Malaria-infected Red Blood Cells Research Articles

On-Chip Impedance Spectroscopy of Malaria-Infected Red Blood Cells.

Malaria is a disease that affects millions of people worldwide, particularly in developing countries. The development of accurate and efficient methods for the detection of malaria-infected cells is crucial for effective disease management and control. This paper presents the electrical impedance spectroscopy (EIS) of normal and malaria-infected red blood cells. An EIS microfluidic device, comprising a microchannel and a pair of coplanar electrodes, was fabricated for single-cell measurements in a continuous manner. Based on the EIS results, the aim of this work is to discriminate Plasmodium falciparum-infected red blood cells from the normal ones. Different from typical impedance spectroscopy, our measurement was performed for the cells in a low-conductivity medium in a frequency range between 50 kHz and 800 kHz. Numerical simulation was utilized to study the suitability parameters of the microchannel and electrodes for the EIS experiment over the measurement frequencies. The measurement results have shown that by using the low-conductivity medium, we could focus on the change in the conductance caused by the presence of a cell in the sensing electrode gap. The results indicated a distinct frequency spectrum of the conductance between the normal and infected red blood cells, which can be further used for the detection of the disease.

Numerical analysis and early detection ofPlasmodium falciparum using a high performanceplasmonic biosensor with an external sensingapproach.

The authors propose a surface plasmon resonance (SPR) sensor based on photonic crystal fibers (PCFs) using three hexagonal ring lattices. The sensor can detect biomolecules with maximum wavelength and amplitude sensitivities of 23,000nm/RIU and 1310.93R I U -1, respectively, in the RI range of 1.32 to 1.42. It can detect infected red blood cells with Plasmodium falciparum for RIs of 1.402, 1.373, 1.395, and 1.383 in various malaria-infected red blood cell stages, including ring phase, trophozoite phase, and schizont phase. Furthermore, the sensor will be able to detect biomolecules such as viruses, proteins, DNA/RNA strands, acetone, ethanol, hexane, isopropanol, hexanol, formic acid, allyl cyanide, and others in its range. With these impressive results and identification capacity, the proposed sensor would benefit the biomaterial field and be appropriate for the early identification of malaria disease.

Quantifying the deformability of malaria-infected red blood cells using deep learning trained on synthetic cells

Quantifying the deformability of malaria-infected red blood cells using deep learning trained on synthetic cells

Classification of Red Blood Cell Abnormality in Thin Blood Smear Images using Convolutional Neural Networks

One of the most common morphological red blood cell abnormalities encountered during routine thin blood smear microscopy for the detection of malaria parasite is the rouleaux formation, which is the stacking together of red blood cells to form a chain. Rouleaux formation signifies an underlying infection and as such microscopists are mandated to report its presence. A lot of work has been done in automating malaria diagnosis using deep learning, but no model has been developed which is capable of detecting rouleaux formation in malaria infected red blood cells. Thus, this study collected 231 peripheral blood smear (PBS) images of normal red blood cell morphology and 231 PBS images with rouleaux morphology. The images were pre-processed and segmented into equal instances of 3044 coloured images of size 750×750 pixels. Two convolutional neural network (CNN) models were developed and trained to classify the images into normal red blood cell morphology or rouleaux morphology. The CNN models were trained on two different image sizes: 300×300 and 500×500. The first CNN model achieved validation accuracy/loss values of 87.91%/0.8177 and 56.58%/1.4090 when trained on images of sizes 300×300 and 500×500 respectively. In the second CNN model, the CNN layers of the first model were replaced with depthwise separable CNN layers, it was also trained on images of sizes 300×300 and 500×500 achieving validation accuracy/loss values of 90.95%/0.2804 and 87.75%/0.5904 respectively. This study demonstrates the capability of CNN models in detecting red blood cell morphology abnormality in thin smear images at an optimal image size of 300×300.

Study on the dielectrophoretic characteristics of malaria-infected red blood cells.

