Malaria, a mosquito-borne infectious disease affecting humans and other animals, is widespread in tropical and subtropical regions. Microscopy is the most common method for diagnosing the malaria parasite from stained blood smear samples. However, this technique is time consuming and must be performed by a well-trained professional, yet it remains prone to errors. Distinguishing the multiple growth stages of parasites remains an especially challenging task.Babesia dataset.In this article, we develop a novel deep learning approach for the recognition of malaria parasites of various stages in blood smear images using a deep transfer graph convolutional network (DTGCN). To our knowledge, this is the first application of graph convolutional network (GCN) on multi-stage malaria parasite recognition in such images. The proposed DTGCN model is based on unsupervised learning by transferring knowledge learnt from source images that contain the discriminative morphology characteristics of multi-stage malaria parasites. This transferred information guarantees the effectiveness of the target parasite recognition. This approach first learns the identical representations from the source to establish topological correlations between source class groups and the unlabelled target samples. At this stage, the GCN is implemented to extract graph feature representations for multi-stage malaria parasite recognition. The proposed method showed higher accuracy and effectiveness in publicly available microscopic images of multi-stage malaria parasites compared to a wide range of state-of-the-art approaches. Furthermore, this method is also evaluated on a large-scale dataset of unseen malaria parasites and the https://github.com/senli2018/DTGCN_2021 under a MIT license.Code and dataset are available at  The main source of malaria is the parasites from the Plasmodium group, which are transmitted to people though the bites of infected mosquitoes. People with malaria experience fever, chill, and a flu-like illness . AccordiMalaria is usually diagnosed by the microscopic examination of blood films, and hundreds of millions of blood films are examined every year for malaria diagnosis , 4. AlthThe aforementioned methods are particularly useful in detecting a single stage of the malaria parasite, normally the ring form. However, the life cycle of the malaria parasites is complicated. The entire cycle involves multiple morphological changes in human blood. As illustrated in To overcome the challenges in both variations and data imbalance for multi-stage malaria parasite recognition, we use a transfer learning strategy that takes advantage of the prior knowledge from the labelled source domain (existing scenario) to train the recognition model and apply it to an unlabelled target domain (even unseen scenario) for detection. On the other hand, the problem of data imbalance can be addressed by implementing graph convolutional network (GCN) on the established topological correlations between source class groups and target features to bridge the different class distribution gaps. Specifically, GCNs have been proposed whereby node features, aggregated from adjacent neighbours and different nodes, can share the same transfer function. Thus, the aggregated nodes can exploit more discriminative information according to the topological graph structure of the node features than directly utilizing CNN on a single image.Babesia, to show the robustness of the DTGCN model. Babesia is a malaria-like parasite that infects RBCs and leads to the disease babesiosis [Babesia are sometimes confused with those of malaria parasites. The excellent results of the present study show that the proposed DTGCN is not only limited to the recognition of malaria parasites but can also effectively solve other microscopic image recognition problems. In general, the proposed DTGCN method can overcome the data variation and the imbalance problem in deep learning–based malaria recognition. Importantly, this DTGCN method can transfer a sufficiently trained recognition model to a completely unlabelled target dataset with unknown differences .In this context, DTGCN is proposed for multi-stage malaria parasite recognition and classification, which consists of a CNN-based feature extractor, a source transfer graph building component, and an unsupervised GCN. A thorough review of the literature reveals that none of the previously reported studies have attempted to explore the advantages of deep learning for multi-stage malaria parasite recognition. To demonstrate the effectiveness of the proposed DTGCN, we conducted experiments on 2 public malaria parasite image datasets, which are available from the Broad Bioimage Benchmark Collection  and the besiosis . With a Plasmodium vivax–infected patients in Manaus, Brazil, and Thailand under 1,000× magnification, annotated by 3 different experts globally. This dataset contains images from 2 classes of uninfected cells (RBCs and leukocytes) and 4 classes of parasitized cells  with Giemsa stain. Although the initial purpose of this dataset was detection of parasitized cells rather than recognition of the various stages of malaria parasites, both bounding box coordinates and corresponding stage labels were provided. A total of 79,672 multi-stage parasitized and uninfected cell images were cropped from raw images according to the given box coordinates. As shown in The multi-stage malaria infected cell images were captured from blood smear samples stained with Giemsa reagent. This image set consists in total of 1,364 images at 1,000× magnification and is publicly available at the Broad Bioimage Benchmark Collection (BBBC) website . All of P. falciparum–infected and 50 healthy patients under 1,000× magnification. Moreover, another 1,100 under-microscope Babesia and 1,100 RBC images were collected to validate the generalizability of our proposed DTGCN modelIn addition, the second malaria parasite recognition task in this article is classification of unseen malaria parasites in a large-scale dataset that has different parameter distributions (such as brightness and imaging equipment settings) from the source training dataset. The BBBC dataset was adapted as the source domain in this multi-stage malaria parasite recognition task, and it was transferred to 2 binary classes for recognition: parasitized class  and uninfected class (RBCs and leukocytes). This dataset, consisting of 13,780 testing images including both malaria parasites and RBCs [K-means clustering algorithm on the final target graph feature representations and achieve the recognition of multi-stage malaria parasites in the target domain . First, CNN is used to extract morphological features from images in each class. Second, a source transfer graph building algorithm is proposed to construct the class correlations between each source class group and target samples by a proposed target-to-center source transfer graph building algorithm according to the source class labels. Transferring the representatively discriminative information from the source into the target domain solves the challenges of dealing with parameter variations in an unfamiliar scenario. Then, the CNN features and source transfer graph are together fed into graph convolutional layers, which are optimized by mentclass2pt{minimAn obvious crucial preliminary step for image recognition is to extract representative morphological features. For the multi-stage malaria parasite recognition task, we use CNN as the backbone network owing to its superior performance in parasite microscopic image recognition , with exfcnn. Then, given the ith source image jth target image d is the dimension of feature vectors), and fcnn. These features are the vectors of pre-softmax units, which attach a UGCN instead of prediction layers.Mathematically, we assume the source data as ResNet50  architecm is the margin setting among the distances. The contrastive loss is to maintain the representative information across different categories by learning a distance metric.Importantly, to guarantee that the CNN features can obtain the identity information for source images, source feature vectors can be constrained by a contrastive loss function, denoted as CON loss,By this constraint, the backbone network To further conduct the transfer learning and solve the problem of data imbalance, this article proposes a UGCN, in which the losses of contrastive