Method For Diagnosis Of Malaria Research Articles

Embedded System‐Based Malaria Detection From Blood Smear Images Using Lightweight Deep Learning Model

ABSTRACTThe disease of malaria, transmitted by female Anopheles mosquitoes, is highly contagious, resulting in numerous deaths across various regions. Microscopic examination of blood cells remains one of the most accurate methods for malaria diagnosis, but it is time‐consuming and can produce inaccurate results occasionally. Due to machine learning and deep learning advances in medical diagnosis, improved diagnostic accuracy can now be achieved while costs can be reduced compared to conventional microscopy methods. This work utilizes an open‐source dataset with 26 161 blood smear images in RGB for malaria detection. Our preprocessing resized the original dimensions of the images into 64 × 64 due to the limitations in computational complexity in developing embedded systems‐based malaria detection. We present a novel embedded system approach using 119 154 trainable parameters in a lightweight 17‐layer SqueezeNet model for the automatic detection of malaria. Incredibly, the model is only 1.72 MB in size. An evaluation of the model's performance on the original NIH malaria dataset shows that it has exceptional accuracy, precision, recall, and F1 scores of 96.37%, 95.67%, 97.21%, and 96.44%, respectively. Based on a modified dataset, the results improved further to 99.71% across all metrics. Compared to current deep learning models, our model significantly outperforms them for malaria detection, making it ideal for embedded systems. This model has also been rigorously tested on the Jetson Nano B01 edge device, demonstrating a rapid single image prediction time of only 0.24 s. The fusion of deep learning with embedded systems makes this research a crucial step toward improving malaria diagnosis. In resource‐constrained settings, the model's lightweight architecture and accuracy enhancements hold great promise for addressing the critical challenge of malaria detection.

Embedded-deep-learning-based sample-to-answer device for on-site malaria diagnosis.

Improvements in digital microscopy are critical for the development of a malaria diagnosis method that is accurate at the cellular level and exhibits satisfactory clinical performance. Digital microscopy can be enhanced by improving deep learning algorithms and achieving consistent staining results. In this study, a novel miLab™ device incorporating the solid hydrogel staining method was proposed for consistent blood film preparation, eliminating the use of complex equipment and liquid reagent maintenance. The miLab™ ensures consistent, high-quality, and reproducible blood films across various hematocrits by leveraging deformable staining patches. Embedded-deep-learning-enabled miLab™ was utilized to detect and classify malarial parasites from autofocused images of stained blood cells using an internal optical system. The results of this method were consistent with manual microscopy images. This method not only minimizes human error but also facilitates remote assistance and review by experts through digital image transmission. This method can set a new paradigm for on-site malaria diagnosis. The miLab™ algorithm for malaria detection achieved a total accuracy of 98.86% for infected red blood cell (RBC) classification. Clinical validation performed in Malawi demonstrated an overall percent agreement of 92.21%. Based on these results, miLab™ can become a reliable and efficient tool for decentralized malaria diagnosis.

Segmentation and Feature Extraction for Malaria Detection in Blood Smears

Malaria remains a critical global health challenge, particularly in tropical and subtropical regions. Early and accurate diagnosis is essential for effective treatment and control. Traditional methods of malaria diagnosis, such as microscopic examination of blood smears, are time-consuming and prone to human error. This study aims to develop an automated system for malaria detection using machine learning techniques, specifically a decision tree classifier. The dataset, sourced from the National Institutes of Health (NIH), comprises 27,558 blood smear images equally divided into Normal and Malaria classes. The preprocessing steps included segmentation using the Canny edge detector and feature extraction using Hu Moments, followed by data normalization to ensure a mean of 0 and variance of 1. The decision tree classifier was trained and evaluated using 5-fold cross-validation, yielding an average accuracy of 77.32%, precision of 77.31%, recall of 77.37%, and F1-Score of 77.48%. These results demonstrate the model's robustness and effectiveness in differentiating between malaria-infected and uninfected images. The study confirms the viability of using Hu Moments for feature extraction and highlights the decision tree classifier's suitability for this task. The proposed method has significant implications for automated malaria diagnosis, potentially improving diagnostic accuracy and efficiency in clinical settings. Future research should validate these findings on diverse datasets, explore advanced classification techniques, and integrate real-time image acquisition to enhance practical applicability. The integration of such automated systems in healthcare can revolutionize malaria diagnosis, especially in resource-limited settings.