Malaria is a tropical disease caused by parasites in the genus Plasmodium, which still presents 241 million cases and nearly 627,000 deaths recently. In this work, we used the dielectrophoresis (DEP) to characterize red blood cells in a microchannel. The purpose of this work is to determine the difference between the normal and the malaria-infected cells based on the DEP characteristics. The samples were infected cells and normal red blood cells, which were either prepared in culture or obtained from volunteers. Diamond-shaped and curved micropillars were used to create different degrees of DEP in the gap between them. The DEP crossover frequencies were observed with the diamond-shaped micropillars. The cell velocity under negative dielectrophoresis (nDEP) at a low frequency was examined with the curved micropillars. The measured lower crossover frequencies were remarkably different between the malaria-infected cells and the normal cells, whereas the higher crossover frequencies were similar among the samples. The velocity under nDEP was lower for the infected cells than the normal cells. The results imply that the malaria infection significantly decreases the capacitance but increases the conductance of the cell membrane, whereas a change in cytoplasmic conductivity may occur in a later stage of infection.

3D reconstruction of unstained weakly scattering cells from a single defocused hologram.

We investigate the problem of 3D complex field reconstruction corresponding to unstained red blood cells (RBCs) with a single defocused off-axis digital hologram. The main challenge in this problem is the localization of cells to the correct axial range. While investigating the volume recovery problem for a continuous phase object like the RBC, we observe an interesting feature of the backpropagated field that it does not show a clear focusing effect. Therefore, sparsity enforcement within the iterative optimization framework using a single hologram data frame cannot effectively restrict the reconstruction to the true object volume. For phase objects, it is known that the amplitude contrast of the backpropagated object field at the focus plane is minimum. We use this information available in the recovered object field in the hologram plane to device depth-dependent weights that are proportional to the inverse of amplitude contrast. This weight function is employed in the iterative steps of the optimization algorithm to assist the object volume localization. The overall reconstruction process is performed using the mean gradient descent (MGD) framework. Experimental illustrations of 3D volume reconstruction of the healthy as well as malaria-infected RBCs are presented. A test sample of polystyrene microsphere bead is also used to validate the axial localization capability of the proposed iterative technique. The proposed methodology is simple to implement experimentally and provides an approximate tomographic solution, which is axially restricted and consistent with the object field data.

Differential Effect of Extracellular Vesicles Derived from Plasmodium falciparum-Infected Red Blood Cells on Monocyte Polarization.

Malaria is a life-threatening tropical arthropod-borne disease caused by Plasmodium spp. Monocytes are the primary immune cells to eliminate malaria-infected red blood cells. Thus, the monocyte's functions are one of the crucial factors in controlling parasite growth. It is reasoned that the activation or modulation of monocyte function by parasite products might dictate the rate of disease progression. Extracellular vesicles (EVs), microvesicles, and exosomes, released from infected red blood cells, mediate intercellular communication and control the recipient cell function. This study aimed to investigate the physical characteristics of EVs derived from culture-adapted P. falciparum isolates (Pf-EVs) from different clinical malaria outcomes and their impact on monocyte polarization. The results showed that all P. falciparum strains released similar amounts of EVs with some variation in size characteristics. The effect of Pf-EV stimulation on M1/M2 monocyte polarization revealed a more pronounced effect on CD14+CD16+ intermediate monocytes than the CD14+CD16- classical monocytes with a marked induction of Pf-EVs from a severe malaria strain. However, no difference in the levels of microRNAs (miR), miR-451a, miR-486, and miR-92a among Pf-EVs derived from virulent and nonvirulent strains was found, suggesting that miR in Pf-EVs might not be a significant factor in driving M2-like monocyte polarization. Future studies on other biomolecules in Pf-EVs derived from the P. falciparum strain with high virulence that induce M2-like polarization are therefore recommended.

Machine learning approach to surface plasmon resonance sensor based on MXene coated PCF for malaria disease detection in RBCs

A photonic crystal fiber (PCF)-based biosensor is proposed with surface plasmon resonance (SPR) sensing approach. This sensor is used for malaria infected red blood cells (RBCs) detection as well as haemoglobin (Hb) concentration detection. A machine learning (ML) algorithm is also implemented and analyzed for the proposed sensor. For SPR mechanism, a Ti3C2Tx thin film is coated over the gold coated PCF. The malaria stages in RBCs are detected by comparing the resonance wavelength of healthy and infected RBCs samples. Finite element method-based simulation is used for analyzing the sensing performance. The measured wavelength sensitivity for ring stage is 12,142 nm/RIU, for trophozoite stage is 9736 nm/RIU and for schizont stage is 8241 nm/RIU. In addition, the Hb concentration in RBCs is detected with maximum wavelength sensitivity of 53 nm/g/dl. Further, the resolution of 10−5 RIU is obtained for the proposed sensor. The low mean squared error of 0.01526 and less than 2% error has been obtained in sensitivity investigation with the ML technique. Due to the enhanced sensing performance and ML approach, the proposed sensor can be the best alternative to the existing malaria sensor.