classification and MMD are switched to create the constraint, rather than the aforementioned CNN features.Generally, GCN provides an effective solution to simulate the correlations between objects in different distributions . Thus, tConsidering the imbalanced data in the source domain, the network transfers the class groups that contain the most representative information by class features. It will be recalled that there exist inherent correlations between the target images and the class groups in the source domain because they belong to the same classes. Inspired by this point, a source transfer graph building mechanism is designed by introducing the source class groups into the target domain.By the CNN feature learning, the source and target image features  by K-means is conducted on the learnt GCN features , and (ii) using a common K-nearest neighbours (KNN) algorithm to formulate the graph (Ours+KNN). On the other hand, this study also evaluates the influence of the deeper CNN in feature learning, which is explored by changing the depth of ResNet: ResNet18, ResNet34, and ResNet50 were tested individually. Thus, we evaluated 2 more models (Ours+ResNet18 and Ours+ResNet34).To demonstrate the superior effectiveness of our DTGCN models, we chose 3 widely used deep learning networks, Visual Geometry Group Network (VggNet) , the GooTo validate the effectiveness of the DTGCN, this study first implemented extensive experiments on multi-stage malaria parasite recognition. We report the training and evaluation of several different models  plot is deployed to show the clustering performance in Fig. To better reveal the recognition result on the 600 testing images, this study uses the confusion matrix to visualize the accuracy of the multi-stage malaria parasite recognition in Fig. To further validate the DTGCN's performance on a larger malaria parasite dataset, the trained models of first 7 comparative methods from the previous subsection were directly tested on a larger-scale dataset that consists of 13,780 testing images with an equal amount of infected and uninfected images. The task was binary classification. To keep the classes consistent, the gametocyte, ring, schizont, and trophozoite stages were grouped as the parasitized cells, and leukocytes and RBCs were considered uninfected cells to conduct the experiment. All models trained on the multi-stage malaria parasite images were directly transferred and tested on the large-scale malaria recognition dataset without further training. As reported in Table This binary classification task based on large-scale data demonstrates that the DTGCN method not only has superior performance in recognizing multi-stage malaria parasites but also works well in an unseen large-scale malaria dataset. This extraordinary capacity saves the expensive labelling work in biomedical image analysis and provides a novel method for unlabelled biomedical image classification. This part shows that DTGCN outperforms other alternative methods on the unseen large dataset of malaria parasites and it further demonstrates the generalizability of DTGCN in malaria parasite recognition.Babesia infects RBCs and leads to the disease babesiosis. Because the clinical and laboratory presentations of babesiosis and malaria are similar (ring-like structure), the ring forms of Babesia are sometimes confused with those of the malaria parasite. Thus, a Babesia parasite recognition test was conducted in this study to distinguish between Babesia parasites and RBCs to evaluate the discriminant ability of the proposed model for other parasites. With the similar ring shapes within infected RBCs, the recognition task was also challenging for transfer learning. Surprisingly, the proposed DTGCN achieved 99.0% accuracy, 99.01% precision, 99.0% recall, and 98.99% F1-score when training the model on 600 Babesia and 600 RBC images. The reason that this model can distinguish Babesia parasites and RBC might be the fact that DTGCN can successfully overcome the problem of insufficient and imbalanced training data. This experiment shows that the proposed DTGCN is effective, flexible, and scalable when presented with a challenging microscopic object recognition task.As mentioned in the Introduction, The proposed DTGCN for multi-stage malaria recognition is composed of 3 major modules: CNN feature learning, target-to-center source transfer graph building, and the UGCN. In this section, we discuss the capacities of each main component of this model and explore the reasons why this collaborative model works so well.To analyse the contribution of CNN feature learning and evaluate the influence of CNN depth, we extracted CNN features by different ResNets with 18, 34, and 50 layers. The relative recognition results are summarized in Tables As the major step of GCN, the graph-building algorithm is extremely important to establish a natural and topological structure for GCN. This article involves a novel graph-building mechanism, named "target-to-center source transfer graph building algorithm," which connects the target image features to the source class groups by measuring the distance between each sample's cluster and source class centers.To show the contribution of the proposed graph-building algorithm for multi-stage malaria parasite recognition, we deploy a well-known KNN graph-building algorithm, which is usually used in GCN, to replace our module. This modified method, named "Ours+KNN" in Tables K-means algorithm on the target CNN features, which were extracted from the sufficiently trained CNN feature learning module. This modification is defined as the Baseline of the proposed DTGCN, and the detailed results on BBBC datasets are reported in Table The crucial module of DTGCN is the proposed unsupervised GCN, which aims to explore the correlations between the unlabelled target samples given the inputs of CNN features and graph. To comprehensively evaluate the contribution of UGCN, we removed this module from DTGCN and directly utilized the In general transfer learning tasks, the source data play an important role in the domain adaptation, and the efficacy of the transferred model is most reliant on the scale of the source data. To evaluate the influence of training data size in our DTGCN, different data sizes from the source domain were randomly selected from each class with equal percentages  to conduct transfer learning on the target multi-stage malaria parasite dataset. In To our knowledge, this study is the first to investigate multi-stage malaria parasite recognition with the use of a deep transfer graph convolutional network (DTGCN) approach. In this article, the DTGCN consists of 3 major components: CNN for feature learning, source transfer graph building, and unsupervised GCN.K-means algorithm. Thus, the proposed framework can achieve multi-stage malaria parasite recognition results by the K-means algorithm on the final graph feature representations from the target domain. What needs to be emphasized is that the proposed DTGCN not only works out the supervised multi-stage malaria parasite recognition task but also does not require any microscopic image labels in the target domain, which means that this model can be transferred to solve an unseen scenario to conduct recognition tasks. The proposed DTGCN can also be applied to other biomedical image recognition tasks that have complicated procedures for data collection and annotation.We designed the source transfer graph building and UGCN for multi-stage parasite recognition, aiming to solve the problems of data variations and imbalanced data. With this knowledge, the transfer learning between labelled source data and unlabelled target data can work in many scenarios. The proposed model first learns the CNN features by a ResNet architecture with constraints of MMD and contrastive losses. It then utilizes the target-to-center–based source transfer graph-building algorithm to connect the source class groups with target samples, to leverage the imbalanced data. After CNN feature learning and graph building, DTGCN uses UGCN to further alleviate the feature distribution gap between source and target domains with a Babesia parasites show that a DTGCN model can also be used for detecting other parasites under microscope.Through experiments on publicly available multi-stage and binary microscopic malaria parasite images, this article has successfully demonstrated that a DTGCN model