Campo DC | Valor | Idioma |
dc.contributor.author | Andrade, Bruno Bezerril | - |
dc.contributor.author | Reis Filho, Antonio | - |
dc.contributor.author | Barros, Austeclino M. | - |
dc.contributor.author | Souza Neto, Sebastião Martins | - |
dc.contributor.author | Nogueira, Lucas de Lima | - |
dc.contributor.author | Fukutani, Kiyoshi Ferreira | - |
dc.contributor.author | Camargo, Erney Plessmann | - |
dc.contributor.author | Camargo, Luís Marcelo Aranha | - |
dc.contributor.author | Barral, Aldina Maria Prado | - |
dc.contributor.author | Duarte, Angelo Amancio | - |
dc.contributor.author | Barral-Netto, Manoel | - |
dc.creator | Andrade, Bruno Bezerril | - |
dc.creator | Reis Filho, Antonio | - |
dc.creator | Barros, Austeclino M. | - |
dc.creator | Souza Neto, Sebastião Martins | - |
dc.creator | Nogueira, Lucas de Lima | - |
dc.creator | Fukutani, Kiyoshi Ferreira | - |
dc.creator | Camargo, Erney Plessmann | - |
dc.creator | Camargo, Luís Marcelo Aranha | - |
dc.creator | Barral, Aldina Maria Prado | - |
dc.creator | Duarte, Angelo Amancio | - |
dc.creator | Barral-Netto, Manoel | - |
dc.date.accessioned | 2012-05-08T12:16:49Z | - |
dc.date.available | 2012-05-08T12:16:49Z | - |
dc.date.issued | 2010 | - |
dc.identifier.issn | 1475-2875 | - |
dc.identifier.uri | http://www.repositorio.ufba.br/ri/handle/ri/5786 | - |
dc.description | p. 1-11 | pt_BR |
dc.description.abstract | Background: Accurate malaria diagnosis is mandatory for the treatment and management of severe cases. Moreover,
individuals with asymptomatic malaria are not usually screened by health care facilities, which further complicates
disease control efforts. The present study compared the performances of a malaria rapid diagnosis test (RDT), the thick
blood smear method and nested PCR for the diagnosis of symptomatic malaria in the Brazilian Amazon. In addition, an
innovative computational approach was tested for the diagnosis of asymptomatic malaria.
Methods: The study was divided in two parts. For the first part, passive case detection was performed in 311
individuals with malaria-related symptoms from a recently urbanized community in the Brazilian Amazon. A crosssectional
investigation compared the diagnostic performance of the RDT Optimal-IT, nested PCR and light microscopy.
The second part of the study involved active case detection of asymptomatic malaria in 380 individuals from riverine
communities in Rondônia, Brazil. The performances of microscopy, nested PCR and an expert computational system
based on artificial neural networks (MalDANN) using epidemiological data were compared.
Results: Nested PCR was shown to be the gold standard for diagnosis of both symptomatic and asymptomatic malaria
because it detected the major number of cases and presented the maximum specificity. Surprisingly, the RDT was
superior to microscopy in the diagnosis of cases with low parasitaemia. Nevertheless, RDT could not discriminate the
Plasmodium species in 12 cases of mixed infections (Plasmodium vivax + Plasmodium falciparum). Moreover, the
microscopy presented low performance in the detection of asymptomatic cases (61.25% of correct diagnoses). The
MalDANN system using epidemiological data was worse that the light microscopy (56% of correct diagnoses).
However, when information regarding plasma levels of interleukin-10 and interferon-gamma were inputted, the
MalDANN performance sensibly increased (80% correct diagnoses).
Conclusions: An RDT for malaria diagnosis may find a promising use in the Brazilian Amazon integrating a rational
diagnostic approach. Despite the low performance of the MalDANN test using solely epidemiological data, an
approach based on neural networks may be feasible in cases where simpler methods for discriminating individuals
below and above threshold cytokine levels are available. | pt_BR |
dc.language.iso | en | pt_BR |
dc.source | DOI: 10.1186/1475-2875-9-117 | pt_BR |
dc.title | Research Towards a precise test for malaria diagnosis in the
Brazilian Amazon: comparison among field microscopy, a rapid diagnostic test, nested PCR, and a computational expert system based on
artificial neural networks | pt_BR |
dc.title.alternative | Malaria Journal | pt_BR |
dc.type | Artigo de Periódico | pt_BR |
dc.identifier.number | v. 9, n. 117 | pt_BR |
Aparece nas coleções: | Artigo Publicado em Periódico (Faculdade de Medicina)
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