B90075 Applied Health Studies

Introduction

The main strategy for the control of malaria is accurate and quick diagnosis along with effective treatment. Early and effective diagnosis of malaria is important for effective management of the disease (Gerstl et al., 2010). As per the regulation of WHO (2018), accurate and fast diagnosis of malaria can be done through manual microscopic detection of the malarial parasite or through the use of the recombinant DNA technology. However, these tests are not 100% effective as it is associated with the generation of false positive results moreover, microscopic detection of malarial parasite does not helps to differentiate between the malarial species which creates a gap in the epidemiology parasitic progression. The following paper aims to critically analyse three peered reviewed journal based on the detection of the malarial parasite. The critical analyse will be done on the based on research structure, ethical consideration, sampling and data presentation. This critical analysis of the three papers will help to judge the strength and limitations of the three papers while getting a detailed insight of the accurate detection of malarial parasite.

Critical analysis

Study conducted by Hemben, Ashley and Tothill (2018) mainly described the development of an affinity sensor in order to detect lactate dehydrogenase of Plasmodium falciparum parasite as one of the biomarker for the detection of malaria. The aim of the study was mainly based on framing of the commercial test kit for the detection of malarial parasite through targeting Plasmodium falciparum’s lactate dehydrogenase (pLD) as a biomarker. The framing of the aim of the assignment was based on the hypothesis of the biosensors. According to Ittarat et al. (2013) and Paul et al. (2016), biosensors have higher potential for the detection of numerous biomarkers at various level of sensitivity. Here pLD was selected as the target biomarker for the detection of malaria because pLD along with parasite aldolase are common marker for the entire Plasmodium species. Moreover, pLD gene is known to contain global genetic variations. Moreover, encouraged from their previous results of the immune-sensor assay by the use of Plasmodium falciparum lactate dehydrogenase detection (PfLDH), Hemben, Ashley and Tothill (2018) aimed devise their study over pLD as the biomarker for the detection of malarial parasite. Moreover, the selected biomarkers also showed greater sensitivity with gold nano-particles and thereby helping to extract positive test effectively. Thus, the overall hypothesis of the paper is succinct as it was based on the previous reported data.  Under the experimental design, the gold sensor was first functionalized with the help of anti-pLDH upon the cleaning of the electrode surface in order to eradicate the impurities. According to Kamra et al. (2016), cleaning of the electrode surface of the gold nano sensor helps in the standardization of the entire set-up. The sensor was then treated in order to block the unreacted groups over the surface of the matrix. This was done before the installation of the sandwich assay in order to detect pLDH in the buffer sample through the use of the dose concentration assay. The nano sensor was optimized in order to obtain the best possible results before the conduction of the concentration assay with pLD serum samples. The methodology followed by Hemben, Ashley and Tothill (2018), is somewhat similar to the structure followed by Ilkhani et al. (2015). Ilkhani et al. (2015) used electrochemical aptamer/antibody based sandwich immunosensor in order to detect epidermal growth factor receptor, a biomarker for cancer cell growth. Ethical consideration does not hold any prominent significance in this research as the pooled human serum (P2918), were purchased from Sigma Aldrich (Dorset, UK) and this product is 100% commercial. The data representation was done through the effective use of the tables, figures and graphs. The presentation of chronoamperometric measurements of the Plasmodium antibody optimization  through and its subsequent capture was done through graphical representation. This is followed the regression analysis of chronoamperometric which is again represented through exponential graph. The level of parasite multiplication in human blood was represented through bar diagram. The colourful representation of the bar diagram helped the readers to understand the difference. The authors also provided screen-shot images in order to highlight the bio-assay of the malarial parasite detection through biomarker. According to Weissgerber et al. (2015), graphical and tabular representation of data is an optimal method for data representation and it helps to readers to easily understand the flow of the research. A significant portion of the data-analysis was done through linear regression model. According to Cameron and Trivedi (2013), regression analysis fall under statistical modeling and it helps in estimation of relationships between the two variable. Regression analysis is suitable statistical test for this research because, it helps to ascertain how the typical value of dependent variable change with independent variable. The in summative conclusion, it can be said that the immunosensor of pLDH for the detection of malarial marker is more sensitive in comparison to the commercially available kit. The performance sensor was compared with the commercially available sensor Plasmodium immunochromatographic malarial kit. The developed sensor was successful in detecting pLDH at a parasitemia concentration of 0.002% with higher sensitivity and cost-effective price. However, the authors highlighted that future experiments are required to be conducted in order to detect the sensitivity of the kit under original bio sample.

