P. vivax lifecycle. In this study, we designed and expressed a P. vivax recombinant modular chimeric protein (PvRMC-1) composed of the main antigenic regions of these vaccine candidates. After structure modelling by prediction, the chimeric protein was expressed, and the antigenicity was assessed by IgM and IgG  ELISA in 301 naturally exposed individuals from the Brazilian Amazon. The recombinant protein was recognized by IgG (54%) and IgM (40%) antibodies in the studied individuals, confirming the natural immunogenicity of the epitopes that composed PvRMC-1 as its maintenance in the chimeric structure. Among responders, a predominant cytophilic response mediated by IgG1 (70%) and IgG3 (69%) was observed. IgM levels were inversely correlated with age and time of residence in endemic areas (p < 0.01). By contrast, the IgG and IgM reactivity indexes were positively correlated with each other, and both were inversely correlated with the time of the last malaria episode. Conclusions: The study demonstrates that PvRMC-1 was successfully expressed and targeted by natural antibodies, providing important insights into the construction of a multistage chimeric recombinant protein and the use of naturally acquired antibodies to validate the construction.The PvCelTOS, PvCyRPA, and Pvs25 proteins play important roles during the three stages of the  Plasmodium falciparum is the most prevalent malaria parasite in Africa and parts of Asia, P. vivax malaria remains the most widely distributed species that predominates in Central and South America, where the prevalence of P. vivax cases was almost 71.5% in 2021 [P. vivax. Furthermore, severe, life-threatening vivax malaria is no longer considered a rare event [P. falciparum sporozoites but is not cross-protective against P. vivax. Therefore, to control malaria resurgence and make progress toward malaria elimination outside Africa, an effective vaccine targeting P. vivax is crucial.Malaria remains a significant public health threat, and the WHO estimates 247 million malaria cases globally in 2021. Between 2019 and 2021, there were an estimated 13.4 million cases [Plasmodium biological cycle, with different antigens stimulating a stage-specific immune response against the pre-erythrocytic and intraerythrocytic, asexual and sexual parasite forms. The pre-erythrocytic strategy involves the generation of an antibody response capable of neutralizing sporozoites and then preventing hepatocyte invasion [Plasmodium spp. life cycle, an ideal antigenic composition for a potential vaccine could be targeted at multiple stages of the parasite. Thus, it becomes essential to explore new antigens to include them in this restricted list of vaccine candidates and, consequently, the use of the most immunogenic regions in chimeric constructs that can culminate in vaccines targeted at multiple stages of parasite development. In this context, three P. vivax proteins involved in different phases of the biological cycle were also explored for vaccine development: PvCelTOS , PvCyRPA (cysteine-rich protective antigen) and Pvs25 (surface protein).In this scenario, the main strategies in malaria vaccine development are based on the invasion ,4,5, geninvasion ,7; erythinvasion ,9 througinvasion ,11 or coinvasion ,13; and invasion ,15. HowePlasmodium species [Plasmodium species [P. berghei sporozoites in inbred and outbred mice [P. berghei parasite expressing the P. falciparum protein [P. vivax.CelTOS  is essential for the cell hepatocyte traversal of the malaria parasite in both mammalian and insect hosts; the mediation of transversal in mammalian hosts is a determinant of the success of malaria infection ,17,18. I species , and vac species . Regardi species . Additiored mice , and mic protein . Moreove protein ,24. ThesP. vivax and belongs to a different protein complex. Nevertheless, CyRPA still has important functions during host cell invasion [P. vivax antigens indicated that CyRPA was the protein that needed less antibody titration to be related to protection [Corresponding to the erythrocytic stage, CyRPA (cysteine-rich protective antigen) plays a role in red blood cell invasion by the merozoite . Due to invasion . A studyotection , reinforotection .P. vivax parasites completely prevented oocyst development in mosquitos [P. falciparum (Pfs25) [As a member of the P25 family of cysteine-rich 25 kDa antigens, Pvs25 has four tandem epidermal growth factor-like domains that are expressed on zygotes and mature ookinetes within mosquitoes ,36,37. Tosquitos . Antibodosquitos ,45, and  (Pfs25) . Additio (Pfs25) . In conjHere, we describe the construction and structural analysis of a multistage chimeric protein, confirming the antigenicity of its epitopes in a naturally exposed population of the Brazilian Amazon, as previously described in other scenario(s) .Using the ChimeraX program version 1.5 , we condFrom this tool, we also explored the PvRMC-1 protein surface A, and wen = 22) of all studied individuals did not remember previous malaria infections in their lives, and 3% (n = 8) reported no previous malaria episode during their entire life. Therefore, 90% (n = 271) of our studied individuals reported at least one malaria episode in the past. Among those who were positive at the time of blood collection (n = 100), the majority (81%) were diagnosed with P. vivax malaria. The number of past infections reported by individuals also varied greatly, ranging from 3 to 18 (mean = 12.04 ± 13.50), and the time elapsed since the last infection varied from 2 to 24 months (mean = 26.60 ± 46.08). Collectively, the evaluated parameters indicated that the studied population had different degrees of exposure and/or immunity.The study population was composed of 301 individuals living in three neighbouring municipalities of the Acre state in the Brazilian Amazon. In this population, the majority were adults, and all individuals were exposed to malaria infection throughout the year . The ageP. vivax, we assessed the IgG reactivity profile against the chimeric recombinant protein using plasma samples from 301 individuals living in three endemic areas of the southwestern Amazon region of Brazil. First, we confirmed that PvRMC-1 was successfully expressed as an antigenic construct and was naturally recognized by antibodies from exposed individuals from the Brazilian Amazon. One hundred sixty-four (54.4%) individuals presented specific antibodies against PvRMC-1, with reactivity index (RI) values ranging from 1.00 to 8.80 (mean = 1.63 ± DP = 1.13)  . Among t = 1.13) .In a complementary way, to evaluate the acquired immune response to more recent stimuli, we evaluated the response against IgM antibodies. Of the 301 individuals, 119 (40%) were responders to IgM antibodies, with reactivity index (IR) values varying from 1.00 to 5.83 (median = 1.34 ± DP = 0.65) .P. vivax  and P. falciparum  at the time of blood collection when compared to individuals residing in the regions studied and who were negative both in microscopic and molecular diagnosis . However, we did not observe significant differences in the frequency and magnitude of response between those infected with P. vivax or P. falciparum  and TREA . Other correlations were observed among other subclasses: IgG3 with IgM isotype  and IgG1 , in addition to IgG3 and IgG4   A,B.In a complementary way, we also evaluated the frequency of high and low responders among the 164 responders to IgG antibodies against chimeric PvRMC-1. Moreover, we evaluated the frequency of IgM and IgG subclass responses in each of these categories.We considered high-responder individuals those with a reactivity index (RI) higher than 2, and we observed that 25 individuals (15%) were high responders, while 139 individuals (85%) were considered low responders A.p = 0.003) and a correlation between low-responder individuals and MSLE .In the high-responder group, we found 16 (64%) responders to IgM, 14 (56%) responders to IgG1, 10 (40%) responders to IgG2, and 17 (68%) responders to IgG3. Among the group of low responders, we found 75 (54%) responders to IgM, 101 (73%) responders to IgG1, 20 (14%) to IgG2, and 96 (69%) to IgG3 B. We didP. vivax. The approaches have three main aims: improved methods for evaluating vaccine potential before clinical trials (as P. vivax has no long-term in vitro culture), description of new vaccine candidates, and optimization of known vaccine candidates. In this study, we focused on the third goal and described the construction and expression of a chimeric protein, PvRMC-1, designed based on our previous works that described B-cell epitopes in conserved regions of PvCyRPA, PvCelTOS, and Pvs25. We also evaluated the acquired antibodies against this protein in a population naturally exposed to malaria in the Brazilian AmazonOver recent years, many advances have been made regarding the exploration of vaccine candidates targeting P. vivax isolates from endemic regions of the Americas and Asia [First, in the design of PvRMC-1, we selected the full sequence of PvCyRPA after the signal peptide (255 amino acids) due to its small size and the moderate polymorphism found among field isolates in our previous work . The twoand Asia ,52,53. Iand Asia ,24. WithPlasmodium proteins have been explored for malaria vaccine development. For Plasmodium falciparum, a multiepitope chimeric antigen containing eight epitopes from this parasite has been explored. This antigen was well recognized by P. falciparum individuals in endemic regions along the China–Myanmar border, despite presenting cross-reactivity with P. vivax-infected individuals [P. vivax MSP1 (PvRMC MSP1) was explored. This antigen contained an extended epitope present in variants of the MSP1 protein. The chimeric protein also showed an improved ability to recognize IgG antibodies from P. falciparum-infected individuals when compared to the recognition made by recombinant PvMSP1 [Plasmodium cycle: merozoite surface protein 1 (PvMSP1) genetically fused to P. vivax P25 (Pvs25), resulting in a recognition of native proteins that was greater for the chimaera than for the protein alone [Chimeric ividuals ,55. Furtt PvMSP1 . More rein alone . Based oin alone ,58 a vacin alone ,61,62,63plasmodium merozoites [The structural vaccinology field has become an important way of accelerating vaccine development, as demonstrated by the COVID-19 pandemic , not onlrozoites . We alsorozoites ,70. Anotrozoites . On the P. vivax and P. falciparum malaria, with the predominance of