A highly invasive malaria parasite has expanded its range to non-migratory birds in North America

Parasite range expansions are a direct consequence of globalization and are an increasing threat to biodiversity. Here, we report a recent range expansion of the SGS1 strain of a highly invasive parasite, Plasmodium relictum , to two non-migratory passerines in North America . Plasmodium relictum is considered one of the world's most invasive parasites and causes the disease avian malaria: this is the first reported case of SGS1 in wild non-migratory birds on the continent. Using a long-term database where researchers report avian malaria parasite infections, we summarized our current understanding of the geographical range of SGS1 and its known hosts. We also identified the most likely geographical region of this introduction event using the MSP1 allele. We hypothesize that this introduction resulted from movements of captive birds and subsequent spillover to native bird populations, via the presence of competent vectors and ecological fitting. Further work should be conducted to determine the extent to which SGS1 has spread following its introduction in North America.

Classification for avian malaria parasite Plasmodium gallinaceum blood stages by using deep convolutional neural networks

The infection of an avian malaria parasite (Plasmodium gallinaceum) in domestic chickens presents a major threat to the poultry industry because it causes economic loss in both the quality and quantity of meat and egg production. Computer-aided diagnosis has been developed to automatically identify avian malaria infections and classify the blood infection stage development. In this study, four types of deep convolutional neural networks, namely Darknet, Darknet19, Darknet19-448 and Densenet201 are used to classify P. gallinaceum blood stages. We randomly collected a dataset of 12,761 single-cell images consisting of three parasite stages from ten-infected blood films stained by Giemsa. All images were confirmed by three well-trained examiners. The study mainly compared several image classification models and used both qualitative and quantitative data for the evaluation of the proposed models. In the model-wise comparison, the four neural network models gave us high values with a mean average accuracy of at least 97%. The Darknet can reproduce a superior performance in the classification of the P. gallinaceum development stages across any other model architectures. Furthermore, the Darknet has the best performance in multiple class-wise classification, with average values of greater than 99% in accuracy, specificity, and sensitivity. It also has a low misclassification rate (< 1%) than the other three models. Therefore, the model is more suitable in the classification of P. gallinaceum blood stages. The findings could help us create a fast-screening method to help non-experts in field studies where there is a lack of specialized instruments for avian malaria diagnostics.

Classification for Avian Malaria Parasite Plasmodium Gallinaceum Blood Stages by Using Deep Convolutional Neural Networks

The infection of an avian malaria parasite (Plasmodium gallinaceum) in domestic chickens presents a major threat to the poultry industry because it causes economic loss in both the quality and quantity of meat and egg production. Computer-aided diagnosis has been developed to automatically identify avian malaria infections and classify the blood infection stage development. In this study, four types of deep convolutional neural networks, namely Darknet, Darknet19, Darknet19-448 and Densenet201 are used to classify P. gallinaceum blood stages. We randomly collected a dataset of 12,761 single-cell images consisting of four parasite stages from ten-infected blood films stained by Giemsa. All images were confirmed by three well-trained examiners. The study mainly compared several image classification models and used both qualitative and quantitative data for the evaluation of the proposed models. In the model-wise comparison, the four neural network models gave us high values with the mean average accuracy of at least 97%. The Darknet can reproduce a superior performance in the classification of the P. gallinaceum development stages across any other model architectures. Furthermore, the Darknet has the best performance in multiple class-wise classification, with average values of greater than 99% in accuracy, specificity, and sensitivity. It also has a low misclassification rate (< 1%) than the other three models. Therefore, the model is more suitable in the classification of P. gallinaceum blood stages. The findings could help us create a fast screening method to help non-experts in field studies where there is a lack of specialized instruments for avian malaria diagnostics.

Effect of Neonicotinoid Exposure on the Life History Traits and Susceptibility to Plasmodium Infection on the Major Avian Malaria Vector Culex pipiens (Diptera: Culicidae)

Culex pipiens complexes play an important role in the transmission of a wide range of pathogens that infect humans, including viruses and filarial worms, as well as pathogens of wildlife, such as the avian malaria parasite (Plasmodium spp.). Numerous biotic and abiotic stresses influence vector-borne pathogen transmission directly, through changes in vector density, or indirectly by changing vector immunocompetence, lifespan, or reproductive potential. Among these stresses, mosquito exposure to sublethal doses of pesticides could have important consequences. In addition to being exposed to pollutants in aquatic breeding sites, mosquitoes can also be exposed to chemicals as adults through their diet (plant nectar). In this study, we explored the impact of mosquito exposure at the larval and adult stages to one of the most commonly used pesticides, imidacloprid, a chemical belonging to the class of the neonicotinoids, on a set of life history traits ranging from development time to fecundity. We also studied the impact of this pesticide on the susceptibility of mosquitoes to infection by the avian malaria parasite, Plasmodium relictum. Surprisingly, we observed no effects of imidacloprid on any of the parameters examined. This result highlights the fact that Culex pipiens mosquitoes do not appear to be susceptible to imidacloprid when exposure doses are close to those measured in the field.