can extract information to boost the accuracy of deep learning. Results on malaria-like The proposed method for multi-stage malaria parasite microscopic image analysis can be immensely helpful in the development of a low-cost, automated malaria diagnostic solution. This can significantly improve efficiency and reduce the need for dedicated pathologists in areas with limited resources.P. vivax  infected human blood smear data are available in the BBBC repository and can be accessed with accession No. BBBC041. The large scale malaria dataset consisting of 13,780 both malaria parasites and RBCs testing images are available in the National Library of Medicine (NLM) respository and can be accessed with accession No. PUB9932. Snapshots of our code and other data further supporting this work are openly available in the GigaScience repository, GigaDB [The , GigaDB .Project name: Deep Transfer Graph Convolutional Networkhttps://github.com/senli2018/DTGCN_2021Project home page: Operating system(s): Windows 10 with Nvidia Geforce 2080Ti GPUProgramming language: Python 3.6.0Other requirements: Pytorch 1.0.0 or higher, Torchvision 0.4.1 or higher, Scipy 1.1.0 or higher, and Numpy 1.17.4 or higherLicense: MITRRID:SCR_020976Biotools: deep_transfer_graph_convolutional_networkhttps://data.broadinstitute.org/bbbc/BBBC041BBBC041: https://lhncbc.nlm.nih.gov/publication/pub9932PUB9932: Supplementary Figure S1. The life cycle of malaria parasites. (a) The intra-erythrocytic cycle of malaria parasites. (b) Examples of multi-stage malaria parasites. Malaria parasites undergo several stages in their complex life cycle. The malaria parasites undergo repeated rounds of asexual multiplication . During the intra-erythrocytic cycle, parasites go through the ring, trophozoite, and schizont stages. In each cycle, a small proportion of parasites begin to develop into the sexual form of the parasite, which is known as a gametocyte.SupplementaryFigure S2. t-SNE performance on large-scale malaria parasite binary classification. The t-SNE plots of VggNet (a), GoogleNet (b), ResNet (c), and Baseline (d) are compared to various DTGCN approaches, including replacing the graph-building graph by KNN algorithm (Ours+KNN) (e) and replacing the CNN backbones with ResNet18 (Ours+Res18) (f), ResNet34 (Our+Res34) (g), and original ResNet50 (Ours+Res50) (h). The t-SNE plots provide a method to evaluate and refine the clustering of each class of sample images. Data points are coloured according to their categories. The performance on large-scale malaria parasite classification is similar to the multi-stage parasite classification, showing that Ours+Res50 is the best discriminated.Supplementary Figure S3. Confusion matrices for the multi-stage malaria parasite classification. The confusion matrices of VggNet (a), GoogLeNet (b), ResNet (c), and Baseline (d) are compared to various DTGCN approaches, including replacing the graph building graph by KNN algorithm (Ours+KNN) (e) and replacing the CNN backbones with ResNet18 (Ours+Res18) (f), ResNet34 (Our+Res34) (g), and ResNet50 (Ours+Res50) (h). The confusion matrix reveals the variation in misclassification between the predicted and true labels. The diagonal cells correspond to samples that are correctly classified. The off-diagonal cells correspond to incorrectly classified samples. It is easy to see that Ours+Res50 (DTGCN) presents the best classification results on the confusion matrix.Supplementary Figure S4. The impact of source data size for recognition accuracy. The DTGCN model is trained by increasing the numbers of source examples as reported in percent of original size . Note that the percentage of used images preserves equivalent ratios of RBC to non-RBC images. For every reported training data size, 5 repeat trainings are performed. The accuracies are calculated and reported as box plots (n = 5). The results of this study support the fact that a large number of training images (≥40%) are necessary for good performance (accuracy >90%). This visualization reveals that dataset size plays an important role in achieving high accuracy in classification.Supplementary Figure S5. Visualization of several convolutional feature maps learned by the top 3 layers of DTGCN. To show the evidence of feature detection in more challenging use case examples, we randomly visualize their feature maps from the top 3 convolutional layers. A feature map generated from convolutional layers can reveal the detailed feature-learning procedure in the deep learning method. The feature map visualization demonstrates that our DTGCN can extract clear morphological features from these selected challenging images, which can prove that our DTGCN has excellent capability in feature representation for challenging multi-stage malaria parasite recognition.Supplementary Table S1. The details of the multi-stage malaria parasite images used in this study. The training images of multi-stage parasites had imbalanced class distribution, where most images captured under the microscope were red blood cells. This table illustrates the numbers of training and test data.Supplementary Table S2. Experimental settings of the compared methods on multi-stage malaria parasite recognition. Details regarding maximum epoch number, batch size for source/target data, learning rate, and optimizer are summarized. The learning rates of each method are initialized with the value in Table S2 and will multiply by 0.1 in each of 50 epochs along with the training. In the modified methods of DTGCN, the maximum epoch number is 50, learning rate is 1e−5, and optimizer is Adam for our network.Supplementary Table S3. Experimental settings of the compared methods on large-scale malaria parasite recognition. Details regarding maximum epoch number, batch size for source/target data, learning rate, and optimizer are summarized. The learning rates of each method are initialized with the value in Table S3 and will multiply by 0.1 in each of 50 epochs along with the training. In the modified methods of DTGCN, the maximum epoch number is 50, learning rate is 1e−5, and optimizer is Adam for our network.Supplementary Algorithm S1. Deep Transfer Graph Convolutional Network Algorithm. This algorithm uses a DTGCN to alleviate the domain gap between the source and target domains to solve the class-imblance problem. This algorithm employs M iterations. Finally, the target images K-means algorithm on the learned GCN features.K-nearest neighbours; MMD: maximum mean discrepancy; RBC: red blood cell; t-SNE: t-distributed stochastic neighbour embedding.CNN: convolutional neural network; GCN: graph convolutional network; GPU: graphics processing unit; KNN: The project is financially supported by the Natural Science Foundation of Shenzhen City (Project No. JCYJ20180306172131515) and the Fundamental Research Funds for the Central Universities (Project No. HIT.NSRIF.2020064).The authors declare that they have no competing interests.Y.Z. contributed to the supervision of the study. Y.Z. and S.L. contributed to the design of the study. S.L., Z.D., and X.M. performed data collection and analysis. All authors contributed to literature review, writing, and critical revision of the manuscript.giab040_GIGA-D-21-00019_Original_SubmissionClick here for additional data file.giab040_GIGA-D-21-00019_Revision_1Click here for additional data file.giab040_GIGA-D-21-00019_Revision_2Click here for additional data file.giab040_GIGA-D-21-00019_Revision_3Click here for additional data file.giab040_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giab040_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giab040_Response_to_Reviewer_Comments_Revision_2Click here for additional data file.giab040_Reviewer_1_Report_Original_SubmissionBarath Narayanan -- 1/31/2021 ReviewedClick here for additional data file.giab040_Reviewer_1_Report_Revision_1Barath Narayanan -- 3/7/2021 ReviewedClick here for additional data file.giab040_Reviewer_2_Report_Original_SubmissionMichael Rung-Tsong Lyu -- 2/12/2021 ReviewedClick here for additional data file.giab040_Reviewer_3_Report_Original_SubmissionChris Armit -- 2/15/2021 ReviewedClick here for additional data file.giab040_Reviewer_3_Report_Revision_1Chris Armit -- 3/4/2021 ReviewedClick here for additional data file.giab040_Reviewer_3_Report_Revision_2Chris Armit -- 3/23/2021 ReviewedClick here for additional data file.giab040_Supplemental_FileClick here for additional data file.