Impedance-based Rapid Diagnostic Tool for Single Malaria Parasite Detection.

This paper presents a custom, low-cost electronic system specifically designed for rapid and quantitative detection of the malaria parasite in a blood sample. The system exploits the paramagnetic properties of malaria-infected red blood cells (iRBCs) for their magnetophoretic capture on the surface of a silicon chip. A lattice of nickel magnetic micro-concentrators embedded in a silicon substrate concentrates the iRBCs above coplanar gold microelectrodes separated by 3 μm for their detection through an impedance measurement. The sensor is designed for a differential operation to remove the large contribution given by the blood sample. The electronic readout automatically balances the sensor before each experiment and reaches a resolution of 15 ppm in the impedance measurement at 1 MHz allowing a limit of detection of 40 parasite/μl with a capture time of 10 minutes. For better reliability of the results, four sensors are acquired during the same experiment. We demonstrate that the realized platform can also detect a single infected cell in real experimental conditions, measuring human blood infected by Plasmodium falciparum malaria specie.

Nmnat3 deficiency in hemolytic anemia exacerbates malaria infection

Malaria is an infectious disease caused by Plasmodium parasites and has high mortality rates, especially among children in African and Southeast Asian countries. Patients with hemolytic anemia are suggested to adapt protective measures against malarial infection. Nicotinamide adenine dinucleotide (NAD+) is a crucial cofactor associated with numerous biological processes that maintain homeostasis in all living organisms. In a previous study, we had demonstrated that the deficiency of nicotinamide mononucleotide adenylyltransferase 3 (Nmnat3), an enzyme catalyzing NAD+ synthesis, causes hemolytic anemia accompanied by a drastic decline in the NAD+ levels in the erythrocytes. It is well known that hemolytic anemia is linked to a reduced risk of malarial infections. In the present study, we investigated whether hemolytic anemia caused by Nmnat3 deficiency is beneficial against malarial infections. We found that Nmnat3 deficiency exacerbated malarial infection and subsequently caused death. Moreover, we demonstrated that the NAD+ levels in malaria-infected Nmnat3 red blood cells significantly increased and the glycolytic flow was largely enhanced to support the rapid growth of malarial parasites. Our results revealed that hemolytic anemia induced by the deletion of Nmnat3 was harmful rather than protective against malaria.

Reducing data dimension boosts neural network-based stage-specific malaria detection

Although malaria has been known for more than 4 thousand years1, it still imposes a global burden with approx. 240 million annual cases2. Improvement in diagnostic techniques is a prerequisite for its global elimination. Despite its main limitations, being time-consuming and subjective, light microscopy on Giemsa-stained blood smears is still the gold-standard diagnostic method used worldwide. Autonomous computer assisted recognition of malaria infected red blood cells (RBCs) using neural networks (NNs) has the potential to overcome these deficiencies, if a fast, high-accuracy detection can be achieved using low computational power and limited sets of microscopy images for training the NN. Here, we report on a novel NN-based scheme that is capable of the high-speed classification of RBCs into four categories—healthy ones and three classes of infected ones according to the parasite age—with an accuracy as high as 98%. Importantly, we observe that a smart reduction of data dimension, using characteristic one-dimensional cross-sections of the RBC images, not only speeds up the classification but also significantly improves its performance with respect to the usual two-dimensional NN schemes. Via comparative studies on RBC images recorded by two additional techniques, fluorescence and atomic force microscopy, we demonstrate that our method is universally applicable for different types of microscopy images. This robustness against imaging platform-specific features is crucial for diagnostic applications. Our approach for the reduction of data dimension could be straightforwardly generalised for the classification of different parasites, cells and other types of objects.