Quality assessment of malaria microscopic diagnosis at the Aristide Le Dantec University Hospital of Dakar, Senegal, in 2020

BackgroundFollowing WHO guidelines, microscopy is the gold standard for malaria diagnosis in endemic countries. The Parasitology-Mycology laboratory (LPM) is the National Reference Laboratory and is currently undergoing ISO 15189 accreditation. In this context, we assessed the performance of the laboratory by confirming the reliability and the accuracy of results obtained in accordance with the requirements of the ISO 15189 standards. This study aimed to verify the method of microscopic diagnosis of malaria at the LPM, in the Aristide Le Dantec hospital (HALD) in Dakar, Senegal.MethodsThis is a validation/verification study conducted from June to August 2020. Twenty (20) microscopic slides of thick/thin blood smear with known parasite densities (PD) selected from the Cheick Anta Diop University malaria slide bank in Dakar were used for this assessment. Six (6) were used to assess microscopists’ ability to determine PD and fourteen (14) slides were used for detection (positive vs negative) and identification of parasites. Four (4) LPM-HALD microscopists read and recorded their results on prepared sheets. Data analysis was done with Microsoft Excel 2010 software.ResultsA minimum threshold of 50% concordance was used for comparison. Of the twenty (20) slides read, 100% concordance was obtained on eight (8) detection (positive vs negative) slides. Four (4) out of the six (6) parasite density evaluation slides obtained a concordance of less than 50%. Thirteen (13) out of the fourteen (14) identification slides obtained a concordance greater than 50%. Only one (1) identification slide obtained zero agreement from the microscopists. For species identification a concordance greater than 80% was noted and the microscopists obtained scores between 0.20 and 0.4 on a scale of 0 to 1 for parasite density reading. The microscopists obtained 100% precision, sensitivity, specificity and both negative and positive predictive values.ConclusionThis work demonstrated that the microscopic method of malaria diagnosis used in the LPM/HALD is in accordance with the requirements of WHO and ISO 15189. Further training of microscopists may be needed to maintain competency.

Development of a Mobile-Based Convolution Neural Network Framework for Rapid Malaria Detection from Blood Smears

Malaria, a vector-borne parasitic disease caused by Plasmodium parasites, continues to pose a substantial threat to global public health. Despite considerable progress in its control and treatment, malaria remains a significant cause of morbidity and mortality, particularly in resource-limited regions. The ability to diagnose malaria infections accurately and swiftly is paramount for effective disease management. Traditional methods of malaria diagnosis, relying on microscopic examination of blood smears, have proven to be accurate but are often hindered by limitations in infrastructure and expertise, particularly in remote and underserved areas. This research focuses on the development of a mobile-based Convolution Neural Network (CNN) framework for rapid malaria parasite detection from blood smear images using Android mobile phones. Blood smear images provide a reasonable and clinically relevant representation of malaria disease which makes it a preferred choice for malaria detection from mobile phones. By leveraging smartphone technology and state-of-the-art deep learning algorithms, this pioneering endeavour aims to normalize malaria diagnostics, providing healthcare workers with a powerful tool for early detection and treatment, irrespective of their location or access to advanced laboratory facilities. The dataset for infected and non-infected parasites was acquired online from Kaggle. The dataset was preprocessed and partitioned into training, testing, and validation sets. The developed model was converted to tflite and was used for the mobile application. A validation accuracy of 94.88% was obtained from the validation set while an accuracy of 95.81% was obtained from the testing set.