Plasmodium falciparum induced blood stage malaria (IBSM) infection, and compared results to individuals with P. vivax infection, in whom coma is rare. Seven healthy, malaria-naïve participants underwent imaging at baseline, and at early symptom onset a median 9 days following inoculation . Participants with P. falciparum infection demonstrated marked lability in radiotracer uptake across all regions of the brain, exceeding expected normal variation (within subject coefficient of variation (wCV): 14.4%) compared to the relatively stable uptake in participants with P. vivax infection (wCV: 3.5%). No consistent imaging changes suggestive of microvascular dysfunction were observed in either group. Neuroimaging in early IBSM studies is safe and technically feasible, with preliminary results suggesting that differences in brain tropism between P. falciparum and P. vivax may occur very early in infection.Cerebral malaria is the most serious manifestation of severe falciparum malaria. Sequestration of infected red blood cells and microvascular dysfunction are key contributing processes. Whether these processes occur in early stage disease prior to clinical manifestations is unknown. To help localize and understand these processes during the early stages of infection, we performed 18-F fluorodeoxyglucose positron emission tomography/magnetic resonance imaging in volunteers with  In Plasmodium falciparum infection, organ-specific sequestration of infected red blood cells and microvascular dysfunction are believed to be key mechanisms underlying severe disease, including the critical complication of cerebral malaria3. P. falciparum virulence and tissue tropism of infected red blood cells is believed to be mediated by expression of specific adhesion molecules on the surface of erythrocytes that mediate sequestration in tissues with the cognate ligand. Adhesion molecule/ligand combinations associated with severe organ-specific disease have been described post-mortem4, and more recently in life, correlated with clinical cerebral malaria on imaging5. In contrast, P. vivax adheres significantly less frequently to microvascular endothelial cells6, and coma is rare7, although generalized endothelial activation and microvascular dysfunction related to systemic inflammation are common9. For both species, headache is frequently an early symptom of clinical infection, but its pathophysiology is poorly understood. Understanding the upstream processes contributing to the cerebral complications of malaria may be useful to help design interventions that may improve clinical outcomes.Severe malaria causes significant global morbidity and mortality11, to significantly increased cerebral volume that may predict a fatal outcome14. Non-human primate studies of cerebral malaria have demonstrated decreased glucose uptake in the anterior circulation of the brain in symptomatic and neurologically intact macaques prior to any parenchymal damage, and not strictly collocated with sequestered parasites at necropsy15. No studies have been performed in earlier stage malaria infection. We have previously used 18-F fluorodeoxyglucose positron emission tomography and magnetic resonance imaging (FDG PET/MRI) to demonstrate differences between P. falciparum and P. vivax in splenic tropism, volume and glucose metabolism in early experimental malaria16, and now present the neuroimaging findings from this study cohort.Neuroimaging studies in uncomplicated and cerebral malaria have led to the identification of a spectrum of changes ranging from localized restricted diffusion in adults hospitalized with uncomplicated malariaP. falciparum to those infected with P. vivax.We performed PET/MRI prior to infection and at symptom onset in participants with experimentally induced blood stage malaria (IBSM). In this study, we sought to investigate if changes in overall glucose metabolism in the brain or in the cerebral circulation were present in early stage infection. We compared findings in participants infected with P. vivax, and four with P. falciparum. All participants developed a measurable parasitemia with expected growth kinetics  in the P. falciparum group and 6042  parasites/mL in the P. vivax group. Although the day of treatment varied between studies, both groups underwent imaging a median 9 days following inoculation. No adverse events related to imaging were recorded and in safety reporting no incidental findings required follow up.A total of 8 healthy, malaria-naïve participants were recruited. All participants underwent baseline imaging within a 7-day interval before inoculation. One participant was enrolled in the imaging study and underwent baseline imaging but was excluded from the IBSM study prior to inoculation due to hypokalemia. This participant did not undergo post-inoculation imaging and was excluded from the analysis. Thus, seven participants underwent post-inoculation imaging. The study population is described in Table 16. All participants reported some symptoms consistent with early malaria infection, and the majority reported headache. All participants in the P. vivax group reported mild headache during the course of the study. Participant 1 reported an intermittent episode, starting the day prior to post-inoculation imaging, and resolving two days following imaging. Participant 2 and 3 reported headache starting on the day of post-inoculation imaging, and resolving four days following, and on the same day of imaging respectively. No other neurological symptoms or abnormal neurological examinations were recorded.Regular physical examinations, and clinician-elicited and self-reported symptoms were recorded for all participants. Safety laboratory results have been reported previouslyP. falciparum group, most participants reported mild headache prior to treatment. Participant 5 reported a headache on the day prior to imaging. Participants 6 and 7 reported intermittent episodes, commencing the day of, and the day prior to post-inoculation imaging, and resolving two days and one day following imaging respectively. Participant 4 reported an episode of mild dizziness that resolved 6 days prior to post-inoculation imaging. No other neurological symptoms or abnormal neurological examinations were recorded.In the All participants underwent matched baseline and post-inoculation neuroimaging protocols. With the exception of Participant 4, all participants had a radiotracer uptake time of approximately 120 min before commencement of neurological PET  for radiotracer uptake across all brain segments was 3.5%, within the defined 10% range of normal inter-scan variability (Table p = 0.354), + 2.6% in the anterior white matter , + 5.1% in the posterior grey matter , + 4.6% in the posterior white matter , + 2.6% in the deep grey matter , and + 3.1% in the corpus callosum . In the splenium of the corpus callosum the mean SUV change was + 1.2%  and the mean ADC (representing the free movement of water) change was + 2.8% . There were no changes suggestive of microvascular dysfunction on qualitative review.In the ty Table . Change ty Table . The meaP. falciparum group, the wCV for radiotracer uptake across brain segments was 14.4% (Table p = 0.645), + 9.0% in the anterior white matter , + 10.4% in the posterior grey matter , + 10.9% in the posterior white matter , + 7.0% in the deep grey matter , and + 13.2% in the corpus callosum . In the splenium of the corpus callosum the mean SUV change was + 13.5%  and the mean ADC change was + 5.5% ; however, Participant 5 experienced an increase in ADC of 27.8%, representing a relative increase in the free movement of water. There were no changes suggestive of microvascular dysfunction on qualitative review and no apparent relationship between parasitemia and radiological findings. The mean percentage change from baseline for reported quantitative imaging metrics was not statistically different between P. falciparum and P. vivax groups  and one a decrease (Participant 7). Increased uptake may be attributed to host cell activation or increased blood flow, while decreased uptake may be related to reduced blood flow, hyperglycemia, or an otherwise generalized reduction in metabolism. Decreased regional glucose uptake has been reported in primate models of cerebral malaria15, suggesting parasite may affect one or some of these mechanisms. Interestingly, in our study uptake decreased in the Participant 7, who had the highest circulating P. falciparum parasitemia, and increased in the participants with lowest (Participants 4 and 5). Although there were no consistent changes in splenial diffusion in either inoculum group, Participant 4 experienced an increase in ADC suggestive of vasogenic edema, which may reflect a change to cerebral blood flow in this individual. We have previously shown abdominal radiotracer uptake changes following experimental malaria infection restricted to the spleen in this cohort16. As a result, changes observed in the brain are an organ-specific rather than global phenomenon.Of the three participants that experienced a change in radiotracer uptake following P. falciparum compared to P. vivax infection. Nonetheless, there are several limitations. Firstly, the small study population size means we were not able to demonstrate a statistical difference between quantitative imaging metrics, only that changes in the P. falciparum group exceed expected inter-scan variability. Although paired imaging has been rigorously standardized, there may be residual concerns about inter-scan variability, the lower limits of detection, and sensitivity of the imaging protocol. Secondly, FDG is non-specific and not able to differentiate between host and parasite activity. Advances in radiochemistry may allow for the future development of parasite-specific radiotracers. Thirdly, we were not able to evaluate the influence of cerebral blood flow on radiotracer uptake. The relative contribution of cerebral blood flow on radiotracer uptake following inoculation could potentially be examined using