P. vivax infections; these findings are in agreement with findings in the literature [In this scenario, the studied population included a majority of rainforest region natives and some migrants from nonendemic Brazilian areas who had lived in the endemic area for more than 10 years. The wide age range of these individuals, the variation in the number of previously recalled infections, and the time elapsed since the last malaria episode indicate differences in exposure and immunity. Moreover, the majority of individuals reported a prior experience with terature ,72. Sincvivax vaccine development has increased over the last few years, especially with erythrocytic antigens such as MSP-1 [P. falciparum antigens have shown that immunity to blood-stage antigens depends on a specific pattern of immunoglobulin subclass response, in which IgG1 and IgG3 antibodies have a link with antibody-dependent cellular inhibition of parasite growth in vitro [P. vivax infection. Not only is IgG responsible for providing naturally acquired immunity against malaria, but it was also already demonstrated for P. falciparum that IgM antibodies could inhibit merozoite invasion and replication in red blood cells (RBCs) by the fixation and activation of complement on the surface of the parasite [P. falciparum infection in children and adults with lifetime malaria exposure. For P. vivax, there are still few reports involving the IgM-mediated response, but it is already known that IgM is persistent in hypoendemic regions, and its levels remain unchanged during acute infection but increase significantly at recovery [The PvRMC-1 construct was broadly recognized by naturally acquired antibodies from individuals living in the southwestern Amazon region of Brazil. We found that over half of the individuals (54.4%) had specific antibodies against PvRMC-1, indicating that the expressed antigen preserved the antigenicity of original epitopes and can be advanced to tests of immunogenicity in animal models. It is important to mention that the use of chimeric construction in as MSP-1 ,73, RBP-as MSP-1 , PvAMA [as MSP-1 , and othas MSP-1 , and froas MSP-1 . It is aas MSP-1 ,33. In ain vitro ,75,76. Min vitro ,78. In tin vitro ,81,82,83parasite . Moreoverecovery . In addirecovery . ConsideFinally, based on previous works that suggested that the humoral response against PvCyRPA, with low antibody levels compared with classical vaccine candidates, is related to protection, we also stratified IgG responders into response levels, subdividing them into high and low responders. The high frequency of low responders was expected since both proteins elicit moderate levels of antibodies in natural exposition ,33. Inten = 155), Guajará (n = 67), and Mâncio Lima (n = 79). Samples from individuals living in nonendemic areas of Rio de Janeiro were considered the control group. Samples and survey data were collected from July 2018 to August 2018, coinciding with the period of increased malaria transmission in the Acre and Amazonas states. A high-risk area situated in the Juruá Valley in the state of Acre, where 99% of autochthonous cases are reported, includes Cruzeiro do Sul (07°37′50″ S/72°40′13″ W) and Mâncio Lima (07°36′49′″ S/72°53′47″ W). The municipality of Guajará (02°58′18″ S/57°40′38″ W) in the state of Amazonas, which is a medium-risk area, has also been included. In Brazil, the risk of contracting malaria is assessed using annual parasitological indexes (API). These indexes help classify transmission areas based on the number of autochthonous cases per 1000 inhabitants, categorizing them as high risk (≥50), medium risk (<50 and ≥10), low risk (<10 and >1), or very low risk (<1). Written informed consent was obtained from all donors, and the study was reviewed and approved by the Fundação Oswaldo Cruz Ethical Committee and the National Ethical Committee of Brazil. The investigations were carried out following the rules of the Declaration of Helsinki.A cross-sectional cohort study was conducted involving 301 individuals from three different endemic areas of Acre: Cruzeiro do Sul , and all epidemiological data were also stored in the Excel program for subsequent analysis.g, 10 min), the plasma was collected and stored at −20 °C and transported to Immunoparasitology Laboratory, Fiocruz—RJ. Thin and thick blood smears of all donors were examined for malaria parasites. The parasitological evaluation was performed by examination of 200 fields at 1000 × 126 magnification under oil immersion, and a research expert in malaria diagnosis examined all slides. In order to enhance the sensitivity of parasite detection, molecular analyses were conducted on all samples. The process involved extracting DNA from the blood samples using the QIAamp DNA blood midi kit  as per the manufacturer’s instructions. Polymerase chain reaction (PCR) was then carried out, employing specific primers for the genus (Plasmodium sp.) and species , following a previously described method [P. vivax and/or P. falciparum in blood smears were subsequently documented and treated using the chemotherapeutic regimen recommended by the Brazilian Ministry of Health.Peripheral blood samples were collected by venipuncture in heparin tubes. After centrifugation . The amino acid sequence was applied in the AlphaFold  using muEscherichia coli and produced from the previous immunological and molecular characterization of four T-cell epitopes and two B-cell epitopes from PvCelTOS [The chimeric recombinant protein PvRMC-1 was expressed in PvCelTOS , two linPvCelTOS , and a 2Antibody binding to PvRMC-1 was evaluated in plasma samples by enzyme-linked immunosorbent assay (ELISA). Briefly, MaxiSorp 96-well plates  were coated with PBS containing 1.0 μg/mL recombinant protein. After overnight incubation at 4 °C, the plates were washed and blocked for 1 h at 37 °C. Individual plasma samples diluted 1:100 in PBS-Tween containing 2% BSA  were added to duplicate wells. After 1 h at 37 °C and three wash cycles with PBS-Tween, bound antibodies were detected with peroxidase-conjugated goat anti-human IgG  diluted at 1:2000, followed by the addition of o-phenylenediamine and hydrogen peroxide. Optical density was identified at 490 nm using a SpectraMax 250 ELISA reader . The results for total IgG or IgM were expressed as reactivity indexes (RIs), which were calculated by the mean optical density of an individual’s tested sample divided by the mean optical density of 10 nonexposed control individuals’ samples, from Rio de Janeiro, plus 3 standard deviations. Subjects were scored as responders to PvRMC-1 if the RI of IgG and IgM against the recombinant protein was higher than one. Additionally, the RIs of IgG subclasses were evaluated in responder individuals by a similar method using peroxidase-conjugated goat anti-human IgG1, IgG2, IgG3, and IgG4 . Reactivity indexes were calculated using OD from exposed individuals/OD from controls plus three SD. This formula is widely used in malaria seroepidemiological studies and is a high standard to guarantee the positivity of studied individuals. The means of the cut-off OD values were 0.178 for IgG1, 0.207 for IgG2, 0.105 for IgG3, and 0.180 for IgG4. For IgG and IgM, the cut-off OD values were 0.333 and 0.223, respectively.p-value < 0.05 was considered significant.All statistical analyses were carried out using Prism 9.0 for Windows . The one-sample Kolmogorov–Smirnoff test was used to determine whether a variable was normally distributed. The Mann–Whitney test was used to compare the RIs of IgG against recombinant PvRMC-1 between the studied groups. Differences in the proportions of the RI of IgG subclasses and epidemiological parameters were evaluated by Fisher’s exact test, and associations between antibody responses and epidemiological data were determined by the Spearman rank test. A two-sided P. vivax infection. To further enhance the potential use of this antigen as a vaccine candidate, additional research is still required to evaluate the immunogenicity of PvRMC-1 in animal models.This study offers significant contributions to the development and construction of a multistage chimeric recombinant protein using key epitopes from PvCYRPA, Pvs25, and PvCelTOS. Additionally, the study explores naturally acquired antibodies to validate this construction. These findings support the creation of innovative approaches for preventing and managing


Plasmodium species challenging. Here, we aimed to establish a deep learning algorithm for identifying and classifying malaria parasites in thin blood smears and evaluate its performance and clinical prospect.Scarcity of annotated image data sets of thin blood smears makes expert-level differentiation among P falciparum, P vivax, P malariae, P ovale, P knowlesi, and P cynomolgi. Peripheral blood samples were obtained from 380 patients diagnosed with malaria. Additionally, blood samples from monkeys diagnosed with malaria were used to analyze P cynomolgi. The accuracy for detecting Plasmodium-infected blood cells was assessed through various evaluation metrics.You Only Look Once v7 was used as the backbone network for training the artificial intelligence algorithm model. The training, validation, and test sets for each malaria parasite category were randomly selected. A comprehensive analysis was performed on 12 708 thin blood smear images of various infective stages of 12 546 malaria parasites, including The total time to identify 1116 malaria parasites was 13 seconds, with an average analysis time of 0.01 seconds for each parasite in the test set. The average precision was 0.902, with a recall and precision of infected erythrocytes of 96.0% and 94.9%, respectively. Sensitivity and specificity exceeded 96.8% and 99.3%, with an area under the receiver operating characteristic curve >0.999. The highest sensitivity (97.8%) and specificity (99.8%) were observed for trophozoites and merozoites.The algorithm can help facilitate the clinical and morphologic examination of malaria parasites. Plasmodium species in thin blood smears. Sensitivity and specificity exceeded 96.8% and 99.3%, respectively, thus enabling improved clinical and morphologic examination of malaria parasites.A deep learning algorithm based on YOLOv7 achieved high accuracy  in identifying and classifying  Plasmodium species. According to the World Health Organization, >247 million malaria infection cases and 619 000 associated deaths occur worldwide [Plasmodium falciparum, P vivax, P malariae, P ovale, P