A simple and purification-free nucleic acid extraction method for rapid diagnosis of malaria

A simple and purification-free nucleic acid extraction method for rapid diagnosis of malaria

Microscopic Diagnosis of Malaria Parasitaemia in Pregnant Women

Malaria is the major cause of morbidity and mortality in the developing countries in which Nigeria is one of them. Malaria during pregnancy is a major cause of maternal death globally. Microscopic analysis of Giemsa stained blood film is still the most suitable method of malaria diagnosis in most developing countries. The aim of this study was to determine malaria parasites number per 100 thick film fields in a Giemsa stained blood film. This is a cross sectional study involving 90 pregnant women within the ages of 19-45 years. Thick and thin smears were prepared on clean glass slide using venous blood. The thick blood smears were stained with 3% Giemsa working solution diluted in pH 7.2 phosphate buffer and examined for the presence of malaria parasites at 100 X oil immersion objective searching 100 fields in each thick smear. Blood smears were examined in the parasitology laboratory of Enugu State University of Science and Technology Teaching Hospital by an experienced microscopist. The level of parasitaemia was indicated in each positive case. The number of parasites in thick blood film was graded using the plus sign: + (1 to 10 parasites per 100 thick film fields); ++ (11 to 100 parasites per 100 thick film fields); +++ (1 to 10 parasites per one thick film field); ++++ (>10 parasites per one thick film field). Bar charts and pie charts were used in this study. Out of 90 pregnant women, 65 (72.2%) of the women had malaria parasite (MP). Out of the 65 pregnant women with MP 19 (21.1%) had one plus (1 to 10 parasites per 100 thick film fields) while 46 (51.1%) had two pluses (11 to 100 parasites per 100 thick film fields). Then 31 (67.4%) of those with two pluses were at age range of 26- 35 years, also 12 (26.1%) of them with two pluses were women that are less than 25 years. However, 25 (27.8%) had no malaria parasite (MP). The findings showed that higher number of the pregnant women had malaria parasites and higher number of individuals positive with malaria parasites had 11 to 100 parasites per 100 thick film fields. Also higher number of pregnant women that had 11 to 100 parasites per 100 thick film fields was at age range of 26- 35 years.

Point-of-Care Diagnosis of Malaria Using a Simple, Purification-Free DNA Extraction Method Coupled with Loop-Mediated Isothermal Amplification-Lateral Flow.

We propose a protocol suitable for point-of-care diagnosis of malaria utilizing a simple and purification-free DNA extraction method with the combination of loop-mediated isothermal amplification assay and lateral flow (LAMP-LF). The multiplex LAMP-LF platform developed here can simultaneously detect Plasmodium knowlesi, P. vivax, P. falciparum, and Plasmodium genus (for P. malariae and P. ovale). Through the capillary effect, the results can be observed by the red band signal on the test and control lines within 5 min. The developed multiplex LAMP-LF was tested with 86 clinical blood samples on-site at Hospital Kapit, Sarawak, Malaysia. By using microscopy as the reference method, the multiplex LAMP-LF showed 100% sensitivity (95% confidence interval (CI): 91.4 to 100.00%) and 97.8% specificity (95% CI: 88.2% to 99.9%). The high sensitivity and specificity of multiplex LAMP-LF make it ideal for use as a point-of-care diagnostic tool. The simple and purification-free DNA extraction protocol can be employed as an alternative DNA extraction method for malaria diagnosis in resource-limited settings. By combining the simple DNA extraction protocol and multiplex LAMP-LF approach, we aim to develop a simple-to-handle and easy-to-read molecular diagnostic tool for malaria in both laboratory and on-site settings.

Robust Image Processing Framework for Intelligent Multi-Stage Malaria Parasite Recognition of Thick and Thin Smear Images.

