Fast detection and quantification of Plasmodium species infected erythrocytes in a non-endemic region by using the Sysmex XN-31 analyzer

Evaluation of new haematology analyser, XN-31, for malaria detection in blood donors: A single-centre study from India

BACKGROUND AND OBJECTIVES

Malaria continues to be a significant public health concern in India, with several regions experiencing endemicity and sporadic outbreaks. The prevalence of malaria in blood donors, in India, varies between 0.02% and 0.07%. Common techniques to screen for malaria, in blood donors and patients, include microscopic smear examination and rapid diagnostic tests (RDTs) based on antigen detection. The aim of this study was to evaluate a new fully automated analyser, XN-31, for malaria detection, as compared with current practice of using RDT.

MATERIALS AND METHODS

Cross-sectional analytical study was conducted to evaluate clinical sensitivity and specificity of new automated analyser XN-31 among blood donors' samples and clinical samples (patients with suspicion of malaria) from outpatient clinic collected over between July 2021 and October 2022. No additional sample was drawn from blood donor or patient. All blood donors and patients' samples were processed by malaria rapid diagnostic test, thick-smear microscopy (MIC) and the haematology analyser XN-31. Any donor blood unit incriminated for malaria was discarded. Laboratory diagnosis using MIC was considered the 'gold standard' in the present study. Clinical sensitivity and specificity of XN-31 were compared with the gold standard.

RESULTS

Fife thousand and five donor samples and 82 diagnostic samples were evaluated. While the clinical sensitivity and specificity for donor samples were 100%, they were 72.7% and 100% for diagnostic samples.

CONCLUSION

Automated haematology analysers represent a promising solution, as they can deliver speedy and sensitive donor malaria screening assessments. This method also has the potential to be used for pre-transfusion malaria screening along with haemoglobin estimation.

The fully automated Sysmex XN-31 hematology analyzer can detect bloodstream form Trypanosoma brucei

BACKGROUND

For fully automated detection and quantification of Plasmodium parasites, Sysmex developed the XN-31 hemocytometer. This study investigated whether the XN-31 can also detect and quantify bloodstream form trypanosomes (trypomastigotes).

METHODS

Axenic cultures of Trypanosoma brucei brucei were used to prepare two dilution series of trypomastigotes in the whole blood of a healthy donor, which were subsequently examined by the XN-31 as well as by microscopic examination of thin and thick blood films. Trypomastigote intactness during the procedures was evaluated by microscopy.

RESULTS

The XN-31 hemocytometer detected trypomastigotes with a detection limit of 26 trypomastigotes/μL. Scattergram patterns of Trypanosoma and Plasmodium parasites were clearly distinct, but current interpretation settings do not allow the identification of trypomastigotes yet, and therefore, need future refinement.

CONCLUSION

Proof of concept was provided for an automated fluorescent flow cytometry method that can detect and quantify Plasmodium spp., as well as Trypanosoma brucei trypomastigotes.

Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review

Malaria is an infectious disease caused by parasites of the genus Plasmodium spp. It is transmitted to humans by the bite of an infected female Anopheles mosquito. It is the most common disease in resource-poor settings, with 241 million malaria cases reported in 2020 according to the World Health Organization. Optical microscopy examination of blood smears is the gold standard technique for malaria diagnosis; however, it is a time-consuming method and a well-trained microscopist is needed to perform the microbiological diagnosis. New techniques based on digital imaging analysis by deep learning and artificial intelligence methods are a challenging alternative tool for the diagnosis of infectious diseases. In particular, systems based on Convolutional Neural Networks for image detection of the malaria parasites emulate the microscopy visualization of an expert. Microscope automation provides a fast and low-cost diagnosis, requiring less supervision. Smartphones are a suitable option for microscopic diagnosis, allowing image capture and software identification of parasites. In addition, image analysis techniques could be a fast and optimal solution for the diagnosis of malaria, tuberculosis, or Neglected Tropical Diseases in endemic areas with low resources. The implementation of automated diagnosis by using smartphone applications and new digital imaging technologies in low-income areas is a challenge to achieve. Moreover, automating the movement of the microscope slide and image autofocusing of the samples by hardware implementation would systemize the procedure. These new diagnostic tools would join the global effort to fight against pandemic malaria and other infectious and poverty-related diseases.

Clinical performance testing of the automated haematology analyzer XN-31 prototype using whole blood samples from patients with imported malaria in Japan

Background

The automated haematology analyzer XN-31 prototype (XN-31p) is a new flow cytometry-based device developed to measure the number and the ratio of malaria-infected red blood cells (MI-RBC) with a complete blood count (CBC). The XN-31p can provide results in about one minute and also can simultaneously provide information on the malaria parasite (Plasmodium) species. In this study, clinical testing of the XN-31p was performed using blood samples from patients with imported malaria in Japan.

Methods

Blood samples were collected from 80 patients who visited the hospital of the National Center for Global Health and Medicine, Tokyo, Japan, for malaria diagnosis from January 2017 to January 2019. The test results by the XN-31p were compared with those by other standard methods, such as microscopic observation, rapid diagnostic tests and the nested PCR.

Results

Thirty-three patients were diagnosed by the nested PCR as being malaria positive (28 Plasmodium falciparum, 2 Plasmodium vivax, 1 Plasmodium knowlesi, 1 mixed infection of P. falciparum and Plasmodium malariae, and 1 mixed infection of P. falciparum and Plasmodium ovale), and the other 47 were negative. The XN-31p detected 32 patients as “MI-RBC positive”, which almost matched the results by the nested PCR and, in fact, completely matched with the microscopic observations. The ratio of RBCs infected with malaria parasites as determined by the XN-31p showed a high correlation coefficient of more than 0.99 with the parasitaemia counted under microscopic observation. The XN-31p can analyse the size and nucleic acid contents of each cell, and the results were visualized on a two-dimensional cytogram termed the “M scattergram”. Information on species and developmental stages of the parasites could also be predicted from the patterns visualized in the M scattergrams. The XN-31p showed a positive coincidence rate of 0.848 with the nested PCR in discriminating P. falciparum from the other species.

Conclusions

The XN-31p could rapidly provide instructive information on the ratio of MI-RBC and the infecting Plasmodium species. It was regarded to be of great help for the clinical diagnosis of malaria.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12936-022-04247-x.