Detection of Plasmodium berghei infected Anopheles stephensi using near-infrared spectroscopy
Detection of Plasmodium berghei infected Anopheles stephensi using near-infrared spectroscopy
复制标题
使用近红外光谱检测伯氏疟原虫感染的史氏按蚊
DOI：
10.1101/195925
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Esperança P
中科院分区：
文献类型：
--
作者：
Esperança P
背景感染疟疾的蚊子比例是一个重要的昆虫学指标，用于评估传播强度和病媒控制干预措施的影响。目前，通过在显微镜下解剖蚊子或使用分子方法来估计具有唾液腺子孢子的蚊子的流行。这些技术是费力的、主观的，并且需要昂贵的设备或培训。本研究评估的潜力，近红外光谱（NIRS），以确定实验室饲养的蚊子感染啮齿动物maligna.MethodsAnopheles stephenismosquito在实验室饲养，并喂养疟原虫berghei感染的血液。在喂食后12天和21天，杀死蚊子，使用NIRS扫描和分析，并立即通过显微镜解剖以确定中肠壁上的卵囊或唾液腺中的子孢子的数量。一个预测分类模型被用来确定寄生虫的患病率和强度状态从spectrum.ResultsThe预测模型正确分类感染性和非感染性蚊子的总体准确率为72%。假阴性和假阳性率分别为30%和26%。虽然NIRS能够区分非传染性和高传染性蚊子，但区分中等传染性群体的准确性较低。多次扫描相同的标本，与重新定位蚊子之间的扫描，显示，以提高准确性。在一个较小的数据集，近红外光谱是无法预测蚊子是否窝藏oocysts.ConclusionsTo我们所知，我们提供的第一个证据表明，近红外光谱可以区分传染性和非传染性蚊子。目前，区分不同的感染强度是具有挑战性的。该分类模型提供了一个灵活的框架，并允许优化不同的错误率，使该技术的灵敏度和特异性根据要求而变化。
BackgroundThe proportion of mosquitoes infected with malaria is an important entomological metric used to assess the intensity of transmission and the impact of vector control interventions. Currently, the prevalence of mosquitoes with salivary gland sporozoites is estimated by dissecting mosquitoes under a microscope or using molecular methods. These techniques are laborious, subjective, and require either expensive equipment or training. This study evaluates the potential of near-infrared spectroscopy (NIRS) to identify laboratory reared mosquitoes infected with rodent malaria.MethodsAnopheles stephensimosquitoes were reared in the laboratory and fed onPlasmodium bergheiinfected blood. After 12 and 21 days post-feeding mosquitoes were killed, scanned and analysed using NIRS and immediately dissected by microscopy to determine the number of oocysts on the midgut wall or sporozoites in the salivary glands. A predictive classification model was used to determine parasite prevalence and intensity status from spectra.ResultsThe predictive model correctly classifies infectious and uninfectious mosquitoes with an overall accuracy of 72%. The false negative and false positive rates were 30 and 26%, respectively. While NIRS was able to differentiate between uninfectious and highly infectious mosquitoes, differentiating between mid-range infectious groups was less accurate. Multiple scans of the same specimen, with repositioning the mosquito between scans, is shown to improve accuracy. On a smaller dataset NIRS was unable to predict whether mosquitoes harboured oocysts.ConclusionsTo our knowledge, we provide the first evidence that NIRS can differentiate between infectious and uninfectious mosquitoes. Currently, distinguishing between different intensities of infection is challenging. The classification model provides a flexible framework and allows for different error rates to be optimised, enabling the sensitivity and specificity of the technique to be varied according to requirements.
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