Malaria is a pressing medical issue in tropical and subtropical regions. Currently, the manual microscopic examination remains the gold standard malaria diagnosis method. Nevertheless, this procedure required highly skilled lab technicians to prepare and examine the slides. Therefore, a framework encompassing image processing and machine learning is proposed due to inconsistencies in manual inspection, counting, and staging. Here, a standardized segmentation framework utilizing thresholding and clustering is developed to segment parasites' stages of P. falciparum and P. vivax species. Moreover, a multi-stage classifier is designed for recognizing parasite species and staging in both species. Experimental results indicate the effectiveness of segmenting thick smear images based on Phansalkar thresholding garnered an accuracy of 99.86%. The employment of variance and new transferring process for the clustered members, enhanced k-means (EKM) clustering has successfully segmented all malaria stages with accuracy and an F1-score of 99.20% and 0.9033, respectively. In addition, the accuracies of parasite detection, species recognition, and staging obtained through a random forest (RF) accounted for 86.89%, 98.82%, and 90.78%, respectively, simultaneously. The proposed framework enables versatile malaria parasite detection and staging with an interactive result, paving the path for future improvements by utilizing the proposed framework on all others malaria species.

Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification

Malaria is a lethal disease responsible for thousands of deaths worldwide every year. Manual methods of malaria diagnosis are time-consuming that require a great deal of human expertise and efforts. Computer-based automated diagnosis of diseases is progressively becoming popular. Although deep learning models show high performance in the medical field, it demands a large volume of data for training which is hard to acquire for medical problems. Similarly, labeling of medical images can be done with the help of medical experts only. Several recent studies have utilized deep learning models to develop efficient malaria diagnostic system, which showed promising results. However, the most common problem with these models is that they need a large amount of data for training. This paper presents a computer-aided malaria diagnosis system that combines a semi-supervised generative adversarial network and transfer learning. The proposed model is trained in a semi-supervised manner and requires less training data than conventional deep learning models. Performance of the proposed model is evaluated on a publicly available dataset of blood smear images (with malaria-infected and normal class) and achieved a classification accuracy of 96.6%.

Malaria Blood Cell Image Classification using Transfer Learning with Fine-Tune ResNet50 and Data Augmentation

Based on the WHO Report related to malaria, it is estimated that there will be 241 million malaria cases and 627,000 deaths from this disease globally in 2020 with the number of deaths increasing yearly. Preventing malaria disease conditions is through early detection. A more quick and precise malaria diagnosis method was required to simplify and reduce the detection process. Medical image classification could be carried out rapidly and precisely using machine learning or deep learning techniques. This research aims to diagnose malaria by classifying images of malaria blood cells using Deep Learning with a Transfer Learning approach. By utilizing various fine-tuning procedures and implementing data augmentation proposed method develops the method from previous studies. Two types of models Frozen ResNet50 and Fine-Tune ResNet50 are being tested. The dataset utilized will be augmented to improve model performance. This study makes use of the "NIH Malaria Cell Images Dataset" a dataset that contains a total of 27,660 image data. It is divided into two classes: parasitized and uninfected. The results are improved from previous research using the fine-tuned VGG16 model with an accuracy of 96% compared to this study using the fine-tuned ResNet50 model which achieved an accuracy score of 98%.

End-point detection of loop-mediated isothermal amplification (LAMP) on malaria by direct observation with colorimetric dyes

In order to ascertain the results of the LAMP technique, different end-point detection methods can be employed. However, these methods require sophisticated equipment. To simplify current end-point detection methods for the diagnosis of malaria, we propose the incorporation of colorimetric dyes: malachite green (MG), phenol red (PR), and xylenol orange (XO) in the LAMP assay. To evaluate the optimum concentration of dyes, 5 different concentrations (50 μM, 75 μM, 100 μM, 125 μM, and 150 μM) were used with buffer pH 8.5 and pH 8.8, respectively. The results showed that 125 μM of MG at pH 8.8 produced the most obvious colour change. A total of 71 clinical blood samples of Plasmodium knowlesi, Plasmodium malariae, Plasmodium vivax, Plasmodium falciparum, and healthy donors were tested using MG-LAMP. It showed 100% sensitivity and specificity. The simplicity and affordability of this method make it ideal to be used as an end-point detection method for malaria diagnosis in resource limited settings.

