Symptom-based clusters of hospitalized patients with severe acute respiratory illness by SARS-CoV-2 in Brazil.

Background: 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.

Spatial Analysis of the Sociodemographic Characteristics, Comorbidities, Hospitalization, Signs, and Symptoms Among Hospitalized Coronavirus Disease 2019 Cases in the State of Rio De Janeiro, Brazil.

After more than 1 year from the beginning of the pandemic, the coronavirus disease 2019 (COVID-19) has reached all continents. The number of infected people is still increasing, and Brazil is among the countries with the highest number of registered cases in the world. In this study, we investigated the profile of hospitalized COVID-19 cases and the eventual clusters of similar areas, using geographic information systems.

Impact of protocol change on individual factors related to course of adverse reactions to chemotherapy for breast cancer.

Purpose: Asthenia, myalgia, arthralgia, mucositis, abdominal pain, diarrhea, and neutropenia are adverse reactions commonly reported by women undergoing chemotherapy. Traditional approaches do not take into account the effect that chemotherapeutic changes and variable interactions can cause in adverse reactions. We aimed to identify the impact of the change of a chemotherapy protocol within the same treatment in profiles associated with adverse reactions.

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

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.

Exploring spatial patterns in the associations between local AIDS incidence and socioeconomic and demographic variables in the state of Rio de Janeiro, Brazil.

Access to antiretroviral therapy (ART), universally provided in Brazil since 1996, resulted in a reduction in overall morbidity and mortality due to AIDS or AIDS-related complications, but in some municipalities of Rio de Janeiro, AIDS incidence remains high. Public health surveillance remains an invaluable tool for understanding current AIDS epidemiologic patterns and local socioeconomic and demographic factors associated with increased incidence. Geographically Weighted Poisson Regression (GWPR) explores spatial varying impacts of these factors across the study area focusing attention on local variations in ecological associations.

Lopinavir Resistance Classification with Imbalanced Data Using Probabilistic Neural Networks.

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 acquired immunodeficiency syndrome in the State of Rio de Janeiro, Brazil: a spatio-temporal analysis of cases reported in the period 2001-2010.

Despite increased funding for research on the human immunodeficiency virus (HIV) and the acquired immunodeficiency syndrome (AIDS), neither vaccine nor cure is yet in sight. Surveillance and prevention are essential for disease intervention, and it is recognised that spatio-temporal analysis of AIDS cases can assist the decision-making process for control of the disease. This study investigated the dynamic, spatial distribution of notified AIDS cases in the State of Rio de Janeiro, Brazil, between 2001 and 2010, based on the annual incidence in each municipality.

Sensitivity and specificity of the World Health Organization dengue classification schemes for severe dengue assessment in children in Rio de Janeiro.

Background: The clinical definition of severe dengue fever remains a challenge for researchers in hyperendemic areas like Brazil. The ability of the traditional (1997) as well as the revised (2009) World Health Organization (WHO) dengue case classification schemes to detect severe dengue cases was evaluated in 267 children admitted to hospital with laboratory-confirmed dengue.

Principal Findings: Using the traditional scheme, 28.

[Determinants of cellular atypia detection in the cervical screening program in Rio de Janeiro, Brazil].

Objective: To identify the main determinants of cellular atypia detection in the cervical screening program in the state of Rio de Janeiro, Brazil, using data from the Cervical Cancer Information System SISCOLO.

Methods: A random sample of 65 535 Pap smears performed in 2007 was obtained from SISCOLO. This sample was used to produce a logistic regression model to identify variables that impact the process of detecting cellular atypia.

A framework to identify gene expression profiles in a model of inflammation induced by lipopolysaccharide after treatment with thalidomide.

Background: Thalidomide is an anti-inflammatory and anti-angiogenic drug currently used for the treatment of several diseases, including erythema nodosum leprosum, which occurs in patients with lepromatous leprosy. In this research, we use DNA microarray analysis to identify the impact of thalidomide on gene expression responses in human cells after lipopolysaccharide (LPS) stimulation. We employed a two-stage framework.

Random-effects models in investigating the effect of vitamin A in childhood diarrhea.

Purpose: By adopting more appropriate and powerful statistical methods that fully exploit longitudinal structure, we re-analyze and extend previously published results from a large community trial to investigate the effect of vitamin A supplementation on the prevalence and severity of diarrhea in young children.

Methods: Generalized linear mixed models were used to allow for repeated measures in a reanalysis of a double-blind, randomized, placebo-controlled community trial conducted in a cohort of children in northeastern Brazil during 1 year. The response variable was weekly number of days with diarrhea for each child, and Markov Chain Monte Carlo methods were used to estimate model parameters.

Modeling diarrhea disease in children less than 5 years old.

Purpose: Identification of the temporal pattern of diarrhea disease in children less than 5 years of age in Rio de Janeiro City (1995-1998) to provide support for decisions about prevention and control of the disease.

Methods: The weekly counts of hospitalizations and deaths due to diarrhea disease were analyzed separately. An initial generalized linear model (GLM) was derived using variables related to weather and month.

Time series analysis of deaths due to diarrhoea in children in Rio de Janeiro, Brazil, 1980-1998.

Diarrhoea is still a considerable public-health problem in developing countries, especially among children aged less than five years. The well-known relationship between seasonal variation and aetiological agents of diarrhoeal diseases helps inform the decisions about the prevention and control measures. The aim of this study was to identify the temporal patterns of deaths due to diarrhoea in children, aged less than five years, in Rio de Janeiro State from 1980 to 1998.

[Temporal pattern of diarrhea hospitalizations and deaths in children, 1995 to 1998, Brazil].

Objective: To analyze the temporal pattern of hospitalization and deaths due to diarrhea among children less than five years old between 1995 and 1998 to support specific prevention actions and disease control strategies.

Methods: Data was collected from the Ministry of Health's Mortality Data System and Hospitalization Data System. Monthly time series of diarrhea hospitalizations and deaths were decomposed into stochastic linear trend, deterministic seasonal and irregular components using structural time series models.