A microfluidic device for capturing malaria-infected red blood cells by magnetophoretic force using an array of V- and W-shaped nickel microstructures

A microfluidic device for capturing malaria-infected red blood cells by magnetophoretic force using an array of V- and W-shaped nickel microstructures

Image Recognition of Malaria-infected Red Blood Cells among Other Normal and Cancer-Mutated Cells Using CNN

Malaria is a contagious infectious disease that is still threatening human life. Malaria morbidity when viewed by province shows that Eastern Indonesia is the area with the highest Annual Parasite Incidence (API), namely Papua, West Papua, NTT, and Maluku. This is a concern for the continued efforts to control and eliminate malaria in these high malaria-endemic areas. There are many strategies to help and prevent, include the possibility of innovation in the diagnostic process. Therefore, to answer how to provide innovation in technology to accelerate the elimination of malaria, this study aims to identify the image of red blood cells which infected with malaria among other normal and leukemia cancer-mutated cells (non-malaria) by making improvements through the proposed new model used. This model is meant to do deep learning using Convolutional Neural Network (CNN). The results obtained in this study show that the success of using the proposed model is influenced by the pre-processing stage, the dropout regularization function, learning rate, and momentum value used. The accuracy value obtained is 0.9660, 0.9693 precision, 0.9626 recall, and an F1 score of 0.9659.

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

BackgroundThe 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.MethodsBlood 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.ResultsThirty-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.ConclusionsThe 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.

Extracellular vesicles from malaria-infected red blood cells: not all are secreted equal.

Extracellular vesicles (EVs) mediate the transfer of molecules between cells and play diverse roles in host-pathogen interactions. Malaria is an important disease caused by intracellular Plasmodium species that invade red blood cells and these red blood cells release EVs. The EVs from infected cells have diverse functions in the disease and an obstacle in understanding how they exert their functions is that multiple EV types exist. In this issue of EMBO reports, Abou Karam and colleagues use sophisticated biophysical techniques to isolate and characterize two EV subpopulations produced by red blood cells infected with Plasmodium falciparum (Abou Karam et al, 2022). The authors show that these EV subpopulations have distinct sizes, protein content, membrane packing, and fusion capabilities, suggesting that EV subpopulations from infected cells could target different cell types and subcellular locations. This work underscores the concept that understanding EV heterogeneity will go hand in hand with understanding EV functions.

Discriminant Analysis PCA-LDA Assisted Surface-Enhanced Raman Spectroscopy for Direct Identification of Malaria-Infected Red Blood Cells.

Various methods for detecting malaria have been developed in recent years, each with its own set of advantages. These methods include microscopic, antigen-based, and molecular-based analysis of blood samples. This study aimed to develop a new, alternative procedure for clinical use by using a large data set of surface-enhanced Raman spectra to distinguish normal and infected red blood cells. PCA-LDA algorithms were used to produce models for separating P. falciparum (3D7)-infected red blood cells and normal red blood cells based on their Raman spectra. Both average normalized spectra and spectral imaging were considered. However, these initial spectra could hardly differentiate normal cells from the infected cells. Then, discrimination analysis was applied to assist in the classification and visualization of the different spectral data sets. The results showed a clear separation in the PCA-LDA coordinate. A blind test was also carried out to evaluate the efficiency of the PCA-LDA separation model and achieved a prediction accuracy of up to 80%. Considering that the PCA-LDA separation accuracy will improve when a larger set of training data is incorporated into the existing database, the proposed method could be highly effective for the identification of malaria-infected red blood cells.

Validation of Effective Extracellular Vesicles Isolation Methods Adapted to Field Studies in Malaria Endemic Regions.

Malaria affects the poorer regions of the world and is of tremendous health and economic burden for developing countries. Extracellular vesicles (EVs) are small vesicles released by almost any cells in the human body, including malaria infected red blood cells. Recent evidence shows that EVs might contribute to the pathogenesis of malaria. In addition, EVs hold considerable value in biomarker discovery. However, there are still significant gaps in our understanding of EV biology. So far most of our knowledge about EVs in malaria comes from in vitro work. More field studies are required to gain insight into their contribution to the disease and pathogenesis under physiological conditions. However, to perform research on EVs in low-income regions might be challenging due to the lack of appropriate equipment to isolate EVs. Therefore, there is a need to develop and validate EV extraction protocols applicable to poorly equipped laboratories. We established and validated two protocols for EV isolation from cell culture supernatants, rodent and human plasma. We compared polyethylene glycol (PEG) and salting out (SA) with sodium acetate for precipitation of EVs. We then characterized the EVs by Transmission Electron Microscopy (TEM), Western Blot, Size-exclusion chromatography (SEC), bead-based flow cytometry and protein quantification. Both protocols resulted in efficient purification of EVs without the need of expensive material or ultracentrifugation. Furthermore, the procedure is easily scalable to work with large and small sample volumes. Here, we propose that both of our approaches can be used in resource limited countries, therefore further helping to close the gap in knowledge of EVs during malaria.

