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. 2022 Oct 14;11(10):1182.
doi: 10.3390/pathogens11101182.

Malaria Detection Accelerated: Combing a High-Throughput NanoZoomer Platform with a ParasiteMacro Algorithm

Shoaib Ashraf  1   2 , Areeba Khalid  1   3   4 , Arend L de Vos  1   5 , Yanfang Feng  1 , Petra Rohrbach  6 , Tayyaba Hasan  1   7
Affiliations

Malaria Detection Accelerated: Combing a High-Throughput NanoZoomer Platform with a ParasiteMacro Algorithm

Shoaib Ashraf et al. Pathogens. .

Abstract

Eradication of malaria, a mosquito-borne parasitic disease that hijacks human red blood cells, is a global priority. Microscopy remains the gold standard hallmark for diagnosis and estimation of parasitemia for malaria, to date. However, this approach is time-consuming and requires much expertise especially in malaria-endemic countries or in areas with low-density malaria infection. Thus, there is a need for accurate malaria diagnosis/parasitemia estimation with standardized, fast, and more reliable methods. To this end, we performed a proof-of-concept study using the automated imaging (NanoZoomer) platform to detect the malarial parasite in infected blood. The approach can be used as a steppingstone for malaria diagnosis and parasitemia estimation. Additionally, we created an algorithm (ParasiteMacro) compatible with free online imaging software (ImageJ) that can be used with low magnification objectives (e.g., 5×, 10×, and 20×) both in the NanoZoomer and routine microscope. The novel approach to estimate malarial parasitemia based on modern technologies compared to manual light microscopy demonstrated 100% sensitivity, 87% specificity, a 100% negative predictive value (NPV) and a 93% positive predictive value (PPV). The manual and automated malaria counts showed a good Pearson correlation for low- (R2 = 0.9377, r = 0.9683 and p < 0.0001) as well as high- parasitemia (R2 = 0.8170, r = 0.9044 and p < 0.0001) with low estimation errors. Our robust strategy that identifies and quantifies malaria can play a pivotal role in disease control strategies.

Keywords: Giemsa-staining; NanoZoomer; ParasiteMacro; Plasmodium falciparum; algorithm; malaria; microscopy; parasite; parasitemia.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Figure 1
Figure 1
The automated NanoZoomer malaria methodology. (1) Samples are prepared on glass slides, (2) stained with the Giemsa-stain and washed, (3) slides are loaded, and imaged with the NanoZoomer, (4) the ParasiteMacro plugin is run, and (5) the parasitemia of all slides is obtained.
Figure 2
Figure 2
Flow chart and image analysis of automated NanoZoomer malaria counting ParasiteMacro. The acquired images were uploaded to imageJ and the created plugin was run.
Figure 3
Figure 3
Scatter plots for association of parasitemia determined by automated counting NanoZoomer ParasiteMacro algorithm versus manual counting. (A) The HB3 (high parasitemia) strain showed a Pearson correlation coefficient of 0.8179, p < 0.0001, (B) while the 7G8 strain (low parasitemia) showed a Pearson correlation coefficient of 0.9377, p < 0.0001.
See this image and copyright information in PMC

References

    1. Favuzza P., de Lera Ruiz M., Thompson J.K., Triglia T., Ngo A., Steel R.W., Vavrek M., Christensen J., Healer J., Boyce C. Dual plasmepsin-targeting antimalarial agents disrupt multiple stages of the malaria parasite life cycle. Cell Host Microbe. 2020;27:642–658.e12. doi: 10.1016/j.chom.2020.02.005. - DOI - PMC - PubMed
    1. World Health Organization . World Malaria Report. WHO Press; Geneva, Switzerland: 2013. Global Malaria Programme; pp. 11–13.
    1. WHO . World Malaria Report 2020. World Health Organization; Geneva, Switzerland: 2020. 20 years of global progress and challenges; pp. 1–151.
    1. Schellenberg D., Menendez C., Kahigwa E., Font F., Galindo C., Acosta C., Schellenberg J.A., Aponte J.J., Kimario J., Urassa H. African children with malaria in an area of intense Plasmodium falciparum transmission: Features on admission to the hospital and risk factors for death. Am. J. Trop. Med. Hyg. 1999;61:431–438. doi: 10.4269/ajtmh.1999.61.431. - DOI - PubMed
    1. Poostchi M., Silamut K., Maude R.J., Jaeger S., Thoma G. Image analysis and machine learning for detecting malaria. Transl. Res. 2018;194:36–55. doi: 10.1016/j.trsl.2017.12.004. - DOI - PMC - PubMed

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