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DNA FROM SALIVA IS A RELIABLE SAMPLE TYPE FOR MALARIA DETECTION

Globally, malaria is still a major problem with approximately 3.3 billion people living in areas at risk of malaria transmission in 106 countries (Centers for Disease Control, 2012). Most of those affected by malaria are young children and pregnant women residing in sub-Saharan Africa. When I was growing up in Lake Victoria, in western Kenya, I would watch kids die of malaria. We lost so many children and adults. So my motivation all along has been to study malaria and get to the depth of this particular disease.

Through my work as a professor, I have met and collaborated with several researchers in Kenya and globally (especially the United States) who have the same goals; to understand the genetic makeup of the human host, the mosquito vector and the malarial parasite, and ultimately work towards controlling the infection. The genetics of malaria is still new – much is still unknown. While the genetic research continues, it is likely that the majority of the children residing in endemic regions in sub-Saharan Africa will be infected with malaria at some point in their lives. With the funding that I have been awarded, I hope to increase the presence of screening labs in these sub-Saharan regions, and to educate others about the disease. For example, in just three years of our initial research on malaria, we were able to reduce the morbidity and mortality rates of children in western Kenya. But the standard methods of sample collection are still not ideal. We are able to collect blood samples from these children; however, this collection method presents a specific set of problems due to its invasive nature. If we are able to identify a non-invasive collection method that can remain stable in these conditions before the samples return to the centers, it will potentially allow us to collect a larger number of samples and reach further across to other affected regions. I recently conducted a preliminary study in P. falciparum malaria regions in western Kenya, with the goal of determining if the malarial parasite can be detected in saliva. The use of saliva, if it presents high enough levels of the parasite in infected children, would offer us a very useful means of sample collection. In this study we collected paired blood and saliva samples from children 4 years and older who presented clinical symptoms of malaria. We decided to use the OMNIgene®•ORAL self collection kits for our saliva samples as it was easy to use and would allow us to store the samples outside without the risk of compromising the sample.

Nowadays the use of rapid diagnostic tests (RDTs) has become more widespread due to the ease of use – a study found the sensitivity and specificity of these to be around 29% and 89% respectively, which highlights room for improvement. Malaria Laboratory located inside Haiti’s National Public Health Lab, Laboratoire National de Santé Publique, situated in the country’s capital, Port-au-Prince. Credit: CDC Yu and colleagues present ‘Malaria Screener’, a novel smartphone-based semi-automated system which can analyse images for malaria screening, combined with a user-friendly interface. The setup, involving an Android smartphone in conjunction with a microscope adaptor, is an affordable setup which may assist malaria diagnosis in resource-limited areas. The application consists of three independent modules on slide-screening, data management and data upload which work together to achieve the desired function – the application is designed to be adaptable and customised, with open-source code hosted on GitHub.

Furthermore, the parasite detection algorithm can be easily swapped – this ensures that other groups in this field of research can experiment with other algorithms and approaches to advance the automation of malaria diagnosis, thus serving as a code base for future developments within the area. Another paper published in 2020 by Kuo and team in Taiwan. developed a malaria detection algorithm which was able to achieve expert-level performance in detecting P. falciparum in thin blood smear images – this further highlights that detection algorithms could be key in the acceleration of developing automated malaria diagnostics. Combining the high-resolution cameras and computing power of modern smartphones, Malaria Screener can screen both thin and thick blood smear images to identify P. falciparum parasites. The research team validated the smartphone application on blood smear images from 150 patients infected with P. falciparum, and 50 control patients in Bangladesh – at a patch level, the application was found to have 96.89% accuracy with 90.92% sensitivity and 97.43% specificity; whilst at patient level the accuracy was 78.00% and the sensitivity and specificity were respectively 79.33% and 74.00%.

The development of Malaria Screener represents a step towards the potential automation of the diagnostic process in malaria through automation of light microscopy examination. Two laboratory technicians with Haiti’s ministry of health (MoH), Mr. Dorelus and Mrs. Byzette, were processing blood samples in order to test for lymphatic filariasis (LF) and malaria, using rapid diagnostic tests in a rural classroom, located in the Nippes Department of Haiti. Credit: Alaine Kathryn Knipes/CDC This may serve as a solution to improve point-of-care diagnosis of malaria in the field in resource-limited settings, through eliminating the need for highly trained personnel. The researchers also integrated additional functions within the app to support the daily work of malaria field workers such as the data management function, allowing them to enter patient information directly into the application database rather than having a separate system. Other papers looking at automation of diagnostic systems in malaria have also shown promise, and the importance of further research in the area to develop new tools to facilitate rapid and easier diagnosis of malaria in areas with limited access to healthcare and laboratory services cannot be overstated.

Another study in 2016 by Luís Rosado and colleagues, based in Portugal, presented a methodology of image processing and analysis methodology using supervised classification to identify P. falciparum in thick smears, using images acquired exclusively using low-cost tools such as smartphones. Further papers have looked at the use of flow cytometry-based haematology analysers as an important adjuvant diagnostic tool in the routine laboratory work-up of febrile patients in or returning from malaria-endemic regions. Innovative diagnostic methodologies combining low-cost equipment maximising efficacy with algorithms for detection from images such as that used in Malaria Screener show potential for a future of automated malaria diagnostics, alleviating the need in resource-limited settings to train personnel and saving lives otherwise lost due to a dearth of equipment and staff to diagnose malaria rapidly and accurately.