The study conducted by Torres et al. (2018) aims to detect the confirmation of the malarial parasite based on the use of automated image recognition. This automated image recognition approach was mainly use in order to eradicate the false positive detection of the malaria due to manual error. The automated protocol mainly used convolutional neural networks in order to eradicate the false positive results. The algorithm based machine-learning and detection of malarial parasite was termed as Autoscope. The hypothesis is based on the study conducted by Poostchi et al. (2018) and Loddo, Di Ruberto and Kocher (2018). Both the studies highlighted that Autoscope algorithm is based on convolutional neural networks CNN) which help in accurate detection of images and subsequent clarification of the living cells. Here Autoscope algorithm was devised in such a way that it generates automated scanning of giemsa-stained blood films in order to detect the presence of the malarial parasite count within the blood irrespective of its geographical variance. The research hypothesis and the aim of the study is well-defined and specific. The title of the research provides a rough view of the aim of the research and abstract of the research provides the succinct summary of the entire research along with the hypothesis undertaken and overall research approach. The analysis of the results highlighted that performance of the diagnostic Autoscope is as par the routine microscopic technique only when the slides have the desired volume of blood. However, lower blood volume, reduce the diagnostic performance of Autoscope. The overall results highlighted the potential importance of artificial intelligence and at the same time reflected the challenges of replicating the adaptiveness of human thought process (Torres et al., 2018). Torres et al. (2018) conducted a cross-sectional observational trail at two primary healthcare facilities in Peru. According to Parahoo (2014) research conducted through collecting data from population observation at specific point of time helps in get on overview of the actual outcome of the process. Thus, the selection of the study approach was in accordance with the scope of the study. The sample size selected by the authors includes 700 participants. The selection criteria include age between 5 to 75 years, history of fever for the past 3 days or had temperature (≥ 37.5 °C). According to Parahoo (2014), large sample size for an observational trail helps to extract unbiased results. The age bracket for the selection of the population was also diverge and this helped to extract unbiased results and detailed overview of the population. The selection of the participation for the study was also done via following the ethical regulations. As mentioned in the research, informed consent was taken from all the selected group of participants. According to Faden, Beauchamp and Kass (2014), informed consent is mandatory before involving any individual in the research. The informed consent contains the purpose of the research, intended and unintended outcomes along with the participants’ role in the research. Only the participants who are willing to participate and have signed informed consent are required to be selected for the study. The representation of the data was done succinctly by the use of diagrams, flow charts and table. The typical thick microscopic images of the parasites was represented with a figure with detailed markings. In order compare the concentration of the blood sample, use in microscope and autoscopy, the WBC count was measure the data of the same is represented in the graphical format. Sampling and the division of the entire sample and the sensitivity of the fetched data was done with the help of flow chart. This detailed representation of the data helped the readers to understand the flow of the research (Weissgerber et al., 2015). Torres et al., 2018 used linear regression analysis in order to quantify the level of parasitaemia between the results obtained through Autoscope and microscope. The regression analysis helped to draw the relation between the accuracy of Autoscope and microscope and thus the use of the statistical test was in accordance with the scope of the results (Cameron and Trivedi 2013). However, the author’s could have performed statistical analysis in order to identify the statistical significance diagnostic performance between Autoscope and microscope.