Improving the Molecular Diagnosis of Malaria: Droplet Digital PCR-Based Method Using Saliva as a DNA Source.

Malaria is an acute febrile disease caused by a protozoan of the genus Plasmodium. Light microscopy (LM) is the gold standard for the diagnosis of malaria. Despite this method being rapid and inexpensive, it has a low limit of detection, which hampers the identification of low parasitemia infections. By using multicopy targets and highly sensitive molecular techniques, it is possible to change this scenario. In this study, we evaluated the performance of droplet digital PCR (ddPCR) to detect Plasmodium DNA obtained from saliva samples (whole saliva and buccal swab) of 157 individuals exposed to malaria transmission from the Brazilian Amazon region. We used the highly sensitive ddPCR method with non-ribosomal multicopy targets for Plasmodium vivax (Pvr47) and Plasmodium falciparum (Pfr364). There was good concordance between the quantitative real-time PCR (qPCR) results from the saliva and blood, except for mixed-species infections. The sensitivity of qPCR was 93% for blood, 77% for saliva, and 47% for swabs. Parasite DNA was not detected in saliva samples in low-density infections compared with the detection in blood samples. ddPCR showed increased sensitivity for detecting Plasmodium in the blood and swabs (99% in blood, 73% in saliva, and 59% in swabs). Notably, ddPCR detected more mixed infections in the blood (15%), saliva (9%), and swabs (18%) than qPCR. Our data showed that the differences between ddPCR and qPCR were the result of a higher number of P. falciparum infections detected by ddPCR. Overall, there was a moderate correlation between parasite densities estimated by the different methods in the blood. Our findings highlight the possibility of using non-invasive sample collection methods for malaria diagnosis by targeting multicopy sequences combined with highly sensitive molecular methods.

Rationalization of the Laboratory Diagnosis for Good Management of Malaria: Lessons from Transitional Methods

Introduction Malaria is an endemic disease in sub-Saharan Africa. In clinical practice, the main concern is the overdiagnosis of malaria leading to inappropriate drug prescription without laboratory confirmation. Objective This study aimed to evaluate clinical examination reliability compared with translational laboratory methods of malaria diagnosis. Methods The study was conducted in Goundi Hospital among hospitalized patients over a seven-month period. Patients were interviewed, and malaria tests done included the Giemsa-stained thick and thin blood smears. Diagnostic accuracy was analysed by calculating sensitivity, specificity, and predictive values. Results Among 1,874 participants, 674 (35.96%) patients had positive Giemsa-stained thick blood films. The rate of positivity is higher for patients under 5 years of age. The parasite densities were between 160 and 84.000 parasites/μL. The threshold pyrogen of the parasitic density was around 10.000 parasites/μL for patients between 0 and 11 months of age, between 1 and 4 years of age, and between 5 and 14 years of age. This threshold was lower for patients over 15 years of age. The study reported some issues in the findings: 60.88% (607/997) cases of fever without positivity of the blood thick smear and 40.13% (284/674) cases of positivity of the thick drop without fever. The positive predictive value of malaria was between 80 and 85% for patients under 5 years of age. This value is lower for patients between 5 and 14 years of age and patients over 15 years of age. Conclusion A presumptive diagnosis of malaria should be confirmed by the laboratory in all suspected cases in all possible scenarios. Every parasitemia should be followed by the calculation of parasitic density. However, for the children under 5 years of age in areas of high transmission, the presumptive diagnosis of malaria in certain circumstances could be considered.

Computational Methods for Automated Analysis of Malaria Parasite Using Blood Smear Images: Recent Advances.