knowlesi, and P cynomolgi [Plasmodium parasites proliferate in human red blood cells (RBCs) after infection, causing periodic systemic chills, fever, and sweating [P falciparum infection can develop into fatal cerebral malaria or involve morbidity of multiple organs, such as acute renal failure and respiratory distress syndrome [Malaria is a mosquito-borne infectious disease caused by sweating . P falcisyndrome . HoweverPlasmodium infection is diagnosed by microscopic examination of peripheral blood smears, polymerase chain reaction (PCR) [Plasmodium, yielding false-positive results [on (PCR) , and anton (PCR) . PCR is  results .Plasmodium species [Plasmodium-infected RBCs, and the antimalarial treatment effect can be simultaneously monitored. However, the reliability of microscopic examination for diagnosing malaria depends largely on experienced morphologic experts and is thus subjective and time-consuming. Furthermore, it is difficult to standardize the microscopic examination of malaria parasites, especially in developing nations with limited experienced parasitologists [The Centers for Disease Control and Prevention has established microscopic examination of Wright-Giemsa–stained thick and thin blood smears as the gold standard for malaria diagnosis . Thick b species . The sevologists .Plasmodium detection based on deep learning, and most of these studies relied on public data sets [Plasmodium species detected in thin blood films from multicenter clinical samples according to the YOLOv7 algorithm.Machine learning and automatic image recognition technologies have recently seen increasing application in disease diagnosis . Implemeata sets . To our Plasmodium species and their development stages in thin blood smears, evaluate its efficacy in augmenting microscopic examinations, and explore its potential clinical applications.Therefore, in this study, we aimed to establish a cutting-edge deep learning algorithm for the identification of P cynomolgi infection. Wright-Giemsa–stained thin blood smears were prepared according to the standardized procedure [Plasmodium species were identified with real-time fluorescent PCR and nested PCR methods . The algorithm framework is shown in The YOLOv7 algorithm was adopted to complete the detection and recognition of The YOLOv7 algorithm adopted the Cross Stage Partial Network as the backbone network . The amoThe Cross Stage Partial Network removed the bottleneck layer  to avoid repeated gradient conduction and useless parameter calculation. The feature map was divided into feature maps A and B, according to the channel dimensions. Feature map A was convoluted and transformed into feature map A″; feature maps A″ and B were fused and transformed into cross stage partial (CSP) blocks; and each block was calculated similarly.To improve small-target detection, YOLOv7 employed the Path Aggregation Network (PAN) algorithm for multiscale detection and feature fusion. The shallow feature map contained accurate location information conducive to network positioning, whereas the deep feature map contained rich high-level semantic information conducive to network classification; the combination of both maps can yield accurate positioning and classification features. The PAN algorithm deepened the feature path based on the feature pyramid network, thereby ensuring that the final classification and regression features contained high-precision location information and rich high-level semantic information to obtain more accurate positioning and classification results .The network structure based on “residual connections” had a high recognition accuracy; however, this was offset by its high memory and video memory consumption during inference, thereby reducing the inference speed and affecting the practical value of the model. Therefore, the structural reparameterization method exploited the “additivity” property of the convolution operation as follows:w1, w2, and w3 were of the same size and the 1 × 1 convolution was treated as a 3 × 3 convolution with all positions except the center point set to 0. This ensured high recognition accuracy, considerably reduced the time required for inference, and maximized the performance of the model in terms of speed and accuracy  + number of categories. Each feature map outputted 64 × 64 × 3, 31 × 31 × 3, and 15 × 15 × 3 detection targets, resulting in 5282 detection results.To suppress redundancy and low-quality test results, the nonmaximum suppression algorithm was used to deduplicate the test results. The nonmaximum suppression algorithm sorted the detection results by category according to the confidence level and calculated the intersection-over-union ratio among all detection frames in each category. The detection frame with the highest confidence was used as the final classification and segmentation result.P falciparum, 3030 P vivax, 225 P malariae, 117 P ovale, 4630 P knowlesi, and 139 P cynomolgi at various stages, comprising 1776 trophozoites, 9285 at the ring stage, 1096 gametocytes, and 389 schizonts , box loss, and confidence loss during model training were recorded.A. Figure 2B shows the original picture, and Figure 2C shows the attention of the neural network to determine parasite characteristics. Red and blue depict high and low attention, respectively. The algorithm exhibited a high level of attention to the actual characteristics of malaria parasites .The total analysis time for 1116 parasites in 782 images was 13 seconds, and the average analysis time for a single image was 0.016 seconds. The average recognition time for a single parasite was 0.014 seconds, and mAP was 0.902. The precision-recall curve of the algorithm is shown in Figure 2For the 1116 parasites in the test set, the recall and precision of the ability to identify infected erythrocytes were 96.0% and 94.9%, respectively. Plasmodium is summarized in The overall sensitivity of the algorithm to parasites was 96.0%  in the medical field, deep learning algorithms can be used to detect malaria parasites . Yet, paPlasmodium species  or lacking image segmentation  for model training [P falciparum in 8145 images from 36 thin blood smears was 0.997, with a specific AUC of 0.995 for the ring stage. The present study yielded comparable results; however, the established algorithm incorporated the identification of 6 Plasmodium species and achieved an mAP of 0.902, which was superior to the mAP (0.885) reported by Kuo et al. Furthermore, Sriporn et al [Plasmodium species in 7000 images from the public data of the National Library of Medicine to be within the ranges of 76.07% to 99.28%, 76.07% to 99.29%, and 78.05% to 99.28%, respectively. Yet, the duration of their process was substantially longer, from 14 to 125 minutes, and the scope of their analysis was limited to a single Plasmodium species. In the present study, the YOLOv7 algorithm was used as the primary technique for detecting Plasmodium. The average analysis time was 0.02 seconds per single field image with dimensions of 15 × 15 mm, which is considerably shorter than that reported by Sriporn et al (1.3–10 seconds per image). The analysis of 22 500 field images from thin blood smears resulted in an overall analysis time of approximately 7.5 minutes, demonstrating the efficacy of the YOLOv7 algorithm in reducing the analysis time. The key factor for the efficiency and accuracy of the YOLOv7 algorithm lies in its resolution of object detection as a regression problem, in which target region and target category predictions were integrated into a single neural network model. This approach enabled the direct transformation of the input image into output object locations and classifications, enhancing the detection speed and precision. In contrast, previous studies have mainly employed traditional image-processing techniques or slower 2-stage detectors, such as faster R-CNN [Previous studies have often used public data sets containing a single training , 26, 27.training  reportedrn et al  reporteder R-CNN , 30, VGGer R-CNN , and SPPPlasmodium species in thin blood smears included in this study. Our findings demonstrated that the proposed algorithm achieved a high level of recognition sensitivity and specificity for the 6 Plasmodium species tested, with values consistently exceeding 96.8% and 99.3%, respectively. Although 5 of these parasites  are common human-infecting Plasmodium species, P cynomolgi can infect monkeys and infrequently humans; thus, we chose to include it in our analysis. Malaria parasites at different development stages have different biological and metabolic characteristics in the host and mosquito, with consequently different degrees of sensitivity and resistance to different types of drugs and treatments. Understanding the development stage of the malaria parasite can thus help clinicians select the most effective treatment plan and improve treatment outcomes [In the present study, 12 708 thin blood smear images with different parasite species and development stages were obtained from multicenter clinical settings. These images were labeled by experienced morphologic experts according to the criteria of the Centers for Disease Control and Prevention. There is a lack of studies on the accuracy of detecting and classifying the 6 P ovale and schizont-stage Plasmodium species was small. Thus, future studies should include a larger number of training sets for these Plasmodium species to improve the accuracy of the algorithm. Moreover, the sample size of the training and validation sets for Plasmodium species other than P falciparum and the ring-stage Plasmodium species was limited. Second, these studies were performed in a reference laboratory, and additional studies are currently underway to test the approach in a clinical setting. In our future studies, we will focus on establishing an AI algorithm to analyze thick blood smear images, which will encompass parasite density calculation and contribute to a more advanced application of deep learning to support malaria diagnosis.This study has some limitations. First, in the training and validation sets, the number of Plasmodium species in thin blood smears. The algorithm exhibited a high degree of sensitivity and specificity, indicating its potential as a valuable tool to aid clinical technologists in the microscopic detection of malaria parasites.In this study, we developed and evaluated an AI algorithm for identifying ofad469_Supplementary_DataClick here for additional data file.