emerging imaging sequences such as resting-state blood oxygen level dependent functional MRI with simultaneous PET28, although the applicability of such a protocol has not previously been evaluated. Finally, as a study of early experimental human infection, the dynamic range of parasitemia and disease severity is greatly restricted compared to clinical disease. As a result, it is difficult to infer any relationship between parasitemia and uptake in our small cohort.Although the study population is small, our findings demonstrate that PET/MRI is feasible in early experimental human malaria infection and the pattern of FDG uptake appears different in P. falciparum infection compared to P. vivax infection, but these changes are not uniform between individuals. This is in keeping with known species-specific tissue tropism and later stage disease complications, and consolidates the notion that these imaging techniques may offer useful insights into disease pathophysiology.There is a paucity of literature examining the organ-specific pathophysiology of early human malaria infection. Understanding upstream events may help us understand the processes contributing to later disease, which may be particularly important for the critical complication of cerebral malaria. We have demonstrated that PET/MRI is feasible in early experimental malaria infection, and that brain glucose metabolism may be more affected by 16. In brief, baseline PET/MRI was performed in the 7 days prior to malaria inoculation on healthy malaria-naïve adults aged 18–55 years participating in IBSM studies. Following inoculation with approximately 2800 viable P. falciparum or approximately 564 viable P. vivax infected red blood cells, peripheral blood parasitemia was monitored at least daily by qPCR29. Post-inoculation imaging was performed near the peak of parasitemia, in the 24 h prior to administration of antimalarial treatment. In addition to standard IBSM study inclusion criteria, participants were required to have a normal fasting glucose and no contraindications to MRI. Due to protocol-specified differences in the contributing studies, the interval between inoculation and post-inoculation imaging varied between studies. Standard safety assessments including regular physical examinations, and clinician-elicited and self-reported symptoms were performed at specified times during the IBSM studies. A total of eight participants were planned for enrollment, based on the availability of funding, IBSM study cohorts, and imaging facilities. The imaging study, and all IBSM studies contributing participants were performed in accordance with the Declaration of Helsinki.This was a prospective, single-center exploratory study performed between 2016 and 2019 at the Herston Imaging Research Facility, Brisbane, Australia. Baseline imaging followed by experimental malaria inoculation and post-inoculation imaging was performed as previously described31. Diet and activity information sheets were provided to assist participant adherence. Participants were inoculated with a standard dosage of approximately 4.0 MBq/kg of the radiotracer FDG prior to PET imaging. No MRI contrast medium was administered.Baseline and post-inoculation FDG PET/MRI were performed on all participants using the 3 Tesla Magnetom Biograph mMR  at the Herston Imaging Research Facility. Detailed methods and imaging protocols are outlined in the Supplementary Methods. All participants completed an institutional MRI safety checklist and the female participant underwent qualitative human chorionic gonadotropin testing to exclude pregnancy prior to imaging. Participants were required to have a normal fasting glucose at enrollment, abstain from strenuous exercise and follow a low carbohydrate diet in the 24-h prior to imaging with a 6-h fasting period immediately prior to help standardize FDG uptakeP. falciparum infection11. ADC represents the relative mobility of water in the tissue, and changes may reflect pathology including relative ischemia13.Imaging was reported qualitatively and with respect to pre-specified quantitative metrics, and was reviewed separately for safety and incidental findings. Post-inoculation imaging was compared to baseline imaging. Reporting nuclear medicine and radiology specialists were not aware of the inoculum species. Mean standardized uptake values (SUVs) were estimated for brain segmentations representing anterior, posterior, and deep regions of interest . SUV is the most commonly used measurement of radiotracer activity, and represents the ratio of tissue radioactivity related to FDG at a given time and the injected dose of radioactivity per kilogram of participant weight. The apparent diffusion co-efficient (ADC) was estimated for a manually defined region of interest in the corpus callosum representing the splenium, as restricted diffusion has been reported in this region in cases of uncomplicated P. vivax and P. falciparum groups. Firstly, to determine if inter-scan variability was outside the expected background variability for PET imaging, the within subject coefficient of variation (wCV) was calculated for each region of interest, and for the brain overall, for each inoculum group as previously described32. A wCV < / = 10% represents expected normal inter-scan variability in the absence of an intervention34. In this study, if experimental human malaria affects imaging parameters, wCV should exceed this normal expected variability. Secondly, assuming that experimental human malaria may affect imaging parameters, the mean ) percentage change from baseline imaging for each region of interest was calculated, for each inoculum group. Parasitemia at the time of post-inoculation imaging was estimated by interpolating the linear rate of change between the nearest log-transformed study parasitemia measurements. In an exploratory analysis, baseline and post-inoculation imaging metrics for each group were compared using paired, two-tailed t-tests. Differences between the mean percentage change from baseline for P. vivax and P. falciparum groups were compared using unpaired t-tests. A p value < 0.05 was considered significant. No adjustments were made for multiple comparisons.Imaging results are presented for the P. falciparum: NCT0286705919 and ACTRN1261900108516720, and one P. vivax: ACTRN1261600017448218. These studies were approved by the QIMR-Berghofer Medical Research Institute Ethics committee. The exploratory imaging study was approved by the QIMR-Berghofer Medical Research Institute, and Queensland Health Metro North Ethics committees. All participants provided written informed consent for the IBSM studies and the exploratory imaging study.Participants were recruited from three IBSM studies: two The datasets generated during and/or analysed during the current study may be available from the corresponding author on reasonable request and with appropriate data sharing agreements in place.Supplementary Information.


In French Guiana, gold miners working illegally represents a major reservoir of malaria. This mobile population, mainly of Brazilian descent, enters the French Guianese forest from neighbouring countries, Suriname and Brazil. A complex and innovative intervention was piloted as a cooperation with the three involved countries involved to control malaria in this specific population. The principle was that health workers called “facilitators” provide the participants with a self-diagnosis and self-treatment kit along with adequate training and material to rapidly manage an episode of malaria symptoms on their own, when they find themselves isolated from health care services.This paper describes the design, development, content of the intervention and players’ organization of this multi-country project, the opportunities and constraints encountered, and the lessons learnt at this stage.The choice not to implement the usual “Test and Treat” approach within the community is mainly driven by regulatory reasons. The content of medical messages tends to balance the tension between thoroughness, accuracy and efficacy. The wide range of tools developed through a participatory approach was intended to cope with the challenges of the literacy level of the target population. Despite the difficulties encountered due to language, regulation differences and distance between partners, cooperation was fruitful, due to the complementary of stakeholders, their involvement at all important stages and regular face-to-face meetings.This experience shows the feasibility of an ambitious project of action-research in a border malaria context, involving several countries and with a mobile and undocumented population. It reveals some factors of success which may be transferable in analogous settings.The online version contains supplementary material available at 10.1186/s12936-021-03748-5. The Guiana Shield is a Region shared by French Guiana, Suriname, Guyana and some parts of Venezuela, Colombia and Brazil Fig. . The soiThis cooperation resulted in the implementation of ‘Malakit’, an innovative pilot project, aiming at evaluating a new strategy to control malaria among migrant gold miners working illegally in French Guiana. The principle was to provide participants with a self-diagnosis and self-treatment kit along with adequate resources to handle rapidly and adequately an episode of malaria symptoms by themselves, when isolated in the Amazon forest . It was Can you describe the intervention fully, so that it can be implemented properly for the purposes of your evaluation, and replicated by others?Despite the emergence of specific reporting guidelines for complex non-pharmacological interventions, descriptions of interventions are still insufficient and inadequate. One of the objectives of this article is to address the following question, as recommended by the UK Medical Research Council (MRC) : Can youAlthough, the protocol of this study was published in 2018 , it seemThe intervention was designed after the initial idea was validated by the partners from