Malaria comes under one of the dangerous diseases in many countries. It is the primary reason for most of the causalities across the world. It is presently rated as a significant cause of the high mortality rate worldwide compared with other diseases that can be reduced significantly by its earlier detection. Therefore, to facilitate the early detection/diagnosis of malaria to reduce the mortality rate, an automated computational method is required with a high accuracy rate. This study is a solid starting point for researchers who want to look into automated blood smear analysis to detect malaria. In this paper, a comprehensive review of different computer-assisted techniques has been outlined as follows: (i) acquisition of image dataset, (ii) preprocessing, (iii) segmentation of RBC, and (iv) feature extraction and selection, and (v) classification for the detection of malaria parasites using blood smear images. This study will be helpful for: (i) researchers can inspect and improve the existing computational methods for early diagnosis of malaria with a high accuracy rate that may further reduce the interobserver and intra-observer variations; (ii) microbiologists to take the second opinion from the automated computational methods for effective diagnosis of malaria; and (iii) finally, several issues remain addressed, and future work has also been discussed in this work.

EMPIRICAL STUDY ON MALARIA DETECTION USING MACHINE LEARNING

Malaria is the third-deadliest infection on the world. Malaria is cause 14 million more cases and 69,000 more deaths in 2020 than it did in 2019. Between 2019 and 2020, India was the only high-burden country to show progress by sustaining a reduction in malaria burden. In 2020, 29 of the 85 malaria-endemic countries accounted for 96 percent of malaria cases. India was responsible for 1.7 percent of malaria cases and 1.2 percent of deaths worldwide. The gold standard for malaria diagnosis is currently microscopic examination of blood films. It is, however, subjective, error-prone, and time-consuming. To address such issues, computational microscopic imaging methods have recently received a lot of attention in the field of digital pathology. As a result, over the last decade, researchers have focused on digital image analysis and computer vision methods for malaria diagnosis. Various contrast enhancement and segmentation methods for microscopic imaging have been discussed in this study for accurate malaria diagnosis.

Performance of rapid diagnostic tests, microscopy, loop-mediated isothermal amplification (LAMP) and PCR for malaria diagnosis in Ethiopia: a systematic review and meta-analysis

BackgroundRapid accurate diagnosis followed by effective treatment is very important for malaria control. Light microscopy remains the “golden standard” method for malaria diagnosis. Diagnostic test method must have sufficient level of accuracy for detecting malaria parasites. Therefore, this study aimed to investigate the diagnostic accuracy of rapid diagnostic tests (RDTs), microscopy, loop-mediated isothermal amplification (LAMP) and/or polymerase chain reaction (PCR) for the malaria diagnosis in Ethiopia.MethodsData bases such as PubMed, PubMed central, Science direct databases, Google scholar, and Scopus were searched from September to October, 2020 for studies assessing the diagnostic accuracy of RDTs, microscopy, LAMP and PCR methods for malaria diagnosis.ResultsA total of 29 studies published between 2001 and 2020 were analysed using review manager, Midas (Stata) and Meta-disc. The sensitivity and specificity of studies comparing RDT with microscopy varies from 79%–100% to 80%–100%, respectively. The sensitivity of LAMP (731 tests) was 100% and its specificity was varies from 85 to 99% when compared with microscopy and PCR. Considerable heterogeneity was observed between studies included in this meta-analysis. Meta-regression showed that blinding status and target antigens were the major sources of heterogeneity (P < 0.05). RDT had an excellent diagnostic accuracy (Area under the ROC Curve = 0.99) when compared with microscopy. Its specificity was quite good (93%–100%) except for one outlier (28%), but lower “sensitivity” was observed when PCR is a reference test. This indicates RDT had a good diagnostic accuracy (AUC = 0.83). Microscopy showed a very good diagnostic accuracy when compared with PCR.ConclusionsThe present study showed that microscopy and RDTs had high efficiency for diagnosing febrile malaria patients. The diagnostic accuracy of RDT was excellent when compared with microscopy. This indicates RDTs have acceptable sensitivities and specificities to be used in resource poor settings as an alternative for microscopy. In this study, LAMP showed an excellent sensitivities and specificities. Furthermore, the need of minimum equipment and relatively short time for obtaining results can made LAMP one of the best alternatives especially for accurate diagnosis of asymptomatic malaria.

