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.

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.
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DOI:
10.1186/1475-2875-9-117
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发表时间:
2010-05-06
期刊:
影响因子:
3
通讯作者:
Barral-Netto M
Barral-Netto M
中科院分区:
医学3区
文献类型:
--
作者:
Andrade BB;Reis-Filho A;Barros AM;Souza-Neto SM;Nogueira LL;Fukutani KF;Camargo EP;Camargo LM;Barral A;Duarte A;Barral-Netto M

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准确的疟疾诊断对于治疗和管理严重病例是必不可少的。此外,无症状疟疾患者通常得不到保健设施的筛查,这使疾病控制工作更加复杂。本研究比较了疟疾快速诊断试验(RDT),厚血涂片法和巢式PCR诊断症状性疟疾在巴西亚马逊河流域的性能。此外,还测试了一种用于诊断无症状疟疾的创新计算方法。这项研究分为两部分。在第一部分中,对来自巴西亚马逊地区最近城市化的社区的311名患有疟疾相关症状的个人进行了被动病例检测。一项横断面调查比较了RDT Optimal-IT、巢式PCR和光学显微镜的诊断性能。研究的第二部分涉及在巴西朗的沿河社区的380名个体中发现无症状疟疾的活跃病例。显微镜,巢式PCR和基于人工神经网络(MalDANN)的专家计算系统,使用流行病学数据的性能进行了比较。巢式PCR被证明是诊断有症状和无症状疟疾的金标准,因为它检测到的病例数最多,特异性最高。令人惊讶的是,在诊断低寄生虫血症的病例中,RDT上级优于显微镜。但在12例间日疟原虫+恶性疟原虫混合感染中,RDT不能区分疟原虫种属。此外,显微镜在无症状病例的检测中表现不佳(正确诊断率为61.25%)。使用流行病学数据的MalDANN系统比光学显微镜更差(正确诊断率为56%)。然而,当输入关于白细胞介素-10和干扰素-γ的血浆水平的信息时,MalDANN性能明显增加(80%正确诊断)。疟疾诊断的RDT可能会发现一个有前途的用途在巴西亚马逊地区整合一个合理的诊断方法。尽管MalDANN测试仅使用流行病学数据的低性能,基于神经网络的方法可能是可行的情况下,更简单的方法用于区分低于和高于阈值细胞因子水平的个人是可用的。
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. 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 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. 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). 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.
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