P < 0.01, Hedges’s g = 1.40, 95% CI 0.84–1.95, I2 = 95.81, 16 studies). The conducted meta-analysis demonstrated a significant elevation in UA levels in patients suffering from severe malaria as compared to non-severe malaria . In summary, these findings provide valuable insights into the potential use of UA as a biomarker for malaria infection and determination of its severity. Further research is needed to validate these findings and to explore the underlying mechanisms that contribute to the elevation of UA levels during malaria infection.Elevated uric acid (UA) levels have been reported in malaria patients and are particularly prominent in severe malaria cases. This study aims to synthesize the difference in UA levels between malaria patients and uninfected controls, and between patients with severe and non-severe malaria. A comprehensive literature search was carried out across databases such as Embase, MEDLINE, Ovid, PubMed, Scopus, ProQuest, and Google Scholar to identify relevant studies for inclusion. The methodological quality of the included studies was evaluated independently by two reviewers using the JBI critical appraisal tool for observational studies. A meta-analysis was performed to calculate the pooled effect sizes, expressed as Hedges' g, with 95% confidence intervals (CIs). The Hedges' g was pooled using the random-effects model. An initial search yielding a total of 1122 articles, and a final total of 19 studies being included in the review. Elevated UA levels were observed more prominently in malaria patients, especially those with severe manifestations, when compared to uninfected controls. The conducted meta-analysis demonstrated a significant elevation in UA levels in patients suffering from malaria as compared to uninfected controls ( The World Health Organization reported an estimated 247 million malaria cases worldwide in 2021, causing approximately 625,000 deaths, a 10% increase from 20191. The disease is transmitted via bites from infected female Anopheles mosquitoes, which carry various Plasmodium species including P. vivax, P. falciparum, P. malariae, P. ovale, and P. knowlesi2. The clinical spectrum of malaria ranges from asymptomatic infections to severe, life-threatening diseases, with severity depending on factors like the Plasmodium species, patient immunity, and other host factors3. Severe malaria can present with complications such as cerebral malaria, severe anemia, acute kidney injury, and respiratory distress4.Malaria is a significant public health issue, predominantly in tropical and subtropical regions5. Severe infections, particularly by P. falciparum and P. vivax, generate substantial oxidative stress, leading to severe damage in multiple organ systems6. In diagnosing and managing malaria, various biomarkers have been identified to characterize the disease's severity. These biomarkers included C-reactive protein (CRP), Angiopoietin 2 (Ang-2), Ang-2/1 ratio, Plasmodium falciparum histidine-rich protein (PfHRP2), and platelet count9.Oxidative stress is a significant factor in malaria's symptoms and pathology. Infected red blood cells (RBCs) trigger an immune response, activating immune cells like macrophages and neutrophils, which release reactive oxygen species (ROS) to eliminate infected RBCs10.Uric acid (UA) is a critical biomarker, being the final product of purine catabolismPlasmodium parasites11. Furthermore, UA is associated with oxidative stress. Evidence suggests that UA embodies dual roles: acting as an oxidative stress marker and potentially offering therapeutic benefits as an antioxidant13. While a multitude of biomarkers provide insights into the status and progression of severe malaria, the significance of UA stands out. Elevated UA levels not only reflect the extent of RBC destruction but also actively influence immune responses and induce inflammation16.During malaria infection, infected RBCs release hypoxanthine. This hypoxanthine is then converted by the enzyme xanthine dehydrogenase in the bloodstream, an enzyme absent both in the infected RBCs and the P. falciparum patients17. This suggests that UA might play a role in triggering various immune cells to release inflammatory cytokines. These inflammatory activities can heighten the symptoms and complications of malaria, underscoring UA's essential role in determining disease severity. It is worth noting that while elevated UA levels are observed in patients with uncomplicated malaria, they are especially pronounced in severe malaria cases20. By comparing UA levels across these varying disease severities, clinicians can achieve a clearer insight into a patient's disease progression, allowing for more tailored therapeutic strategies21. Consequently, this study seeks to elucidate the differences in UA levels between malaria patients and uninfected controls, as well as between patients with severe and non-severe malaria.Increased plasma UA levels in malaria patients correlate with a surge in inflammatory cytokines such as tumor necrosis factor (TNF)-α, interleukin (IL)-6, IL-10, and IL-8 in 22. The protocol of the systematic review was registered at PROPERO (CRD42023431878).The systematic review employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) as its reporting guideline for systematic reviews and meta-analyses23.A comprehensive literature search was carried out across databases such as Embase, MEDLINE, Ovid, PubMed, Scopus, ProQuest, and Google Scholar to identify relevant studies for inclusion. The search was conducted with a combination of keywords and MeSH terms relating to “malaria,” and “uric acid,” as “ AND ” Table . The seaStudies were included if they met the following criteria: (1) observational studies  that reported on UA levels in malaria patients and uninfected controls or in severe and non-severe malaria patients, (2) studies that provided sufficient data (mean/median and standard deviation/interquartile range) to calculate effect sizes, and (3) studies published in English. Reviews, editorials, case reports, and studies lacking full-text availability were excluded.The study selection process involved two stages. In the initial stage, titles and abstracts of identified studies were independently screened by two reviewers  based on the inclusion and exclusion criteria. In the subsequent stage, full texts of the shortlisted studies were thoroughly assessed for eligibility. Any discrepancies were resolved through discussion, and if required, a third reviewer (KUK) was consulted.Plasmodium, diagnostic methods for malaria, the type of blood samples collected, and both qualitative and quantitative data pertinent for analysis. It is important to note that while quantitative studies are typically subjected to meta-analysis, qualitative studies more commonly utilize the meta-synthesis or meta-summary approach24.Data extraction was conducted independently by two reviewers  using a standardized data extraction form. Any discrepancies were resolved through discussion, and if required, a third reviewer (AM) was consulted. The extracted information from each study encompassed author names, year of publication, study design, geographical continent, age group of participants, species of 25. Studies were assessed based on selection, comparability, and outcome for cohort and case–control studies, and selection, comparability, and exposure for cross-sectional studies. Discrepancies were resolved through discussion, or a third reviewer (KUK) was consulted when necessary.The methodological quality of the included studies was evaluated independently by two reviewers  using the JBI critical appraisal tool for observational studies26. The meta-analysis was performed to calculate the pooled effect sizes, expressed as Hedges' g, with 95% confidence intervals (CIs). The Hedges' g was pooled using the random-effects model27. Heterogeneity among studies was assessed using the I2 statistic, with values > 50% indicating substantial heterogeneity28. Subgroup analyses were performed based on publication years, study design, geographical continent, age group of participants, species of Plasmodium, diagnostic methods for malaria, and the type of blood samples collected. Meta-regression analyses were conducted to explore potential sources of heterogeneity. Sensitivity analysis, or leave-one-out analysis, was conducted to assess the robustness of the results. Publication bias was examined visually using funnel plots and statistically with Egger's test, and the trim-and-fill method was employed to adjust for publication bias. All analyses were performed using Stata software version 17.0, with a P-value < 0.05 considered statistically significant, unless otherwise stated.Thematic synthesis was employed to capture these overarching themes and narratives, synthesizing the collective qualitative insights35. The exclusions at this stage were based on the following: 14 were animal studies, nine were in vitro studies, seven were conference abstracts, six were reviews, three were studies on uric acid in a single group of malaria patients but without a control group, and one was a study on uric acid levels before and after treatment. In addition to the initial databases, we also reviewed Google Scholar and reference lists to broaden eligibility criteria. Four studies were identified from Google Scholar39, and three were identified in reference lists42, resulting in a final total of 19 studies being included in the review42  and P. vivax (10.5%). A subset of studies investigated multiple Plasmodium species, with 15.8% examining both P. falciparum and P. vivax, and another 10.5% looking into mixed infections involving P. falciparum, P. vivax, and other species. Participant cohorts ranged across all age groups, with a slightly higher proportion of studies focusing on adults (42.1%). Microscopy was the predominant method for malaria detection (63.2%), with some studies utilizing combined methodologies. The blood samples for uric acid measurement were equally divided between serum and plasma (42.1% each), with some studies unspecified , and employed cross-sectional (63.2%), cohort (21.1%), and case–control designs (15.8%). The geographical distribution was nearly balanced between Africa (42.1%) and Asia (57.9%), with a preponderance of studies from Nigeria and India. The studies predominantly focused on ed Table .Table 1C.30, Ayede et al.31, Bertrand et al.20, Bhardwaj et al.32, Karua and Kishan41, Okon et al.34, Prabhu et al.42, and Iwalokun et al.19. However, some studies such as Erel et al.38, Idonije et al.4, and Thurnham et al.35 had an unclear status regarding the inclusion criteria and addressing confounding factors. Among the case–control studies, Ebrahim et al.37, Hatim et al.40, and Olisekodiaka et al.39 lacked identification of confounding factors and strategies to address them. In the cohort studies, Chandrashekhar et al.33 and Lopera-Mesa et al.17 lacked identification of confounding factors, strategies to address them, and details on follow-up time. Das et al.36 study also lacked identification of confounding factors, strategies to address them, and details on follow-up time. Moreover, Das BS and Nanda NK6 study was unclear regarding the utilization of strategies to address incomplete follow-up  was identified across 11 studies. However, studies published before 2010 did not present a statistically significant difference. In terms of study design, both cross-sectional and case–control studies showed significantly higher UA levels in malaria cases (P < 0.01), while cohort studies did not. Geographically, studies from both Africa and Asia consistently revealed higher UA levels in malaria cases (P < 0.01). When assessing age groups, children and adults had significantly higher UA levels (P < 0.01), while groups with unspecified age or encompassing all age groups did not demonstrate statistical significance. UA levels varied across different Plasmodium species, with significantly higher UA levels identified in P. falciparum and mixed infection cases (P < 0.01). Cases with P. vivax also had higher UA levels, but the significance level was less robust (P = 0.04). The diagnostic method for