Berzosa et al. (2018) conducted a study in Equatorial Guinea (EG) in order to perform a comparison with three different diagnostic methods that are used for the detection of malarial parasite and this includes microscopy, recombinant DNA technology (RDT) and polymerase chain reaction (PCR). The aim of the result is based on the sensitivity and cost-effectiveness of three different methods that can be used for the detection of malarial parasite in blood. Microscopy is used as the most common approach for the detection of malarial parasite in blood. It has sensitivity of 50 to 500 parasites per micro-liter. This inexpensive technique however requires supreme expertise and lacks accuracy under mixed infection condition or low parasitaemia (Hänscheid, 2003). Moreover, in EF, microscopy approach is unavailable in majority of the rural areas of EG. Moreover, microscopic detection of Plasmodium fails to highlight the species different as existing between Plasmodium ovale and Plasmodium faliciparum. Highlighting the different between the two is crucial for epidemiological mapping (WHO, 2018).  In EG, the common technique used for the detection of Malaria is RDT. However, RDT cam also yield false positive results due to gene deletion (pLDH) and gene duplication in some other parasites (Mathison and Pritt, 2017). This mis-diagnosis can lead to the overuse of the anti-parasitic drug leading to inappropriate treatment (Ugah et al., 2017). So, the authors aimed to study the accuracy of PCR which requires low sample volume for the detection of malaria and study is efficacy in comparison to the other available techniques. However, the scope of the research is not significant as it is not clear that how a poor country like EG, who cannot even afford microscopy can employ semi-nested multiplex PCR (SnM-PCR) for malaria detection, which also demands expertise to run the machine. The analysis of the results highlighted that the false positive findings of the microscopy is higher (19.4%), in comparison to RDT testing (13.3%). The false negative results of RDT can be detected with the help of SnM-PCR. This approach will help to devise the malarial parasite detection in an effective and in an accurate way (Berzosa et al., 2018). A total of 1724 sample samples were tested for microscopy, RDT and SnM-PCR. The sample greater than 1000 is suitable for statistical analysis and helps in the proper analysis for data for the extraction of the significant results. Sampling was undertaken with the use of multistage, stratified cluster strategy. It was assuming that the expected malaria prevalence is 50%. Rural villages and urban neighborhoods were selected randomly and the probability was proportional to the size to improve the overall accuracy in the sample design (Parahoo, 2014). However, the study did not highlight whether the participants, whose blood samples was taken for the study were informed about the scope of the research and the expected outcome. This is against the ethical rule of the research (Faden, Beauchamp and Kass, 2014). The authors used cross-sectional survey model in order to conduct the research. As discussed previously, cross-sectional research design helps to get on overview of the actual outcome of the process and this approach is significant in order to ascertain the best detection method for malaria over a mass population (Parahoo 2014). The presentation of the data was done in tabular format, which provided separate view of the P value and the percentage of the confidence interval. This helped the readers to get a detailed overview of the statistical analysis. Thus it can be said that effective use of the tables helped in detailed data presentation. The authors also used flow chart in order to highlighted the bifurcation of the sample as per the false positive and false negative results. Furthermore, graph was used in order to highlight the sensitivity of the microscopic and RDT analysis in detail. The analysis of the data was done through statistical analysis and this helped to extract the statistically significant results.

Conclusion

Thus from the above analysis of the three peered-reviewed journals in relation to the malarial parasite detection, it can be concluded that lactate dehydrogenase is an important marker gene present in the malarial parasite, Plasmodium faliciparum. This maker gene of the malarial parasite genome can be used as a biomarker for the commercial for the accurate and fast detection of malaria. This overall approach is feasible under the application of gold-nano particles, which will act as an immunosensor for the detection pLDH. The sensor is highly sensitive and cost-effective options for the detection of the parasite load within the tenure of 2 hours of infection. The overall study design followed the required ethical approach and study structure. In the domain of overall rationale of the research, it can be concluded that the research is effective in mobile network devise and thus can be used for the proper impact assessment and evaluation. The analysis of the second paper highlighted that automated microscopy can be used as an important tool for the detection of malarial parasite and it helps to eradicate the false positive and false negative results as obtained from the manual microscopic. It is a perfect amalgamation of artificial intelligence and technology. The analysis of the third article highlighted that in remote areas the use of microscopy for the detection of malarial parasite is not feasaible due to scarcity of professional expertise for handling microscope and lack of proper funding. In some cases RDT is used as a feasible options, but this technology might also generate false negative results due to gene duplication. However, the percentage of false negative results as detected through RDT is less than microscope and the false negative results can further be analyzed through qPCR in order to review the false positive negative results. The overall research structure of the three papers is succinct and provided a new direction for the early detection of malaria.

References

Berzosa, P., de Lucio, A., Romay-Barja, M., Herrador, Z., González, V., García, L., Fernández-Martínez, A., Santana-Morales, M., Ncogo, P., Valladares, B. and Riloha, M., (2018).’ Comparison of three diagnostic methods (microscopy, RDT, and PCR) for the detection of malaria parasites in representative samples from Equatorial Guinea’. Malaria journal, 17(1), p.333.

