a mixed-effect model was used. Moreover, publication bias and sensitivity analysis were evaluated. RESULTS: The findings of the included studies were consistent in stating the contribution of comorbidities, gender, age, smoking status, obesity, acute kidney injury, and D-dimer as a risk factor to increase the requirement for advanced medical care. The analysis results showed that the pooled prevalence of mortality among hospitalized patients with COVID-19 was 17.62% (95% CI 14.26-21.57%, 42 studies and 423,117 patients). Older age has shown increased risk of mortality due to coronavirus and the pooled odds ratio (pOR) and hazard ratio (pHR) were 2.61 (95% CI 1.75-3.47) and 1.31 (95% CI 1.11-1.51), respectively. A significant association were found between COVID-19 mortality and male (pOR = 1.45; 95% CI 1.41-1.51; pHR = 1.24; 95% CI 1.07-1.41), and current smoker (pOR = 1.42; 95% CI 1.01-1.83). Furthermore, risk of mortality among hospitalized COVID-19 patients is highly influenced by patients with Chronic Obstructive Pulmonary Disease (COPD), Cardiovascular Disease (CVD), diabetes, hypertension, obese, cancer, acute kidney injury and increase D-dimer. CONCLUSION: Chronic comorbidities, complications, and demographic variables including acute kidney injury, COPD, diabetes, hypertension, CVD, cancer, increased D-dimer, male gender, older age, current smoker, and obesity are clinical risk factors for a fatal outcome associated with coronavirus. The findings could be used for disease's future research, control and prevention.

The association of smoking status with sars-cov-2 infection, hospitalization and mortality from covid-19: A living rapid evidence review with bayesian meta-analyses (version 7)

Abstract: AIMS: To estimate the association of smoking status with rates of (i) infection, (ii) hospitalization, (iii) disease severity and (iv) mortality from SARS-CoV-2/COVID-19 disease. DESIGN: Living rapid review of observational and experimental studies with random-effects hierarchical Bayesian meta-analyses. Published articles and pre-prints were identified via MEDLINE and medRxiv. SETTING: Community or hospital, no restrictions on location. PARTICIPANTS: Adults who received a SARS-CoV-2 test or a COVID-19 diagnosis. MEASUREMENTS: Outcomes were SARS-CoV-2 infection, hospitalization, disease severity and mortality stratified by smoking status. Study quality was assessed (i.e. 'good', 'fair' and 'poor'). FINDINGS: Version 7 (searches up to 25 August 2020) included 233 studies with 32 'good' and 'fair' quality studies included in meta-analyses. Fifty-seven studies (24.5%) reported current, former and never smoking status. Recorded smoking prevalence among people with COVID-19 was generally lower than national prevalence. Current compared with never smokers were at reduced risk of SARS-CoV-2 infection [relative risk (RR) = 0.74, 95% credible interval (CrI) = 0.58-0.93, τ = 0.41]. Data for former smokers were inconclusive (RR = 1.05, 95% CrI = 0.95-1.17, τ = 0.17), but favoured there being no important association (21% probability of RR ≥ 1.1). Former compared with never smokers were at somewhat increased risk of hospitalization (RR = 1.20, CrI = 1.03-1.44, τ = 0.17), greater disease severity (RR = 1.52, CrI = 1.13-2.07, τ = 0.29) and mortality (RR = 1.39, 95% CrI = 1.09-1.87, τ = 0.27). Data for current smokers were inconclusive (RR = 1.06, CrI = 0.82-1.35, τ = 0.27; RR = 1.25, CrI = 0.85-1.93, τ = 0.34; RR = 1.22, 95% CrI = 0.78-1.94, τ = 0.49, respectively), but favoured there being no important associations with hospitalization and mortality (35% and 70% probability of RR ≥ 1.1, respectively) and a small but important association with disease severity (79% probability of RR ≥ 1.1). CONCLUSIONS: Compared with never smokers, current smokers appear to be at reduced risk of SARS-CoV-2 infection, while former smokers appear to be at increased risk of hospitalization, increased disease severity and mortality from COVID-19. However, it is uncertain whether these associations are causal.

Predictors of in-hospital COVID-19 mortality: A comprehensive systematic review and meta-analysis exploring differences by age, sex and health conditions.

Abstract: Objective Risk factors for in-hospital mortality in confirmed COVID-19 patients have been summarized in numerous meta-analyses, but it is still unclear whether they vary according to the age, sex and health conditions of the studied populations. This study explored these variables as potential mortality predictors. Methods A systematic review was conducted by searching the MEDLINE, Scopus, and Web of Science databases of studies available through July 27, 2020. The pooled risk was estimated with the odds ratio (p-OR) or effect size (p-ES) obtained through random-effects meta-analyses. Subgroup analyses and meta-regression were applied to explore differences by age, sex and health conditions. The MOOSE guidelines were strictly followed. Results The meta-analysis included 60 studies, with a total of 51,225 patients (12,458 [24.3%] deaths) from hospitals in 13 countries. A higher in-hospital mortality risk was found for dyspnoea (p-OR = 2.5), smoking (p-OR = 1.6) and several comorbidities (p-OR range: 1.8 to 4.7) and laboratory parameters (p-ES range: 0.3 to -2.6). Age was the main source of heterogeneity, followed by sex and health condition. The following predictors were more markedly associated with mortality in studies with patients with a mean age ≤60 years: dyspnoea (p-OR = 4.3), smoking (p-OR = 2.8), kidney disease (p-OR = 3.8), hypertension (p-OR = 3.7), malignancy (p-OR = 3.7), diabetes (p-OR = 3.2), pulmonary disease (p-OR = 3.1), decreased platelet count (p-ES = -1.7), decreased haemoglobin concentration (p-ES = -0.6), increased creatinine (p-ES = 2.4), increased interleukin-6 (p-ES = 2.4) and increased cardiac troponin I (p-ES = 0.7). On the other hand, in addition to comorbidities, the most important mortality predictors in studies with older patients were albumin (p-ES = -3.1), total bilirubin (p-ES = 0.7), AST (p-ES = 1.8), ALT (p-ES = 0.4), urea nitrogen (p-ES), C-reactive protein (p-ES = 2.7), LDH (p-ES = 2.4) and ferritin (p-ES = 1.7). Obesity was associated with increased mortality only in studies with fewer chronic or critical patients (p-OR = 1.8). Conclusion The prognostic effect of clinical conditions on COVID-19 mortality vary substantially according to the mean age of patients. PROSPERO registration number CRD42020176595.