the three countries involved i.e. the distribution at border resting sites of a self-diagnostic and self-treatment kit to gold miners, to be used when needed, within the context of their activity on the French Guianese territory . The desThe choice not to develop a usual community-based approach as implemented in several places in the world with great success, such as in Suriname, is mainly for regulatory reasons . In Frengarimpeiros [garimpeiros were accessible, available, and willing to give time for health studies, and more specifically, to answer questions about their activity and their health [Illegal gold mining is well organized; each person has a role to play. Social life is also structured and has its own codes. However, it is not a proper pyramidal organization and communities are transient, due to the absence of a permanent commitment to bosses and to the great mobility of many impeiros . Even wir health . ComplemPlasmodium falciparum, the most common among garimpeiros at this period [Plasmodium vivax malaria attacks (but not to prevent relapses). The ultimate goal was to prevent the emergence of artemisinin-resistant Plasmodium falciparum parasites by addressing the risks associated with the practices of the study population.It was decided, in consensus with partners, that the main objective would be to increase the use of an appropriate and complete malaria treatment against s period , includiThe strategy of training key persons within a community to perform diagnostic tests to other people has been widely used in low income countries. Despite heterogeneity of efficacy and cost-effectiveness after scaling-up, the feasibility of training laypersons to perform RDT, even with a low education level, has already been demonstrated –16.One innovative aspect of Malakit is the self-performed RDT test in case of malaria symptoms, which has only been implemented previously in research among travellers. Study designs varied with diverse types of RDTs and with instruction methods ranging from simple written instructions to actual training. Conclusions of these studies have a limited transferability due to the specific contexts, such as the possibility to consult a health professional, the risk of false negative inherent to the type of test, or the limitations of the study design –21. HoweP. falciparum, P. vivax, Plasmodium ovale and Plasmodium malariae), is easy to read (two-band test) and can withstand storage at temperatures up to 40 °C (mean temperature range in the Amazon being 25 to 27 °C); it was thus the best RDT candidate [The Malakit intervention hypothesized that with adapted directives and prior supervised practice of self-RDT, gold miners could then perform a self-RDT, whatever their literacy level, even while in the forest with malaria symptoms. The Access Bio’s CareStart™ Malaria pLDH (Pan) can detect (but not distinguish) the four main species of human malaria parasites , and is the one recommended by the respective health authorities in the three countries involved in the project, in accordance with World Health Organization (WHO) recommendations .A single dose of primaquine is recommended for its gametocytocidal effect in addition to treatment regimens . To thisThe desired characteristics of the container of the kit were to be waterproof, resistant and easily transportable to fit gold miners’ environment, and to contain sufficient information to decrease the risk of misuse in case someone obtained it without training. Around 40 gold miners were shown several design options on the Surinamese border and contributed to the creation of the final prototype.The container of the kit was made of plastic, wallet-sized (i.e. 20 by 15 cm)  for hammocks completed the kit.The function of the facilitators, main protagonists of the intervention, was to carry out inclusion and follow-up visits as described in Fig. The contents of the theoretical training could not be covered in a single session. Experience showed that it was much more successful to split the training into two parts. In the first part, the objective was to become proficient in performing a first visit . The second part focused on mastering data collection during follow-up visits, which implies debriefing the participants about their episode(s) of symptoms, use(s) of the kit and movements since the last visit on a distribution site. Due to administrative (obtaining of visa) and logistical constraints, it was not possible to gather all facilitators and supervisors for one training session. Therefore, three sessions were provided, which were refined and improved over time. Simulations and confrontations with the reality in the field during the first training allowed optimizing the structure of the inclusion process. Smaller audiences also enabled to address partially the issue of language differences and heterogeneity of background and education levels.All messages are presented in the Table Some specific points of the intervention required the consultation of experts in order to choose between different options:Completeness of the treatment:Take your medication entirely if you have a positive RDT” or “If you start treatment, you have to finish”, considering that the person may take the medication with a negative diagnosis. The first option was agreed upon in order not to give too many messages and to avoid the suggestion that it is correct to take the medication without a positive diagnosis of malaria.Interrogation between the two following message options: “Message to avoid risk of overdose for children:The question was either to indicate a dosage according to weight and age or to state that the kit is only for adults (over 15 years old). Eventually the main message was that the kit is for personal use only, and participants with symptomatic children were specifically advised to consult a health professional.Indication for paracetamol medication:Take paracetamol when the RDT is negative” as opposed to Coartem® intake in case of positive RDT, and “Take paracetamol when you feel fever or pain/ache; paracetamol is the sole medication you can take if the RDT is negative”. The second option was selected since paracetamol can be regardless of the outcome of the RDT, and to stress on the importance of positivity for malaria treatment.The two options were: “Contraindications:If you take medication for the heart, start the malaria treatment as soon as you have a positive test but also seek medical care as soon as possible.” As described in Table The risk–benefit balance was finally in favour of the absence of interdiction of taking the antimalarial medication considering that it is very tricky at a personal level to identify contraindications. The contraindication of cardiac history for Coartem® was addressed by giving the following message: “P. vivax:Instructions regarding possible relapses of P. vivax and in case of persistence of symptoms. Indeed, recommending to use the malakit as a first-aid kit and then to go to a health centre for a professional opinion and specific malaria diagnosis did not seem relevant, especially if the person feels better after using a kit.In order to avoid giving instructions that would probably not be followed, it was decided to recommend consulting a health centre only in case of suspicion of relapse of garimpeiros were asked to perform a self-RDT with only drawings as explanation. This allowed for example to determine the need to number the images to indicate the right order in which the images are read. Participants were also involved in adjusting the instructions of medication use to their literacy by changing the level and amount of information, disposition of images and text. For example, thanks to their contribution, pictures were added to indicate the time of intake of Coartem®, i.e. a sun for the morning and a moon for the evening  assessing feasibility, (2) drafting the protocol and all study tools, (3) seeking funds and managing the bulk of the project funds, (4) searching for investigation centres, (5) searching for technical, scientific and institutional partnerships and agreements drafting, (6) informing and training of the persons in charge of the investigation, (7) contributing to the logistical implementation of the study, (8) developing, implementing and managing the information system, (9) supervising the study intervention delivery, (10) analysing data and disseminating results and (11) communicating about the project.The CIC-AG’s promotion department was in charge of the clinical trial monitoring of the study with a dedicated clinical research associate. The strategy followed the recommendations of the Orpal study , which wThe National Malaria Control Programme (NMCP) has a strong experience in health promotion and primary healthcare among gold miners on the field . The FouThe French Guianese border is far from the centres of Brasilia and Rio de Janeiro and from Macapá  but both central and local authorities were involved as much as possible at all stages of project development. A local non-government organization (NGO), DPAC fronteira (or “DPAC”), with a history of social and health mediation with the study population was chosen to implement the project. DPAC also had experience in French-Brazilian international cooperation. They were in charge of staff recruitment and management of the Brazilian study onsite. The principal investigator and scientific counterpart in Brazil was a researcher working on malaria epidemiology at the Foundation Oswaldo Cruz (Fiocruz) in Rio de Janeiro in order to complement scientifically the DPAC field of competence Fig. .The responsibility of the realization of the endline study Orpal was given to the Fiocruz along the Brazilian border and to the CIC-AG along the Surinamese border. Key considerations for successful cooperation between countries were (1) the formulation of a convergent goal and joint efforts to achieve it, (2) solicitation and involvement of all stakeholders at each important phase of the project, (3) fruitful interactions