Classification for Avian Malaria Parasite Plasmodium Gallinaceum Blood Stages by Using Deep Convolutional Neural Networks

Background: The infections of an avian malaria parasite (Plasmodium gallinaceum) in domestic chickens presents a major threat to poultry industry because it cause economical loss in both quality and quantity of meat and egg productions. Deep learning algorithms have been developed to identify avian malaria infections and classify its blood stage development. Methods: In this study, four types of deep convolutional neural networks namely Darknet, Darknet19, darknet19_448x448 and Densenet 201 are used to classify P. gallinaceum blood stages. We randomly collected dataset of 10,548 single-cell images consisting of four parasite stages from ten-infected blood films stained by Giemsa. All images were confirmed by three well-trained examiners. Results: In the model-wise comparison, the four neural network models gave us high values in the mean average precision at least 95%. Darknet can reproduce a superior performance in classification of the P. gallinaceum development stages across any other model architectures. In addition, Darknet also has best performance in multiple class-wise classification, scoring the average values of greater than 99% in accuracy, specificity, sensitivity, precision, and F1-score.Conclusions: Therefore, Darknet model is more suitable in the classification of P. gallinaceum blood stages than the other three models. The result may contribute us to develop the rapid screening tool for further assist non-expert in filed study where is lack of specific instrument for avian malaria diagnostic.

Gene regulation of the avian malaria parasite Plasmodium relictum, during the different stages within the mosquito vector

The malaria parasite Plasmodium relictum is one of the most widespread species of avian malaria. As is the case in its human counterparts, bird Plasmodium undergoes a complex life cycle infecting two hosts: the arthropod vector and the vertebrate host. In this study, we examine the transcriptome of P. relictum (SGS1) during crucial timepoints within its natural vector, Culex pipiens quinquefasciatus. Differential gene-expression analyses identified genes linked to the parasites life-stages at: i) a few minutes after the blood meal is ingested, ii) during peak oocyst production phase, iii) during peak sporozoite phase and iv) during the late-stages of the infection. A large amount of genes coding for functions linked to host-immune invasion and multifunctional genes was active throughout the infection cycle. One gene associated with a conserved Plasmodium membrane protein with unknown function was upregulated throughout the parasite development in the vector, suggesting an important role in the successful completion of the sporogonic cycle. Transcript annotation further revealed novel genes, which were significantly differentially expressed during the infection in the vector as well as upregulation of reticulocyte-binding proteins, which raises the possibility of the multifunctionality of these RBPs. We establish the existence of highly stage-specific pathways being overexpressed during the infection. This first study of gene-expression of a non-human Plasmodium species in its natural vector provides a comprehensive insight into the molecular mechanisms of the common avian malaria parasite P. relictum and provides essential information on the evolutionary diversity in gene regulation of the Plasmodium’s vector stages.

Plasmodium transmission differs between mosquito species and parasite lineages

Factors such as the particular combination of parasite–mosquito species, their co-evolutionary history and the host's parasite load greatly affect parasite transmission. However, the importance of these factors in the epidemiology of mosquito-borne parasites, such as avian malaria parasites, is largely unknown. Here, we assessed the competence of two mosquito species [Culex pipiens and Aedes (Ochlerotatus) caspius], for the transmission of four avian Plasmodium lineages (Plasmodium relictum SGS1 and GRW11 and Plasmodium cathemerium-related lineages COLL1 and PADOM01) naturally infecting wild house sparrows. We assessed the effects of parasite identity and parasite load on Plasmodium transmission risk through its effects on the transmission rate and mosquito survival. We found that Cx. pipiens was able to transmit the four Plasmodium lineages, while Ae. caspius was unable to transmit any of them. However, Cx. pipiens mosquitoes fed on birds infected by P. relictum showed a lower survival and transmission rate than those fed on birds infected by parasites related to P. cathemerium. Non-significant associations were found with the host–parasite load. Our results confirm the existence of inter- and intra-specific differences in the ability of Plasmodium lineages to develop in mosquito species and their effects on the survival of mosquitoes that result in important differences in the transmission risk of the different avian malaria parasite lineages studied.