malaria (microscopy or microscopy/RDT) and types of blood samples (serum or plasma) also showed significant differences in UA levels. However, UA levels in cases where diagnostic methods and types of blood samples were not specified did not reach statistical significance . However, data for the years 2000–2009 were not applicable. In terms of study design, cohort studies showed higher UA levels in severe cases with marginal significance , while cross-sectional studies did not reach statistical significance . Regionally, higher UA levels in severe cases were significant in Africa , while the significance was not reached in Asia . In terms of age groups, significantly higher UA levels in severe cases were found in both children and adults, but with a higher degree of heterogeneity in the adult subgroup. Among the Plasmodium species, data for P. falciparum were applicable, but the results were not statistically significant. Similarly, the diagnostic method used for malaria did not significantly influence the UA levels between the groups. In terms of blood sample types, serum samples showed significant differences , while plasma samples also reached statistical significance . This variation in results indicates that the meta-analysis outcomes in this context were not robust, and the overall conclusions were sensitive to the removal of individual studies.On the other hand, the results were different when this leave-one-out analysis was applied to the comparison of UA levels between severe and non-severe malaria cases. In this case, the significance of the results varied depending on the study that was excluded , indicating the presence of publication bias likely attributable to the disproportionate influence of smaller studies.Visual inspection of the funnel plot unveiled an asymmetrical distribution  found a significant increase in UA levels among malaria patients, whereas older studies did not. It is possible that recent improvements in diagnostic and analytical techniques might have contributed to these results. As for the study design, both cross-sectional and case–control studies revealed a significant increase in UA levels in malaria cases, suggesting that these designs might be more sensitive at detecting this difference. The meta-analysis also reveals that the geographic location and age group of the participants play a crucial role in determining the UA levels. Studies conducted in Africa and Asia consistently showed elevated UA levels in malaria patients, which could reflect the high malaria burden in these regionsP. falciparum and P. vivax infections. This variability could be attributed to the unique pathogenesis and disease manifestations between P. falciparum and P. vivax infections44. The fact that cases of P. falciparum and mixed infections exhibited significantly higher UA levels suggests a potentially more severe inflammatory response associated with these types of infections. For different age groups, both adult and pediatric patients had higher UA levels, underscoring the potential of UA as a biomarker for malaria across all age groups. Nevertheless, UA may be used as a biomarker for malaria severity only in children because there was no difference  in UA between severe and non-severe malaria among studies that enrolled adults.Interestingly, UA levels varied between 34, underscored a significant increase in UA levels in patients with severe malaria compared to those with non-severe malaria. Elevated UA levels appear to be associated with severe malaria; however, this association seems to be influenced by factors such as geographical location and the age group of participants, as demonstrated in the subgroup meta-analysis. Given the observed heterogeneity among the studies reviewed, the findings from the meta-analysis should be approached with caution. The diversity in methodologies and objectives of the primary studies inherently adds complexity to a comprehensive interpretation. While this review identifies a potential link between UA levels and malaria severity, it is essential for readers and researchers to contextualize each primary study's aims and methods when drawing conclusions from these insights. Moreover, delving deeper into the mechanisms underlying this association is vital, not only for a clearer understanding of malaria's pathophysiology but also for uncovering potential therapeutic targets, enhancing predictions of disease severity, and tailoring treatments more effectively.The current meta-analysis included six studies due to the availability of quantitative dataPlasmodium infection, specifically in human dendritic cells, is facilitated by the presence of UA precipitates derived from P. falciparum. This process involves the maturation of dendritic cells, leading to elevated expression levels of cell surface co-stimulatory molecules such as CD40, CD80, and CD8646. In a similar vein, a reduction in UA levels in vivo has the potential to mitigate the host's inflammatory response and alleviate associated pathology46. A previous study revealed that the formation of UA through the degradation of Plasmodium-derived hypoxanthine/xanthine triggers the release of TNF, which is a cytokine associated with the response to Plasmodium infections47. Moreover, inhibiting the formation of UA significantly diminishes the secretion of cytokines such as TNF, IL-6, IL-1β, and IL-10, which are known to be involved in the response to P. falciparum infections and are associated with the pathogenesis of malaria48. Another study indicated that patients with P. vivax malaria exhibit lower levels of uric acid during the acute phase compared to the convalescent phase, suggesting clinical improvement resulting from antimalarial treatment or as a negative finding in P. vivax malaria49. Conversely, it has been reported that UA levels increase during the acute phase of P. falciparum malaria and correlate with disease severity17. The elevated levels of UA contribute to the pathogenesis of P. falciparum malaria by inducing endothelial pathology, as evidenced by the basal plasma levels of UA and endothelial biomarkers, which rise during episodes of uncomplicated malaria and escalate further with increasing disease severity50.Previous in vitro investigations have demonstrated that the induction of the innate immune response to I2 value, indicating substantial heterogeneity among the included studies. This heterogeneity, resulting from differences in study design, population demographics, geographical location, diagnostic methods, and other factors, complicates the task of drawing definitive conclusions. Thirdly, although our meta-regression analyses factored in several variables, there may still be unconsidered confounding factors that could potentially influence the observed relationship between malaria severity and UA levels. Fourthly, the limited number of studies included in our analysis constrains the breadth of our conclusions, making it challenging to generalize across all malaria patients or geographical locations. Lastly, the limited data available for both severe and non-severe malaria patients may impinge on the reliability of the comparison made between these two groups. Consequently, future research should aim to address these limitations for a more comprehensive understanding of the topic.The present study is subject to certain limitations. Firstly, there was a publication bias detected in the meta-analysis results, which may lead to an overestimation of the effect size. Secondly, our meta-analysis demonstrated a high In light of the objective to synthesize the difference in UA levels between malaria patients and uninfected controls, as well as between patients with varying malaria severity, the review found evidence supporting the potential role of UA as a distinguishing biomarker. Elevated UA levels were observed more prominently in malaria patients, especially those with severe manifestations, when compared to uninfected controls. However, it is crucial to approach these findings with caution due to certain study-related variables that could influence results. While the findings underscore UA's potential as an indicative marker for malaria infection and its severity, further research is imperative to validate these observations and delve deeper into the mechanisms prompting the surge in UA levels during a malaria infection.Supplementary Table 1.Supplementary Table 2.Supplementary Table 3.Supplementary Table 4.Supplementary Table 5.


Plasmodium falciparum malaria infections within a longitudinal study conducted in Kinshasa, Democratic Republic of Congo (DRC) between 2015-2017. Children and adults were sampled at biannual household surveys and routine clinic visits. Among 9,089 samples from 1,565 participants, incidences of P. malariae, P. ovale spp., and P. falciparum infections by 1-year were 7.8% (95% CI: 6.4%-9.1%), 4.8% (95% CI: 3.7%-5.9%) and 57.5% (95% CI: 54.4%-60.5%), respectively. Non-falciparum prevalences were higher in school-age children, rural and peri-urban sites, and P. falciparum co-infections. P. falciparum remains the primary driver of malaria in the DRC, though non-falciparum species also pose an infection risk. As P. falciparum interventions gain traction in high-burden settings, continued surveillance and improved understanding of non-falciparum infections are warranted.Reports suggest non-falciparum species are an underappreciated cause of malaria in sub-Saharan Africa but their epidemiology is ill-defined, particularly in highly malaria-endemic regions. We estimated incidence and prevalence of PCR-confirmed non-falciparum and  P. malariae, P. ovale spp., in Kinshasa, Democratic Republic of Congo and compare to P. falciparum, which known to be common in the region.Non-falciparum malaria may cause a significant disease burden in highly endemic regions, but epidemiological data is limited. In this study, the authors estimate the incidence and prevalence of  Plasmodium malariae and Plasmodium ovale spp. have increased across sub-Saharan Africa, where more than 95% of global malaria cases and deaths occur1. Plasmodium falciparum is the primary cause of malaria morbidity and mortality in the region. However, molecular surveys have confirmed co-circulating non-falciparum species, and suggest a rise in prevalence in regions where P. falciparum has declined6. Despite this, our understanding of infection risk and clinical burden posed by non-falciparum species is limited, particularly within regions of high P. falciparum transmission.Reports of non-falciparum malaria caused by 7, indicating a critical need to understand the full landscape of malaria transmission and infection in the country. The DRC harbors the second highest burden of malaria worldwide, accounting for 12% of global malaria cases and 13% of malaria-related deaths as of 20201. Transmission is largely stable and perennial throughout the country, with an estimated 97% of the population living in high malaria transmission regions for eight or more months per year10. While the majority of the country is characterized as hyper- and holo-endemic for malaria11, transmission can vary within and across provinces due to environmental and geographical factors such as urbanicity, population density, land use, temperature, and rainfall12. In Kinshasa Province, home to the capital city of Kinshasa, malaria transmission is lower within the central urban areas, and increases in the outer peri-urban and rural zones10.The Democratic Republic of Congo (DRC) is one of 11 malaria “High Burden, High Impact” countries designated by the World Health Organization (WHO)P. falciparum accounts for the vast majority of infections in the DRC, P. malariae and P. ovale spp. have been documented at low-level prevalences15; however, their epidemiology and clinical impact remains poorly understood. Existing non-falciparum prevalence estimates were largely derived from cross-sectional studies, limiting risk assessment, or focused on a singular age group or symptomatic status, which restricts broader generalizability21. The absence of reliable field diagnostics for these less-common infections complicates surveillance efforts and case management. Gold-standard microscopy methods can detect non-falciparum parasites, but insufficient training and specialization ultimately limits the sensitivity and specificity for non-falciparum infections22. Widely-used malaria rapid diagnostic tests (RDTs) in Africa cannot distinguish P. malariae or P. ovale spp. infections. While most RDTs detect P. falciparum-specific histidine-rich protein 2 (HRP2), and some also detect Plasmodium lactate dehydrogenase (LDH), no P. malariae and P. ovale spp.