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利用近红外光谱技术检测感染伯氏疟原虫的斯氏按蚊。

Detection of Plasmodium berghei infected Anopheles stephensi using near-infrared spectroscopy.

机构信息

MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1PG, UK.

Department of Life Sciences, Imperial College London, South Kensington, London, SW7 2AZ, UK.

出版信息

Parasit Vectors. 2018 Jun 28;11(1):377. doi: 10.1186/s13071-018-2960-z.

Abstract

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

摘要

背景

感染疟疾的蚊子比例是评估传播强度和病媒控制干预效果的重要昆虫学指标。目前,通过在显微镜下解剖蚊子或使用分子方法来估计带有唾液腺孢子的蚊子的流行率。这些技术既繁琐,又主观,并且需要昂贵的设备或培训。本研究评估了近红外光谱(NIRS)识别实验室饲养的感染鼠疟的蚊子的潜力。

方法

用实验室饲养的按蚊(Anopheles stephensi),并以感染伯氏疟原虫(Plasmodium berghei)的血液为食。在感染后 12 天和 21 天,杀死蚊子,用 NIRS 扫描和分析,然后立即通过显微镜解剖,以确定中肠壁上的卵囊数量或唾液腺中的孢子。使用预测分类模型从光谱中确定寄生虫的流行率和强度状况。

结果

预测模型正确地将感染性和非感染性蚊子分类,总体准确率为 72%。假阴性和假阳性率分别为 30%和 26%。虽然 NIRS 能够区分非感染性和高度感染性的蚊子,但区分中程感染组的准确性较低。对同一标本进行多次扫描,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,NIRS 无法预测蚊子是否携带卵囊。

结论

据我们所知,这是首次提供证据表明 NIRS 可以区分感染性和非感染性的蚊子。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,允许优化不同的错误率,根据需要改变技术的灵敏度和特异性。

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