Detection of Plasmodium berghei infected Anopheles stephensi using near-infrared spectroscopy.
Detection of Plasmodium berghei infected Anopheles stephensi using near-infrared spectroscopy.
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DOI：
10.1186/s13071-018-2960-z
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发表时间：
2018-06-28
影响因子：
3.2
通讯作者：
Churcher TS
中科院分区：
文献类型：
--
作者：
Esperança PM;Blagborough AM;Da DF;Dowell FE;Churcher TS
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携带唾液腺子孢子的蚊子的流行率。这些技术费力、主观，并且需要昂贵的设备或培训。这项研究评估了近红外光谱 (NIRS) 识别实验室饲养的感染啮齿动物疟疾的蚊子的潜力。史氏按蚊在实验室中饲养，并以伯氏疟原虫感染的血液为食。喂食后 12 天和 21 天后，杀死蚊子，使用 NIRS 进行扫描和分析，并立即通过显微镜进行解剖，以确定中肠壁上的卵囊或唾液腺中的子孢子的数量。使用预测分类模型根据光谱确定寄生虫流行率和强度状态。该预测模型正确分类了传染性和非传染性蚊子，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度较低。对同一样本进行多次扫描，并在扫描之间重新定位蚊子，可以提高准确性。在较小的数据集上，近红外光谱仪无法预测蚊子是否含有卵囊。据我们所知，我们提供了第一个证据，证明 NIRS 可以区分传染性和非传染性蚊子。目前，区分不同感染强度具有挑战性。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，从而使技术的灵敏度和特异性能够根据要求而变化。
The 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. Anopheles stephensi mosquitoes were reared in the laboratory and fed on Plasmodium berghei infected 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. The 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. To 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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影响因子：
3
作者：
Delves, Michael J.;Sinden, Robert E.
通讯作者：
Sinden, Robert E.
影响因子：
3.2
作者：
Sikulu, Maggy;Killeen, Gerry F.;Dowell, Floyd E.
通讯作者：
Dowell, Floyd E.
DOI：
10.1016/0035-9203(93)90124-9
发表时间：
1993-05-01
影响因子：
2.2
作者：
TASSANAKAJON, A;BOONSAENG, V;PANYIM, S
通讯作者：
PANYIM, S
DOI：
10.1016/0169-7439(93)85002-x
发表时间：
1993-03-01
影响因子：
3.9
作者：
DEJONG, S
通讯作者：
DEJONG, S
影响因子：
3
作者：
Kilama M;Smith DL;Hutchinson R;Kigozi R;Yeka A;Lavoy G;Kamya MR;Staedke SG;Donnelly MJ;Drakeley C;Greenhouse B;Dorsey G;Lindsay SW
通讯作者：
Lindsay SW