-specific RDTs are currently available. Further, clinical diagnosis of a non-falciparum infection is not feasible in the absence of symptoms attributed specifically to P. malariae or P. ovale spp. These infections are complicated to detect in malaria-endemic regions such as the DRC where P. falciparum co-infection is common, making it difficult to distinguish symptoms caused specifically by these non-falciparum species. P. malariae infections have been associated with acute febrile illness and anemia25 as well as chronic nephrotic syndrome27. P. ovale spp. infections commonly result in low morbidity, although severe complications have been documented in case reports29. The clinical relevance of P. falciparum co-infection with non-falciparum species is also unclear, with posited suppressive effects observed in one study4, while another detected severe malaria among mixed-infections24. These diagnostic challenges are exacerbated by low parasite densities characteristic of non-falciparum infections, which require the use of sensitive laboratory assays that are not suitable for routine field use.While P. malariae and P. ovale spp. infections have occasionally been dismissed as rare infections that are not clinically impactful in high-burden countries like the DRC. However, improved characterization of their epidemiology and clinical implications is needed to determine their proper place in malaria programmatic and surveillance efforts. Leveraging a 34-month longitudinal cohort study conducted across diverse sites in Kinshasa Province, DRC, we sought to determine the epidemiology of P. malariae and P. ovale spp. infections in the context of high P. falciparum transmission. We performed high-throughput, real-time polymerase chain reaction (PCR) on samples collected from both asymptomatic and symptomatic participants of all ages as part of a large longitudinal study of non-falciparum malaria infection. We estimated incidence, associated factors, and clinical features associated with P. malariae and P. ovale spp. infections over time, and as compared to P. falciparum infections.n = 143) and urban  health areas compared to rural . Sixty-seven percent (n = 1050) of those in the main survey population also had ≥1 symptomatic clinic visit during the 34-month passive follow-up period and were included in the clinic-based analysis, comprising 218 (90.1%) of the 242 enrolled households. Participants in the clinic subpopulation had 3407 total clinic visits, with a median  of 2  visits per person and 11  per household.A total of 1591 individuals were enrolled in the parent cohort across 242 households and seven sites in Kinshasa Province, DRC. Among these, 1565 (98.4%) participants had a baseline dried blood spot (DBS) sample available for analysis and were included in this study, contributing 5682 total survey visits. Participant follow-up by analysis population is displayed in Fig. Baseline demographic and clinical characteristics for the survey population are summarized in Table Baseline characteristics were similar between those also included in the symptomatic clinic subpopulation, and the full survey population (Supplementary Table n = 1518) and 99.6% (n = 1559) of participants were PCR-negative for a P. malariae and P. ovale spp. infection at baseline, respectively; 69.1% (n = 1081) were PCR-negative for a P. falciparum infection at baseline. These PCR-negative participants comprised at-risk populations for malaria incidence estimation.Participant characteristics across follow-up visits are summarized in Supplementary Table P. malariae and P. ovale spp. infections were 7.8% (95% CI: 6.4%–9.1%) and 4.8% (95% CI: 3.7%–5.9%), respectively, encompassing the first incident infections detected at survey or clinic visits. Comparatively, 57.5% (95% CI: 54.4%–60.5%) of at-risk participants acquired a P. falciparum infection by 1-year  and 3.4% (95% CI: 2.4%–4.3%), respectively, including single- and mixed-species infections, whereas the cumulative incidence of a P. falciparum infection by 1-year was 42.8% (95% CI: 39.6%–45.8%) within the survey population . Time to the first detected P. ovale spp. infection was similar between children <5 and 5–14 years throughout follow-up, whereas school-aged children 5–14 years old experienced a faster time to P. malariae infection, particularly towards the end of follow-up driven by symptomatic clinic-based infections. The cumulative incidence of first P. falciparum infection was also higher among children <5 and 5–14 years old, compared to adults aged ≥15, although P. falciparum incidences were high (≥50%) across all age categories by 1-year (p < 0.001). No differences in the cumulative incidence of P. ovale spp. and P. falciparum infection by sex were detected , although P. malariae infection incidence was higher among males than females (p = 0.03).Cumulative incidences of first P. malariae infections, 70 (4.5%) participants had 78 P. ovale spp. infections, and 990 (63.3%) participants had 1976 P. falciparum infections detected. Of all 5682 tested samples collected from these participants across household follow-up surveys, 3.3% (95% CI: 2.8%–3.8%) were PCR-positive for a P. malariae infection, 1.4% (95% CI: 1.1%–1.7%) for a P. ovale spp. infection, and 34.9% (95% CI: 33.2%–36.5%) for a P. falciparum infection. The majority of P. malariae and P. ovale spp. infections in the survey-based population were mixed-species , and were predominately co-infected with P. falciparum. In contrast, the vast majority of P. falciparum infections were single-species .Among 1565 participants across all baseline and follow-up household surveys in the approximately 2-year active surveillance period, 159 (10.2%) participants had 186 PCR-confirmed P. malariae prevalence across household survey visits remained steady throughout active follow-up, fluctuating between 3.0% at baseline, 2.4% at follow-up 1, 4.1% at follow-up 2, and 3.7% at the final household survey  detected at the second follow-up visit in the rainy season  patients had 95 P. ovale spp. infections detected. However, prevalence of P. falciparum was higher in symptomatic clinic patients compared to the survey population, with 789 (75.1%) patients having 2009 P. falciparum infections. From the clinic subpopulation, 4.0% (95% CI: 3.3%–4.7%) of all 3407 tested clinic samples across the clinic-based surveillance follow-up period were PCR-positive for a P. malariae infection, 2.8% (95% CI: 2.2%–3.4%) for a P. ovale spp. infection, and 58.7% (95% CI: 56.5%–60.8%) for a P. falciparum infection.Similar prevalences to those detected in the survey population were observed among the symptomatic clinic subpopulation, despite longer clinic-based follow-up. Among 1050 participants who visited study clinics in the approximately 3-year clinic-based surveillance follow-up period, a total of 120 (11.4%) patients had 135 P. malariae and P. ovale spp. infections by participant throughout the full study is depicted in Supplementary Fig. n = 58/247) of all participants who had a P. malariae infection, and in 21.6% (n = 30/139) of those who had a P. ovale spp. infection during the 34-month study period. Three participants each had a P. malariae infection detected 4 times throughout follow-up. In comparison, P. falciparum infections re-occurred frequently, with 75.2% (n = 914/1216) of those who had a P. falciparum infection having more than one during follow-up, and 22.4% (n = 272/1216) having ≥5 P. falciparum infections detected during the 34-month study period.The timeline of all P. malariae infection in the survey-based population was highest among school-aged children aged 5–14 years at 5.6% (95% CI: 4.4%–6.7%), though still low overall, followed by children <5 years (3.4% [95% CI: 2.1%–4.7%]), and lowest among those aged ≥15 (1.7% [95% CI: 1.2%–2.2%]). Conversely, children <5 years experienced a slightly higher prevalence of P. ovale spp. infection compared to school-aged children (2.3% [95% CI: 1.3%–3.3%] vs. 1.9% [95% CI: 1.2%–2.7%]), although prevalences across both age groups were low overall, and lower than P. malariae prevalence in these age groups; 0.7% (95% CI: 0.4%–1.0%) of adults ≥15 years had a P. ovale spp. infection at study survey visits.Children had non-falciparum infections more frequently than adults. Prevalence of P. malariae and P. ovale spp. infections are shown in Fig. P. falciparum prevalence differences (PD) in Supplementary Fig. P. falciparum coinfection was associated with an increased prevalence of both P. malariae (PD 0.05 [95% CI: 0.04–0.06]) and P. ovale spp. (PD: 0.02 [95% CI: 0.02–0.03]) infections. In comparison to those with average wealth, higher wealth was associated with a decreased prevalence of P. malariae (PD: -0.01 [95% CI: -0.03 to -0.002]) and P. ovale spp. (PD: -0.01 [95% CI: -0.02 to -0.00]) infection. School-aged children 5–14 years old were associated with a higher prevalence of P. malariae (PD: 0.04 [95% CI: 0.03–0.05]) and P. ovale spp. (PD: 0.01 [95% CI: 0.005–0.02]), infection as compared to participants 15 and older and also as compared to children aged <5 for P. malariae (PD: 0.02 [95% CI: 0.005–0.04]). Additionally, both P. malariae and P. ovale spp. infection prevalences were higher among participants living in rural  and peri-urban  sites as compared to an urban setting.Factors associated with the prevalence of P. malariae (PD: 0.02 [95% CI: 0.01–0.04]) and P. ovale spp. , infections, compared to adults ≥15 and older; however, no differences in prevalence of P. malariae and P. ovale spp. were observed between children <5 years and adults ≥15 and older in this symptomatic population . In contrast to the survey population, no associations were observed with P. falciparum co-infection for either non-falciparum species in the clinic-based analysis . P. malariae infection prevalences were higher among rural (PD: 0.05 [95% CI: 0.04–0.06]) and peri-urban (PD: 0.03 [95% CI: 0.02–0.05]) sites as compared to urban in the clinic-based analysis, whereas no difference in P. ovale spp. prevalence was observed between rural and urban settings among symptomatic participants (PD: 0.01 [95% CI: -0.002–0.02]).Similarly, in the clinic-based analysis, school-aged children were also associated with an increased prevalence of P. malariae (PD: -0.006 [95% CI:-0.018–0.006]) and P. ovale spp. infections (PD: 0.007 [95% CI: -0.002–0.017]), as compared to no species-specific malaria infection among the survey population , although associations were no longer significant when stratifying by anemia severity, comparing moderate-to-severe cases vs. mild or no anemia for P. malariae (PD: 0.012 [95% CI: -0.006–0.029]), or for P. ovale spp. , while median (IQR) P. falciparum parasite densities in the total population were higher at 267 p/µL . Non-falciparum parasite densities remained low when stratified by survey population , vs. symptomatic clinic sub-population . Parasite density distributions were slightly higher among P. malariae infections than P. ovale spp. infections. We did not detect any differences in estimated P. malariae or P. ovale spp. parasite density distributions between mixed- vs. single-species P. malariae or P. ovale spp. infections in the total population , or across specific population types. Parasite densities are summarized in Supplementary Table n = 591 total).Estimated median (IQR) parasite densities were low (<50 parasites [p]/µL) among all non-falciparum infections in the study  and P. falciparum- P. ovale co-infections  as compared to single-species P. falciparum infections among the survey population. However, a lower median P. falciparum parasite density was observed within mixed