Image Classification in JPEG Compression Domain for Malaria Infection Detection.

Digital images are usually stored in compressed format. However, image classification typically takes decompressed images as inputs rather than compressed images. Therefore, performing image classification directly in the compression domain will eliminate the need for decompression, thus increasing efficiency and decreasing costs. However, there has been very sparse work on image classification in the compression domain. In this paper, we studied the feasibility of classifying images in their JPEG compression domain. We analyzed the underlying mechanisms of JPEG as an example and conducted classification on data from different stages during the compression. The images we used were malaria-infected red blood cells and normal cells. The training data include multiple combinations of DCT coefficients, DC values in both decimal and binary forms, the “scan” segment in both binary and decimal form, and the variable length of the entire bitstream. The result shows that LSTM can successfully classify the image in its compressed form, with accuracies around 80%. If using only coded DC values, we can achieve accuracies higher than 90%. This indicates that images from different classes can still be well separated in their JPEG compressed format. Our simulations demonstrate that the proposed compression domain-processing method can reduce the input data, and eliminate the image decompression step, thereby achieving significant savings on memory and computation time.

A discrete dielectrophoresis device for the separation of malaria-infected cells.

Malaria is a serious disease caused by Plasmodium parasites that infect red blood cells (RBCs). This paper presents the continuous separation of malaria-infected RBCs (iRBCs) from normal blood cells. The proposed method employed the discrete dielectrophoresis (DEP) in a microfluidic device with interdigitated electrodes. Our aim is to treat a sample having high concentration of cells to realize high throughput and to prevent the clogging of the microchannel with the use of the discrete DEP. The discrete DEP force for deflecting cells in the device was controlled by adjusting the magnitude, frequency, and duty cycle of the applied voltage. The effectiveness of the proposed method was demonstrated by separating the malaria-infected cells in samples having a cell concentration of 106 cells/µl. From experimental results, we determined the enrichment that is needed to enhance the detection in the case of low parasitemia. The enrichment of the infected cells at the device output was 3000 times as high as that of the input containing 1 infected cell to 106 normal cells. Therefore, the proposed method is highly effective and can significantly facilitate the detection of the infected cells for the identification of Malaria patients.

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

BackgroundDue to increased travel from endemic countries, malaria occurs more frequently in non-endemic regions. It is a challenge for diagnostic laboratories in non-endemic countries to provide reliable results, as experience of staff is often limited to only a few cases per year. This study evaluated the diagnostic accuracy of the fully automated Sysmex XN-31 malaria analyzer in a routine diagnostic setting in a non-endemic region was evaluated.MethodsSamples from 112 patients suspected for malaria were examined by the Sysmex XN-31 analyzer to determine the absolute count of malaria-infected red blood cells count (MI-RBC/µL). Microscopic examination of both Quantitative Buffy Coat capillary tubes and thick and thin blood films were used as reference methods. Limits of blank (LoB), detection (LoD) and quantification (LoQ) were investigated using an in vitro Plasmodium falciparum culture. Nine hundred twenty samples of patients with RBC abnormalities were included to determine which RBC abnormalities trigger indeterminate or false positive results.ResultsNo false positive nor false negative results were obtained for the examined patient samples suspected for malaria. For 3% of samples an indeterminate result by the XN-31 was obtained. The Passing-Bablok regression line for diagnostic accuracy of the parasitaemia was y = 39.75 + 0.7892 × showing a positive bias of about 21% when comparing the MI-RBC results to microscopy. The LoB, LoD and LoQ were calculated to be 4.7, 5.9, and 19.0 infected RBC/μL, respectively. From the 920 abnormal RBC samples collected, 4.6% resulted in a false positive MI-RBC result and almost half of the samples produced indeterminate results. These results were related to increases in nucleated red blood cells, reticulocytes and other abnormal RBC morphologies such as sickle cells.ConclusionsBased on the results, the XN-31 is a fast and reliable screening method in the detection and quantification of Plasmodium species in patients However, if an abnormal red blood cell morphology is present, the results of the XN-31 should be interpreted with caution as false positive results can be caused by interfering abnormal erythrocytes.