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Visible microspectrophotometry coupled with machine learning to discriminate the erythrocytic life cycle stages of P. falciparum malaria parasites in functional single cells
Analyst ( IF 3.6 ) Pub Date : 2022-05-12 , DOI: 10.1039/d2an00274d John A Adegoke 1 , Hannah Raper 1 , Callum Gassner 1 , Philip Heraud 1 , Bayden R Wood 1
Analyst ( IF 3.6 ) Pub Date : 2022-05-12 , DOI: 10.1039/d2an00274d John A Adegoke 1 , Hannah Raper 1 , Callum Gassner 1 , Philip Heraud 1 , Bayden R Wood 1
Affiliation
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Malaria was regarded as the most devastating infectious disease of the 21st century until the COVID-19 pandemic. Asexual blood staged parasites (ABS) play a unique role in ensuring the parasite's survival and pathogenesis. Hitherto, there have been no spectroscopic reports discriminating the life cycle stages of the ABS parasite under physiological conditions. The identification and quantification of the stages in the erythrocytic life cycle is important in monitoring the progression and recovery from the disease. In this study, we explored visible microspectrophotometry coupled to machine learning to discriminate functional ABS parasites at the single cell level. Principal Component Analysis (PCA) showed an excellent discrimination between the different stages of the ABS parasites. Support Vector Machine Analysis provided a 100% prediction for both schizonts and trophozoites, while a 92% and 98% accuracy was achieved for predicting control and ring staged infected RBCs, respectively. This work shows proof of principle for discriminating the life cycle stages of parasites in functional erythrocytes using visible microscopy and thus eliminating the drying and fixative steps that are associated with other optical-based spectroscopic techniques.
中文翻译：
可见显微分光光度法与机器学习相结合以区分功能性单细胞中恶性疟原虫疟原虫的红细胞生命周期阶段
在 COVID-19 大流行之前，疟疾一直被认为是 21 世纪最具破坏性的传染病。无性血液分期寄生虫 (ABS) 在确保寄生虫的存活和发病机制方面发挥着独特的作用。迄今为止，还没有光谱报告在生理条件下区分 ABS 寄生虫的生命周期阶段。红细胞生命周期阶段的识别和量化对于监测疾病的进展和恢复非常重要。在这项研究中，我们探索了与机器学习相结合的可见显微分光光度法，以在单细胞水平上区分功能性 ABS 寄生虫。主成分分析 (PCA) 显示了 ABS 寄生虫不同阶段之间的极好区分。支持向量机分析为裂殖体和滋养体提供了 100% 的预测，而预测控制和环分期感染的红细胞的准确度分别为 92% 和 98%。这项工作证明了使用可见显微镜来区分功能性红细胞中寄生虫生命周期阶段的原理证明，从而消除了与其他基于光学的光谱技术相关的干燥和固定步骤。
更新日期：2022-05-12
中文翻译：
可见显微分光光度法与机器学习相结合以区分功能性单细胞中恶性疟原虫疟原虫的红细胞生命周期阶段
在 COVID-19 大流行之前，疟疾一直被认为是 21 世纪最具破坏性的传染病。无性血液分期寄生虫 (ABS) 在确保寄生虫的存活和发病机制方面发挥着独特的作用。迄今为止，还没有光谱报告在生理条件下区分 ABS 寄生虫的生命周期阶段。红细胞生命周期阶段的识别和量化对于监测疾病的进展和恢复非常重要。在这项研究中，我们探索了与机器学习相结合的可见显微分光光度法，以在单细胞水平上区分功能性 ABS 寄生虫。主成分分析 (PCA) 显示了 ABS 寄生虫不同阶段之间的极好区分。支持向量机分析为裂殖体和滋养体提供了 100% 的预测，而预测控制和环分期感染的红细胞的准确度分别为 92% 和 98%。这项工作证明了使用可见显微镜来区分功能性红细胞中寄生虫生命周期阶段的原理证明，从而消除了与其他基于光学的光谱技术相关的干燥和固定步骤。
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