P. falciparum- P. malariae infections (n = 84) compared to P. falciparum single-species infections (n = 1834) in the clinic-based sub-population ; this association was not observed for P. falciparum- P. ovale co-infections , although the sample size for mixed P. falciparum-P. ovale co-infections was smaller than for single-species P. falciparum infections.Interestingly, differences in the distribution of ion Fig. . A higheP. malariae and P. ovale spp. malaria infections were detected across all sites included in this study, affecting children and adults across a range of P. falciparum transmission intensities in Kinshasa Province, DRC. In the largest longitudinal study of non-falciparum malaria conducted to-date in the DRC, we observed an 8% cumulative incidence of first P. malariae infection and 5% incidence of first P. ovale spp. infection within 1-year, including both asymptomatic and symptomatic infections. Over half of the study population in this high malaria-burdened country experienced an incident P. falciparum infection within 1-year from baseline, confirming the central importance of P. falciparum in the DRC. Over 70% of the non-falciparum infections detected occurred as co-infections with P. falciparum. Though P. falciparum was the dominant species, we detected a considerable risk of infection by a non-falciparum species, particularly for children under 15 years of age. These findings underscore the need to consider these neglected species in areas of high P. falciparum transmission.P. malariae infection during the study, and 5% had at least one P. ovale spp. infection, including both mixed- and single-species infections. Similar prevalences were observed among symptomatic individuals during clinic-based surveillance. Prevalences generally align with previous estimates of non-falciparum infections in the DRC derived from cross-sectional studies30. Infection prevalences were stable across the 34-month follow-up, differing from recent studies in neighboring countries, which have detected increases in P. malariae and P. ovale infection prevalence over time, particularly as P. falciparum prevalences have started to decline6. The high proportion of participants with P. malariae who had infection detected more than once during the study (24%) is notable given that this parasite can persist in the blood at low parasite density for long periods of time32. Chronic P. malariae infection may be associated with deleterious clinical outcomes, such as severe anemia and chronic nephrotic syndrome33. Though we could not distinguish whether multiple P. malariae infections detected in an individual were due to chronic occurrences or acute reinfection events, future work may allow us to differentiate these infection types. Such work is necessary given a dearth of evidence on the commonality of chronic P. malariae infection, as well as reinfection rates for non-falciparum species in regions highly endemic for P. falciparum.Ten percent of household survey participants had at least one P. malariae or P. ovale spp. infection were similar but not identical to those infected with P. falciparum. School-aged children were found to have higher prevalences of P. malariae infection than children under 5 years of age, following similar age-related infection associations with P. falciparum that have been previously established in the region35. Dissimilar from P. falciparum infection patterns, symptomatic children under 5 had comparable P. malariae infection prevalences as those experienced by symptomatic adults aged 15 and older. All Plasmodium species were generally more common in rural and peri-urban sites, and less common in the wealthiest households.Populations experiencing a P. malariae infection prevalence and anemia of any severity in the symptomatic clinic subpopulation, which translated to a 1.5% absolute increase in prevalence compared to those without anemia. Although, this association was no longer significant when stratifying by anemia severity, and was not detected among P. ovale spp. infections in this study. Similarly, no association between non-falciparum infections and fever was detected within the survey- or clinic-based analyses, though misclassification of fever is possible if fever-reducing medicines were taken prior to clinic visits, as data on use of fever-reducing medicines was not captured in this study. Prior studies have posited a protective effect of co-infection with P. falciparum and non-falciparum species on reduced clinical outcomes, compared to single-species P. falciparum infections37, although this has not been consistently observed across studies39. We did not assess causal effects of non-falciparum infection on clinical outcomes in our study due to the limited number of positive samples (though no crude associations were detected in the unadjusted models), including the possibility of attenuated P. falciparum symptoms due to mixed-species infection.We observed a crude association between P. falciparum parasite densities were higher in mixed infections with P. malariae or P. ovale spp. in the household survey-based, but not clinic-based, analysis. One possible explanation is that P. falciparum increases its virulence to compete with non-falciparum species within the asymptomatic host, though this is an unproven hypothesis that our data are insufficient to evaluate. Non-falciparum parasite densities observed in this study were low, as is characteristic of P. malariae and P. ovale spp. infections. We did not identify any difference in non-falciparum parasite density between mixed and single-species non-falciparum infections.Interestingly, P. malariae and P. ovale species. Fourth, some misclassification of seasonality may be present, as local classifications for seasonality by month, rather than rainfall amounts, were used to define rainy seasons. Finally, our observational study design is not immune to the risk of unmeasured confounding, although no causal relationships were assessed in this study. Associated factors depict crude associations only, and while the sample size was relatively large, findings may be affected by the lower prevalence of P. malariae and P. ovale events.Our incidence and prevalence estimates should be considered lower bounds of the true incidence and prevalence in the study catchment area owing to several limitations of the study. First, the biannual frequency of household surveys missed infections that occurred and resolved between study visits. Also, while the longitudinal design provides helpful insight into risk of infection surrounding these species, the study’s sample size and duration of the study likely limited our ability to detect changes in the prevalence of these less-common species over time in the DRC, where gains in malaria control have not been achieved as in other settings. Second, while loss to follow-up was low overall in the study, missed study visits, early loss to follow-up, and differences in study clinic attendance across sites may have introduced selection bias into the study. Participants may have elected to visit alternative health facilities for malaria care instead of presenting to study clinics. However, all participants were eligible for free malaria treatment at study clinics to encourage use of the study facilities for care and increase representativeness of the clinic subpopulation to the overall study population. Third, our duplex PCR assay detected low density infections with a limit of detection between 1 and 10 p/µL, but would likely miss infections of parasite densities below this range, which are known to occur with P. malariae and P. ovale species in a region heavily affected by P. falciparum malaria. Though less common and impactful than P. falciparum, P. malariae and P. ovale spp. infections occurred across all sites and subpopulations, and were often detected within symptomatic clinic-based cases in this large longitudinal study in the DRC. Malaria research and control efforts focused on P. falciparum should also consider these neglected species, particularly in school-aged children and rural communities as part of efforts to progress toward malaria elimination.This study provides insight into the epidemiology of 40. In brief, households were selected at random within the seven chosen villages, and household members were enrolled into the study following screening for eligibility criteria and provision of informed consent, or assent for minors. Participants were followed prospectively through biannual household visits , and at visits to local study health facilities, as-needed, for presentation of fever or other malaria symptoms   Fig. . Three lHousehold surveys were conducted between February 2015 and October 2016 (20 months of active surveillance), with baseline and second follow-up surveys largely occurring during the rainy season, and first and third  follow-up surveys occurring during the dry season. Symptomatic clinic visits continued beyond the completion of household surveys, through December 2017, for 34 months of passive surveillance.P. falciparum-specific HRP2 and pan-Plasmodium LDH) antigens . Those positive by RDT (defined as a positive result for either antigen) were referred to study clinics for antimalarial treatment. DBS samples were also collected from all consenting participants at each visit using Whatman 3MM filter paper , and were stored with desiccant at -20 °C for future molecular testing.At baseline and follow-up surveys, household-level and individual-level questionnaires were collected to ascertain demographic information, household characteristics , health status and clinical data . All participants were screened for malaria infection at each visit using a combination RDT detecting Brief clinical questionnaires were collected from participants at each symptomatic clinic visit throughout the study, alongside malaria RDTs, DBS samples, and hemoglobin testing. Participants were treated at health facilities according to local clinical judgment, as needed, including artemisinin-based combination therapy (ACT) for malaria infection, or referral for blood transfusion if severely anemic.Plasmodium species. DNA was extracted from DBS samples using Chelex and saponin41. P. falciparum parasitemia was identified using a quantitative real-time duplex PCR assay targeting the LDH and human β-tubulin genes, as previously described42. We tested samples for P. malariae and P. ovale spp. parasitemia using a semi-quantitative, duplex real-time PCR assay targeting the 18S ribosomal subunit  < 40. P. falciparum samples were run in duplicate and were considered positive if at least one sample amplified at a Ct <38, or both amplified between Cts ≥38 and <40, as previously described45. Semi-quantitative P. malariae and P. ovale spp. parasite densities were estimated from standard curves made using serially-diluted 18 S rRNA plasmid DNA (BEI Resources: #MRA-179 and #MRA-180), assuming six plasmid copies per parasite genome46. Non-falciparum parasitemias were then multiplied by 4.0 to account for a 4-fold dilution of blood from three DBS punches47, improving comparability with P. falciparum parasitemia quantification. DNA samples with evidence of dehydration were rehydrated prior to PCR testing using 50 µL of molecular-grade water; rehydrated samples were excluded from parasite density analyses. Negative (water) controls were included on all PCR runs. P. vivax was not tested in this study as previously published estimates of P. vivax prevalence in Kinshasa Province from a similar time period (2013-14) indicated low prevalence in the region (0 to <2%)30.Samples underwent molecular testing at the University of North Carolina Chapel Hill to distinguish All participants with a baseline DBS available for testing were included in the study. The Kinshasa longitudinal cohort study population provided two distinct types of data utilized in this study: (1) The “survey-based” analysis consists of participant survey data and clinical samples collected at active surveillance household visits for all those enrolled at baseline, and (2) the “clinic-based” analysis consists of participant clinical survey data and samples