Cameron, A.C. and Trivedi, P.K., (2013). Regression analysis of count data (Vol. 53). Cambridge university press.

Faden, R.R., Beauchamp, T.L. and Kass, N.E., (2014). ‘Informed consent, comparative effectiveness, and learning health care’. N Engl J Med, 370(8), pp.766-768.

Gerstl, S., Dunkley, S., Mukhtar, A., De Smet, M., Baker, S. and Maikere, J., (2010). ‘Assessment of two malaria rapid diagnostic tests in children under five years of age, with follow-up of false-positive pLDH test results, in a hyperendemic falciparum malaria area’, Sierra Leone. Malaria journal, 9(1), p.28.

Hänscheid, T., (2003). ‘Current strategies to avoid misdiagnosis of malaria’. Clinical Microbiology and Infection, 9(6), pp.497-504.

Hemben, A., Ashley, J. and Tothill, I.E., (2018). ‘An immunosensor for parasite lactate dehydrogenase detection as a malaria biomarker–Comparison with commercial test kit’. Talanta, 187, pp.321-329.

Ilkhani, H., Sarparast, M., Noori, A., Bathaie, S.Z. and Mousavi, M.F., (2015). ‘Electrochemical aptamer/antibody based sandwich immunosensor for the detection of EGFR, a cancer biomarker, using gold nanoparticles as a signaling probe’. Biosensors and Bioelectronics, 74, pp.491-497.

Ittarat, W., Chomean, S., Sanchomphu, C., Wangmaung, N., Promptmas, C. and Ngrenngarmlert, W., (2013). ‘Biosensor as a molecular malaria differential diagnosis’. Clinica Chimica Acta, 419, pp.47-51.

Kamra, T., Chaudhary, S., Xu, C., Montelius, L., Schnadt, J. and Ye, L., (2016). ‘Covalent immobilization of molecularly imprinted polymer nanoparticles on a gold surface using carbodiimide coupling for chemical sensing’. Journal of colloid and interface science, 461, pp.1-8.

Loddo, A., Di Ruberto, C. and Kocher, M., (2018). ‘Recent Advances of Malaria Parasites Detection Systems Based on Mathematical Morphology’. Sensors, 18(2), p.513.

Mathison, B.A. and Pritt, B.S., (2017).’ Update on malaria diagnostics and test utilization’. Journal of clinical microbiology, pp.JCM-02562.

Parahoo, K., (2014). Nursing research: principles, process and issues. Macmillan International Higher Education.

Paul, K.B., Kumar, S., Tripathy, S., Vanjari, S.R.K., Singh, V. and Singh, S.G., (2016). ‘A highly sensitive self assembled monolayer modified copper doped zinc oxide nanofiber interface for detection of Plasmodium falciparum histidine-rich protein-2: targeted towards rapid, early diagnosis of malaria’. Biosensors and Bioelectronics, 80, pp.39-46.

Poostchi, M., Silamut, K., Maude, R., Jaeger, S. and Thoma, G., (2018). ‘Image analysis and machine learning for detecting malaria’. Translational Research.

Torres, K., Bachman, C.M., Delahunt, C.B., Baldeon, J.A., Alava, F., Vilela, D.G., Proux, S., Mehanian, C., McGuire, S.K., Thompson, C.M. and Ostbye, T.,( 2018).’ Automated microscopy for routine malaria diagnosis: a field comparison on Giemsa-stained blood films in Peru’. Malaria journal, 17(1), p.339.

Ugah, U.I., Alo, M.N., Owolabi, J.O., Okata-Nwali, O.D., Ekejindu, I.M., Ibeh, N. and Elom, M.O., (2017). ‘Evaluation of the utility value of three diagnostic methods in the detection of malaria parasites in endemic area’. Malaria journal, 16(1), p.189.

Weissgerber, T.L., Milic, N.M., Winham, S.J. and Garovic, V.D., (2015). ‘Beyond bar and line graphs: time for a new data presentation paradigm’. PLoS biology, 13(4), p.e1002128.

World Health Organisation [WHO]. (2018). Malaria Overview of Diagnostic Testing. [Accessed on: 1^st November 2018]. https://www.who.int/malaria/areas/diagnosis/overview/en/