between institutions, (4) scientific structures and social associations in the field, (5) the consistency of the main players in each country.The language of the study population and of the facilitators was Brazilian Portuguese, however, other actors spoke French, English, or Dutch. The main protagonists of the project were either located in Paramaribo, Cayenne, Rio de Janeiro or along the Maroni and Oyapock border rivers. The distance and transport constraints, and the linguistic differences were the main constraints for communication, which is the key to successful project management. Providing enough time, person-time and funds for sufficient bilateral and international meetings, and planning the systematic presence of professional interpreters during trainings and meetings, are factors identified from this experience to enhance better communication between stakeholders. Furthermore, digital tools can be useful for advancement between two face-to-face meetings but imply allowing time for training of players to ensure commitment to the tool.. Brazil and Suriname. In Brazil, the organization of the principal investigator, Fiocruz, conducted the translation and the submission of the protocol internally and to the Brazilian national committee. In Suriname, ethical clearance submission with the national committee was also delegated to the Surinamese principal investigator. Study insurance for each country was contracted by the sponsor. Differences in what constitutes good clinical practices were identified between countries in the course of the project, but adaptation efforts were made on each side. For example, those responsible for the investigation of both countries (Suriname and Brazil) contributed well to the monitoring carried out by the research associate. A better prior knowledge of the regulations in each country can probably save time. Administrative procedures such as the registration of health products with Brazil’s National Sanitary Regulatory Authority and Genetic heritage System, customs formalities or visa applications were barriers to smooth cooperation. A letter of intention between all countries at higher administrative levels may be a solution to these issues.In France, the sponsor of a study is responsible for ethical clearance in countries where participants are included, i.eThe supply chain management for this project was a complicated and time-consuming task due to delays in transferring funds, absence of customs at the border between French Guiana and Brazil which has made the transportation of materials across borders more complex, long and complicated procedures for donations of medical products and shipping mistakes from provider. The timing of such an international initiative should take into account these possible problems.The Surinamese coordination suggested two safety measures to protect the participants, i.e. the implementation of a participants advocacy and the assembling of a Data Safety Monitoring Board (DSMB). For the first one, the Surinamese coordination partnered with the Brazilian embassy that made available a hotline number for Malakit participants. Each participant was told during inclusion that a person independent of the project could be contacted if he/she had any concerns or questions that could not be shared with the people of the project, such as an inappropriate behaviour of a facilitator.For the DSMB, the sponsor contacted four malaria experts around the world who had access to safety information such as reports of the RDT quality control, side effects or misuse indicators. They gathered twice to discuss on this information to give recommendations and ask for clarification when needed.This article presents the development of the Malakit intervention, opportunities, constraints and lessons learnt at this stage of the project. Despite a complex international context implying distance working, cultural and language variety, regulation barriers, and a sensitive and mobile target population, this unprecedented project was implemented in a rather short period of time i.e. about one year and a half between the initial idea and the first Malakit distribution. Some of the ingredients underlying the successful setup of the project are (1) the complementarity of stakeholders i.e. health professionals, public health specialists, research clinical specialists, information system specialists, malaria experts, illustrator, health and social mediation specialists, actors in contact with the study population and community representatives, (2) the consideration of feasibility information, (3) the participatory approach in developing a wide range of complementary materials and tools adapted to the community, and (4) good external communication on the project to all partners, health institutions and the scientific community. This experience confirms that reduction of the administrative procedures, such as visa and custom formalities can smoothen efficient implementation of activities in border areas, as reported in literature review .Uncontrolled land borders have been identified as a risk factor for the re-introduction of malaria since the 1960s . Border This initiative shows that an action research project with an innovative approach involving three countries concerned by border malaria is possible despite many challenges. Such a project can also stimulate further cooperation and may provide a model to be transferred to other regions with similar contexts.Additional file 1. Contextual differences between countries involves in the Malakit project. The malaria and gold mining context of French Guiana, Suriname and Brazil are described here.Additional file 2. Malakit pouch. Picture of a malakit closed.Additional file 3. Malakit pouch opened. Picture of a malakit opened.Additional file 4. RDT pocket of the malakit. Picture of the RDT pocket heads (illustrated instructions) and tails.Additional file 5. Treatment pocket of the malakit. Picture of the Treatment pocket heads (illustrated instructions) and tails.Additional file 6. Malakit training course. Content of the training.Additional file 7. Tools and contents for facilitator training. The different tools and their content used to train the facilitators in their tasks.Additional file 8. RDT video. A tutorial video to perform a self-diagnostic test with a voice-over in Portuguese.Additional file 9. Animated video. A teaser video which presents the Malakit project.Additional file 10. Training posters. The drawings used by the facilitators to train the Malakit participants.


The meta-analysis showed that malaria and leptospirosis co-infections occurred by chance . The prevalence of malaria in leptospirosis co-infection among febrile patients in the included studies was low. Co-infection was likely to occur by chance. However, as clinical symptoms of leptospirosis patients were non-specific and not distinguishable from symptoms of malaria patients, clinicians caring for febrile patients in an area where those two diseases are endemic should maintain a high index of suspicion for both diseases and whether mono-infections or co-infections are likely. Recognition of this co-infection may play an important role in reducing disease severity and treatment duration.Malaria and leptospirosis are important cosmopolitan infections that have emerged with overlapping geographic distribution, especially in tropical and subtropical regions. Therefore, co-infection with malaria and leptospirosis may occur in overlapping areas. The present study aimed to quantify the prevalence of malaria and leptospirosis co-infection among febrile patients. The association between malaria and leptospirosis infections was also investigated. Relevant studies that had reported malaria and leptospirosis co-infection were identified from PubMed, Scopus, and Web of Science. The risk of bias of the studies was assessed using the Joanna Briggs Institute (JBI) Critical Appraisal Tool. The pooled prevalence of malaria and leptospirosis co-infections among febrile patients and the pooled prevalence of leptospirosis infection among malaria patients were estimated using random effect models. The association between malaria and leptospirosis infection among febrile patients was estimated using random effect models. The outcomes of each study were shown in a forest plot in point estimate and 95% confidence interval (CI). Heterogeneity among the included studies was assessed using Cochran’s Q and quantified using I-squared statistics. For leptospirosis, subgroup analyses of countries, diagnostic tests, and participants’ age groups were performed to specify prevalence in each subgroup. Publication bias was assessed by funnel-plot visualization. Of the 2370 articles identified from the databases, 15 studies met the eligibility criteria and were included for qualitative and quantitative syntheses. Most of the included studies were conducted in India , Thailand , and Cambodia . Most of the enrolled cases were febrile patients (5838 cases) and malaria-positive patients (421 cases). The meta-analysis showed that the pooled prevalence of malaria and leptospirosis co-infection (86 cases) among febrile patients was 1% (95% CI: 1–2%, I Plasmodium spp.: P. falciparum, P. vivax, P. malariae, P. ovale curtisi, P. ovale wallikeri, and P. knowlesi [P. cynomolgi might be a cause of malaria in humans in Cambodia [P. falciparum infection; however, a lesser proportion of severe malaria could also be caused by other Plasmodium spp. [Malaria in humans is caused by one of six knowlesi . Recent Cambodia , ThailanCambodia , and MalCambodia ,5,6. TheCambodia . SymptomCambodia . If leftium spp. ,11,12,13ium spp. ,15.Leptospira [Leptospirosis is one of the most important zoonotic diseases caused by pathogenic species of the spirochete bacteria ptospira ,17. Thisptospira ,19. Nowaptospira ,21. Prevptospira . At leasptospira . The iniptospira ,23. Thisptospira . Therefoptospira . Severe ptospira ,19. Tranptospira . Howeverptospira ,25. RiskMalaria and leptospirosis are important