collected from the subset of the survey-based population who had samples collected at the baseline household enrollment visit, and who visited study clinics as-needed when symptomatic during follow-up (passive surveillance).Samples derived from all study touchpoints (household surveys + clinic visits) were combined to estimate overall infection burden, and also stratified by population type (survey vs. clinic) to account for differences between their predominantly asymptomatic vs. symptomatic natures. Characteristics between the broader survey population and the nested clinic subpopulation were assessed .The primary outcomes in this study were P. malariae, P. ovale spp., and P. falciparum infections were calculated using Kaplan-Meier methods among participants who were PCR-negative at baseline for the Plasmodium species of interest. Incidences were estimated as the inverse of the survival function, measuring time (in months) to the first detected infection since baseline. Participants in the survey population were censored at the time of their last study household visit. Participants in the clinic subpopulation were censored at the end of passive surveillance follow-up, as participants were eligible to continue visiting study health clinics even after the final active follow-up, if symptomatic. Similarly, in the total population, participants who were lost to follow-up prior to the final household survey and had never visited study clinics during follow-up were censored at the time of their last study household visit. Otherwise, those in the total population who completed the final household follow-up were censored at the end of the full study period. Participants dropped out of the at-risk population at the time of their first infection event. We assumed no competing risks for malaria infection since infection by one species would not preclude infection by another, and measured mortality events in the study were low (n = 26). Demographic characteristics associated with incidence of first infection were evaluated by log-rank testing of stratified cumulative incidence curves.Cumulative incidences of first P. malariae, P. ovale spp., and P. falciparum were estimated overall and by species across follow-up. Factors associated with non-falciparum infection prevalences were evaluated using crude linear binomial generalized estimating equation (GEE) models accounting for repeated testing of participants over time. Possible factors associated with malaria infection were modeled individually. An exchangeable working correlation matrix was assumed for GEE models, and robust standard errors were estimated for calculation of 95% confidence intervals (CIs). While three-level clustering of participants nested within households, and households within village sites is present in the study, this clustering structure was too complex to account for using mixed-effect modeling because of the low number of P. malariae and P. ovale outcomes detected in this study. Therefore, the GEE model was used. We also assessed the frequency of multiple same-species non-falciparum infections detected within a participant throughout follow-up; however, these were not distinguished between acute reinfection events, or chronic carriage of a prior infection.Prevalences of ,48. Quintiles were collapsed into three categories for analysis, and wealth at baseline was carried forward for all subsequent study visits. Seasonality was defined by month, classifying October through April as the rainy season. Anemia severity at symptomatic clinic visits was classified according to hemoglobin (Hb) level, following WHO categories for age-, sex-, and pregnancy-specific cut-points49; severe anemia was defined as Hb<7.0 g/dL for children <5 years, and Hb <8.0 g/dL for all others. Cases were categorized as any anemia vs. no anemia, and comparing moderate/severe vs. mild/no anemia cases. Fever was defined as an axillary temperature >37.5 °C as measured at symptomatic clinic visits, or as self-reported fever within the prior week at baseline and follow-up surveys. Missing data were summarized and excluded from statistical analyses.Household wealth was computed through adaptation of the Demographic and Health Survey method and categorized into quintiles as previously describedtidycmprsk, ggsurvfit, and gee packages. The full list of R packages used in analysis is listed in Supplemental Table p-values were two-sided. Informed consent, and assent where required, was obtained from all participants or their legal guardians prior to study enrollment and sampling. The study was approved by the Institutional Review Boards at the University of North Carolina at Chapel Hill (IRB#: 14-0489) the University of Iowa (#201701201), and the Kinshasa School of Public Health (ESP/CE/015/014).Dataset construction and cleaning were performed using SAS (version 9.4), and analyses were conducted in R (version 4.0.2) primarily using Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Malaria and dengue fever are the leading causes of acute, undifferentiated febrile illness. In Africa, misdiagnosis of dengue fever as malaria is a common scenario. Through a systematic review of the published literature, this study seeks to estimate the prevalence of dengue and malaria coinfection among acute undifferentiated febrile diseases in Africa.Relevant publications were systematically searched in the PubMed, Cochrane Library, and Google Scholar until May 19, 2023. A random-effects meta-analysis and meta-regression were used to summarize and examine the prevalence estimates.Plasmodium falciparum and acute dengue virus coinfection prevalence increased significantly from 0.9% during 2008–2013 to 3.8% during 2014–2017 and to 5.5% during 2018–2021 (p = 0.0414).Twenty-two studies with 22,803 acute undifferentiated febrile patients from 10 countries in Africa were included. The meta-analysis findings revealed a pooled prevalence of malaria and dengue coinfection of 4.2%, with Central Africa having the highest rate (4.7%), followed by East Africa (2.7%) and West Africa (1.6%). Continent-wide, There was a high and increasing prevalence of malaria and acute dengue virus coinfection in Africa. Healthcare workers should bear in mind the possibility of dengue infection as a differential diagnosis for acute febrile illness, as well as the possibility of coexisting malaria and dengue in endemic areas. In addition, high-quality multicentre studies are required to verify the above conclusions.Protocol registration number: CRD42022311301. Acute undifferentiated febrile illness (AUFI) is one of the most frequent reasons for seeking healthcare in Africa . AUFI usPlasmodium species, namely, Plasmodium falciparum, Plasmodium vivax, Plasmodium malariae, and Plasmodium ovale, with a variable geographic distribution. P. falciparum accounts for nearly all malaria deaths in sub-Saharan Africa, which bears over 90% of the global malaria burden  OR "Dengue Virus"[Mesh] OR dengue [tiab] OR dengue fever [tiab] OR breakbone fever [tiab] OR break bone fever [tiab] OR dengue virus*[tiab])) OR  AND (dengue [tiab] OR dengue fever [tiab] OR breakbone fever [tiab] OR break bone fever[tiab] OR dengue virus*[tiab]) NOT MEDLINE[sb])) NOT systematic [sb].The Joanna Briggs Institute System for the Unified Management, Assessment, and Review of Information (JBI SUMARI) tool  was used2 statistic and Cochran’s Q-test were used to measure the heterogeneity of the included studies, and meta-regression analysis was used to investigate the factors associated with heterogeneities in stratified meta-analyses. The publication bias was evaluated using Begg and Mezumdar rank correlation tests and assessed the relationship between malaria prevalence and dengue fever prevalence using spearman correlation. A subgroup analysis was performed to determine the prevalence by study time period and region and calculated the odds ratio (OR) and 95% CI of prevalence to estimate the effect of age and gender. A p value < 0.05 was considered to indicate statistical significance. The data was analysed using Comprehensive Meta-analysis (Version 3) software.The random-effect models was used to determine the prevalence estimates and their 95% confidence intervals (CIs). The IThis study was performed without patient or public involvement.The article screening and selection process is depicted in the PRISMA flowchart Fig. . A totalThe range or interquartile rage (IQR) of age of participants was reported in 20 and two of the 22 studies, while the mean or median age were stated in 14 and six studies, respectively. The mean or median age was not mentioned in two studies , 45. EigThe included studies’ JBI checklist scores varied from four to seven Table . No stud2 = 95.18; Q test p = 0.00), and no significant publication bias was observed . The high degree of heterogeneity was significantly related to the study time period (p = 0.0414)  per 1000 AUFI cases. This estimate substantially increased from 9 (95% CI 2–35) during 2008–2013 to 38 (95% CI 21–67) during 2014–2017 and then to 55 (95% CI 34–86) during 2018–2021  in West Africa to 27 (95% CI 7–97) in East Africa to 47 (95% CI 22–98) in Central Africa  was observed between malaria and dengue fever prevalence among acute undifferentiated febrile illnesses (AUFI) in Africa during 2008–2021 continent-wide Fig. . In all Malaria has a complicated pathophysiology, causing pathologic alterations in all bodily systems. Direct red blood cell destruction and nonspecific inflammatory and immune responses are the major mechanisms involved . SimilarPlasmodium and dengue virus coinfection occur when both of these mosquito-borne diseases occur simultaneously in an individual, which may increase the severity and duration of one or both [ or both . The fir or both . About 2Based on the meta-analysis, the pooled prevalence of malaria and dengue fever coinfection was 4.2%, and the highest rate was recorded in Central Africa (4.7%), followed by East Africa (2.7%) and West Africa (1.6%). This result is lower than the finding of a study  on a metP. falciparum and dengue virus grew significantly from 0.9% between 2008 and 2013 to 3.8% between 2014 and 2017 and 5.5% between 2018 and 2021. This could be due to increased global transportation network dynamics, population movement, and climate change [Across the African continent, the prevalence of co-infection with In the study, children were more affected by coinfection than adults. Children are more susceptible to mosquito-borne illnesses because they are exposed to mosquito bites for longer periods during dangerous hours . MoreovePlasmodium falciparum was the only malaria parasite specified in the coinfection among the included studies, as nearly all malaria cases in Africa are caused by P. falciparum [Furthermore, lciparum .The study showed a high and increasing trend of malaria and dengue coinfection prevalence in many parts of Africa. Nevertheless, healthcare workers misdiagnose dengue or malaria-dengue as malaria alone due to the institutionalisation of malaria as the primary febrile illness in the region by international development organizations and national malaria control programs , limitedThe study had some strengths and limitations. A large sample size, good-quality included studies, no evidence of publication bias among the included studies, and subgroup analysis were some of the strengths of the study; however, restriction to those published in English only, including single-centred facility-based studies, a small sample size in six studies , 35, 39,In general, a high prevalence of malaria and dengue virus coinfection among acute undifferentiated febrile patients was found in Africa, with variable rates across regions. Children were more affected by the coinfection than adults. Healthcare workers should bear in mind the possibility of dengue infection as one of the differential diagnoses for acute febrile illness, as well as the possibility of coexisting malaria and dengue in endemic areas. In addition, high-quality multicentre studies are required to verify the above conclusions and gain more insights into malaria and dengue virus coinfection on the continent.