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

Malaria Journal

Research

Sebastião M Souza-Neto¹  Lucas L Nogueira²  Bruno B Andrade³  Antonio Reis-Filho³  Kiyoshi F Fukutani³  Manoel Barral-Netto⁴  Aldina Barral⁴  Austeclino M Barros⁵  Ângelo Duarte⁶  Erney P Camargo⁷  Luís MA Camargo⁸ 

[1] Centro de Pesquisas Gonçalo Moniz (FIOCRUZ), Bahia, Brazil;Departamento de Ciências Biológicas, Universidade Estadual de Santa Cruz (UESC), IlhéusBahia, Brazil;Centro de Pesquisas Gonçalo Moniz (FIOCRUZ), Bahia, Brazil;Departamento de Microbiologia e Parasitologia, Universidade Federal de Santa Catarina, Florianópolis, Brazil;Centro de Pesquisas Gonçalo Moniz (FIOCRUZ), Bahia, Brazil;Faculdade de Medicina da Bahia, Universidade Federal da Bahia (UFBA), Brazil;Centro de Pesquisas Gonçalo Moniz (FIOCRUZ), Bahia, Brazil;Faculdade de Medicina da Bahia, Universidade Federal da Bahia (UFBA), Brazil;Instituto de Investigação em Imunologia (iii), Instituto Nacional de Ciência e Tecnologia (INCT), São Paulo, Brazil;Departamento de Ciência da Computação/Faculdade Ruy Barbosa, Salvador, Brazil;Departamento de Tecnologia, Universidade Estadual de Feira de Santana (UEFS), Feira de Santana, Brazil;Unidade Avançada de Pesquisa, Instituto de Ciências Biológicas V, Universidade de São Paulo (USP), Rondônia, Brazil;Unidade Avançada de Pesquisa, Instituto de Ciências Biológicas V, Universidade de São Paulo (USP), Rondônia, Brazil;Faculdade de Medicina, Faculdade São Lucas, Rondônia, Brazil;

关键词: Malaria;    Malaria Case;    Mixed Infection;    Malaria Diagnosis;    Asymptomatic Malaria;   

DOI  :  10.1186/1475-2875-9-117

 received in 2010-01-12, accepted in 2010-05-06,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundAccurate 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.MethodsThe 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 cross-sectional 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.ResultsNested 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).ConclusionsAn 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.

【 授权许可】

Unknown   
© Andrade et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.