cosmopolitan infections that have emerged with overlapping geographic distribution, especially in tropical and subtropical regions . TherefoThe protocol of this systematic review was registered at PROSPERO with ID CRD42021255898. Reports of the systematic review followed the PRISMA 2020 statement .Potentially relevant articles were searched in PubMed, Scopus, and Web of Science by using keyword combinations specific for malaria and leptospirosis, as provided in All types of study designs that reported malaria and leptospirosis were considered. Studies were selected according to the following eligibility (inclusion/exclusion) criteria: (1) Cross-sectional studies, longitudinal studies, case-control studies, cohort studies, and observational studies were included. The inclusion of all types of studies allowed us to maximize the number of included studies to represent the pooled prevalence of co-infection globally and (2) only human studies with malaria and leptospirosis infections by laboratory diagnosis, such as microscopic diagnosis, culture, molecular diagnosis, rapid diagnostic test (RDT), and serology, were included. The following studies were excluded: diagnosis of malaria and leptospirosis infection by clinical diagnosis only , animal studies, in vitro studies, assay performance, review articles, case reports, and case series. The participant/population (P), outcome of interest (I), and contexts (Co) were applied to the key question.Study selection was based on the eligibility criteria. Articles were retrieved from the databases using the search strategy. All articles were imported into Endnote software for management. All studies were reviewed by two independent authors (MK and WM). First, duplicates were screened and removed. Second, the titles and abstracts of the articles were reviewed. Unrelated articles were excluded and then the remaining articles were examined for full texts. Studies that met the eligibility criteria were included and those that did not were excluded, with the explained reasons. For any discrepancies between the two authors during study selection, another author (PW) served as a third author to create consensus.The two authors (MK and WM) extracted data from each included study to the pilot Excel datasheet before data analysis. The following data were extracted: name of the first author, year of publication, country, year study conducted, study design, characteristics of participants enrolled, age, gender, number of patients with co-infection, number of patients with malaria, number of patients with leptospirosis, diagnostic test(s) for malaria, and diagnostic test(s) for leptospirosis. The data were cross-checked by another author (PW) to assure the accuracy of the method.The risk of bias in the studies was assessed independently by two reviewers (MK and WM) according to the Joanna Briggs Institute (JBI) Critical Appraisal Tools for cross-sectional study . Any disThe data extracted from all of the included studies were narratively synthesized to provide a qualitative account of the data extracted from the included studies. The qualitative syntheses involved an explanation of the characteristics of the included studies, including study design, participants, study location, age, number of patients with co-infections, number of patients with malaria, number of patients with leptospirosis, and diagnostic tests for both malaria and leptospirosis. The quantitative synthesis involved the use of statistical analysis to pool the outcome. The first outcome of this study was the pooled prevalence of malaria and leptospirosis co-infections among febrile patients. The secondary outcome was the pooled prevalence of leptospirosis infection among malaria patients. The tertiary outcome was the pooled odds of malaria and leptospirosis co-infections among febrile patients. All outcomes were estimated using the random effect models and assumed that heterogeneity existed among the included studies. The outcomes of each study were shown in the forest plot in the point estimate and their 95% confidence interval (CI). The summarized outcome of interest was also shown in the forest plots. The heterogeneity among the included studies was assessed using Cochran’s Q and quantified using I-squared statistics. In the presence of substantial heterogeneity, the outcomes were pooled using the random effect model. For leptospirosis, the subgroup analyses of countries, diagnostic tests, and participants’ age groups were performed to specify the prevalence in each subgroup. Publication bias was assessed by visualizing a funnel plot. The meta-analysis was conducted using Stata ver. 14 .Overall, 2370 articles were retrieved from three databases: 542 from PubMed, 1232 from Scopus, and 596 from the Web of Science. After duplicates were removed, 1487 studies were screened for titles and abstracts. After 247 non-relevant articles were excluded, 125 articles were examined for full texts. Of the 125 articles examined for full text, 111 articles were excluded for the following reasons: 55 had no data on co-infection of malaria and leptospirosis, 25 had no malaria cases, 13 were review articles, 9 were case reports or case series for malaria and leptospirosis, and 9 had no leptospirosis cases. Fourteen articles ,39,40,41Fifteen studies included in the present study were prospective observational studies  ,34,37,41The quality of the included studies was assessed using the checklist see  for anal2: 85.2%), 1% , and 6% (95% CI: 3–12%), respectively , 0% in Jamaica (95% CI: 0–1%), 1% in Colombia (95% CI: 0–5%), and 1% in India . Overall, the pooled prevalence of malaria and leptospirosis co-infection (86 cases) among febrile patients was 1%  , 2% in Tanzania (95% CI: 1–4%), 1% in Thailand  .2: 0%), 9% , 9% (95% CI: 4–16%), 6% (95% CI: 3–12%), and 8% (95% CI: 5–12%), respectively , 11% in Thailand , 9% in Cambodia (95% CI: 7–11%), 8% in Tanzania (95% CI: 4–16%), 7% in Jamaica (95% CI: 4–12%), and 4% in Venezuela (95% CI: 2–8%) , 25% in Colombia (95% CI: 5–70%), 12% in India  .2: 99.3%), 7% in adults , and 24% in all age groups . Overall, the pooled prevalence of leptospirosis infection (186 cases) among malaria patients was 13%   .The odds of malaria and leptospirosis co-infections (86 cases) among febrile patients (5838 cases) were estimated using the data of 10 studies ,39,40,41Publication bias among studies that included analysis of the pooled prevalence of leptospirosis infection among malaria patients was performed using funnel plot, Egger’s test, and Contour enhanced funnel plot. The funnel plot between effect size  and standard error of the ES (seES) showed the asymmetrical distribution of the outcomes of two studies .p: 0.07, coefficient: 2.75, standard error: 1.38, t; 1.99). The contour-enhanced funnel plot showed missing studies in non-significant areas (p > 0.01) indicating that the cause of funnel plot asymmetry may more likely be due to publication bias . However, the subgroup of countries showing highest co-infection was in Bangladesh (5%) and lower in Tanzania (2%), Thailand, Jamaica, Colombia, and India. In addition, the pooled prevalence of leptospirosis infection among malaria patients was high (13%). However, the heterogeneity of the prevalence was subsided by the subgroup of countries that showed the highest prevalence in Bangladesh (75%) and lower in Colombia 25%), India (12%), Thailand (11%), Cambodia (9%), Tanzania (8%), Jamaica (7%), and Venezuela (4%). The high prevalence of leptospirosis infection among malaria patients in Bangladesh could be explained by the seropositivity for leptospirosis being stable throughout seasonality, while malaria had a peak in the rainy season when conditions for the vector seem to be favorable. In addition, high proportions of asymptomatic malaria-positive adults were identified in this country, indicating a greater probability for semi-immunity by increasing age . Moreovenfection . While tnfection . The titnfection ,43. In anfection . ELISA i%, India nfection . Techniq32 genes ,45. Real32 genes . Nested serovars .Leptospira from rodents to humans [Subgroup analysis of age groups showed that that the pooled prevalence of leptospirosis infection among malaria patients was highest in all age groups (24%), while lower prevalence was demonstrated in studies that enrolled specific groups, such as children (8%) and adults (7%). This result was consistent with the report showing severe leptospirosis occurred more among adolescents than children and adults  Moreovero humans  or exceso humans . In Bango humans . Therefoo humans .The present study had limitations. First, the limited number of studies reported concurrent malaria and leptospirosis infection. Therefore, the limited data, such as clinical laboratory characteristics and also the outcome of coinfected patients that might differ from malaria or leptospirosis mono-infection, could be used to investigate using a meta-analytical approach. Secondly, the pooled prevalence of malaria and leptospirosis co-infection among febrile patients or the pooled prevalence of leptospirosis infection among malaria patients were demonstrated with high prevalence heterogeneity across studies or countries. Therefore, the pooled prevalence of co-infection might not be estimated precisely and should be interpreted with the prevalence of an individual study.In conclusion, the low prevalence of malaria in leptospirosis co-infection among febrile patients occurred among the included studies. Co-infection was likely to occur by chance. However, clinical symptoms of leptospirosis patients were non-specific and not distinguishable from symptoms of malaria patients. Therefore, clinicians caring for febrile patients in an area where these two diseases are endemic should maintain a high index of suspicion for both diseases, particularly during the peak incidence seasons and whether mono-infections or co-infections are likely. The recognition of co-infection may be an important factor in reducing disease severity and treatment duration.