scholarly journals Bloodmeal Identification and Detection of Avian Malaria Parasite From Mosquitoes (Diptera: Culicidae) Inhabiting Coastal Areas of Tokyo Bay, Japan

2009 ◽  
Vol 46 (5) ◽  
pp. 1230-1234 ◽  
Author(s):  
Kyeong Soon Kim ◽  
Yoshio Tsuda ◽  
Akio Yamada
Keyword(s):  
Malaria Parasite ◽  
Avian Malaria ◽  
Coastal Areas ◽  
Tokyo Bay ◽  
Avian Malaria Parasite ◽  
Bloodmeal Identification

Blood Meal Identification and Prevalence of Avian Malaria Parasite in Mosquitoes Collected at Kushiro Wetland, a Subarctic Zone of Japan

2011 ◽  
Vol 48 (4) ◽  
pp. 904-908 ◽  
Author(s):  
Hiroko Ejiri ◽  
Yukita Sato ◽  
Kyeong Soon Kim ◽  
Yoshio Tsuda ◽  
Koichi Murata ◽  
...  
Keyword(s):  
Blood Meal ◽  
Malaria Parasite ◽  
Avian Malaria ◽  
Avian Malaria Parasite ◽  
Blood Meal Identification

scholarly journals Molecular characterization and distribution of Plasmodium matutinum, a common avian malaria parasite

Parasitology ◽  
2017 ◽  
Vol 144 (13) ◽  
pp. 1726-1735 ◽  
Author(s):  
GEDIMINAS VALKIŪNAS ◽  
MIKAS ILGŪNAS ◽  
DOVILĖ BUKAUSKAITĖ ◽  
VAIDAS PALINAUSKAS ◽  
RASA BERNOTIENĖ ◽  
...  
Keyword(s):  
Molecular Markers ◽  
Molecular Characterization ◽  
Malaria Parasite ◽  
Avian Malaria ◽  
Clinical Signs ◽  
Molecular Data ◽  
Infected Blood ◽  
Avian Malaria Parasite ◽  
Thrush Nightingale

SUMMARYSpecies of Plasmodium (Plasmodiidae, Haemosporida) are widespread and cause malaria, which can be severe in avian hosts. Molecular markers are essential to detect and identify parasites, but still absent for many avian malaria and related haemosporidian species. Here, we provide first molecular characterization of Plasmodium matutinum, a common agent of avian malaria. This parasite was isolated from a naturally infected thrush nightingale Luscinia luscinia (Muscicapidae). Fragments of mitochondrial, apicoplast and nuclear genomes were obtained. Domestic canaries Serinus canaria were susceptible after inoculation of infected blood, and the long-lasting light parasitemia developed in two exposed birds. Clinical signs of illness were not reported. Illustrations of blood stages of P. matutinum (pLINN1) are given, and phylogenetic analysis identified the closely related avian Plasmodium species. The phylogeny based on partial cytochrome b (cyt b) sequences suggests that this parasite is most closely related to Plasmodium tejerai (cyt b lineage pSPMAG01), a common malaria parasite of American birds. Both these parasites belong to subgenus Haemamoeba, and their blood stages are similar morphologically, particularly due to marked vacuolization of the cytoplasm in growing erythrocytic meronts. Molecular data show that transmission of P. matutinum (pLINN1) occurs broadly in the Holarctic, and the parasite likely is of cosmopolitan distribution. Passeriform birds and Culex mosquitoes are common hosts. This study provides first molecular markers for detection of P. matutinum.

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

2021 ◽  
Author(s):  
Veerayuth Kittichai ◽  
Morakot Kaewthamasorn ◽  
Suchansa Thanee ◽  
Rangsan Jomtarak ◽  
Kamonpob Klanboot ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Malaria Parasite ◽  
Avian Malaria ◽  
Superior Performance ◽  
Rapid Screening ◽  
Plasmodium Gallinaceum ◽  
Deep Convolutional Neural Networks ◽  
Avian Malaria Parasite

Abstract 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.

Prevalence of avian malaria parasite in mosquitoes collected at a zoological garden in Japan

2009 ◽  
Vol 105 (3) ◽  
pp. 629-633 ◽  
Author(s):  
Hiroko Ejiri ◽  
Yukita Sato ◽  
Risa Sawai ◽  
Emi Sasaki ◽  
Rei Matsumoto ◽  
...  
Keyword(s):  
Malaria Parasite ◽  
Avian Malaria ◽  
Zoological Garden ◽  
Avian Malaria Parasite
Sign in / Sign up

Export Citation Format

Share Document