Magneto-optical diagnosis of symptomatic malaria in Papua New Guinea

Improved methods for malaria diagnosis are urgently needed. Here, we evaluate a novel method named rotating-crystal magneto-optical detection (RMOD) in 956 suspected malaria patients in Papua New Guinea. RMOD tests can be conducted within minutes and at low cost. We systematically evaluate the capability of RMOD to detect infections by directly comparing it with expert light microscopy, rapid diagnostic tests and polymerase chain reaction on capillary blood samples. We show that compared to light microscopy, RMOD exhibits 82% sensitivity and 84% specificity to detect any malaria infection and 87% sensitivity and 88% specificity to detect Plasmodium vivax. This indicates that RMOD could be useful in P. vivax dominated elimination settings. Parasite density correlates well with the quantitative magneto-optical signal. Importantly, residual hemozoin present in malaria-negative patients is also detectable by RMOD, indicating its ability to detect previous infections. This could be exploited to reveal transmission hotspots in low-transmission settings.

Detection of Plasmodium falciparum malaria in 1 h using a simplified enzyme-linked immunosorbent assay

Malaria is a parasitic disease caused by protists of the genus Plasmodium, which are transmitted to humans through the bite of infected female Anopheles mosquitoes. Analytical methodologies and efficient drugs exist for the early detection and treatment of malaria, and yet this disease continues infecting millions of people and claiming several hundred thousand lives each year. One of the reasons behind this failure to control the disease is that the standard method for malaria diagnosis, microscopy, is time-consuming and requires trained personnel. Alternatively, rapid diagnostic tests, which have become common for point-of-care testing thanks to their simplicity of use, tend to be insufficiently sensitive and reliable, and PCR, which is sensitive, is too complex and expensive for massive population screening.In this work, we report a sensitive simplified ELISA for the quantitation of Plasmodium falciparum lactate dehydrogenase (Pf-LDH), which is capable of detecting malaria in 45–60 min. Assay development was founded in the selection of high-performance antibodies, implementation of a poly-horseradish peroxidase (polyHRP) signal amplifier, and optimization of whole-blood sample pre-treatment. The simplified ELISA achieved limits of detection (LOD) and quantification (LOQ) of 0.11 ng mL−1 and 0.37 ng mL−1, respectively, in lysed whole blood, and an LOD comparable to that of PCR in Plasmodium in vitro cultures (0.67 and 1.33 parasites μL−1 for ELISA and PCR, respectively). Accordingly, the developed immunoassay represents a simple and effective diagnostic tool for P. falciparum malaria, with a time-to-result of <60 min and sensitivity similar to the reference PCR, but easier to implement in low-resource settings.

Portable Impedance Analyzer as a Rapid Screening Tool for Malaria: An Experimental Study With Culture and Blood Infected Samples by Early Forms of Plasmodium Falciparum.

Infection by the parasite of malaria is a serious healthcare problem for populations residing primarily in tropical and subtropical countries. Early detection of the disease is essential to reduce both the mortality rate and spreading of the disease in the infected areas. Current methods for malaria diagnosis still rely on microscopic analysis of blood smears, which is a time-consuming and expensive process, in addition of requiring trained examiners to perform the analysis. In this paper, we introduce a novel fast screening tool for malaria based on a portable blood impedance analyzer. The simultaneous multi-tone injection of current and voltage detection of the device allow reducing the screening time (order of seconds) while enhancing the differences in impedance signal registered among frequencies to increase parasitemia level discrimination. We went further to demonstrate the possibility of directly applying the device on blood samples collected from volunteers to distinguish between infected and non-infected samples. Minimum parasitemia level discriminated was 0.0078% (or, equivalently 390 parasites/μl), which closely approaches the international standard set to 200 parasites/μl. Finally, we also show the frequency dependency of ring-synchronized and unsynchronized blood samples, which can constitute the basis for development of a point-of-care and laboratory-free method to distinguish different stages of malaria infection in economic stagnated communities.